Download Marine fog: A review

Atmospheric Research 143 (2014) 142–175
Contents lists available at ScienceDirect
Atmospheric Research
journal homepage: www.elsevier.com/locate/atmos
Marine fog: A review
Darko Koračin a,b,⁎, Clive E. Dorman c, John M. Lewis a,d, James G. Hudson a,
Eric M. Wilcox a, Alicia Torregrosa e
a
b
c
d
e
Desert Research Institute, Reno, NV, USA
University of Split, Croatia
Scripps Institution of Oceanography, San Diego, CA, USA
NOAA, National Severe Storms Laboratory, Norman, OK, USA
U.S. Geological Survey, Western Geographic Science Center, Menlo Park, CA, USA
a r t i c l e
i n f o
Article history:
Received 30 July 2013
Received in revised form 18 December 2013
Accepted 26 December 2013
Available online 4 January 2014
Keywords:
Marine fog
Review
a b s t r a c t
The objective of this review is to discuss physical processes over a wide range of spatial scales
that govern the formation, evolution, and dissipation of marine fog. We consider marine fog as
the collective combination of fog over the open sea along with coastal sea fog and coastal land
fog. The review includes a history of sea fog research, field programs, forecasting methods, and
detection of sea fog via satellite observations where similarity in radiative properties of fog top
and the underlying sea induce further complexity. The main thrust of the study is to provide
insight into causality of fog including its initiation, maintenance, and destruction. The interplay
between the various physical processes behind the several stages of marine fog is among the
most challenging aspects of the problem. An effort is made to identify this interplay between
processes that include the microphysics of fog formation and maintenance, the influence of
large-scale circulations and precipitation/clouds, radiation, turbulence (air–sea interaction),
and advection. The environmental impact of marine fog is also addressed. The study concludes
with an assessment of our current knowledge of the phenomenon, our principal areas of
ignorance, and future lines of research that hold promise for advances in our understanding.
© 2013 Published by Elsevier B.V.
Contents
1.
2.
3.
4.
Prologue . . . . . . . . . . . . . . . . . . . . . .
Introduction
. . . . . . . . . . . . . . . . . . . .
History of sea fog research . . . . . . . . . . . . . .
Marine fog field programs . . . . . . . . . . . . . .
4.1.
1850s, Maury and ship observations . . . . . .
4.2.
1913 International ice patrol off Labrador . . .
4.2.1.
1925–1927 Meteor Expedition in eastern
4.3.
1910–1960 Oregon, California and Baja California
4.4.
1970s, California Coast, CEWCOM, US Navy . . .
4.5.
1980–2000s, ONR, other California coastal studies
4.6.
Field programs after the 2000s
. . . . . . . .
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
Atlantic
. . . .
. . . .
. . .
. . . .
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
⁎ Corresponding author at: Desert Research Institute, 2215 Raggio Parkway, Reno, NV 89512-1095, USA.
E-mail address: [email protected] (D. Koračin).
0169-8095/$ – see front matter © 2013 Published by Elsevier B.V.
http://dx.doi.org/10.1016/j.atmosres.2013.12.012
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
143
144
145
147
147
149
149
149
149
150
150
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
5.
Causative factors for marine fog . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.
Advection fog — warmer air flowing over a colder sea (cold sea fog or cold fog) .
5.1.1.
Advection and air mass trajectory history . . . . . . . . . . . . . . .
5.1.2.
Advection over upwelled U.S. West Coast waters . . . . . . . . . . .
5.1.3.
Fog during coastally-trapped disturbances
. . . . . . . . . . . . . .
5.1.4.
Haar off British Isles . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.5.
Merging synoptic and local effects — Yellow Sea fog . . . . . . . . . .
5.1.6.
Frequency and persistence of fog over the Yellow Sea . . . . . . . . .
5.1.7.
Importance of air–sea interaction for fog evolution . . . . . . . . . .
5.2.
Advection fog — colder air flowing over a warmer sea (warm sea fog or warm fog)
5.2.1.
Petterssen's and Pilié's viewpoints . . . . . . . . . . . . . . . . . .
5.2.2.
“Steam fog” — advected air much colder than the sea surface
. . . . .
5.3.
Air mass transformation leading to fog — processes at an elevated level . . . . .
5.3.1.
Inversion and cloud forcing leading to sea fog . . . . . . . . . . . . .
5.3.2.
Warm advection depressing the inversion — triggering mechanisms . .
5.3.3.
Synoptic forcing for fog formation . . . . . . . . . . . . . . . . . .
5.3.4.
Local circulations and advection of aerosols . . . . . . . . . . . . . .
5.3.5.
Fog and precipitation . . . . . . . . . . . . . . . . . . . . . . . .
5.4.
Environmental impact on terrestrial systems
. . . . . . . . . . . . . . . . .
5.4.1.
Marine fog — hydrologic impacts on ecosystem dynamics . . . . . . .
5.4.2.
Marine fog — chemical and microbiological impacts . . . . . . . . . .
5.4.3.
Marine fog — thermodynamic ecosystem impacts . . . . . . . . . . .
5.4.4.
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.
Microphysics of fog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.
Marine fog forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.
Initial climatology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2.
Surge expansion with aviation . . . . . . . . . . . . . . . . . . . . . . . .
7.3.
After WWII . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.4.
Growth of operational numerical models . . . . . . . . . . . . . . . . . . .
7.5.
Satellite development and expansion . . . . . . . . . . . . . . . . . . . . .
7.6.
The elusive sub-mesoscale and microscale . . . . . . . . . . . . . . . . . . .
8.
Remote sensing of marine fog . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.
Epilog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. Prologue
While studying atmospheric and oceanic processes, we
realize that they are occurring on a wide spectrum of scales
intrinsically coupled through complex interactions. In a more
general sense, as we view the development of physics in the
twentieth century and the early part of the twenty-first
century, theorists have marveled at the interplay of the very
large (the universe) and the very small (the elementary
particles). To quote from the noted theorist John Wheeler:
Quantum mechanics is often described as the theory of the
very small. A true statement, as far as it goes. Quantum
mechanics is an absolute necessity, and an everyday tool, in
explaining how molecules, atoms, photons, electrons, and
other particles behave. It is of no consequence in explaining
motion of spacecraft, planets, comets, and whole galaxies.
So what does the quantum have to do with the universe?
Perhaps everything, because in any fundamental theory of
existence, the large and the small cannot be separated.
[(Wheeler and Ford, 1998, p. 329)]
A corollary to Wheeler's statement is the impossibility of
explaining the phases of marine fog (existence, maintenance,
and destruction) without accounting for the very small (the
microphysics of fog droplet formation) and the very large
143
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
150
150
150
150
152
152
152
153
153
153
153
154
154
154
155
155
157
157
157
158
158
159
159
159
163
164
164
165
165
166
166
167
169
170
171
(hemispheric circulations) as well as the intermediate scales
of motion. Since a typical dimension of fog condensation
nuclei is 0.1 μm (10−5 cm) or less and the synoptic-scale
processes linked to marine fog are on a scale of 108 cm or
more, a conservative estimate of the ratio of length scales for
marine fog is about 1013. For the universe as a whole, the
ratio of one of the smallest structures (the dimension of a
proton: 10−13 cm) to the largest (the distance across the
universe: 1029 cm) is 1042 (Ford, 1968).1
Fig. 1 schematically displays the collection of processes
that are central to the phases of marine fog — ranging from
the large/synoptic scale (advection, subsidence, cloud) to
subsynoptic scale (land–sea breezes, sea surface temperature
structure, and oceanic upwelling) to the mesoscale (radiation, surface fluxes) and to the smallest scale (droplets and
aerosol within the foggy air mass). And precipitation, always
a challenging aspect of any geophysical problem, plays a
double role by moistening and cooling of the subcloud layer
and generating cloud thickening and lowering of the cloud
base to eventual fog formation, but also inducing fog dissipation
in some cases. The solution demands a coupled set of governing
1
In the decades since Kenneth Ford made his estimates of this ratio, there
is convincing evidence that distances significantly smaller than the size of a
proton exist, yet precise quantitative measurement of these distances is
difficult to confirm (Cary and Michael Hwang http://htwins.net).
144
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
Fig. 1. Schematic of the main processes governing the formation, evolution, and dissipation of marine fog.
equations that describe the dynamics, moist thermodynamics,
radiation, microphysics, and turbulence processes — a formidable mathematical/physical problem. It is no wonder that this
geophysical problem attracted some of the most able scientific
minds of the early twentieth century: Geoffrey Ingram (“G.I.”)
Taylor (1915, 1917), Harold Jeffreys (1918), Anders Ångström
(1920), and Ira Bowen (1926).
The difficulties and the challenges of marine fog investigation, however, do not come without esthetic rewards. One only
needs to experience the splendor of a sea fog advance along the
ocean's shoreline to be captivated by the phenomenon. Fig. 2 is a
picturesque example of the majesty of sea fog as it advances
toward the pier at the Scripps Institution of Oceanography in La
Jolla, California. The turrets that form atop the fog bank give
evidence of the buoyant plume and the leading edge is dynamic.
strong warm-season subsidence and surface winds out of the
north–northwest favor the formation of marine fog (Leipper,
1994; Filonczuk et al., 1995). These high frequency areas of
marine fog give evidence of the differing factors that lead to
fog over the world sea.
Although we mention in the further text many of the
investigations and associated areas of frequent fog occurrence, here are some of the examples:
2. Introduction
Marine fog is a worldwide phenomenon occurring at both
coastal and open ocean areas, especially frequent over the
northwestern Atlantic and Pacific Oceans (Fig. 3). Here the
warm western boundary currents in each of these oceans, the
Gulf Stream (Atlantic) and Kuroshio Current (Pacific), abut
the cold oceanic flows neighboring Labrador and Kamchatka,
respectively. The processes that give rise to these foggy areas
are discussed in Section 5.
Fig. 3 also indicates an area of significant marine fog
frequency off the U.S. West Coast. In this area, the cold
upwelled ocean water along the coast in association with
Fig. 2. Sea fog approaching Scripps Pier.
Photo by www.flickr.com.
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
145
Fig. 3. Sea fog frequency [% of the observations indicating moderate to dense fog (not including mist)] in Jun, Jul, and Aug (1885–1933). Chart 53 from Atlas of the
Climatic Charts of the Oceans (U.S. Department of Agriculture, 1938).
U.S. West Coast
Palmer (1917), Byers (1930), Anderson (1931),
Leipper (1948), Oliver et al. (1978), Pilié et al.
(1979), Hudson (1980), Koračin et al. (2001, 2005a,
b), Lewis et al. (2003, 2004), Thompson et al. (2005),
Johnstone and Dawson (2010) and O'Brien et al.
(2013).
South American West Coast (Ecuador–Peru–Chile coast)
Schemenauer and Cereceda (1991) and Garreaud et al.
(2008).
Atlantic Coast (U.S.–Canada)
Taylor (1917), Willett (1928), Gultepe et al. (2006c),
Tardif and Rasmussen (2008), Toth et al. (2010) and Yang
et al. (2010).
Yellow Sea
Gao et al. (2007), Zhang et al. (2009), Heo and Ha
(2010), Kim and Yum (2010, 2012) and Zhou and Du
(2010).
South China Sea
Huang et al. (2011).
From a pragmatic viewpoint, knowledge of marine fog climatology and the ability to forecast it is of utmost importance. It
is estimated that 32% of all accidents at sea worldwide and 40% in
the Atlantic occur in the presence of dense fog (Tremant, 1987).
The disruption of trade and endangerment to life are always at
the forefront of its assessment (Johnson and Graschel, 1992;
Croft et al., 1995; Garmon et al., 1996; Kim and Yum, 2010; Leem
et al., 2005). And from an ecological viewpoint, changes in its
frequency have devastating effects on ecology (Johnstone and
Dawson, 2010). Some have even suggested that the economic
and human losses associated with fog and low visibilities are
comparable to the losses from other weather disasters such as
tornadoes and hurricanes (Gultepe et al., 2007b).
In this review, we pay particular attention to the wide range
of small- and large-scale processes that are germane to the
phenomenon of marine fog. A brief history of research is
presented in Section 3 and expanded upon at various junctures
in the paper. Section 4 reviews the worldwide field programs
aimed at analyzing the fog and the centerpiece of the contribution is found in Section 5 — an explanation of the known
causative factors associated with marine fog. The microphysics
of fog formation is discussed in Section 6, and an assessment of
forecast capabilities and detection of fog via satellite observations are found in Sections 7 and 8, respectively. The Epilog
summarizes our knowledge of this phenomenon and lists those
areas where ignorance remains. Finally, we speculate on lines of
research that hold promise for advancement in our understanding of the phenomenon.
Southern Africa
3. History of sea fog research
Taljaard and Schumann (1940), Cermak (2012) and
van Schalkwyk and Dyson (2013).
As in all modern science, collection and analysis of observations is the starting point for research. Further, in the presence
of the great challenge to predict sea fog — its origin, maintenance, and decay — the climatology of sea fog assumes
paramount importance. The first known “archive” of sea fog
Arabian Peninsula
Bartok et al. (2012).
146
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
comes from the two Icelandic Sagas that include records of sea
voyages from Iceland to northeastern North America in the AD
1050s through the early AD 1100s (over nine hundred years
ago) — a new world called the “Wineland” by Leif Eriksson and
the other Viking explorers (Bergthórsson, 2000). Bergthórsson, a
renowned Icelandic meteorologist and protégé of Carl-Gustaf
Rossby, pays particular attention to the meteorological and
oceanographic events encountered during these voyages. Further, he reconstructs weather charts from information in the
Sagas that give evidence of the sea fog that the explorers encountered (Bergthórsson, 2000, Ch. 2). And as shown on the
climatic charts of fog over the world sea that came from the
extensive logs at Seewarte and the Naval Observatory in late19th century, the preponderance of sea fog along the track of
these voyages from Iceland to Newfoundland exhibits the largest
frequency in the world — ≈30% frequency over the Grand Banks
of Newfoundland during the warm season (Fig. 3). Augmentation of these data resources continues with collection at the
Hadley Climate Centre in England in coordination with COADS
(Coupled Ocean and Atmospheric Data Set). Excellent local sea
fog climatology based on COADS has appeared in publications
such as those from the Scripps Institution of Oceanography in La
Jolla, California [see, for example, Filonczuk et al. (1995)]. Fig. 4
presents information on the monthly frequency of sea fog off the
California coast that has been extracted from data in Filonczuk et
al. (1995).
Taylor made the most celebrated study of sea fog during
the summer of 1913 over the Grand Banks of Newfoundland.
Fig. 4. Relative monthly frequencies (in percentages) of sea fog hours along the California coast by regions for the period 1949–1991.
These data were extracted from Filonczuk et al. (1995). The plot is reproduced from Lewis et al. (2004). For more details, see Koračin et al. (2005b).
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
The study was made in response to the RMS Titanic disaster
when the British Government and some shipping companies
equipped an expedition on an old 230-ton wooden whaling
ship, the S.S. [steam ship] Scotia (see Lewis et al., 2004 for
details). Although the primary purpose of the expedition was
to track icebergs from the glaciers of Greenland, the analysis of
meteorological conditions that led to sea fog was also a central
theme. One of Taylor's recollections of the expedition follows:
“… of the 806 occasions on which meteorological observations were taken during the voyage of the whaling ship
Scotia over the Banks, there was fog 141 times…It would
have been impossible to examine every case in detail to see
how the fog arose, but in certain cases I succeeded in raising
the kite to explore the upper air, and in most of those
observations I traced the cause of the fog which prevailed at
the time of the ascent. In every one of these cases, it turned
out that the fog was due to air blowing off warm water on to
the cold water of the Banks.”
[(Taylor, 1917, p. 250)]
Fig. 5 shows the track of air for a particular case in July 1913
and the associated thermodynamic structure in the presence of
a foggy layer over the Banks. In this case, Taylor was able to track
the surface air for a period of nine days through the judicious
use of observations from merchant vessels. The sea surface
temperatures were obtained from weekly charts published by
the British Meteorological Office. Analysis of the kite sounding at
the terminal point of the trajectory along with knowledge of the
sea surface temperature along the trajectory led Taylor to reason
that the air column, characterized by a well-mixed adiabatic
state immediately above the sea surface on 18 July, appears
above 700 m on 25 July. Extension of this profile to the surface
gives a temperature of 26 °C (79 °F) that is close to the sea
surface temperature measured by a merchant ship at the origin
of the trajectory on 18 July. He concluded that after 18 July, the
air traveled over progressively colder water and cooled as the
result of turbulence (shear turbulence in the presence of a stable
stratification). From the measurements of relative humidity and
the saturated state in the lower atmosphere, Taylor concluded
that the fog extended to 700 ft (210 m ASL). The high frequency
of sea fog in the northwestern Pacific Ocean displayed in Fig. 3
stems from the same action — air flowing over the Kuroshio
current to a location above the cold waters of the Oyashio
Current that emanates from the Bering Sea. In Taylor's study, the
cold water stemmed from the Labrador Current that flows over
the Grand Banks from its Arctic Ocean point of origin.
Taylor was an expert in the theory of turbulence (Taylor,
1915) and he clearly understood the action of turbulence at
the air–sea interface. It was Petterssen (1936, 1938, 1939) who
expanded the view of turbulence as a factor in sea fog generation.
He studied sea fog generation off the southern California coastline and realized that it was buoyancy of low-level air that
stemmed from contact with a warmer sea surface (air temperature initially colder than sea surface). If the lifting condensation
level (level where buoyant air reaches condensation) is below
the level of the inversion, then stratus forms. And with longwave
cooling at the top of the stratus that mitigates solar warming,
negative buoyancy can be created and lead to cooling of the
subcloud layer and subsequent sea fog formation (the stratus
thickening/lowering phenomenon for fog creation). It was
147
Douglas (1930) that first postulated and measured this
negative buoyancy as it led to the haar, sea fog over the
North Sea. Others who have made early contributions to
understanding the influence of radiation as a contributor to
sea fog are Emmons and Montgomery (1947) and Leipper
(1948, 1994). Again, expanded discussion of contributions
by these investigators is found in Lewis et al. (2004).
As a complement to this brief history that has emphasized
contributions from the United Kingdom and the United States,
we refer the reader to the book by Wang (1985). Although
Wang does not view the development of sea fog research from
a historical perspective, he includes a broad-based bibliography
and presents a host of interesting analyses of sea fog in the East
Asian area. Further valuable introduction to sea fog processes
from a pedagogical viewpoint is found in Roach (1995).
4. Marine fog field programs
Field programs designed to establish a climatic base for sea
fog effectively began with the international agreement on
collection of ship observations in the 1850s. The first surface
measurements and kite soundings in fog-prone areas that
supported this climatological effort took place in 1913 as part
of the International Ice Patrol over the Grand Banks. It was the
same program discussed in the previous section in which G.I.
Taylor collected meteorological measurements associated with
fog. In addition to the measurements discussed earlier, Taylor
developed fog diagrams that related surface variables to fog
occurrence (discussed further in Section 5.1.1). In the following
decade, the German survey vessel Meteor took surface observations and made kite soundings over the Atlantic in the latitude
band 20 °N to 60 °S. This monumental field exercise became
known as the Meteor Expedition (German: Deutsche Atlantik
Expedition) of 1925–1927. As part of this field program, observations of inversion-base height were made in the eastern
subtropical zone of the south Atlantic and in the eastern
boundary upwelling zones where fog is extensive. In the period
1910–1960, data were collected from lighthouses along the U.S.
West Coast in conjunction with aircraft soundings and finally
balloon soundings. These data were used to identify mesoscale
characteristics important to fog formation. The 1970s saw singlepurpose scientific ships along the U.S. West Coast that focused on
identifying small-scale atmospheric and oceanic structures in the
upwelling zones that occurred in the presence of coastal fog. And
again along the U.S. West Coast during the 1980–2000 period, a
series of instrumented aircraft flights complemented observations from automated coast and buoy stations to study coastal
fog. After 2000, satellite microwave measurements of foggy
areas were used to improve microphysical parameterization
for numerical forecast models. Highlights from the various
field programs follow.
4.1. 1850s, Maury and ship observations
The most basic climatology, including fog for 71% of the Earth,
was directed by Matthew Fontaine Maury when he served as
superintendent of the United States Naval Observatory between
1842 and 1861. The systematic collection and analysis of surface
meteorological and oceanographic observations from ships' daily
weather logs were the basis for this climatology (discussed in
Maury's well-known book, The Physical Geography of the Sea,
148
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
Fig. 5. Figures from Taylor (1917): (a) path history of air, and (b) temperature and relative humidity profiles obtained from an instrumented kite where the sea
surface temperature is indicated by the arrow along the abscissa.
From Lewis et al. (2004).
1861; reviewed in Lewis, 1996). Maury organized the first true
international meteorological conference in Brussels in 1853 for
the world-wide collection of marine data. As part of the
collection strategy, the world oceans were divided into
1° × 1° latitude–longitude squares that were used to
generate monthly analyses — a practice that continues to
this day. Results are found in atlases such as The Atlas of the
Climatic Charts of the Oceans (U.S. Dept. of Agriculture,
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
1938), later the U.S. Navy Marine Climatic Atlas of the World
(U.S. Navy, 1955), then a CD-ROM version (http://gcmd.nasa.
gov/index.html) that is available on the Internet as The
International Comprehensive Ocean–atmosphere Data Set
(ICOADS). The ship weather observation was formatted into
the “ship synoptic code” to be complete and systematic for
transmission (U.S. Weather Bureau, 1930) which includes variables directly related to fog occurrence and its nature with
factors such as weather type, horizontal visibility, ceiling obscuration, humidity, and sea and air temperatures. For most any
modern study being initiated on marine fog, the first step is to
gather the ship observations and associated analyses initially
organized by Maury. We have followed this practice by making
use of charts from the Atlas of the Climatic Charts of the Oceans
(Sections 2 and 5).
4.2. 1913 International ice patrol off Labrador
4.2.1. 1925–1927 Meteor Expedition in eastern Atlantic
As stated above, Germany sponsored the Meteor Expedition in 1925–1927 (Von Ficker, 1936; Spiess, 1985). Accurate
measurements were made with balloon wind soundings and
box kite soundings with an attached temperature recorder.
As a result, the air temperature inversion height was mapped
for the eastern Atlantic and it remains the only ocean-scale
analysis based directly on measurements. This inversion base
was so low at a point off southwest Africa that the top of the
Meteor's mast was above the air temperature inversion base.
As later recognized from measurements along the California
coast, a similar strong, air temperature inversion capping a
cool, moist marine layer is a critical factor in fog formation
and maintenance in that area.
4.3. 1910–1960 Oregon, California and Baja California
In addition to the Grand Banks, a well-recognized marine
fog area with different dynamics occurs over the coastal waters
off Oregon, California and Baja California. Due to the surface
pressure forces associated with the summertime subtropical
anticyclone over the Pacific Ocean and the heat low over the
southwest U.S., the winds along the California are from the
north–northwest and they serve to drive ocean upwelling that
brings cold water to the surface. A result of the contrast
between the cold ocean surface and the dry, subsiding air is an
atmospheric marine layer with a cold moisture source at the
base capped by dry, hot air, which gives rise to conditions
conducive to fog.
Reports on the basic fog conditions along the California–
Oregon coast began to appear in the 1910s through the 1960s.
These are: the eastern side of the northeast Pacific anticyclone
dominating the area with flow toward the equator; wind driven
upwelling of cold water with the coldest sea surface temperature
close to the coast; and a shallow, cold, moist atmospheric marine
layer containing fog and stratocumulus clouds capped by a
strong, sharp air temperature inversion. The earliest accounts of
these conditions were based upon lighthouse observations for
climatology (Palmer, 1917), aircraft soundings (Willett, 1928;
Anderson, 1931) and synoptic setting (Willett, 1928; Petterssen,
1938). Crucial to fog in this area is the distinctive air temperature
inversion layer which is an important controlling factor, and
stratus, often closely related to fog or a stage in its cycle, has been
149
the focus of many early field programs reviewed by Kloesel
(1992).
Clarity of the atmospheric conditions accompanying fog,
stratus, and cumulus in the eastern Pacific Ocean came with
tropical–subtropical studies by Herbert Riehl and his students
at the University of Chicago near the end of WWII (Riehl et al.,
1951; reviewed in Lewis et al., 2012). Of particular interest in
this marine fog review is the series of upper-air soundings that
were obtained from four stationary U. S. Navy ships that
operated on the flight path from San Francisco to Honolulu
during the period July to October 1945. Observations from
these ships defined the height and depth of the summertime
inversion over the subtropical latitudes of the eastern Pacific
Ocean. Morris Neiburger and his colleagues at UCLA continued
the line of research started by Riehl with special emphasis
on the stratus cloud in the eastern Pacific (Neiburger et al.,
1961). The work of Neiburger and his associates has formed
the backbone for subsequent studies involving near-surface
winds, marine conditions and marine clouds. A major result
is that the air temperature inversion is lowest (below 500 m
ASL) and most persistent during the extended summer
between Cape Mendocino in northern California and Point
Conception in southern California. Going westward, the
inversion base height rises quickly from the North and
Central California coast, and then more slowly to be above
1200 m ASL at Hawaii.
4.4. 1970s, California Coast, CEWCOM, US Navy
In the 1970s, the Calspan Corporation (a component of the
Cornell Aeronautical Laboratory) and the Naval Postgraduate
School, Monterey, CA participated in the Cooperative Experiment
in West Coast Oceanography and Meteorology (CEWCOM). The
Naval Air Systems Command supported the program. As part of
this experiment, a series of single-ship cruises took place along
the California coast from northern California to San Diego at
distances as far as 500 km from the coastline. On seven cruises,
30 fog events were encountered. Especially along the northern
California coastline, the fog areas were characterized by strong,
low air temperature inversions and wind-driven coastal upwelling that occurred in patches. Using data from this experiment,
Pilié et al. (1979) reported five different conditions under which
fog develops. One condition indicates that fog is triggered by
instability and mixing of colder air over warm water patches, so
that the fog layer grows along downwind trajectories. A second
condition links fog development within a stratus layer to
radiative cooling atop the cloud layer. The cooling leads to
negative buoyancy so that the stratus lowers and thickens to
create fog. A third condition relates fog formation to low level
convergence, growth of the entire layer, and lifting of the
inversion base height. The fourth condition favors fog formation
over the coastal waters in response to radiative cooling over the
adjacent land at night. In wintertime, a nocturnal land breeze
advects the cool air out to sea. Finally, another instance is a
complex land–coastal interaction that occurs along southern
California to form fog during the fall–winter–spring in
association with a “Santa Ana” — a warm offshore, dry wind
regime. Fog in response to the Santa Ana flow regime was
originally proposed by Leipper (1948). Focusing on southern
California, radar, acoustic sounder, aircraft, satellite, radiosonde, ship and coastal observations were employed to sort out
150
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
shallow marine air movement over water with different
temperatures (Noonkester, 1979; Pilié et al., 1979). Warm, dry
offshore flow lowers the marine inversion and passes over
cooler water where fog forms. As stated in earlier discussions, an
important factor in fog maintenance and growth is longwave
cooling at the fog top.
4.5. 1980–2000s, ONR, other California coastal studies
With knowledge of the large-scale factors influencing fog
climatology, smaller scale, difficult to measure aspects of fog
events became a focus along the California coast during the last
decades of the 20th century. Long aircraft flights west of San
Francisco in September 1981 found that cooler air moving over
warmer water created instability and vertical mixing that
dissipated fog (Telford and Chai, 1993). Ship and coastal
automated surface observations illuminated the smaller scale,
diurnal variation and higher concentrations of fog occurrence
along California (Filonczuk et al., 1995; Lundquist and Bourcy,
1999). A coastally trapped atmospheric event with a shallow
bore pushing poleward along the central California coast
created a shallow stratus-coastal-cloud bore that developed
downward into fog by pushing warmer air over colder water
(Dorman et al., 1998). The changing trajectories of near-surface
air during transient weather events were linked to both the
formation and dissipation of sea fog (Lewis et al., 2003; Koračin
et al., 2001, 2005b).
4.6. Field programs after the 2000s
In the 2000s, fog field experiments tended to focus on
surface observations and near-surface observations that were
co-located with satellite measurements. The field work was
designed with an eye on strengthening operational numerical forecast models — model development that needed
guidance on parameterization of the moist physics associated
with sea fog.
FRAM-L was the marine phase taking place in the summer
of 2006 along the Nova Scotia, Atlantic coast that was part of a
larger fog program that included continental fog. Its major
strength was the use of optical probes for detailed measurement of droplets and visibility at the surface combined with
satellite microwave radiometer data to examine inferred fog
properties that will be used to develop microphysical parameterizations that could be incorporated into numerical forecast
models (Toth et al., 2010).
5. Causative factors for marine fog
The wide range of processes governing marine fog have been
mentioned earlier and displayed in Fig. 1. Earlier studies have
tended to focus on a limited number of these known processes
whereas recent work has often tended to dismiss significant
results from the early studies — in part due to a restrictive
viewpoint tied to the goals of funded research projects. Further,
many studies have failed to be even-handed in their attention to
the phases of marine fog — often emphasizing initiation and
duration without consideration of processes that maintain and
destruct fog. As is known by those who have investigated sea fog,
once formed, it tends to persist. Essentially, generative processes
counteract destructive processes. A negative feedback process is
at work. In this section that strives to identify most of the
causative factors related to marine fog, the dynamical/physical
processes are divided into the following categories: cold sea fog,
warm sea fog, and elevated and land forcing. And each of these
major themes is further subdivided. Although we have made an
effort to avoid overlap in our review, by necessity some overlap
occurs — especially noticeable with information in Section 4
(Marine fog field programs) and itemization of causative factors
found in this section.
5.1. Advection fog — warmer air flowing over a colder sea (cold
sea fog or cold fog)
The sea fog investigated by Taylor (1917) has come to be
labeled advection fog — a fog that is generated through the
action of air movement over a surface with a different
temperature. In Taylor's study, the warm/moist air initially
over the Gulf Stream was transported northward and encountered progressively colder sea surface temperatures on its path
to Newfoundland. This we label cold sea fog in contrast to the
opposite case where cold air is transported over progressively
warmer water — the situation studied by Petterssen off the coast
of southern California and labeled warm sea fog (discussed in
Section 5.2).
5.1.1. Advection and air mass trajectory history
The panels of Fig. 6 are complements to the sea fog
frequency chart shown in Fig. 3. Focusing on the high-frequency
fog area over the northwestern Atlantic, we note sea surface
temperatures (SSTs) as low as 10 °C north of Newfoundland. In
the other cardinal compass directions relative to Newfoundland,
the SSTs are significantly warmer (the American continent to
the west and the Gulf Stream to the south and east). An airflow
originating from any of these warmer areas can potentially
induce cooling of the near-surface marine air and eventually
produce fog (Fig. 6). Taylor concluded that all fluid states
(moisture–temperature structure) in these cases must map
onto a straight line in the q–Θ plane (q — total water mixing
ratio; Θ — potential temperature) which passes through the
surface state (q0, th0) and has a slope determined by the ratio of
the air–sea moisture and heat fluxes. If the turbulence effects are
dominant and radiation effects are negligible, Oliver et al.
(1978) showed that this condition implies that temperature and
moisture fluxes are the same and that the distributions of q–q0
and Θ–Θ0 and their turbulence correlations must be functions of
z/Lm and z/z0, where Lm is the Monin–Obukhov length, z is the
height, and z0 is the roughness length. To achieve this state,
Taylor concluded that one of the most important factors was
adaptation and modification of an air mass along the long
overwater trajectory originating over the warm sea surface and
continuing over the cold ocean current. This use of the similarity
theory complements Taylor's analysis.
5.1.2. Advection over upwelled U.S. West Coast waters
Another fog-prone area is the U.S. West Coast, which is
characterized by cold upwelled sea surface in the warm season,
a shallow atmospheric marine layer with frequent northerly
and northwesterly flows and occasional offshore flows, and
subsidence that maintains a strong low-level marine inversion.
As itemized in the Introduction, there have been numerous
studies that have investigated West Coast fog. Byers (1930)
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
151
Fig. 6. a. Same as Fig. 3, except for temperature differences (air temp − sea surface temp). b. Same as Fig. 3, except for wind regime.
studied summertime fog over this area and hypothesized that
the fog forms by saturation of moderately cold air moving over
the colder sea surface. Other investigators including Leipper
(1948, 1994) have challenged the premise that fog forms
simply as a consequence of cooling and moistening of the
transported air. According to these studies and based on many
observations and data analysis, a primary mechanism for fog
formation is as follows: In the warm season, offshore flows
(linked to an anticyclone over the northwestern U.S.) descend
over the coastal mountains and additionally warm by compression and adiabatic heating from 5 °C to as high as 24 °C
(Leipper, 1994) (Fig. 7). This warm offshore flow effectively
depresses the marine inversion almost to the surface and fog
forms in a very shallow marine layer. Once fog forms, other
processes such as the longwave radiation at the fog top induce
cooling and turbulent mixing that initiate fog growth. It should
be noted that Johnstone and Dawson (2010) also support the
scenario with positive correlations between fog occurrence and
Oakland upper air temperatures above the marine layer and
negative correlations within the cooler than normal marine
layer.
So, to summarize this scenario, fog does not form within
the warm advected air, but it forms in the extremely shallow
marine layer over the cold ocean waters. The warm advected
air serves to lower the marine inversion almost to the surface
and effectively contain a shallow and moist marine layer.
Pilié et al. (1979) used observations and conceptual modeling
and also concluded that one of the main mechanisms for the
152
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
propagate northward (Nuss et al., 2000). These conditions
appear to represent a characteristic case of warm and moist air
propagating northward over cold upwelled water [documented
by Dorman et al. (1998)]. However, Thompson et al. (2005)
argued that according to their sensitivity tests from 3D
simulations using the COAMPS model (Coupled Ocean–atmosphere Mesoscale Prediction System; Hodur, 1997; Hodur et al.,
2002), the main driver for the fog maintenance during the
northward propagation was due to wind convergence and
associated rising motions at the leading edge of the cloud/fog.
The convergence and rising motions change the stability of the
marine layer from strongly stable to a shallow and cool mixed
layer with relative humidity increasing upward. Future studies
including observations and advanced modeling are needed to
further investigate these hypotheses based on testing particular
model sensitivity to some of the processes such as surface fluxes.
Fig. 7. A schematic diagram of Leipper's conceptual model of sea fog
formation during a hot-spell event. The upper portion of the diagram shows
the surface airflows associated with the position of the Pacific anticyclone.
The inset shows the relative position of clear air, fog, and stratus relative to
the coastline. See text for further details.
The plot is reproduced from Lewis et al. (2004).
fog formation in the warm season over the West Coast is that a
warm advected offshore flow erodes the marine inversion
almost to the surface and allows saturation of this near-surface
marine layer. Koračin et al. (2005b) simulated processes in this
scenario of fog formation in the very shallow marine inversion
capped by the strong low-level inversion. The importance of
longwave cooling in rapid fog growth was discussed as well as
the necessity of high vertical resolution in resolving radiation
fluxes that are crucial for fog evolution.
5.1.3. Fog during coastally-trapped disturbances
Interestingly, some authors have proposed physical mechanisms different from the classical mechanisms for cold sea fog
mentioned above. One example is related to fog over the cold
upwelled West Coast water that appears during occasional
coastally-trapped disturbances [CTD, also called coastallytrapped wind reversals (Nuss et al., 2000; Thompson et al.,
2005)]. The CTD events usually occur several times each year
over the West Coast during the warm season synoptic setup
(anticyclone in the northern Pacific and low over the U.S.
southwest). In these areas, persistent northerly and northwesterly flow regimes are interrupted by a period of opposing
southerly flow near the coast where clouds and fog are spread
over several hundred kilometers of the coast and rapidly
5.1.4. Haar off British Isles
The haar is a well-known sea fog that occurs most
frequently along the east coast of Scotland. The unusual name
comes from the speech of the inhabitants of Lincolnshire
County on the east coast of England. This fog has been traced to
the cooling of warm air masses by the relatively cold waters of
the North Sea (east of Scotland). British meteorologists Hubert
Lamb and C.K.M. Douglas are credited with an early investigation of haar that gave evidence of both classic cold sea fog
process augmented by radiative cooling atop the fog layer
(Douglas, 1930; Lamb, 1943). Lamb's study made it clear that
local land–sea circulations were often active in haar where
foggy air at sea was brought to land by a sea breeze. The poor
visibility is often extreme with haar (visibility b 25 m). Further
insight into haar is shown by Taylor (1987) and Findlater et al.
(1989), among others.
5.1.5. Merging synoptic and local effects — Yellow Sea fog
The classic concepts of cold sea fog are in evidence from
studies of fog over the Yellow Sea and the East and South China
Sea. There are a significant number of observational and
modeling studies aimed at understanding the active physical
processes governing fog in this area. The main backdrop includes
synoptic and local effects. In March–April, heating of the air over
central-eastern China usually induces a shallow anticyclone over
the cool Yellow Sea and the northern East China Sea that
includes a prominent inversion at 100 to 350 m altitude. On the
west flank of the anticyclone, this setup generates persistent
southerlies which advect warm and humid air over the cool
waters of the Yellow Sea and East China Sea that leads to fog
occurrence (Zhang et al., 2009). In July–August, the East Asian–
western Pacific monsoon initiates a strong shift from southerlies
to easterlies over those two areas that brings drier air to the
region that erodes the marine inversion and leads to disappearance of the fog.
Additionally to mechanisms for fog along the U.S. West
Coast due to offshore flow and lowering of the marine inversion (Leipper, 1948, 1994; Koračin et al., 2005a), Zhang et al.
(2009) indicate that the offshore flow from the heated
continent (at about 925 hPa) strengthens the marine inversion
and traps the moisture in a shallow marine layer. Compared to
the central Yellow Sea, the sea surface approaching the Korean
coast is even colder due to intense tidal mixing that promotes
more fog events (Cho et al., 2000; Zhang et al., 2009).
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
Numerical simulations of sea fog over the Yellow Sea have
been conducted by Gao et al. (2007). Their results using
Mesoscale Model 5 (MM5; Grell et al., 1994) showed that
formation of a surface-based inversion was a critically important
element in the formation of sea fog. The inversion led to development of a shallow, moist marine layer and formation of a
thermal internal boundary layer at the base. Sea fog formed in
response to gradual cooling and moistening by turbulent
mixing. Dissipation of fog was caused by the shift in advection
from warm and moist southerlies to dry and cold northerlies.
5.1.6. Frequency and persistence of fog over the Yellow Sea
Kim and Yum (2010) analyzed the climatologies of sea fog
along the Korean coast and open ocean and categorically
separated events into coastal and open ocean fog–sea fog.
Although there were significantly more cases of coastal fog,
the sea fog (cold sea fog being more frequent than warm sea
fog) was characterized by longer durations. Based on the
synoptic setup discussed above, the occurrence of cold fog
during the warm season is in agreement with results from
Zhang et al. (2009) while the occurrence of warm sea fog was
usually from January to May. Kim and Yum (2010) noted that
the dew point preceding the cold fog was greater than the
SST. On sea fog days, this dew point was approximately 5 °C
greater than on fog-free days. Consequently, fog was formed
by cooling of already sufficiently humid air.
Using numerical simulations, Kim and Yum (2012) investigated a case of cold fog over the Yellow Sea which they found
to occur more frequently than warm sea fog. They simulated
this fog event with both a 1D model (Bott and Trautmann,
2002) and a 3D model — the Weather Research and Forecasting
(WRF) model (Skamarock et al., 2008). They indicated that
some processes relevant to cold sea fog begin with cooling of
the overlaying warmer air by radiative cooling and turbulent
mixing which in turn leads to a thermally stable internal
boundary layer. The moisture loss due to the downward
moisture flux near the surface is balanced by the continuous
advection of the moisture — this keeps the dew point
quasi-constant and, due to net cooling, the air mass eventually
reaches saturation.
5.1.7. Importance of air–sea interaction for fog evolution
Heo and Ha (2010) used coupled and uncoupled versions
of the COAMPS model (Hodur, 1997; Hodur et al., 2002) and
the ROMS ocean model (Shchepetkin and McWilliams, 2004)
to investigate cold sea fog cases and a steam fog case over the
Yellow Sea. Regarding the cold sea fog, there was advection of
warm and moist air over the cold coastal waters that were
additionally cooled compared to offshore regions by tidal
mixing (Cho et al., 2000). Simulated trajectories showed that
the warm air mass coming from the south was gradually cooled
over the SST that decreased northward. Condensation occurred, thus inducing negative latent heat flux and increasing
stability near the sea surface causing weakening of the winds
and restricting downward mixing of drier air. Decreasing SST
along the advection trajectory appears to be a significant
pre-cursor for fog formation allowing cooling and moistening
of the near-surface air. This also maintained the fog until the
southerly advection was reduced. Heo and Ha (2010) noted
that uncoupled simulations showed underestimation of
153
stability, and they were unable to represent these processes
relevant to the formation of fog.
Huang et al. (2011) studied observations during a cold sea
fog event over southern China coastal waters with warm air
advection over the cooler waters. They suggested that the
marine boundary layer consisted of two major sub-layers
characterized by mechanical turbulence near the sea surface
and thermal radiation turbulence effects due to longwave
cooling from the stratus/fog top. They indicated that a thermal
turbulence interface was observed between 180 and 350 m
altitude that separates these sub-layers. Fog dissipation or/and
lifting to stratus was attributed to reduction of moisture
transport, rapid lifting of the fog top and associated entrainment. Results from this study indicate that the thermal
turbulence interface persisted even when the fog lifted to
form stratus. In short, the thermal turbulence interface has a
significant role in maintaining stratus. This could motivate
future observational and modeling studies to further understand these observed characteristics.
5.2. Advection fog — colder air flowing over a warmer sea
(warm sea fog or warm fog)
Another concept of fog formation stems from advection of
colder air over the warm sea where saturation occurs in
response to mixing of the cold and sufficiently moist air with
warm/moist air. Taylor (1917) also gave some attention to
this process of warm fog formation.
5.2.1. Petterssen's and Pilié's viewpoints
Lewis et al. (2004) reviewed the work by Petterssen
regarding fog formation due to advection of colder air over
warm water (Petterssen, 1936, 1938, 1939). Petterssen discussed
warm sea fog in the context of mixing — i.e., mixing of two
unsaturated air masses, one the incoming cool and unsaturated
air and the other the near-surface warm and saturated or
near-saturated air. The vertical mixing is a response to buoyancy.
Sufficient mixing and radiative cooling then lead to saturation of
the air mixture.
Some of the main processes during this type of fog are
discussed in detail by Pilié et al. (1979). They collected
shipboard measurements in a situation characterized by fog
at the site of the ship but fog-free upwind of the ship. There was
a surface inversion layer on the upwind side that effectively
trapped moist near-saturated air at the air–sea interface. When
this layer traversed warm sea, the stability was eroded. Mixing
and saturation then took place between the warm and moist
air at the sea surface and the advancing cool, moist air (Fig. 8).
The temperature decreased in the fog layer due to mixing and
radiative cooling and a superadiabatic layer was maintained
near the surface. The cold fog layer also represents a sink for
moisture that consequently enhances evaporation from the
warm sea surface.
Another study of warm fog evolution was shown by Kim
and Yum (2010). They analyzed data from sea fog (cold and
warm) and also coastal fog over the west coast of Korea. Their
measurements indicated that the air temperature slightly
dropped when the fog was formed over the warm waters.
They concluded that the dew point increase, with the
difference between the air temperature and the dew point
during the fog mature phase ranging from 0.5 to 1.9 °C, is due
154
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
Fig. 8. (Left) Profile sketch of the upwind edge of a fog patch formed over warm water, 30 Aug 1972; (right) Selected vertical profiles at indicated positions
relative to fog edge, 30 Aug 1972.
From Pilié et al. (1979).
to moisture transport from the surface and is the dominant
process for fog formation. They indicated that this is possibly
due to longwave cooling of the fog top which overpowers
warming from the sea surface. Notably, Pilié et al. (1979) also
observed the effect of fog cooling over the warm waters (Fig. 8;
their Figs. 2 and 3). Others also demonstrated pre-conditioning
cooling and/or fog cooling over warm waters. Koračin et al.
(2001) used 1D simulations emulating cooling of a Lagrangian
air mass along a long overwater trajectory due to longwave
cooling from the cloud/fog that can eventually overpower
warming from the surface. With a continuous supply from the
surface and cooling-generated instability and turbulence, this
process can lead to fog formation. These effects are further
elaborated on in Section 5.3.2.
5.2.2. “Steam fog” — advected air much colder than the sea
surface
When a stream of cold, dry air (typically originating over
land) traverses a much warmer sea surface, a “steam fog” can
occur. Such events also occur with regularity over lakes in
wintertime. The large heat capacity of water leads to slower
cooling of the lake water relative to the land mass (especially
notable in late fall and early winter). In both cases, i.e., over lakes
or oceans, the latent and sensible heat fluxes are extreme (often
reaching values of 100s or more of W m−2 for both sensible and
latent heat fluxes). The steam fog is frequently observed in the
Arctic (Saunders, 1964; Økland and Gotaas, 1995; Gultepe et al.,
2003). Further observations and simulations, especially for cases
of larger air–sea temperature differences, are needed to reveal
the role of SST in steam fog.
5.3. Air mass transformation leading to fog — processes at an
elevated level
The earlier discussion of sea fog clearly indicates that much
of the attention has focused on the relationship between the
surface atmospheric layer and the ocean surface. However,
there are certainly other pathways that can bring the surface
layer to near saturation over the sea. Some of the other
possibilities occur in response to elevated processes above the
surface layer. These include convergence and divergence above
the surface layer that can change the subsidence and inversion
dynamics on the top of the surface layer. Above this surface
layer, the presence of clouds, haze, and particulate matter alter
the upward and downward radiation. Precipitation can also
affect surface layer processes. Slight changes in these factors can
tip the balance for fog formation. However, a near-saturation
condition can also persist without fog formation where the sum
of elevated processes is a significant factor. Once formed, there
are tremendous differences between cloudy and cloud-free
marine layers with respect to thermal, radiative, and turbulence
processes that favor maintenance of a cloudy layer (Tjernström
and Koračin, 1995).
5.3.1. Inversion and cloud forcing leading to sea fog
Koračin et al. (2001) studied a transient-season (April) sea
fog over the U.S. West Coast in the presence of an along-coast
northerly and northwesterly flow. They emphasized a need to
consider the sea fog formation and evolution in a Lagrangian
framework that allows for understanding air mass modification
along a long over-water trajectory. In essence, these researchers
followed Taylor's (1917) lead on considering air mass transformation in a Lagrangian framework. The modification was
characterized by the sea being warmer than the air with
continuous cooling of the marine air along the trajectory.
According to their analysis of observations and a highresolution 1D model, they concluded that the main mechanism
for fog formation was cloud-top cooling and associated turbulent
mixing, supplying moisture due to a positive heat flux, and all in
the presence of intense subsidence, maintained strong marine
inversion, and shrinking of the marine layer along the advection
trajectory (Fig. 9). The 1-D model was run in a Lagrangian mode
where the advection was emulated by varying the SST over a
spatial trajectory estimated from the weather analysis. The
downward propagation of cloud cooling can be seen clearly in
this time–height cross section of air temperature (Fig. 9). The
onset of cooling first occurs at higher elevations and gradually
descends to lower levels. For example, the net cooling begins
after 12 h of simulation at 200 m, while it takes 2 more hours for
the cooling to reach the 100-m level. Significant cooling occurred
after 39 h when the elevated air driven by cloud-top cooling
merged with the near-surface layer. Cloud-top cooling and
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
155
Fig. 9. Contours of ambient temperature (°C) for the baseline 1D Lagrangian simulation. Contour interval is 0.5 °C. See the text for further details.
From Koračin et al. (2001).
subsidence appear to be dominant factors for fog formation and
evolution during cloud lowering along the advection trajectory.
Their sensitivity tests showed that there is an optimum strength
of the inversion and that moisture above the inversion controls
the longwave cooling and entrainment processes.
5.3.2. Warm advection depressing the inversion — triggering
mechanisms
A triggering mechanism for saturation within the shallow
marine layer is still under investigation and debate. As mentioned earlier, Koračin et al. (2005b) used a high-resolution 1D
model to simulate depression of the marine inversion and
consequent fog formation. In this case, differential advection is
present — warm air advection above the inversion and cold air
below the inversion. Their hypothesis is that the increased
longwave flux divergence at the top of a shallow marine layer
with sufficiently uniform moisture leads to condensation at the
top of this shallow layer, which then rapidly propagates to the
surface due to further increases in longwave cooling at the fog
top. Their simulations show that longwave cooling at the
interface of the warm, dry air and the moist, cool air at the very
top of the depressed marine layer is the dominant process for fog
formation. Sufficient mechanical and radiatively-driven turbulence initiate saturation at the very top of the marine layer,
which rapidly propagates downward to the sea surface within
a very shallow marine layer. Consequently, longwave cooling at
the fog top is the main driver of the fog growth that is limited
by the strength of the subsidence and gradual warming and
drying while growing and mixing vertically. Their sensitivity
study (Fig. 10) clearly indicates that the model setup (vertical
resolution and physics options), and initial conditions (lower
boundary — SST, humidity and temperature profiles within the
marine layer, and properties above the inversion such as
humidity, temperature, and subsidence) significantly influence
fog initiation and evolution.
These tests clearly indicate a need for sophisticated
ensemble modeling providing probabilities for fog occurrence,
evolution, and dissipation (further discussion in the Epilog).
5.3.3. Synoptic forcing for fog formation
Lewis et al. (2003) re-analyzed the case studied by
Koračin et al. (2001) focusing on the larger-scale influences
on fog formation and dissipation. They contrasted synoptic
conditions for fog and fog-free events and found that the
156
20
250
10
Heating (K/day)
Height (m)
0
150
100
−10
−20
−30
−40
50
−50
0
0
20
40
60
80
Hour
100
120
140
160
−60
5
10
15
20
25
30
35
40
45
Hour
Fig. 10. (Left) Evolution of the simulated fog top for the baseline run (solid line), a case with colder sea-surface temperature by 2 °C (○), a case with warmer sea-surface temperature by 2 °C as compared to the baseline run
(*), a case with larger near-surface inversion (×), and a case with a drier hot-air layer (+), as compared with the baseline run; (right) time series of the simulated average net heating of the fog layer (°C day−1) due to
longwave and shortwave radiative heat transfer for the baseline simulation with 180 vertical points (solid), the test run with 90 vertical points (dash–dot), and the test run with 45 vertical points (dashed line) within
1200 m.
From Koračin et al. (2005b).
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
200
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
strength and evolution of the synoptic-scale subsidence,
strength, and height of the marine inversion, air–sea temperature difference, cloudiness at the top of the marine layer as well
as air-mass transformation along trajectories were critically
important factors in sea fog formation. Their back-trajectory
analysis showed that trajectory residence time over the land
significantly reduced fog formation over the sea. Koračin et al.
(2005a) performed 3D simulations of the same case from
Koračin et al. (2001). The simulations were able to demonstrate
that the intensity of air mass modification during this advection
significantly depended on whether there were clouds along the
trajectories and whether the trajectories had residence time
over the land or ocean. The model was able to simulate overall
cooling of the marine layer (due to longwave cooling at the
cloud top) in spite of a gradual increase in the SST along the
trajectory. A scale analysis of the model results showed that
longwave cooling at the cloud and fog top overpowered surface
sensible and latent heat fluxes and entrainment in cases of
transformation of cloudy marine layer over long over-water
trajectories. Fog dissipation was significantly influenced by the
development of land-driven circulations as a result of the
complex interplay among advection, synoptic evolution, and
specifics of local circulations. Displacement and weakening of
the horizontal synoptic pressure gradients and the consequent
decrease of the marine winds allowed for the development of
offshore flows that induced warming and drying and consequent fog dissipation.
5.3.4. Local circulations and advection of aerosols
While categorizing fog by advection properties, an important component is advection of aerosols and fog condensation
nuclei that bears importance for fog both over the sea and land.
The advection can be due to local circulations or synoptic
patterns. Transport of sea aerosols and water vapor onto the
land during sea breeze circulations can trigger fog over the land
where the condensation nuclei came from the sea (Bartok et al.,
2012). Obviously, fog can form over the land and then drift to
the sea during a land breeze and vice versa during a sea breeze
(Pilié et al., 1979). This further complicates investigation of the
origin of fog and physical processes relevant to fog formation.
These issues are comprehensively addressed in Section 6
(microphysics of fog).
5.3.5. Fog and precipitation
Some of the early studies indicated that cloud precipitation
cools and moistens the subcloud layer and lowers the condensation level. Pilié et al. (1979) describe this process as cloud
thickening. It should be noted that in many cases, this process is
inaccurately called “cloud lowering”. The term “cloud lowering”
should be reserved for the whole entity of cloud lowering
(including the cloud top) and this is actually explained in the
study by Koračin et al. (2001) where subsidence overpowered
marine turbulence and the whole inversion lowered along the
trajectory of the air mass.
As mentioned earlier, Tardif and Rasmussen (2008) showed
that the effect of precipitation evaporating and moistening the
subcloud layer can occur for a wide range of precipitation
intensities and can strongly influence fog formation, evolution
and dissipation. Although the majority of the cases fall into
categories of light precipitation, rain showers and snow as
pre-condition forcing can also lead to fog formation. They claim
157
that the moistening is a more dominant process compared to
evaporative cooling leading to fog formation. In a subsequent
study, Tardif and Rasmussen (2010) used a Lagrangian-frame
simulation of a microphysical column model and showed that
evaporation from raindrops departing from equilibrium, i. e., the
latent heat loss from droplet evaporation does not balance
sensible heat flux from the ambient air, facilitates fog formation.
Consequently, they indicate that the non-equilibrium state of the
falling raindrops needs to be taken into account while considering precipitation fog events.
5.4. Environmental impact on terrestrial systems
Advecting marine fog transports water and materials onto
adjacent coastal and terrestrial systems, profoundly altering the
environmental conditions of these systems. The transport of fog
water droplets and their associated soluble ions, dust particles,
microorganisms, and mixtures of organic and inorganic reaction
products changes the hydrologic (Bruijnzeel et al., 2005;
Dawson, 1998), thermodynamic (Madej et al., 2006; Iacobellis
and Cayan, 2013), nutrient (Weathers and Likens, 1996; Ewing
et al., 2009; González et al., 2011), and toxicological (Wurl and
Obbard, 2004) regimes of ecosystems along the coast. Although
the interactions are variable and complex, several prominent
ecological mechanisms associated with each type of flux
(water, energy, and microscopic particulates both inorganic
and organic) have been elucidated.
The strong ecological response to the spatial and temporal
variability of marine fog results in discernible impacts on diurnal
to seasonal (Fischer et al., 2009; Carbone et al., 2013), interannual to decadal (Cereceda et al., 2007; Garreaud et al., 2008),
and longer scales (Gutiérrez et al., 2008; Williams et al., 2008).
The strong biogeographic climate signature of transported
marine fog water and minerals has led to terrestrial proxies for
paleoclimate change in coastal upwelling. Fog associated species
such as coastal redwoods [Sequoia sempervirens ((D. Don)
Endl.)], coastal fog opportunists such as Bishop pines (Pinus
muricata (D. Don)), and endemic fog-dependent species such as
Torrey pine (Pinus torreyana ssp. insularis (Haller)) and Chilean
Tique [Aextoxicon punctatum (Ruiz and Pav)] have provided
pollen (Heusser, 1998; Barron and Anderson, 2011) and
tree-ring based chronologies (Roden et al., 2009; Williams et
al., 2008; Gutiérrez et al., 2008; Díaz et al., 2001) that are used as
paleoclimate proxies (Poole and van Bergen, 2006) in the
interpretation of atmosphere–ocean linkages associated with
major climate events. Johnstone and Dawson (2010) related
how, for example, over the last 8 millennia, expansion and
contraction of fog associated vegetation is interpreted as a
synchronous response to marine fog-conducive upwelling
conditions that are an expression of ENSO/PDO variations
(Barron and Anderson, 2011). In the context of longer
paleoecological timescales, fog imbued coastal regions now
act as refugia for forest ecosystems that were once distributed
across North America but do not tolerate current combinations
of arid and freezing conditions such as redwood forests that are
now restricted to a narrow 50 km belt along the California
coast (Ahuja, 2009; LePage et al., 2005; Johnstone and Dawson,
2010). Reported decline of summer fog along the California
coast (Johnstone and Dawson, 2010) and Hokkaido Island,
Japan (Sugimoto et al., 2013) could result in a cascade of
ecosystem responses should the trend continue.
158
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
5.4.1. Marine fog — hydrologic impacts on ecosystem dynamics
One of the earliest records of hydrologic transport from
marine fogs into coastal ecosystems is the “sacred” fountain
tree, attributed to Pliny the Elder. In 1764, George Glas described
it as a laurel [Ocotea foetens (Aiton) (Benth. & Hook.f)] on the
Canary island of El Hierro used by indigenous people until its
uprooting during a hurricane in 1610 (Gioda et al., 1995). In
recent times, coastal drylands adjacent to eastern ocean basins
such as California, Chile, and Namibia have been used as a
productive laboratories to understand the mechanisms of
hydrologic input from marine fog at scales from microbiological
to landscape. In the Mediterranean climate system of California,
precipitation from the strong cyclonic activity occurs during the
winter with little to no precipitation falling during the
anticyclonic dry summer period. By late summer the water
deficit experienced by plants as they transpire more water than
is available to maintain tissue turgor is at its most extreme, and
if unabated will cause a plant to wilt and die.
Two mechanisms effectively transfer liquid water from
marine fogs into ecosystems. The first is fog drip, where fog
water accumulates on surfaces such as leaves, needles, or stems
then drips or is channeled to the soil surface providing
moisture to shallow roots (Means, 1927; Oberlander, 1956;
Azevedo and Morgan, 1974; Ingraham and Matthews, 1995;
Corbin et al., 2005; Simonin et al., 2009; Roth-Nebelsick et al.,
2012) or to collection containers for human use (Schemenauer
and Cereceda, 1994). The second is foliar uptake where
leaf-wetting fog events increase foliar hydration when water
moves directly through leaf pores and surfaces into internal
tissues along a water potential gradient (Limm et al., 2009;
Burgess and Dawson, 2004; Vasey et al., 2012; Eller et al.,
2013). Critically important features for this mechanism are the
absolute liquid water content of the marine fog event and
deposition rate (Lovett, 1984; Slinn, 1982; Joslin et al., 1990;
Katata et al., 2010; Hiatt et al., 2012). The first is predominantly
a function of cloud condensation nuclei (CCN) composition and
path history while the second is influenced by wind speed,
wind direction, and dew-point depression. Liquid water inputs
based on annual averages from flat screen fog collectors and fog
gauges range from 0.2 to 15 l/m2/day (Schemenauer and
Cereceda, 1991; Larrain et al., 2002; Juvik et al., 2011; Klemm
et al., 2012).
Marine fog also increases the relative humidity, suppressing transpiration and effectively reducing plant water deficit
(Ritter et al., 2009; Fischer et al., 2009; Williams et al., 2008).
This effect is also notable in regions with a predominant
nocturnal marine fog layer that acts to reduce nighttime
transpiration rates (Dawson et al., 2007; Alvarado-Barrientos
et al., 2013). The increase in available water and reduction of
evaporative stress has marked impact on overall ecosystem
productivity. At the canopy level, forest photosynthesis and
carbon uptake is substantially higher during fog events than
on clear, sunny days — a result of diffuse radiation on canopy
CO2 (Carbone et al., 2013). At the soil level, increased
microbial activity measured as soil respiration also increases
available nutrients leading to an overall increase in ecosystem productivity (Carbone et al., 2011).
5.4.2. Marine fog — chemical and microbiological impacts
Fogs have long been observed to carry more than water
resulting in both deleterious and beneficial impacts. The
same mechanisms that can lead to fog formation, such as
anticyclone mediated temperature inversion, can trap and
concentrate urban, industrial, and agricultural aerosols to toxic
levels. Despite an extensive literature on nutrient and pollutant
deposition flux from fog (Weathers et al., 1986; Collett et al.,
2002) much is still poorly understood about the impact of
marine fog chemistry and microbiota on humans and ecosystems. Concerns over impacts from deposition nitrogen oxides
and anthropogenic sulfur dioxide (which are also found in
marine fogs) have resulted in regulations on industrial and
urban emissions (Lynch et al., 2000). Other impacts such as
asthmatic or allergic reactions to constituents in marine fogs
have not been adequately studied or regulated. In part, this
results from the complexity of marine chemical composition
and the reactions that occur on multiple temporal scales as well
as the difficulty of ascribing causal linkage within an epidemiological context.
The marine fog air mass is a complex and reaction rich
aqueous environment that reflects its ocean origin and path
trajectory. The droplet “core” varies in size depending on
chemical composition and can have a variety of surface films
(Fig. 11) that are altered over time (Donaldson and Vaida,
2006). The interstitial space between fog droplets can contain
inactivated or hydrophobic dry aerosol particles and aerosol
precursor gases. These include, in varying quantities and for
various durations, ionic ocean salts, anthropogenic and naturally occurring sulfate and organic compounds, atmospheric
particles entrained from above such as wind-blown mineral
dust and trace metals, and bioaerosols.
Large differences in reported marine fog water chemistry
(Gundel et al., 1994, Northern California; Klemm et al., 1994,
New England; Watanabe et al., 2001, Japan; Yue et al., 2012,
South China Sea) suggest a strong geographic and temporal
response of marine fog to ambient conditions. This is also
mirrored in the differences between terrestrial and marine fog
chemistry, the latter tending to have higher concentrations of
inorganic ions, lower pH, and lower primary sulfates (Kimball
et al., 1988). In contrast, marine fog of longer exposure has
higher concentrations of secondary ammonium sulfate and
nitrate (Chow et al., 1996). The bidirectional coastal flux
between marine fog and anthropogenic sulfur dioxide (the
precursor of sulfuric acid and sulfate aerosols) from copper
smelters in Chile is an example of inland-sourced aerosols
affecting marine fog formation and evolution. The reactions
that occur in the fog air mass as it moves from the ocean to land
surfaces and toward a dissipation or deposition location defines
the material composition and size and, as a consequence, the
type and level of impact on the receiving body, either organism
or landscape unit.
Fig. 11. Atmospheric processing of a 0.2 μm organic marine aerosol.
From Ellison et al. (1999).
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
High chloride ion concentrations from sea spray-originating
CCN result in a much more corrosive atmospheric environment
causing cars to rust faster in seaside cities (Ma et al., 2009). The
impacts of other ions including nitrogen and sulfur compounds
are beneficial for nutrient limited ecosystems but deleterious
when in excess of ecosystem needs. When marine fog scavenges excess ammonium sulfate, ammonium nitrate, and other
air pollutants, air quality improves however the subsequent
deposition (acid rain) results in deleterious impacts on vegetation and water bodies. Essential plant nutrients transported by
marine fog such as nitrogen, calcium, and to some extent sulfur
have measurable beneficial impacts on the growth of forest,
shrub, lichen, and fungal communities (Weathers et al., 2000;
Ewing et al., 2009; Maphangwa et al., 2012).
The highly curved surface microlayer of bursting ocean
bubbles is an important interface of hydration microphysics.
Toxicologically significant pollutants such as chlorinated hydrocarbons, organotin compounds, petroleum hydrocarbons, polycyclic aromatic hydrocarbons (PAH), and heavy metals are up to
500 times more concentrated in this sea-surface microlayer (top
1–1000 μm) than in the underlying bulk water column (Wurl
and Obbard 2004). The flux of these materials into marine fog
aerosols and their epidemiological impact is not yet well
quantified.
Airborne particles of biological origin such as fungal spores,
bacteria, algae, viruses, pollen, excretions, and biotic fragments of
varying size represent a significant fraction of air particles
(Després et al., 2012). Experiments in 1769 by Spallanzani to
disprove spontaneous generation initiated the field of aerobiology. Fog temperature, chemical composition, and acidity affect
the concentration of bacteria and yeasts but not of molds (Fuzzi
et al., 1997). Metabolic activity of aerosol microbes can
transform the chemical constituents of fog aerosols affecting
the microstructure, wettability, and hydration of the aerosols
(Deguillaume et al., 2008). The sources and impacts of
microbial aerosols are not yet well understood. Urbano et al.
(2011) used both culture dependent and independent techniques such as DNA clone libraries to examine 55 sequences
obtained from a coastal pier in southern California. The majority
of these were fungi and bacteria taxa commonly found from
terrestrial sources suggesting a beach or intertidal source.
Dueker et al. (2012) used 16S rRNA sequencing on bacterial
cultures from ocean and onshore coastal Maine collections
during foggy and clear days and found that cultures from foggy
days were dominated by ocean surface microbial aerosols and
microbial viability was enhanced when fog was present.
The epidemiology of airborne material has a rich literature
(Fernstrom and Goldblatt, 2013) but the specific flux and
consequence from marine fog is sparse. Many different atmospheric aerosols that are also constituents of marine fogs have
known human health effects such as viruses, cyanobacteria
(Genitsaris et al., 2011), dust (Sandstrom and Forsberg, 2008),
and heavy metals such as mercury (Tchounwou et al., 2003).
Pandemics that can affect large portions of the global population
that also have an aerosol/droplet component, such as the
1918–1920 Spanish Flu, are increasingly investigated using an
atmospheric aerosol conceptual model (Fuhrmann, 2010). The
epidemiological requirement to ascertain unambiguous causal
linkage makes the challenge of identifying impacts of marine fog
on human health more challenging. The inter-connectedness of
the air–ocean–land–anthropic system at global, mesoscale, and
159
local scales increases the potential for an environmental burden
associated with marine fogs. Efforts are underway to investigate
the environmental burden in marine fog of several categories of
toxicologically damaging constituents such as mercury (Weiss
Penzias et al., 2012), nitrosamines (Cornell et al., 2003), and
other organic compounds (Herckes et al., 2013).
5.4.3. Marine fog — thermodynamic ecosystem impacts
Marine fog-related reductions in daytime temperature and
increases in nighttime temperature moderate climatic conditions
for coastal ecosystems and reduce extremes in climate variability
(Lebassi-Habtezion et al., 2011; Iacobellis and Cayan, 2013). The
impact of the thermal moderation has wide ranging effects both
for human communities and natural resources. Marine fog
reduction of thermal extremes decreases emergency response
to extreme heat events and human mortality (Gershunov and
Johnston, 2011). Energy consumption is reduced in several
sectors such as irrigation pumping and air conditioning thereby
lowering energy infrastructure costs (Miller et al., 2008;
Lebassi et al., 2010). Impacts on coastal wildlife from marine
fog are also widespread. Sessile organisms such as intertidal
marine invertebrates experience high mortality during low
tide events coinciding with anomalously low summertime fog
conditions (Helmuth et al., 2006). Marine fog blocks shortwave
radiation during California summer months when coastal
stream flow is low helping to reduce salmon mortality from
thermal stress. Fisheries managers in the eastern Pacific are
greatly concerned that the 20th century trend (Johnstone and
Dawson, 2010) of 33% declines in coastal fog could continue.
5.4.4. Conclusion
Marine fogs provide many valuable ecosystem services with
most not yet fully quantified. Once considered primarily for the
hazard it posed to ship, aviation, and other transportation sectors, it is now also recognized for beneficial properties (Bendix et
al., 2011). Climatological projections of future marine fog
conditions (O'Brien et al., 2013; Haensler et al., 2011; Tseng et
al., 2012) are of extreme interest to many social and management sectors including energy (Gonzalez-Cruz et al., 2013),
emergency response (Gershunov and Johnston, 2011), coastal
fisheries (King et al., 2011), and biodiversity (Ackerly et al.,
2012). Interdisciplinary inquiry will help advance our understanding of the environmental impacts of marine fog and
quantify the ecosystem services provided by marine fog.
6. Microphysics of fog
Most of what is known about fog microphysics pertains to
land fog. Nevertheless, it is suspected that results obtained
from land-based fog observations are applicable in principle
to sea fogs and especially to fogs near the coastline.
Probably the greatest difference between fog and cloud
microphysics is the greater perceived importance of smaller
droplets in fog compared to most aloft cloud studies. This is
partly due to the greater prevalence of smaller droplets in more
polluted environments that are more frequent at the surface
where most of the pollution originates. Another reason is the
greater importance of the 2nd moment of droplet sizes compared to higher moments that are more important for many
cloud studies; i.e., precipitation, radar measurements. This lower
moment, which is directly related to the prime motivation of
160
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
most fog studies, visibility, attaches more importance to smaller
droplets.
One of the earliest investigations of fog microphysics was
conducted by Pedersen and Todsen (1960). They were the
first to note bimodal drop size distributions, which were also
occasionally observed by May (1961) at 1–2 and 15–25 μm
diameters. Garland (1971) found bimodality (Fig. 12) in half
of a more extensive investigation of 25 rural England fogs,
where small droplet concentrations exceeded 500 cm−3.
He attributed the bimodality to the difference between the
larger activated droplets and the smaller unactivated droplets.
Activated droplet sizes exceeded their critical sizes (at the peak
of the Kohler curve of each particle) because the ambient
supersaturation (S) exceeded the critical S (Sc) of the particles
upon which these droplets had formed. Unactivated droplets
had grown on particles with Sc higher than ambient S. Garland
noted that although the unactivated (haze) droplets did not
contribute much to the liquid water content of the fogs, their
disproportionate surface area made a greater relative contribution to visibility reduction because in nine of the twenty five
fogs these haze droplets contributed 30% of the atmospheric
extinction and in five of the fogs they produced 60% of the
extinction. Roach et al. (1976) also noted that the activated
droplet mode contributed greatly to the liquid water content
(LWC) but little to visibility reduction. Elias et al. (2009) also
found that small unactivated haze droplets produced the
majority of the extinction in polluted Paris fogs. Fig. 13 shows
larger fog droplet size distributions in rather unpolluted fogs at
Albany, New York (Fuzzi et al., 1984).
Roach et al. (1976) were the first to point out the importance
of gravitational collection of droplets at the ground, which
considerably reduced LWC from 1–2 g m−3 to b0.3 g m−3.
Gultepe et al. (2007b) noted that most fogs have LWC 0.01–
0.4 g m−3. Roach et al. (1976) also placed more importance on
microphysics by noting that the droplets themselves contribute to the radiative cooling that sustains the fog. Although
Roach (1976) predicted fog S of a few hundredths of a percent,
estimates of fog S by matching activated mode fog droplet
concentrations (Nc) with CCN concentrations (NCCN) measured
at various S, inferred 0.8% S in rural England fogs (Roach et al.,
1976). This type of inference of fog or cloud S by matching
NCCN(S) (CCN spectra) with nearby measured Nc is dubbed
effective S (Seff). With CCN measurements at much lower S by
using an isothermal haze chamber (IHC), Fitzgerald (1978)
inferred Seff of 0.055–0.79% in sea fogs off Nova Scotia. With
similar equipment, Hudson (1980; Fig. 14) found fog Seff of
0.06–0.11% at four different locations along the U.S. West Coast,
including one on a ship at sea (Fig. 14b).
These similar Seff in spite of very different NCCN indicated the
validity of the proportionality between NCCN and measured Nc.
Another measurement at San Diego, California in more polluted
conditions showed Seff ≪ 0.04% where the haze droplets alone
reduced visibility well below 1 km (Fig. 14). Gultepe et al.
(2007b) noted that the distinction between activated and unactivated droplets is much less in more polluted environments.
On the other hand, measurements in mountain impacted stratus
by Hudson and Rogers (1986) showed higher Seff in the cleaner
San Marcos Pass, California (Seff N 0.1%) than at the more
polluted Henninger Flats (0.02–0.05% Seff), which was immediately downwind of Los Angeles. This seemed to confirm
predictions of Twomey (1959) that Seff should be inversely
related to NCCN. Similar aircraft measurements in stratus clouds
off the US west coast indicated slightly higher Seff of ~0.2%
(Hudson, 1983). This was confirmed by more extensive aircraft
Fig. 12. Droplet concentrations in various size intervals.
From Garland (1971).
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
161
Fig. 13. Volume percent distribution of fog droplets taken at different times during the October 1, 1982 fog event. The solid line represents the integral percent
volume as a function of droplet size. The data from the Particle Measuring Systems — Forward Scattering Spectrometer Probe 100 are integrated on 12-min
periods. The droplet size interval taken into account is 4–47 μm diameter.
From Fuzzi et al. (1984).
measurements of stratus off the US west coast by Hudson et al.
(2010) and Hudson and Noble (2014) where a wider range of
NCCN and Nc definitively showed the expected decrease of Seff
with NCCN predicted by Twomey (1959), with clean stratus
showing Seff N 1%. This was quite different from fog Seff with
similarly low NCCN and Nc on a ship off the west coast where Seff
was 0.06% (Hudson, 1980). Further measurements with the
same CCN instruments and similar droplet instruments in
radiation fog at Albany New York showed Seff of 0.026–0.20%
whereas similar measurements in impacted stratus at Whiteface
Mountain, New York showed Seff of 0.14–0.35% (Hudson, 1984).
Thus, in all comparisons fog seemed to show a lower Seff than
Fig. 14. Data showing ambient Nc (×) and simultaneous cumulative drop distributions (+) within the isothermal haze chamber (IHC). The third distributions (○) take
the IHC curves and move the sizes from r100 (the size in the IHC, which is at 100% RH or 0% S) to rc (critical radius; rc = 30.5 r100). This curve does not represent any real
drop distribution but is only used to determine Seff, from the r at the intersection of the × and ○ lines so that Sc = 7.1 × 10−2 / rc, which was 0.06% (a) and (b) and
≪0.04% (c). Visibility calculated from the ambient fog drop distribution was 6000 m (a), 1900 m (b), and 670 m (c). (a) Yaquina Head, Oregon, 0121–0125 PST 26 July
1977; (b) on board the R.V. Wecoma, 30 km off Oregon coast, 0730–0740 PST 29 July 1977; (c) San Diego, CA 0030–0040 PST 27 December 1975.
From Hudson (1980).
162
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
stratus that was either detached from the surface or impacted on
mountains. This Seff difference between fog and stratus is
probably partly due to the lower vertical velocities of fog due to
the inhibition of motion due to the proximity of the surface.
Meyer et al. (1980) differentiated haze from fog conditions
in that visual range (V) which depends strictly on particle
concentrations in unsaturated haze conditions but changes
abruptly to dependence on particle/droplet size at V ~ 1–2 km
where activation to fog/cloud conditions occurs (Fig. 15).
In haze conditions, where there are no activated droplets, V
depended only on particle concentrations and very little on
particle sizes, but in activated fog conditions particle/droplet
size first became equal to particle concentration effects and
then overtook particle concentration dependence (Fig. 15b).
The larger sized activated mode tends to persist more than the
smaller sized unactivated mode during fog dissipation stages.
Precipitation often accompanies fog, especially fogs associated with frontal passages. Tardif and Rasmussen (2007) show
precipitation fog as one of the four major fog types along with
radiation, advection, and cloud-base-lowering fogs. Fig. 16a
shows a fog case with precipitation throughout the fog while
Fig. 16b shows precipitation only after the fog.
Fig. 16c shows relationships between fog visibility and
precipitation, which shows that at high precipitation rates
visibility is much lower than estimated from earlier studies and
that visibility decreases much more gradually with precipitation rate than the earlier studies. Furthermore, drizzle seemed
to have more effect than rain on visibility. The huge scattering
of the data shows that more detail of the drop and droplet sizes
are needed to better relate to visibility. Haeffelin et al. (2005)
reported that light precipitation contributed to the reduction in
visibility in three quarters of non-precipitation types of fogs.
Recent research that has revisited Sean Twomey's interesting conjectures of over 50 years ago (Twomey, 1959)
gives evidence of the interaction of vertical motion fluctuations in stratus (turbulence) and cloud microphysics. Though
clouds are generally caused by rising air (positive vertical
velocity [W]), unlike cumulus clouds, stratus clouds cannot
have net positive W because they are usually vertically
confined. Thus, over sufficient distances mean W is usually
zero in stratus. This is why the fluctuations of W rather than
mean W are generally considered in stratus (Peng et al., 2005).
Hudson and Noble (2014) showed that droplet concentrations
(Nc) were proportional to standard deviations of W (σw) in
polluted stratus clouds where Nc was not correlated with CCN
concentrations (NCCN) as was the case in cleaner air masses.
Hudson et al. (2010) demonstrated the suppression of cloud
supersaturation (S) by higher NCCN that had been predicted by
Twomey (1959). At these lower values of S, cumulative CCN
spectra are generally steeper (i.e., higher k; i.e., greater NCCN
differences per S difference). Twomey (1959) pointed out that
as k increases, Nc switches from predominant dependence on
NCCN to predominant dependence on W. So at the higher k that
is more relevant as S is depressed, variations of W rather than
variations of NCCN become more important for determining Nc
and there is a higher correlation between Nc and W than
between Nc and NCCN (Hudson and Noble, 2014). In the case of
fog, this is σw rather than W. Twomey (1959) said that high k
makes W more important than NCCN for determining Nc, but it
is the high NCCN and high k of the low S portion of the CCN
spectrum that makes W or σw variations more important than
NCCN variations for determining Nc.
The studies described here affirm the contention in the
introductory paragraphs of the relative importance of smaller
droplets in fogs compared to clouds. Smaller droplets have
greater relative significance for visibility and chemistry,
which are more important for fogs than for most cloud
studies. Thus, even unactivated haze droplets take on much
more significance for fogs compared to clouds. Furthermore,
this is exacerbated by the greater losses due to fallout and
impaction of the larger droplets, which is more prevalent
closer to the surface, where there are also obstacles such as
vegetation (mainly trees). Fogs can even occur without
supersaturation, especially in more polluted environments,
Fig. 15. Visual range versus (a) cumulative aerosol concentrations and (b) rms diameter squared (mean surface diameter squared). Solid vertical lines indicate
one standard deviation.
From Meyer et al. (1980).
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
163
Fig. 16. (a, b) Visibility, RH, T, and PRR time series from observations of two fogs and the results for a proposed parameterization (Vis-RHw), green line is
parameterization. (c) Vis-vs-PR relationships for rain and drizzle, shown with red solid lines and red dashed lines, respectively, overlaid on all data points (red dots).
From Gultepe et al. (2009).
where supersaturations are suppressed by competition among
droplets. The impact of pollution (higher CCN concentrations)
seems to be more recognizable in fogs where the smaller
droplets take on more importance and the larger droplets also
fall out.
7. Marine fog forecasting
Two-way communication is essential for weather forecasts —
timely receipt of observations at the operational prediction
center and delivery of the forecast product to clients. This process
164
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
was initiated by land telegraph that connected stations across
continents by 1870; undersea cables linked the world by 1900.
At sea, long-distance communications began with early radio,
which received a big expansion in 1912 with the sinking of the
Titanic in fog, followed by a steady stream of technical
improvements that later included satellites and ultimately the
World Wide Web.
Weather observations at sea were taken with basic
instruments read by eye and transmitted by radio. This evolved
to automated instruments on ships that recorded information
digitally and transmitted electronically. The era of satellite
measurements has made it possible to sample the entire world
ocean, beginning with sea surface temperatures, later including
estimates of winds based on cloud tracking, and vertical
thermal structure from radiance soundings, and finally surface
wind based on sea-state structure.
Over the past hundred years, fog forecasts have steadily
improved. In the early 20th century, only the ship-based
climatology was available. With the development of aviation
during these same decades, land forecast centers were established and issued forecasts based on hand-plotted and analyzed
maps, fog-prediction diagrams, and subjective experience of the
synoptic meteorologist. By the beginning of WWII, weather
forecasting was viewed in terms of the synoptic situation or
so-called synoptic typing. The evolution of a weather pattern for
periods up to a week was based on the analog method —
matching current weather with a similar pattern in the past and
using the historical pattern as a guide for the current forecast. As
mentioned in the history of sea fog research (Lewis et al., 2004),
C. K. M. Douglas had an encyclopedic memory of historical
weather patterns and the D-Day forecast benefitted greatly from
his memory of past weather. After WWII, the advent of the
digital computer led to computer-based numerical weather map
analyses that fed into the dynamical prediction models —
essentially objective analyses and forecasts based on the physics
of the atmosphere and the supporting numerical methods
required to solve the governing equations. With knowledge
that came from numerical prediction experiments/simulations
on the global scale in the 1960s and beyond, operational forecast
models began to produce products over the oceanic areas by the
1970s. Nevertheless, the coarse resolution of the early global
models made it impossible to capture the small-scale processes
related to sea fog initiation and maintenance.
The focus of this section is forecasting fog at sea including
those situations where the marine influence laps over the
coast. Forecasting is intrinsically perishable — it ceases to be
useful to the operational forecaster after a given time point. The
“nowcast” is a short-period extrapolation of analyses based on
current observations and background information such as a
forecast from an earlier time and/or climatology. The longerrange forecast relies on an accurate initial condition and a
dynamical model. The merit of a forecast is judged on its ability
to improve on the climatological background state.
In the following, we supply details on some of the major
issues that have faced sea fog forecasting.
7.1. Initial climatology
Basic climatology of marine fog over the world sea was
developed through the international collection of weather
observations from ships and lighthouses (Fig. 17) starting in
the mid-1800s (see Section 4, Marine fog field programs). An
example of a California coastal fog climatology based upon
ship and coastal stations was shown in Fig. 4.
Over time, the number of stations and data volume
transmission expanded — critically important for weather
forecasts on the synoptic scale. Significant weather data transmission by telegraph was transcontinental by the later 1800s;
transoceanic undersea cables were well established by 1900, and
ship radio was expanding by the 1920s.
Early prediction was based upon fog-prediction diagrams
(Petterssen, 1956). The main client was a coastal airport where
the forecast relied on surface observations such as air
temperature and dew point depression along with sounding
data that delivered wind shear used to predict the chance of
fog, its hour of onset, severity (dense/moderate/light fog), and
time of breakup. While Taylor (1917) was among the first to
use this technique at sea, forecasts based on these subjective
principles remained in use well into late 20th century until
numerical fog prediction replaced this approach.
7.2. Surge expansion with aviation
Dramatic expansion of operational weather surface observations so important for the understanding of fog and
short-term forecasts occurred with the initial development of
Fig. 17. Marine fog envelops the West Quoddy Head Lighthouse and a Coast Guard vessel in Penobscot Bay, both on the Maine coast and under the influence of the
Labrador Current/Gulf Stream complex. Coastal fog climatology often depends upon lighthouse and ship observations.
Photos from http://www.photolib.noaa.gov/index.html and http://www.snodoglog.com/09Summer-Pg5.html.
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
commercial aviation, especially air mail and passenger service
over land in the late 1920s and 1930s (George, 1960, http://
celebrating200years.noaa.gov/foundations/aviation_weather/
#get). Interest in marine fog forecasting tended to be restricted
to coastal airports subject to fog that included bays. In these
early days, there were a disproportionate number of floatplane
operations due to the major travel junctions at coastal bays and
the limited number and quality of land runways (Johnson,
2009). The forecasting technique tended to be based on a
combination of climatology, fog-prediction diagrams and a
subjective estimate of the synoptic weather trend. The latter for
some stations was a subjective analysis from a central agency
relying on experience, climatology, surface weather observations and soundings, performed by skilled forecasters such as
C.K.M. Douglas and H.H. Lamb in Great Britain (Lewis et al.,
2004) and the National Weather Service in the U.S. (http://
www.noaa.gov/features/protecting_1208/weatherservice.
html). Some forecasters maintained a reputation of superiority over machines in forecasting fog long into the era of
central, computer-based forecasts, due to the difficulty
of such systems in accurately expressing the subsynoptic
scale, never mind the microscale, in the early stages of their
development.
More systematic collection of surface observations and
balloon soundings at fixed sea locations began with the
development of Ocean Weather Ships (in part the response
to a Pan-American aircraft accident over the Pacific Ocean in
1938) and the heightened trans-North Atlantic ship and air
traffic with the escalation of WWII. A dozen Ocean Weather
Ship stations were in the north Atlantic and three were in the
north Pacific starting about 1940 and ending in the 1970s
(Adams, 2010).
7.3. After WWII
Following WWII, the weather forecasting industry in both
government and private sectors expanded and flourished. Central to these expansions were the world wide communication
circuits that morphed from land, undersea cable and radio
teletype to the World Wide Web at century's end. In the 1950s–
1960s, most surface observations were made by human observers at airports, encoded onto paper forms and transmitted by
teletype. By 2000, most of these observations were digitized by
automated weather devices and entered into the World Wide
Web. Satellite detection of marine fog lagged, but has improved
with use of visual and IR bands as well as satellite-based
soundings for enhanced measurement of the synoptic structure
over water. By the late 20th century, the expanded coverage that
came with numerical weather prediction included all of the
world's temperate latitudes (see Section 7.4). The forecast of low
cloud and properties of the marine layer (including fog) still need
considerable development.
Although numerical weather prediction's development was
initially slow following WWII, advances in both computing
power and theories of atmospheric processes led to improved
models and faster execution times by the mid-1960s. By the
1980s, satellite-based measurement of SST, clouds and temperature structure through inversion of radiance measurements led to improved initial conditions for the numerical
models — especially noticeable over the data-sparse oceans.
165
7.4. Growth of operational numerical models
The main approach to marine fog forecasting starts with
the larger scale motions. Output from the large-scale models
generally provides boundary conditions for the subsynoptic
and mesoscale models that cover a coherent geographical area
such as a specific coast or a small section of an ocean and the
adjacent area. The horizontal resolution of these local models
approaches 10 km or better, although upper-air observations
cannot resolve structures at this resolution. The benefits of fog
forecasts that use output from these small-scale models in
concert with climatology have received some attention (Wells,
2007), but the sample size is limited.
Many coastal stations, such as U.S. Gulf Coast stations,
forecast fog based upon a conceptual decision-tree process
that is a mix of climatology (large scale and station specifics),
observations (local surface, local sounding, satellite imagery),
centrally produced numerical guidance and analyses, local
model diagnostic software, and qualitative assessment of
cloud condensation nuclei (Croft et al., 1997). This combination of factors is used to forecast fog initiation, visibility, and
breakup of fog.
Bartok et al. (2012) present a recent example of the use of a
high resolution, 3-dimensional numerical model (WRF) coupled
with a one-dimensional fog model that employs boundary layer
processes and parameterized microphysics to forecast the
occurrence of fog for road traffic on the north coast of the
United Arab Emirates with significant skill. The fogs are formed
at night from Persian Gulf marine air advected over land.
Comparisons of a forecast with corresponding satellite image
are shown in Fig. 18, where a correct forecast is when fog is
forecasted and occurs (27 out of 84 cases), a correct negative
forecast is when fog is forecasted to not occur and does not (16
out of 84 cases), and a false alarm is when fog is forecasted but
does not (19 out of 84 cases). Not shown is the case when fog
occurs when not forecasted, which happened only in a small
proportion of the cases studied (5 out of 84 cases).
It has been suggested that improvements could be made
from better integration of operationally available surface based
measurements, remote measurements (satellite, ground based
profilers), and numerical model outputs (Ellrod, 1995; Isaac et
al., 2006). Another example is Zhou et al. (2007) who propose
two different approaches that use an operational numerical
model — one improves the probability of fog occurrence while
the other also improves liquid water content which is required
for visibility forecasts.
A variation of working on a numerical weather prediction
system itself is to base fog prediction on multi-mesoscale
models related in subgroupings to form systems (Zhou et al.,
2007; Zhou and Du, 2010). An advantage is that this allows a
relative check on how individual models and various groupings
perform on deterministic and probabilistic forecasts. Ensemblebased forecasts are significantly better than those based on
individual models.
Another alternative to sea fog forecasting comes with the
pattern recognition method (a statistical methodology as
opposed to a dynamic prediction methodology). Among these
statistical methods is the classification and regression tree
(CART) method — a decision tree-building technique (Lewis,
2000). The advantage of this approach is that when the
available data sources, including numerical models, are not
166
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
Fig. 18. Comparison pairs of fog forecast (left, WRF model, relative humidity in units of percent, gray scale with white = 100%; arrows are 10-m flow streamlines)
with corresponding satellite image (right, EUMET satellite) for north coast of the United Arab Emirates at time and date posted in upper left. White areas denote
foggy regions. See text for discussion.
Figure adapted from Bartok et al. (2012).
strongly related to fog formation, combinations of indirect
factors can sometimes improve the forecast. Lewis (2000)
applied this technique to fog forecasting at the Kunsan Air Base
in Korea. His model input included sea surface temperatures,
land-based surface observations, upper-air soundings, and
output from a numerical weather prediction model. Forecasting for fog has been also conducted using artificial neural
networks (e.g., Fabbian et al., 2007) and for cloud ceiling and
visibility using fuzzy logic (e.g., Hansen, 2007) and neural
networks (Marzban et al., 2007).
7.5. Satellite development and expansion
The analysis of current observations is the essential component of the “nowcast” — the prediction on the order of several
hours. As expected, satellite observations are crucial for the
nowcast over the oceanic areas. The visible imagery can
generally detect thick cloud, but thin cloudy areas including
foggy areas are difficult to detect — essentially transparent
during the daylight hours. The infrared radiance measurements
at night generally fail to differentiate between the temperature of
the fog top and the temperature of the sea surface. Nevertheless,
work is underway to differential these temperature based on
radiance differences in the shorter- and longer-waves in the
infrared spectrum (Eyre et al., 1984; Ellrod, 1995). A filtering
process helps identify those image pixels with partial coverage
by fog. Operational algorithms for daytime detection of fog and
low stratus have been proposed by Bendix et al. (2006) (Terra
MODIS) and Cermak and Bendix (2007) (Meteosat SEVIRI).
Satellite-based fog detection over a range of oceanic scales
is paramount to improving the fog forecasts at sea (Ellrod
and Gultepe, 2007). Bendix et al. (2006) have proposed a fog
detection algorithm for the MODIS instrument that includes
channels in the near infrared that reportedly can detect fog to
500 m resolution.
7.6. The elusive sub-mesoscale and microscale
A major obstacle to operational numerical prediction of fog
is the inability to directly incorporate fog microphysics into the
model. In part, this is due to increased computational demands
that come with the microphysics parameterization (Müller et
al., 2007; Gultepe et al., 2006c; Tardif and Rasmussen, 2010).
Further, as presented and discussed in Section 6 (Microphysics
of marine fog), equations governing the microphysics of fog
droplets are not easily linked with the equations that govern a
mesoscale model. That is, the parameterization is not straightforward and generally involves serious assumptions. A lot of
uncertainty is also present in unknown initial and boundary
conditions of aerosols. Another challenge is to optimally couple
the background climatology with the observations. Thus, on
both the large- and small-scale, factors critical to fog and its
evolution are not easily incorporated into a forecast system
(Stoelinga and Warner, 1999; Gultepe et al., 2006a).
There has been considerable work on the relationships
between the sub-mesoscale or microscale and fog. To give an
impression of the complexities missed by operational models,
some of the work on treatment of physical processes and needs
for their parameterizations in models follows.
Fog occurs in aerosol-laden surface air with high relative
humidity, ranging from undersaturated to slightly supersaturated (Pruppacher and Klett, 1997); it is a mixture of micronsize haze (unactivated) particles and activated particles
reaching 10s of microns in size (Pinnick et al., 1978; Hudson,
1980; Gerber, 1981). Fog's microstructure and life cycle depend
on the properties of aerosols (Bott, 1991), as does superstation
(Pilié et al., 1975; Gerber, 1991). Fog droplets are generally
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
smaller than cloud droplets; fog liquid water content is
generally small, and most fog liquid water content ranges
from 0.01 to 0.4 g m−3 (Gultepe et al., 2007b).
Liquid water content is related to droplet concentration
(Gerber, 1981, 1991; Garcia-Garcia et al., 2002; Fuzzi et al.,
1992), gravitational settling of larger drops (Bott et al., 1990),
and droplet size (Jiusto, 1981). Liquid water content has been
empirically related to extinction coefficients (Eldridge, 1971;
Tomasi and Tampieri, 1976; Kunkel, 1984; Gultepe et al.,
2006a) while liquid water content times the droplet number
concentration has been related to visibility (Gultepe et al.,
2006a), an important forecasting characteristic.
The role of small-scale turbulence in fog has been investigated by Zdunkowski and Barr (1972), Turton and Brown
(1987), and Musson-Genon (1987). Turbulence exchange coefficients in the nocturnal boundary layer with fog have been
explored in Turton and Brown (1987) and with a second-order
turbulence closure in Nakanishi and Niino (2004, 2006).
The cooling of moist air by radiative flux divergence has
been analyzed by Duynkerke (1991). Another complexity is
that clouds above the surface layer increase the downward
longwave radiation, reducing the longware radiation loss at the
top of the fog which is important to fog dynamics (Gultepe et
al., 2007a).
The role and importance of advection terms and their role in
fog formation and evolution have been shown by Guédalia and
Bergot (1994). Related is that large-eddy simulations show
distinct flow regimes in different stages of fog layer evolution
(Nakanishi, 2000).
Fog has been explored using models of different dimensions. Local surface measurements were assimilated into a
1-dimensional fog model (Bergot et al., 2005). Oliver et al.
(1978) used a second-order closure model to investigate
turbulence radiation properties in fog. Koračin et al. (2001,
2005b) used a 1D model in a Lagrangian framework and also a
3-dimensional model (Koračin et al., 2005a) to simulate
inversion and cloud forcing leading to fog. Bott et al. (1990)
employed a 2-dimensional fog model to examine the effects of
fog microphysics and radiation processes. Ballard et al. (1991)
and Pagowski et al. (2004) used 3-dimensional models to
explore fog variations. On the other hand, Bretherton et al.
(1999), Duynkerke et al. (1999) and Teixeira (1999) used
single-column versions of existing 3-D models that have been
used for fog studies. Muller (2006) developed a 1-D variation
assimilation scheme for surface observations and coupled two
1-D models with several operational 3-D models to produce an
ensemble forecast. Zhou and Du (2010) have developed a
multimodel mesoscale ensemble prediction system for fog and
showed that the ensemble-based forecasts are in general
superior to the single control forecasts.
As noted in the previous paragraph, a major restriction for
fog forecasting improvement was that forecasting is done
largely through operational numerical models that do not deal
directly with the fog physics but employ coefficients and
parameterizations. To improve this, the field project FRAM
included data from coastal and continental sites (Gultepe et al.,
2006a; Gultepe and Milbrandt, 2009; Toth et al., 2010). The
marine phase took place in the summer of 2006 along the Nova
Scotia Atlantic coast. Extensive measurements were made of the
lower atmosphere, including variables known to be important
for fog but not operationally available such as aerosol size and
167
concentration, cloud-base height, droplet size, droplet number
concentration, liquid water content, liquid water path, radiative
fluxes, vapor mixing ratio, precipitation type and intensity, 3D
wind speed and turbulence, and wind profiler with RASS while
satellite observations were included from GOES, MODIS Terra.
The data can be used to develop and improve microphysical
parameterizations which will be incorporated into numerical
forecast models. An example parameterization is that of
visibility versus the inverse of the liquid water content times
the cloud water drop number concentration (Nd) (Gultepe et al.,
2006b). Another example is the concentration weighted particle
terminal velocity times liquid water content versus a function
based upon the liquid water content and Nd.
Future improvement of world-wide forecasting of marine
fog will be based upon better observations of fog that will most
likely be through technical developments of satellite based,
remotely sensed systems such as hyperspectral channels to
provide a basis to improve detection algorithms (Ellrod and
Gultepe, 2007). Better remotely sensed observations will, in
turn, provide a basis for comparison with large scale operational numerical models to deal with their lack of sensitivity in
the smaller scale, near sea surface conditions to better handle
fog forecasting.
It is expected that fog forecasting will become at least
somewhat more realistic via algorithm development to make
up for unmeasured variables that are essential throughout the
life of fog (i.e., drop size distribution, condensation nuclei,
sub-grid scale motions and structure) as well as to have a basis
to forecast useful but previously unapproachable variables that
characterize fog conditions such as visibility and fog depth.
In recent years there has been strong interest in the evolution
of open sea and coastal zone fog from a climatological perspective. While global models are still operating with coarse
horizontal and vertical resolutions, regional climate models (e.g.,
O'Brien et al., 2013) are showing some promise in addressing
this issue.
8. Remote sensing of marine fog
Satellite imagery is used to identify low clouds and fog both
in conjunction with in-situ field studies (Gultepe et al., 2009)
and operationally by meteorological services (Molenar et al.,
2000). The standard approach to detecting fog relies on
detection of passive infrared emission or scattering from the
cloud-top. This is achieved by evaluating the difference between
the narrow-band brightness temperature observed in the
infrared window region of the spectrum (typically a wavelength
in the 10–12 μm range) and a brightness temperature observed
in the near-infrared (typically in the 3–4 μm range). The
emissivity of both increases with cloud depth, but the difference
is relatively uniform for clouds thicker than approximately
100 m (Ellrod, 1995). For low, liquid water clouds, the infrared
window channel emissivity is greater than the near-infrared
emissivity, leading to a positive difference (window channel
minus near-infrared channel) in the observed brightness
temperatures that increases with cloud thickness (Ellrod,
1995). At night, the difference is small (generally less than
2 °C) for clear-sky conditions and increases to as much as
approximately 5 °C for thick stratus or stratocumulus clouds.
Thin cirrus clouds yield a negative brightness temperature
difference that distinguishes them from lower liquid water
168
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
stratus clouds. Fig. 19 shows an operational application of the
technique applied to GOES 15 geostationary satellite imagery
depicting nighttime low clouds and fog intruding on the San
Francisco Bay Area. In addition to the presence of fog, depth of
the fog based upon the brightness temperature difference
(Ellrod, 1995) is also estimated operationally.
During the day, the signal of reflected near-infrared
radiation from the sun overwhelms the emission signal.
However, the near-infrared reflectance of low liquid water
cloud is substantially brighter than the reflectance of surface or
cirrus clouds, which generally yields a large negative brightness temperature difference useful for daytime detection of fog
and low stratus clouds (Fig. 20).
The infrared brightness temperature difference technique
is applicable over the ocean and a wide variety of land surface
types. It has been applied in a range of fog studies (Ellrod,
1995; Lee et al., 1997; Bendix, 2002).
One fundamental limitation of this approach is that it does
not precisely discriminate cloud near the surface from higherlevel stratocumulus or altostratus clouds. Higher-level clouds
may either obscure fog below or be mistaken for low-level
clouds. Furthermore, even for low clouds, the infrared emission
signatures of the cloud measured from above yield little
information regarding the proximity of the cloud base to the
surface. One solution to this problem is combining satellite data
with information about surface conditions. Gultepe et al.
(2007a) report that successful fog detection rates using satellite
imagery compared against surface monitoring stations in
Canada are only between 0.26 and 0.32, largely owing to the
presence of mid- or high-level clouds. However, they improve
the rate to between 0.55 and 1.00 by using numerical weather
prediction model-derived near-surface temperature estimates.
They report a false alarm rate of only 0.10. Similarly, Zhang and
Yi (2013) use a climatology of sea surface temperature to
discriminate fog from low stratus clouds over seas adjacent to
China. They find that the difference between the temperature at
the fog top and the sea surface temperature differs between
cases of fog from cases of low stratus cloud. Hence the monthlymean climatology of SST is used to determine a dynamic
threshold on infrared brightness temperature useful for detecting fog.
Daytime application of the brightness temperature difference technique is also complicated by large variations in
near-infrared illumination from the sun through the day. Data
from Lee et al. (1997) documents the large daytime variation in
the brightness temperature difference through the day, and
demonstrate that the daytime detection of fog and low cloud is
made more reliable by first estimating the near-infrared
reflectance, which is a considerably more stable quantity
through the day, and using the high reflectance of low stratus
clouds to distinguish fog and low cloud from less reflective
cirrus, ocean, or land surface. Nevertheless, further ambiguity in
the brightness temperature difference is present in the dawn
and dusk hours, when the weak signal in reflected near-infrared
radiation renders fog indistinguishable from the surface in the
brightness temperature difference. Lee et al. (2011) make use of
the cloud-free visible reflectance derived from 15 days of prior
satellite imagery to identify scenes that are brighter than the
cloud-free case and distinguish brighter fog in the visible band
from darker surface. Based on the daytime and nighttime
Fig. 19. Low clouds and fog (yellow pixels) intruding on the San Francisco Bay Area at 0300 LST July 22, 2013 determined from the difference of 3.9 μm and
10.7 μm brightness temperature measured by GOES 15 geostationary satellite. Cirrus clouds (blue and black pixels) exhibit the opposite sign of the brightness
temperature difference.
Image obtained from the NOAA Satellite and Information Service (http://www.star.nesdis.noaa.gov/smcd/opdb/aviation/fog.html).
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
Fig. 20. Infrared window channel brightness temperature (“longwave”) and
near-infrared channel brightness temperature (“shortwave”) from GOES-9
satellite imagery illustrating the change in sign of the brightness temperature difference at sunset.
From Lee et al. (1997).
brightness temperature difference and the additional dawn/
dusk criterion, Lee et al. (2011) present a smooth 24-hour fog
detection algorithm.
The limited visibility caused by fog is a consequence of the
microphysical aspects of the cloud. Detection of the potential
presence of fog using satellites is valuable, but further
interpretation of the potential impact on visibility relies on
retrieval of the microphysical properties of the clouds. The
reflectance of near-infrared radiation, which has so far been
discussed as a means of detecting daytime fog, is also
substantially dependent upon the size of the cloud droplets. A
cloud composed of smaller drops enhances the reflectance of
near-infrared radiation relative to a cloud with larger drops.
Near-infrared reflectance, when combined with a measure of
visible reflectance (for example at 0.64 μm), has been used to
perform a combined retrieval of cloud optical thickness and
cloud droplet effective radius (Nakajima and King, 1990).
These data are now produced routinely using imager data such
as that from the MODIS instrument (Platnick et al., 2003).
Wetzel et al. (1996) compare retrievals of effective radius and
169
optical thickness for a fog case over California against in situ
profiles of fog droplet sizes. Based on a favorable agreement,
they discuss the potential to estimate the surface visual range
in the presence of fog based on some assumptions about the
vertical profile of the drop sizes. MODIS retrievals for another
fog case are presented in Gultepe et al. (2009). Retrievals of
cloud drop number concentration are also made by satellite
with some success (Rausch et al., 2010), which could be
applied to translating satellite observations to surface visibility
estimates. This retrieval, however, also relies on an assumption
for the vertical structure — in this case the adiabatic model for
the vertical variability of LWC and drop effective radius.
Active remote sensing of fog can eliminate some of the
ambiguity of satellite remote sensing, but at present is limited
to select surface sites, and hence does not offer the global
coverage of satellite data. Visible light is strongly attenuated by
clouds, therefore lidar technology deployed from the surface is
useful for detecting cloud base, such as with operational
ceilometers. However, such technology cannot profile a fog
layer. Suborbital aircraft and satellite lidar systems can identify
cloud top heights with substantially greater precision than
passive infrared imaging techniques, but are similarly unable to
determine whether the cloud base reaches the surface.
Radar systems have been used to study the structure and
evolution of fog layers. Operational weather radars operating at
centimeter wavelengths are designed to detect precipitation
sized cloud drops, but the smaller drops typical of fog layers do
not effectively scatter radar signals at these wavelengths.
Experimental cloud radars, however, have been deployed at
35 GHz and 95 GHz frequencies to study the layer thickness
and vertical structure of cloud layers. Hamazu et al. (2003)
describe a scanning Doppler radar system at 35 GHz and use it
to describe the variability of reflectivity within a sea fog
case. Gultepe et al. (2009) and Boers et al. (2013) evaluate the
prospects for determining surface visibility from radar reflectivity. While relationships between the two quantities are
apparent (Fig. 21), Boers et al. (2013) conclude that the
visibility–reflectivity relationship varies as the fog layer evolves.
A 94 GHz satellite cloud radar, CloudSat, is in orbit,
offering the prospect of global fog studies. However, weak
sensitivity and clutter attributable to the surface return
complicates the observation of cloud properties near the
surface by space-borne radar.
Operational techniques for satellite remote sensing of sea
fog based on passive visible and infrared imagery are now
routinely applied to monitoring fog events. These techniques work best at night and are limited in their ability to
distinguish fog at the surface from ordinary low stratus
clouds. Active remote sensing of fog by radar shows
potential for retrieving visibility estimates in fog layers, but
is so far limited to experimental cases rather than widespread monitoring.
9. Epilog
Viewed historically, we have been investigating fog at sea
for exactly 100 years if we label G.I. Taylor's monumental work
of 1913 as the initial point in time. What do we know about the
life cycle of sea fog commencing with this early investigation?
First of all, the factors identified by Taylor have stood the test of
time — Lagrangian trajectories of initially warm/moist air that
170
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
–
–
–
–
Fig. 21. Visibility against cloud radar reflectivity (“Z”) for samples from the
FRAM field campaign based in Ontario, Canada.
From Gultepe et al. (2009).
is cooled from below by colder ocean temperatures and mixed
vertically by velocity-shear turbulence. Since then, we also
know that turbulence in an unstably stratified air mass (warm
ocean temperatures relative to the overlying air) very effectively mixes the air upward and entrains air from atop the
mixed layer. Further, we more clearly understand the warming
and cooling of the fog layer in response to the processes of both
long- and short-wave radiation; and the “capping lid” — marine
inversion atop the fog layer is generally a response to
subsidence associated with the semi-permanent anticyclones
over the oceans. These are among the processes that have been
thoroughly investigated and we have some confidence that
their action is understood.
What do not we know about the cycle of sea fog?
Certainly we are ignorant of the complex interactions of the
various processes, some of which were mentioned above.
And part of the dilemma rests on our inability to observe
these processes individually, let alone collectively. And this
should come as no surprise since the phenomenon exhibits
extreme scales — a range of ~ 1013.
Among the issues that are poorly understood are the
following:
– Accurate estimates/predictions of subsidence elude us.
– Models cannot maintain a strong inversion.
– Many model parameterizations have been calibrated for
different conditions (frequently over the land).
– Models with coarse resolution cannot fully represent
coastal local circulations.
– Ocean input to atmospheric models is generally too
coarse.
– Over-ocean measurements are sparse and non-existent in
many areas.
– Model coupling (atmosphere–ocean) and surface fluxes
are frequently approximated.
– Initial state, composition, and history of air masses
including fog condensation nuclei are generally unknown.
– Condensation triggering for various subsaturation and
supersaturation scenarios is difficult to represent in models
with limited condensation parameterizations.
Detailed microphysics and precipitation are usually simplified in model parameterizations.
Physical processes relevant to marine fog in Lagrangian and
Eulerian frameworks need to be more closely examined.
Fog is an elusive target for airborne field programs;
airborne measurements in fog are limited by the Federal
Aviation Authority (FAA) and other agencies.
Climatology of marine air characteristics is poorly known
over many regions of the world sea.
Improvement in our knowledge of sea fog will undoubtedly
come from detection of sea fog through satellite observations.
While limitations persist in the quantitative information
attainable about fog from satellites, the vastly improved
spatio-temporal sampling of the oceans afforded by satellites
and the demonstrated ability of satellite detection of fog events
suggests that many more fog events can be identified and
studied with the aid of satellites.
Of course, we will gain much from measurements of sea fog
in field programs that incorporate observations from ship and
coastal stations. The future trend will emphasize programs that
amalgamate observations in the hope of clarifying the interactions of processes. Field measurements will be combined with
model output to obtain the most accurate representation of a
system state that stretches across the scales from synoptic to
microphysical.
From the many modeling studies of sea fog, essentially
numerical experiments/simulations/forecasting that started in
the immediate post WWII period, it becomes clear that
deterministic forecasting of sea fog onset and its duration has
generally been unsuccessful. The extreme sensitivity of model
output to elements of control [initial conditions, boundary
conditions, and forcing (empirical/physical parameterization)],
in concert with the chaotic nature of dynamic prediction, is at the
heart of prediction inaccuracy. Ensemble prediction is a possibility, but it comes with complications when applied to sea fog. The
complications arise because the phenomenon is discontinuous
with an impulsive start and an abrupt end. The fundamentals of
ensemble prediction applied to discontinuous dynamical systems
such as this one are in their infancy. Without doubt, however, this
area of investigation is needed and promising.
When will we be able to make accurate forecasts of sea fog
onset and its duration? At this time, we are unable to answer
this question with any degree of certainty. However, we know
from the history of geophysical science that improvements in
understanding and forecasting come incrementally with
dependence on better observations (both temporal and
spatial) that lead to improved four-dimensional analyses
which in turn lead to improved dynamical forecasts. And not
only from models, but advances will come from the individual
scientists (observationalists, analysts, and theoreticians) with
their phenomenological viewpoints — viewpoints that lead to
conjectures or hypotheses that when followed to their terminal
points contribute to the incremental advance.
Acknowledgments
Two of the authors (Koračin and Lewis) acknowledge
support from the Office of Naval Research grant N00014-
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
00-1-0524. Funding for this project was partially provided by
NASA UCSD contract 20053743 (Dorman), DOE ASR DESC0009162 (Hudson), and (Torregrosa) US Geological Survey
Climate and Landuse Change Program, Gordon and Betty Moore
Foundation Grant #2861, and California Landscape Conservation
Collaborative. We also want to express our gratitude to the late
Professor Dale Leipper, a protégé of Harald Sverdrup at Scripps in
the late 1940s–early 1950s, who was an inspiration to those of us
who had the privilege of working with him. All authors are
grateful to Mr. Travis McCord of the Desert Research Institute for
his thorough editorial efforts.
References
Ackerly, D.D., Ryals, R.A., Cornwell, W.K., Loarie, S.R., Veloz, S., Higgason, K.D.,
Silver, W.L., Dawson, T.E., 2012. Potential Impacts of Climate Change on
Biodiversity and Ecosystem Services in the San Francisco Bay Area.California
Energy Commission (Publication number: CEC-500-2012-037).
Adams, Michael R., 2010. Ocean Station: Operations of the U.S. Coast Guard,
1940–1977.Nor'Easter Press, Eastpoint, Maine978-0-9779200-1-3.
Ahuja, M.R., 2009. Genetic constitution and diversity in four narrow endemic
redwoods from the family Cupressaceae. Euphytica 165 (1), 5–19.
Alvarado-Barrientos, M.S., Asbjornsen, H., Holwerda, F., 2013. Is nighttime
transpiration enhanced after fog events? In IX International Workshop
on Sap Flow 991, pp. 133–139.
Anderson, J., 1931. Observations from airplanes of cloud and fog conditions
along the southern California coast. Mon. Weather Rev. 59, 264–270.
Ångström, A., 1920. Applications of heat radiation measurements to problems
of the evaporation from lakes and heat convection at their surfaces. Georg.
Ann. 2, 237–252.
Azevedo, J., Morgan, D.L., 1974. Fog precipitation in coastal California forests.
Ecology 55 (5), 1135–1141.
Ballard, S., Golding, B., Smith, R., 1991. Mesoscale model experimental forecasts
of the Haar of northeast Scotland. Mon. Weather Rev. 191, 2107–2123.
Barron, J.A., Anderson, L., 2011. Enhanced Late Holocene ENSO/PDO expression
along the margins of the eastern North Pacific. Quat. Int. 235 (1), 3–12.
Bartok, B., Bott, A., Gera, M., 2012. Fog prediction for road traffic safety in a
coastal desert region. Bound.-Layer Meteorol. 145 (3), 485–506.
Bendix, J., 2002. A satellite-based climatology of fog and low-level stratus in
Germany and adjacent areas. Atmos. Res. 64, 3–18.
Bendix, J., Thies, B., Nauss, T., Cermak, J., 2006. A feasibility study of daytime
fog and low stratus detection with TERRA/AQUA-MODIS over land.
Meteorol. Appl. 13, 111–125.
Bendix, J., Eugster, W., Klemm, O., 2011. Fog—boon or bane? Erdkunde 65
(3), 229–232.
Bergot, T., Carrer, D., Noilhan, J., Bougeault, P., 2005. Improved sit-specific
numerical prediction of fog and low clouds: a feasibility study. Weather
Forecast. 20, 627–646.
Bergthorsson, P., 2000. The Wineland Millennium.Mal og menning,
Reykjavik, Iceland (304 pp.).
Boers, R., Baltink, H.K., Hemink, H.J., Bosveld, F.C., Moerman, M., 2013. Groundbased observations and modeling of the visibility and radar reflectivity in a
radiation fog layer. J. Atmos. Ocean. Technol. 30, 288–300.
Bott, A., 1991. On the influence of the physico-chemical properties of aerosols
on the life cycle of radiation fogs. Bound.-Layer Meteorol. 56, 1–31.
Bott, A., Trautmann, T., 2002. PAFOG—a new efficient forecast model of radiation
fog and low-level stratiform clouds. Atmos. Res. 64 (1–4), 191–203.
Bott, A., Sievers, U., Zdunkowski, W., 1990. A radiation fog model with a
detailed treatment of the interaction between radiative transfer and fog
microphysics. J. Atmos. Sci. 47, 2153–2166.
Bowen, I.S., 1926. The ratio of heat losses by conduction and evaporation
from water surfaces. Phys. Rev. 27, 779–787.
Bretherton, C.S., Krueger, S.K., Wyant, M.C., Bechtold, P., Van Meijgaard, E.,
Stevens, B., Teixeira, J., 1999. A GCSS boundary-layer cloud model
intercomparison study of the first ASTEX Lagrangian experiment.
Bound.-Layer Meteorol. 93 (3), 341–380.
Bruijnzeel, L.A., Eugster, W., Burkard, R., 2005. Fog as a hydrologic input. In:
Anderson, M.G., McDon nell, J.J. (Eds.), Encyclopedia of Hydrological
Sciences, 1. John Wiley & Sons, Inc, Chichester, pp. 559–582.
Burgess, S.S.O., Dawson, T.E., 2004. The contribution of fog to the water
relations of Sequoia sempervirens (D. Don): foliar uptake and prevention
of dehydration. Plant Cell Environ. 27 (8), 1023–1034.
Byers, H.R., 1930. Summer sea fogs of the central California coast. Univ. Calif.
Publ. Geogr. 3 (5), 291–338.
171
Carbone, M.S., Still, C.J., Ambrose, A.R., Dawson, T.E., Williams, A.P., Boot,
C.M., Schimel, J.P., 2011. Seasonal and episodic moisture controls on
plant and microbial contributions to soil respiration. Oecologia 167
(1), 265–278.
Carbone, M.S., Park Williams, A., Ambrose, A.R., Boot, C.M., Bradley, E.S.,
Dawson, T.E., Schaeffer, S.M., Schimel, J.P., Still, C.J., 2013. Cloud shading
and fog drip influence the metabolism of a coastal pine ecosystem. Glob.
Chang. Biol. 19, 484–497. http://dx.doi.org/10.1111/gcb.12054.
Cereceda, P., Larrain, H., Osses, P., Farías, M., Egaña, I., 2007. The spatial and
temporal variability of fog and its relation to fog oases in the Atacama
Desert, Chile. Atmos. Res. 87 (3–4), 312–323. http://dx.doi.org/10.1016/
jatmosres2007.11.012.
Cermak, J., 2012. Low clouds and fog along the south-western African coast—
satellite-based retrieval and spatial patterns. Atmos. Res. 116, 15–21.
Cermak, J., Bendix, J., 2007. Dynamical nighttime fog/low stratus detection
based on Meteosat SEVIRI data: a feasibility study. Pure Appl. Geophys.
164, 1179–1192.
Cho, Y.-K., Kim, M.-O., Kim, B.-C., 2000. Sea fog around the Korean Peninsula.
J. Appl. Meteorol. 39, 2473–2479.
Chow, J.C., Watson, J.G., Lowenthal, D.H., Countess, R.J., 1996. Sources and
chemistry of PM10 aerosol in Santa Barbara County, CA. Atmos. Environ.
30 (9), 1489–1499.
Collett Jr., J.L., Bator, A., Sherman, D.E., Moore, K.F., Hoag, K.J., Demoz, B.B.,
Rao, X., Reilly, J.E., 2002. The chemical composition of fogs and
intercepted clouds in the United States. Atmos. Res. 64, 29–40.
Corbin, J.D., Thomsen, M.A., Dawson, T.E., D'Antonio, C.M., 2005. Summer
water use by California coastal prairie grasses: fog, drought, and
community composition. Oecologia 145 (4), 511–521.
Cornell, S.E., Jickells, T.D., Cape, J.N., Rowland, A.P., Duce, R.A., 2003. Organic
nitrogen deposition on land and coastal environments: a review of
methods and data. Atmos. Environ. 37 (16), 2173–2191.
Croft, P.J., Darbe, D.L., Garmon, J.F., 1995. Forecasting significant fog in
southern Alabama. Natl Weather Dig. 19, 10–16.
Croft, P.J., Pfost, R.L., Medlin, J.M., Johnson, G.A., 1997. Fog forecasting for
the southern region: a conceptual model approach. Weather Forecast.
12, 545–556.
Dawson, T.E., 1998. Fog in the California redwood forest: ecosystem inputs
and use by plants. Oecologia 117 (4), 476–485.
Dawson, T.E., Burgess, S.S., Tu, K.P., Oliveira, R.S., Santiago, L.S., Fisher, J.B.,
Simonin, K.A., Ambrose, A.R., 2007. Nighttime transpiration in woody
plants from contrasting ecosystems. Tree Physiol. 27 (4), 561–575.
Deguillaume, L., Leriche, M., Amato, P., Ariya, P.A., Delort, A.M., Pöschl, U., Morris,
C.E., 2008. Microbiology and atmospheric processes: chemical interactions of
primary biological aerosols. Biogeosci. Discuss. 5 (1), 841–870.
Després, V.R., Huffman, J.A., Burrows, S.M., Hoose, C., Safatov, A.S., Buryak, G.,
Jaenicke, R., 2012. Primary biological aerosol particles in the atmosphere: a review. Tellus B 64.
Díaz, S.C., Touchan, R., Swetnam, T.W., 2001. A tree-ring reconstruction of
past precipitation for Baja California Sur, Mexico. Int. J. Climatol. 21,
1007–1019. http://dx.doi.org/10.1002/joc.664.
Donaldson, D.J., Vaida, V., 2006. The influence of organic films at the air–
aqueous boundary on atmospheric processes. Chem. Rev. 106 (4),
1445–1461.
Dorman, C.E., Armi, L., Bane, J.M., Rogers, D.P., 1998. Sea surface mixed layer
during the 10–11 June 1994 California coastally trapped event. Mon.
Weather Rev. 126, 600–619.
Douglas, C., 1930. Cold fogs over the sea. Meteorol. Mag. 65, 133–135.
Dueker, M.E., O'Mullan, G.D., Weathers, K.C., Juhl, A.R., Uriarte, M., 2012.
Coupling of fog and marine microbial content in the near-shore coastal
environment. Biogeosciences 9 (2), 803–813.
Duynkerke, P.G., 1991. Radiation fog: a comparison of model simulations
with detailed observations. Mon. Weather Rev. 119, 324–341.
Duynkerke, P.G., Jonker, P.J., Chlond, A., Van Zanten, M.C., Cuxart, J., Clark, P.,
Sanchez, E., Martin, G., Lenderink, G., Teixeira, J., 1999. Intercomparison
of three- and one-dimensional model simulations and aircraft observations of stratocumulus. Bound.-Layer Meteorol. 92, 453–487.
Eldridge, R.G., 1971. The relationship between visibility and liquid water
content of fog. J. Atmos. Sci. 28, 1183–1186.
Elias, T., Haeffelin, M., Drobinski, P., Gomes, L., Rangognio, J., Bergot, T., Chazette,
P., Raut, J.-C., Colomb, M., 2009. Particulate contribution to extinction of
visible radiation: pollution, haze, and fog. Atmos. Res. 92, 443–454.
Eller, C.B., Lima, A.L., Oliveira, R.S., 2013. Foliar uptake of fog water and
transport belowground alleviates drought effects in the cloud forest
tree species, Drimys brasiliensis (Winteraceae). New Phytol. 199,
151–162.
Ellison, G.B., Tuck, A.F., Vaida, V., 1999. Atmospheric processing of organic
aerosols. J. Geophys. Res. 104 (D9), 11633-11.
Ellrod, G.P., 1995. Advances in the detection and analysis of fog at night
using GOES multi-spectral infrared imagery. Weather Forecast. 10,
606–619.
172
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
Ellrod, G.P., Gultepe, I., 2007. Inferring low cloud base heights at night for
aviation using satellite infrared and surface temperature data. In: Fog
and boundary layer clouds: fog visibility and forecasting. Birkhäuser
Basel, pp. 1193–1205.
Emmons, G., Montgomery, R., 1947. Note on the physics of fog formation.
J. Meteorol. 4, 206.
Ewing, H.A., et al., 2009. Fog water and ecosystem function: heterogeneity in
a California redwood forest. Ecosystems 12, 417–433.
Eyre, J.R., Brownscombe, J.L., Allan, R.J., 1984. Detection of fog at night using
Advanced High Resolution Radiometer (AVHRR) imagery. Meteorol.
Mag. 113, 66–271.
Fabbian, D., de Dear, R., Lellyett, S., 2007. Application of artificial neural network
forecasts to predict fog at Canberra International Airport. Weather
Forecast. 22, 372–381.
Fernstrom, A., Goldblatt, M., 2013. Aerobiology and its role in the transmission
of infectious diseases. J. Pathog. 2013, 13. http://dx.doi.org/10.1155/2013/
493960.
Filonczuk, M.K., Cayan, D.l.R., Riddle, L.G., 1995. Variability of marine fog
along the California coast. SIO-Reference, No 95-2. Climate Research
Division, Scripps Institution of Oceanography, University of California,
San Diego (http://www.escholarship.org/uc/item/2kc7x97f#page-1).
Findlater, J., Roach, W., McHugh, B., 1989. The haar of north-east Scotland. Q.
J. R. Meteorol. Soc. 115, 581–608.
Fischer, D.T., Still, C.J., Williams, A.P., 2009. Significance of summer fog and
overcast for drought stress and ecological functioning of coastal
California endemic plant species. J. Biogeogr. 36, 783–799.
Fitzgerald, J.W., 1978. A numerical model of the formation of droplet spectra
in advection fogs at sea and its applicability to fogs off Nova Scotia.
J. Atmos. Sci. 35, 1522–1535.
Ford, K.W., 1968. Basic Physics.Blaisdell Publishing Company (968 pp.).
Fuhrmann, C., 2010. The effects of weather and climate on the seasonality of
influenza: what we know and what we need to know. Geogr. Compass 4
(7), 718–730.
Fuzzi, S., Castillo, R.A., Jiusto, J.E., Lala, G., 1984. Chemical composition of
radiation fog water at Albany, New York, and its relationship to fog
microphysics. J. Geophys. Res. 89, 7159–7164.
Fuzzi, S., Facchini, M.C., Orsi, G., Lind, J.A., Wobrock, W., Kessel, M., Maser, R.,
Jaeschke, W., Enderle, K.H., Arends, B.G., Berner, A., Solly, I., Kruisz, C.,
Reischl, G., Pahl, S., Kaminski, U., Winkler, P., Ogren, J.A., Noone, K.J.,
Hallberg, A., Fierlinger-Oberlinninger, H., Puxbaum, H., Marzorati, A.,
Hansson, H.-C., Wiedensohler, A., Svenningsson, I.B., Martinsson, B.G.,
Schell, D., Georgii, H.W., 1992. The Po Valley fog experiment 1989. Tellus B
44, 448–468. http://dx.doi.org/10.1034/j.1600-0889.1992.t01-4-00002.
Fuzzi, S., Mandrioli, P., Perfetto, A., 1997. Fog droplets—an atmospheric
source of secondary biological aerosol particles. Atmos. Environ. 31 (2),
287–290.
Gao, S., Lin, H., Shen, B., Fu, G., 2007. A heavy sea fog event over the Yellow
Sea in March 2005: analysis and numerical modeling. Adv. Atmos. Sci.
24, 65–81.
Garcia-Garcia, F., Virafuentes, U., Montero-Martinez, G., 2002. Fine-scale
measurements of fog-droplet concentrations: a preliminary assessment.
Atmos. Res. 64, 179–189.
Garland, J.A., 1971. Some fog droplet size distributions obtained by an
impaction method. Q. J. R. Meteorol. Soc. 97, 483–494.
Garmon, J.F., Darbe, D.L., Croft, P.J., 1996. Forecasting significant fog on the
Alabama coast: impact climatology and forecast checklist development.
NWS Technical Memorandum NWS SR-176 (16 pp.).
Garreaud, R., Barichivich, J., Christie, D.A., Maldonado, A., 2008. Interannual
variability of the coastal fog at Fray Jorge relict forests in semiarid Chile.
J. Geophys. Res. Biogeosci. 113 (G4) (2005–2012).
Genitsaris, S., Kormas, K.A., Moustaka-Gouni, M., 2011. Airborne algae and
cyanobacteria: occurrence and related health effects. Front. Biosci. 3,
772–787.
George, J.J., 1960. Weather Forecasting for Aeronautics.Academic Press
297–372.
Gerber, H.E., 1981. Microstructure of a radiation fog. J. Atmos. Sci. 38,
454–458.
Gerber, H.E., 1991. Supersaturation and droplet spectral evolution in fog.
J. Atmos. Sci. 48, 2569–2588.
Gershunov, A., Johnston, Z., 2011. The California heat wave 2006 with impacts
on statewide medical emergency: a space–time analysis. Geography
Research Forum, vol. 31, pp. 6–31.
Gioda, A., Maley, J., Guasp, R.E., Baladón, A.A., 1995. Some low elevation fog
forests of dry environments: applications to African paleoenvironments.
Tropical Montane Cloud Forests. Springer, US, pp. 156–164.
González, A.L., Fariña, J.M., Pinto, R., Pérez, C., Weathers, K.C., Armesto, J.J.,
Marquet, P.A., 2011. Bromeliad growth and stoichiometry: responses to
atmospheric nutrient supply in fog-dependent ecosystems of the hyperarid Atacama Desert, Chile. Oecologia 167 (3), 835–845.
Gonzalez-Cruz, J., Sequera, P., Molina, Y., Picon, R., Pillich, J., Ghebreegziabhe,
A.T., Bornstein, B., 2013. 3.01 — Climate and energy vulnerability in coastal
regions: the case for US Pacific and northeast corridor coastal regions. In:
Pielke, Roger (Ed.), Climate Vulnerability. Academic Press, Oxford,
pp. 3–35. http://dx.doi.org/10.1016/B978-0-12-384703-4.00302-6.
Grell, G.A., Dudhia, J., Stauffer, D.R., 1994. A description of the fifth-generation
Penn State/NCAR mesoscale model (MM5). NCAR Tech. Note NCAR/TN398 + STR (122 pp.).
Guédalia, D., Bergot, T., 1994. Numerical forecasting of radiation fog. Part II: a
comparison of model simulations and several observed fog events. Mon.
Weather Rev. 122, 1231–1246.
Gultepe, I., Milbrandt, J.A., 2009. Probabilistic parameterizations of visibility
using observations of rain precipitation rate, relative humidity, and
visibility. J. Appl. Meteorol. 49, 36–46.
Gultepe, I., Isaac, G., MacPherson, I., Marcotte, D., Strawbridge, K., 2003.
Characteristics of moisture and heat fluxes over leads and polynyas, and
their effect on Arctic clouds during FIRE. ACE Atmos. Ocean 41, 15–34.
Gultepe, I., Muller, M.D., Boybeyi, Z., 2006a. A new warm fog parameterization scheme for numerical weather prediction models. J. Appl. Meteorol.
45, 1469–1480.
Gultepe, I., Müller, M.D., Boybeyi, Z., 2006b. A new visibility parameterization for warm fog applications in numerical weather prediction models.
J. Appl. Meteorol. 45, 1469–1480.
Gultepe, I., Cober, S.G., King, P., Isaac, G., Taylor, P., Hansen, B., 2006c. The fog
remote sensing and modeling (FRAM) field project and preliminary
results. AMS 12th Cloud Physics Conference, July 9–14, 2006, Madison,
Wisconsin, USA (Print in CD, P4.3).
Gultepe, I., Pagowski, M., Reid, J., 2007a. A satellite-based fog detection
scheme using screen air temperature. Weather Forecast. 22, 444–456.
Gultepe, I., Tardif, R., Michaelides, S.C., Cermak, J., Bott, A., Bendix, J., Muller,
M.D., Pagowski, M., Hansen, B., Ellrod, G., Jacobs, W., Toth, G., Cober, S.G.,
2007b. Fog research: a review of past achievements and future
perspectives. Pure Appl. Geophys. 164, 1121–1159.
Gultepe, I., Pearson, G., Milbrandt, J.A., Hansen, B., Platnick, S., Taylor, P.,
Gordon, M., Oakley, J.P., Cober, S.G., 2009. The fog remote sensing and
modeling field project. Bull. Am. Meteorol. Soc. 90, 341–359.
Gundel, L.A., Benner, W.H., Hansen, A.D.A., 1994. Chemical composition of
fog water and interstitial aerosol in Berkeley, California. Atmos. Environ.
28 (16), 2715–2725.
Gutiérrez, A.G., Barbosa, O., Christie, D.A., del-Val, E., Ewing, H.A., Jones, C.G.,
Marquet, P.A., Weathers, K.C., Armesto, J.J., 2008. Regeneration patterns
and persistence of the fog dependent Fray Jorge forest in semiarid Chile
during the past two centuries. Glob. Change Biol. 14, 161–176. http://
dx.doi.org/10.1111/j.1365-2486.2007.01482.x.
Haeffelin, M., Barthès, L., Bock, O., Boitel, C., Bony, S., Bouniol, D., Chepfer, H.,
Chiriaco, M., Cuesta, J., Delanoe, J., Drobinski, P., Dufresne, J.L., Flamant, C.,
Grall, M., Hodzic, A., Hourdin, F., Lapouge, F., Lemaitre, Y., Mathieu, A., Morille,
Y., Naud, C., Noel, V., OH'irok, B., Pelon, J., Pietras, C., Protat, A., Romand, B.,
Scialom, G., Vautard, R., 2005. SIRTA, a ground-based atmospheric observatory for cloud and aerosol research. Ann. Geophys. 23, 253–275.
Haensler, A., Cermak, J., Hagemann, S., Jacob, D., 2011. Will the southern African
west coast fog be affected by future climate change? Results of an initial fog
projection using a regional climate model. Erdkunde. 261–275.
Hamazu, K., Hashiguchi, H., Wakayama, T., Matsuda, T., Doviak, R.J., Fukao, S.,
2003. A 35-GHz scanning Doppler radar for fog observations. J. Atmos.
Ocean. Technol. 20, 972–986.
Hansen, 2007. A fuzzy logic based analog forecasting system for ceiling and
visibility. Weather Forecast. 22, 1319–1330.
Helmuth, B., Broitman, B.R., Blanchette, C.A., Gilman, S., Halpin, P., Harley,
C.D., O'Donnell, M.J., Hofmann, G.E., Menge, B., Strickland, D., 2006.
Mosaic patterns of thermal stress in the rocky intertidal zone:
implications for climate change. Ecol. Monogr. 76 (4), 461–479.
Heo, K.-Y., Ha, K.-J., 2010. A coupled model study on the formation and
dissipation of sea fogs. Mon. Weather Rev. 138, 1186–1205.
Herckes, P., Valsaraj, K.T., Collett Jr., J.L., 2013. A review of observations of
organic matter in fogs and clouds: origin, processing and fate. Atmos.
Res. 132, 434–449.
Heusser, L., 1998. Direct correlation of millenial-scale changes in western
North American vegetation and climate with changes in the
California Current system over the past 60 kyr. Paleoceanography
13 (3), 252–262.
Hiatt, C., Fernandez, D., Potter, C., 2012. Measurements of fog water
deposition on the California central coast. Atmos. Clim. Sci. 2, 525–531.
Hodur, R.M., 1997. The Naval Research Laboratory's Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). Mon. Weather Rev. 125,
1414–1430.
Hodur, R.M., Hong, X., Doyle, J.D., Pullen, J., Cummings, J., Martin, P., Rennic,
M.A., 2002. The Coupled Ocean/Atmosphere Mesoscale Prediction
System (COAMPS). Oceanography 15, 88–98 (Wash DC).
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
Huang, H., Liu, H., Jiang, W., Huang, J., Mao, W., 2011. Characteristics of the
boundary layer structure of sea fog on the coast of Southern China. Adv.
Atmos. Sci. 28 (6), 1377–1389.
Hudson, J.G., 1980. Relationship between fog condensation nuclei and fog
microstructure. J. Atmos. Sci. 37, 1854–1867.
Hudson, J.G., 1983. Effects of CCN on stratus clouds. J. Atmos. Sci. 40 (480),
486.
Hudson, J.G., 1984. CCN measurements within clouds. J. Clim. Appl. Meteorol.
23 (42), 51.
Hudson, J.G., Noble, S., 2014. CCN and vertical velocity influences on droplet
concentrations and supersaturations in clean and polluted stratus
clouds. J. Atmos. Sci. 71, 312–331.
Hudson, J.G., Rogers, C.F., 1986. Relationship between critical supersaturation and cloud droplet size: implications for cloud mixing processes.
J. Atmos. Sci. 43 (2341), 2359.
Hudson, J.G., Noble, S., Jha, V., 2010. Stratus cloud supersaturations. Geophys.
Res. Lett. 37, L21813. http://dx.doi.org/10.1029/2010GL045197.
Iacobellis, S.F., Cayan, D.R., 2013. The variability of California summertime
marine stratus: impacts on surface air temperatures. J. Geophys. Res.
http://dx.doi.org/10.1002/jgrd.50652 (Online Access published July, 19,
2013).
Ingraham, N.L., Matthews, R.A., 1995. The importance of fog-drip water to
vegetation—Point Reyes peninsula, California. J. Hydrol. 164 (1–4), 269–285.
Isaac, G.A., Bailey, M., Cober, S.G., Donaldson, N., Driedger, N., Glazer, A., Gultepe,
I., Hudak, D., Korolev, A., Reid, J., Rodriguez, P., Strapp, J.W., Fabry, F., 2006.
Airport vicinity icing and snow advisor. AIAA 44th Aerospace Sci. Meeting
and Exhibit, Reno Nevada, 9–12 January 2006, AIAA-2006-1219.
Jeffreys, H., 1918. Some problems of evaporation. Phil. Mag. 35 (6th series),
270–280.
Jiusto, J.E., 1981. Fog structure. In: Hobbs, P.V., Deepak, A. (Eds.), Clouds,
Their Formation, Optical Properties and Effect. Academic Press (495 pp.).
Johnson, E.R., 2009. American Flying Boats and Amphibious Aircraft: An
Illustrated History.McFarland, Jefferson, North Carolina (376 pp.).
Johnson, G.A., Graschel, J., 1992. Sea fog and stratus: a major aviation and
marine hazard in the northern Gulf of Mexico. Symp. on Weather
Forecasting. Amer. Meteor. Soc., Atlanta, GA, pp. 55–60.
Johnstone, J.A., Dawson, T.E., 2010. Climatic context and ecological
implications of summer fog decline in the coast redwood region. PNAS
2010. http://dx.doi.org/10.1073/pnas.0915062107.
Joslin, J.D., Mueller, S.F., Wolfe, M.H., 1990. Tests of models of cloudwater
deposition to forest canopies using artificial and living collectors. Atmos.
Environ. 24A, 3007–3019 (journal.pone.0034603).
Juvik, J.O., DeLay, J.K., Kinney, K.M., Hansen, E.W., 2011. A 50th anniversary
reassessment of the seminal ‘Lana'i fog drip study’ in Hawai'i. Hydrol.
Process. 25 (3), 402–410.
Katata, G., Nagai, H., Kajino, M., Ueda, H., Hozumi, Y., 2010. Numerical study
of fog deposition on vegetation for atmosphere–land interactions in
semi-arid and arid regions. Agric. For. Meteorol. 150 (3), 340–353.
Kim, C.-K., Yum, S.-S., 2010. Local meteorological and synoptic characteristics
of fogs formed over Incheon international airport in the west coast of
Korea. Adv. Atmos. Sci. 27, 761–776.
Kim, C.-K., Yum, S.-S., 2012. A numerical study of sea-fog formation over cold
sea surface using a one-dimensional turbulence model coupled with the
weather research and forecasting model. Bound.-Layer Meteorol. 143,
481–505.
Kimball, K.D., Jagels, R., Gordon, G.A., Weathers, K.C., Carlisle, J., 1988.
Differences between New England coastal fog and mountain cloud water
chemistry. Water Air Soil Pollut. 39 (3–4), 383–393.
King, J.R., Agostini, V.N., Harvey, C.J., McFarlane, G.A., Foreman, M.G.G.,
Overland, J.E., Di Lorenzo, E., Bond, N.A., Aydin, K.Y., 2011. Climate forcing
and the California Current ecosystem. ICES J. Mar. Sci. 68, 1199–1216.
Klemm, O., Bachmeier, A.S., Talbot, R.W., Klemm, K.I., 1994. Fog chemistry at
the New England coast: influence of air mass history. Atmos. Environ. 28
(6), 1181–1188.
Klemm, O., Schemenauer, R.S., Lummerich, A., Cereceda, P., Marzol, V., Corell,
D., Fessehaye, G.M., 2012. Fog as a fresh-water resource: overview and
perspectives. Ambio 41 (3), 221–234.
Kloesel, K.A., 1992. A 70-year history of marine stratocumulus cloud field
experiments off the coast of California. Bull. Am. Meteorol. Soc. 73,
1581–1585.
Koračin, D., Lewis, J., Thompson, W.T., Dorman, C.E., Businger, J.A., 2001.
Transition of stratus into fog along the California coast: observations and
modeling. J. Atmos. Sci. 58, 1714–1731.
Koračin, D., Businger, J.A., Dorman, C.E., Lewis, J.M., 2005a. Formation,
evolution, and dissipation of coastal sea fog. Bound.-Layer Meteorol. 117,
447–478.
Koračin, D., Leipper, D.F., Lewis, J.M., 2005b. Modeling sea fog on the U.S.
California coast during a hot spell event. Geofizika 22, 59–82.
Kunkel, B.A., 1984. Parameterization of droplet terminal velocity and
extinction coefficient in fog models. J. Clim. Appl. Meteorol. 23, 34–41.
173
Lamb, H., 1943. Haars or North Sea Fogs on the Coasts of Great
Britain.Meteorology Office Publication, M. O. 504.
Larrain, H., Velasquez, F., Cereceda, P., Espejo, R., Pinto, R., Osses, P., Schemenauer,
R.S., 2002. Fog measurements at the site ‘Falda Verde’ north of Chan˜aral
compared with other fog stations of Chile. Atmos. Res. 64, 273–284.
Lebassi, B., González, J.E., Fabris, D., Bornstein, R., 2010. Impacts of climate
change in degree days and energy demand in coastal California. J. Sol.
Energy Eng. 132 (3) (031005-1).
Lebassi-Habtezion, B., González, J., Bornstein, R., 2011. Modeled large scale
warming impacts on summer California coastal cooling trends.
J. Geophys. Res. 116, D20114. http://dx.doi.org/10.1029/2011JD015759.
Lee, T.F., Turk, F.J., Richardson, K., 1997. Stratus and fog products using GOES8-9 3.9-μm data. Weather Forecast. 12, 664–677. http://dx.doi.org/
10.1175/1520-0434(1997)012b0664:SAFPUGN2.0.CO;2.
Lee, J.-R., Chung, C.-Y., Ou, M.L., 2011. Fog detection using geostationary
satellite data: temporally continuous algorithm. Asia–Pac. J. Atmos. Sci.
47, 113–122.
Leem, H.H., Kim, D.H., Song, K.W., 2005. The importance of fog prediction at the
Incheon Int'l Airport through flight delay and cancel statistics. J. Korean Soc.
Aeronaut. Space Sci. 13, 1–10 (in Korean with English abstract).
Leipper, D., 1948. Fog development at San Diego, California. J. Mar. Res. 7,
337–346.
Leipper, D.F., 1994. Fog on the U.S. West Coast, a review. Bull. Am. Meteorol.
Soc. 72, 229–240.
LePage, B.A., Williams, C.J., Yang, H. (Eds.), 2005. The Geobiology and Ecology
of Metasequoia, vol. 22. Springer.
Lewis, J.M., 1996. Winds over the world sea: Maury and Köppen. Bull. Am.
Meteorol. Soc. 77, 935–952. http://dx.doi.org/10.1175/1520-0477.
Lewis, R.J., 2000. An Introduction to Classification and Regression Tree
(CART) Analysis.Department of Emergency Medicine, Harbor-UCLA
Medical Center, Torrance, CA.
Lewis, J.M., Koračin, D., Rabin, R., Businger, J., 2003. Sea fog off the California
coast: viewed in the context of transient weather systems. J. Geophys.
Res. Atmos. 108 (D15), 4457. http://dx.doi.org/10.1029/2002JD002833.
Lewis, J.M., Koračin, D., Redmond, K.T., 2004. Sea fog research in the United
Kingdom and United States: a historical essay including outlook. Bull.
Am. Meteorol. Soc. 85, 395–408.
Lewis, J.M., Fearon, M.G., Klieforth, H.E., 2012. Herbert Riehl: intrepid and
enigmatic scholar. Bull. Am. Meteorol. Soc. 93, 963–985. http://
dx.doi.org/10.1175/BAMS-D-11-00224.1.
Limm, E.B., Simonin, K.S., Bothman, A.G., Dawson, T.E., 2009. Foliar water
uptake: a common water acquisition strategy for plants of the redwood
forest. Oecologia 161, 449–459.
Lovett, G.M., 1984. Rates and mechanisms of cloud water deposition to a
subalpine balsam fir forest. Atmos. Environ. 18, 361–371.
Lundquist, J., Bourcy, T.B., 1999. California and Oregon Humidity and Coastal
Fog SIO, Reference No. 99-17.
Lynch, J.A., Bowersox, V.C., Grimm, J.W., 2000. Changes in sulfate deposition
in eastern USA following implementation of Phase I of Title IV of the
Clean Air Act Amendments of 1990. Atmos. Environ. 34, 1665–1680.
Ma, Y., Li, Y., Wang, F., 2009. Corrosion of low carbon steel in atmospheric
environments of different chloride content. Corros. Sci. 51 (5), 997–1006.
Madej, M.A., Currens, C., Ozaki, V., Yee, J., Anderson, D.G., 2006. Assessing
possible thermal rearing restrictions for juvenile coho salmon (Oncorhynchus kisutch) through thermal infrared imaging and in-stream
monitoring, Redwood Creek, California. Can. J. Fish. Aquat. Sci. 63,
1384–1396. http://dx.doi.org/10.1139/F06-043.
Maphangwa, K.W., Musil, C.F., Raitt, L., Zedda, L., 2012. Differential interception
and evaporation of fog, dew and water vapour and elemental accumulation
by lichens explain their relative abundance in a coastal desert. J. Arid
Environ. 82, 71–80.
Marzban, C., Leyton, S., Colman, B., 2007. Ceiling and visibility forecasts via
neural nets. Weather Forecast. 22 (3), 466–479.
Maury, M.F., 1861. Physical Geography of the Sea and Its Meteorology.
May, K.R., 1961. Fog droplet sampling using a modified impactor technique.
Q. J. R. Meteorol. Soc. 87, 534–548.
Means, H.R., 1927. Fog precipitated by trees. Science 66, 402–403.
Meyer, M.B., Jiusto, J.E., Lala, G.G., 1980. Measurements of visual range and
radiation-fog (haze) microphysics. J. Atmos. Sci. 37, 622–629.
Miller, N.L., Hayhoe, K., Jin, J., Auffhammer, M., 2008. Climate, extreme heat, and
electricity demand in California. J. Appl. Meteorol. Climatol. 47, 1834–1844.
Molenar, D.A., Schrab, K.J., Purdom, J.F.W., 2000. RAMSDIS contributions to
NOAA satellite data utilization. Bull. Am. Meteorol. Soc. 81, 1019–1030.
Muller, M.D., 2006. Numerical Simulation of Fog and Radiation in Complex
Terrain. (Ph.D. Thesis) Stratus, 12. University of Basel (103 pp.).
Müller, M.D., Schmutz, C., Parlow, E., 2007. A one-dimensional ensemble
forecast and assimilation system for fog prediction. J. Pure Appl. Geophys.
164, 1241–1264 (Special issue).
Musson-Genon, L., 1987. Numerical simulation of a fog event with a onedimensional boundary layer model. Mon. Weather Rev. 115, 592–607.
174
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
http://dx.doi.org/10.1175/1520-0493(1987)115b0592:
NSOAFEN2.0.CO;2.
Nakajima, T., King, M.D., 1990. Determination of the optical thickness and
effective particle radius of clouds from reflected solar radiation
measurements. Part I: theory. J. Atmos. Sci. 47, 1878–1893.
Nakanishi, M., 2000. Large-eddy simulation of radiation fog. Boundary Layer
Meteorol. 94, 461–493.
Nakanishi, M., Niino, H., 2004. An improved Mellor–Yamada level-3 model
with condensation physics: its design and verification. Boundary Layer
Meteorol. 112, 1–31.
Nakanishi, M., Niino, H., 2006. An improved Mellor–Yamada level-3 model:
its numerical stability and application to a regional prediction of
advection fog. Boundary Layer Meteorol. 119, 397–407.
Neiburger, M., Johnson, D.S., Chien, C.W., 1961. Studies of the Structure of the
Atmosphere Over the Eastern Pacific Ocean in Summer.University of
California Press (94 pp.).
Noonkester, V.R., 1979. Coastal marine fog in southern California. Mon.
Weather Rev. 107, 830–851.
Nuss, W.A., et al., 2000. Coastally trapped wind reversals: progress toward
understanding. Bull. Am. Meteorol. Soc. 81, 719–743. http://dx.doi.org/
10.1175/1520-0477(2000)081b0719:CTWRPTN2.3.CO;2.
O'Brien, T.A., Sloan, L.C., Chuang, P.Y., Faloona, I.C., Johnstone, J.A., 2013.
Multidecadal simulation of coastal fog with a regional climate model.
Clim. Dyn. 40, 2801–2812.
Oberlander, G.T., 1956. Summer fog precipitation on the San Francisco
peninsula. Ecology 37, 851–852.
Økland, H., Gotaas, Y., 1995. Modelling and prediction of steam fog. Beitr.
Phys. Atmos. 68, 121–131.
Oliver, D., Lewellen, W., Williamson, G., 1978. The interaction between
turbulent and radiative transport in the development of fog and lowlevel stratus. J. Atmos. Sci. 35, 301–316.
Pagowski, M., Gultepe, I., King, P., 2004. Analysis and modeling of an extremely
dense fog event in Southern Ontario. J. Appl. Meteorol. 43, 3–16. http://
dx.doi.org/10.1175/1520-0450(2004)043b0003:AAMOAEN2.0.CO;2.
Palmer, A.K., 1917. Fog along the California Coast. Mon. Weather Rev. 45, 496.
Pedersen, K., Todsen, M., 1960. Some measurements of the microstructure of
fog and stratus in the Oslo area. Geophys. Pub., 21. Norske Videnskaps
Akad, Oslo, pp. 1–15.
Peng, Y., Lohmann, U., Leaitch, W.R., 2005. Importance of vertical velocity
variations in the cloud droplet nucleation process of marine stratus clouds.
J. Geophys. Res. D21213. http://dx.doi.org/10.1029/2004JD004922.
Petterssen, S.V., 1936. On the causes and forecasting of the California fog.
J. Aerosp. Sci. 3, 305–309.
Petterssen, S.V., 1938. On the causes and forecasting of the California fog.
Bull. Am. Meteorol. Soc. 19, 49–55.
Petterssen, S., 1939. Some aspects of formation and dissipation of fog.
Geofys. Publ. 12, 15–22.
Petterssen, S., 1956. Weather analysis and forecasting. Weather and Weather
Systems, vol. II. McGraw-Hill, New York (266 pp.).
Pilié, R.J., Mack, E.J., Kocmond, W.C., Rogers, C.W., Eadie, W.J., 1975. The life
cycle of valley fog. Part I: micrometeorological characteristics. J. Appl.
Meteorol. 14, 347–363.
Pilié, R.J., Mack, E.J., Rogers, C.W., Katz, U., Kocmond, W.C., 1979. The
formation of marine fog and the development of fog-stratus systems
along the California coast. J. Appl. Meteorol. 18, 1275–1286.
Pinnick, R.G., Hoihjelle, D.L., Fernandez, G., Stenmark, E.B., Lindberg, J.D.,
Hoidale, G.B., 1978. Vertical structure in atmospheric fog and haze
and its effects on visible and infrared extinction. J. Atmos. Sci. 35,
2020–2032.
Platnick, S., King, M.D., Ackerman, A., Menzel, W.P., Baum, B.A., Riédi, J.C.,
Frey, R.A., 2003. The MODIS cloud products: algorithms and examples
from Terra. IEEE Trans. Geosci. Remote Sens. 41, 459–473.
Poole, I., van Bergen, P.F., 2006. Physiognomic and chemical characters in
wood as palaeoclimate proxies. Plants and Climate Change. Springer,
Netherlands, pp. 175–196.
Pruppacher, H.R., Klett, J.D., 1997. Microphysics of Clouds and Precipitation2nd
ed. Kluwer Pub. Inc., Boston (954 pp.).
Rausch, J., Heidinger, A., Bennartz, R., 2010. Regional assessment of microphysical
properties of marine boundary layer cloud using the PATMOS-x dataset.
J. Geophys. Res. 115, D23. http://dx.doi.org/10.1029/2010JD014468.
Riehl, H., Yeh, T.C., Malkus, J., La Seur, N., 1951. The northeast trade of the
Pacific Ocean. Q. J. R. Meteorol. Soc. 77, 598–626.
Ritter, A., Regalado, C.M., Aschan, G., 2009. Fog reduces transpiration in tree
species of the Canarian relict heath-laurel cloud forest (Garajonay
National Park, Spain). Tree Physiol. 29 (4), 517–528.
Roach, W.T., 1976. On the effect of radiative exchange on the growth by
condensation of a cloud or fog droplet. Q. J. R. Meteorol. Soc. 102,
361–372.
Roach, W.T., 1995. Back to basics: fog: part 3—the formation and dissipation
of sea fog. Weather 50, 80–84.
Roach, W., Brown, R., Caughey, S.J., Garland, J.A., Readings, C.J., 1976. The
physics of radiation fog: I—a field study. Q. J. R. Meteorol. Soc. 102,
313–333.
Roden, J.S., Johnstone, J.A., Dawson, T.E., 2009. Intra-annual variation in the
stable oxygen and carbon isotope ratios of cellulose in tree rings of coast
redwood (Sequoia sempervirens). The Holocene 19 (2), 189–197.
Roth-Nebelsick, A., Ebner, M., Miranda, T., Gottschalk, V., Voigt, D., Gorb, S.,
Stegmaier, T., Sarsour, M., Linke, J., Konrad, W., 2012. Leaf surface
structures enable the endemic Namib desert grass Stipagrostis sabulicola
to irrigate itself with fog water. J. R. Soc. Interface. 9, 1965–1974. http://
dx.doi.org/10.1098/rsif. 2011.0847.
Sandstrom, T., Forsberg, B., 2008. Desert dust: an unrecognized source of
dangerous air pollution? Epidemiology 19 (6), 808–809.
Saunders, P.M., 1964. Sea smoke and steam fog. Q. J. R. Meteorol. Soc. 90,
156–165.
Schemenauer, R.S., Cereceda, P., 1991. Fog-water collection in arid coastal
locations. Ambio 303–308.
Schemenauer, R.S., Cereceda, P., 1994. Fog collection's role in water planning
for developing countries. Natural Resources Forum, 18. Wiley, p. 2.
Shchepetkin, A.F., McWilliams, J.C., 2004. The regional oceanic modeling
system: a split-explicit, free-surface, topography-following-coordinate
ocean model. Ocean Modell. 9, 347–404.
Simonin, K.S., Santiago, L.S., Dawson, T.E., 2009. Fog interception by Sequoia
sempervirens (D. Don) crowns decouples physiology from soil water
deficit. Plant Cell Environ. 32, 882–892.
Skamarock, W., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D., Duda, M.G.,
Huang, X.-Y., Wang, W., 2008. A description of the advanced research
WRF version 3. NCAR Technical Note NCAR/TN-475 + STR. http://
dx.doi.org/10.5065/D68S4MVH.
Slinn, W.G.N., 1982. Predictions for particle deposition to vegetative
canopies. Atmos. Environ. 16, 1785–1794.
Spiess, F., 1985. In: Emery, W.J. (Ed.), The Meteor Expedition: Scientific
Results of the German Atlantic Expedition. Amerind Pub. Co (429 pp.).
Stoelinga, M.T., Warner, T.T., 1999. Nonhydrostatic, mesobeta-scale model
simulations of cloud ceiling and visibility for an east coast winter
precipitation event. J. Appl. Meteorol. 38, 385–404.
Sugimoto, S., Sato, T., Nakamura, K., 2013. Effects of synoptic-scale control on
long-term declining trends of summer fog frequency over the Pacific
side of Hokkaido Island. J. Appl. Meteorol. Climatol. 52 (10), 2226–2242.
Taljaard, J.J., Schumann, T.E.W., 1940. Upper air temperatures and humidities
at Walvis Bay, southwest Africa. Bull. Am. Meteorol. Soc. 21, 293–296.
Tardif, R., Rasmussen, R.M., 2007. Event-based climatology and typology of fog
in the New York City region. J. Appl. Meteorol. Climatol. 46 (8), 1141–1168.
Tardif, R., Rasmussen, R.M., 2008. Process-oriented analysis of environmental conditions associated with precipitation fog events in the New York
City region. J. Appl. Meteorol. Climatol. 47, 1681–1703.
Tardif, R., Rasmussen, R.M., 2010. Evaporation of non-equilibrium raindrops
as a fog formation mechanism. J. Atmos. Sci. 67 (2), 345–364.
Taylor, G.I., 1915. Eddy motion in the atmosphere. Philos. Trans. R. Soc. Lond. Ser. A
215, 1–26 (Containing Papers of a Mathematical or Physical Character).
Taylor, G.I., 1917. The formation of fog and mist. Q. J. R. Meteorol. Soc. 43, 241–268.
Taylor, W., 1987. Mesoscale case study: project haar. Met Office Met. O.11
Tech. Note 239.
Tchounwou, P.B., Ayensu, W.K., Ninashvili, N., Sutton, D., 2003. Review:
environmental exposure to mercury and its toxicopathologic implications for public health. Environ. Toxicol. 18 (3), 149–175.
Teixeira, J., 1999. Simulation of fog with the ECMWF prognostic cloud
scheme. Q. J. R. Meteorol. Soc. 125, 529–553.
Telford, J.W., Chai, S.K., 1993. Marine fog and its dissipation over warm
water. J. Atmos. Sci. 50, 3336–3349.
Thompson, W.T., Burk, S.D., Lewis, J., 2005. Fog and low clouds in a coastally
trapped disturbance. J. Geophys. Res. 110, D18.
Tjernström, M., Koračin, D., 1995. Modeling the impact of marine stratocumulus on the boundary-layer structure. J. Atmos. Sci. 52, 863–878.
Tomasi, C., Tampieri, F., 1976. Features of the proportionality coefficient in
the relationship between visibility and liquid water content in haze and
fog. Atmosphere 14, 61–76.
Toth, G., Gultepe, I., Milbrandt, J., Hansen, B., Pearson, G., Fogarty, C.,
Burrows, W., 2010. The environment Canada handbook on fog and fog
forecasting. Her Majesty the Queen in Right of Canada, Represented by
the Minister of the Environment. ISBN: 978-1-100-52518-1 (93 pp., Cat.
no. En56-231/2010).
Tremant, M., 1987. La prévision du brouillard en mer. Météorologie Maritime
et Activities Océanographique Connexes. Rapport No. 20. TD no. 211.
World Meteorological Organization, Geneva, Switzerland.
Tseng, Y.-H., Chien, S.-H., Jin, J., Miller, N.L., 2012. Modeling air–land–sea
interactions using the integrated regional model system in Monterey
Bay, California. Mon. Weather Rev. 140, 1285–1306.
Turton, J.D., Brown, R., 1987. A comparison of a numerical model of radiation
fog with detailed observations. Q. J. R. Meteorol. Soc. 113, 37–54.
D. Koračin et al. / Atmospheric Research 143 (2014) 142–175
Twomey, S., 1959. The nuclei of natural cloud formation, II; the supersaturation in natural clouds and the variation of cloud droplet concentration. Geophys. Pure Appl. 43, 243–249.
U.S. Department of Agriculture, 1938. Atlas of the Climatic Charts of the
Oceans. Publication No. 1247, Prepared under the supervision of W. F.
McDonald, 130 charts. U.S. Weather Bureau.
U.S. Weather Bureau, 1930. International Radio Weather Code for Use on
United States Selected Ships. W.B No. 1005.U.S. Government Printing
Office, Washington, DC, USA (14 pp.).
Urbano, R., Palenik, B., Gaston, C.J., Prather, K.A., 2011. Detection and
phylogenetic analysis of coastal bioaerosols using culture dependent
and independent techniques. Biogeosciences 8 (2), 301–309.
US Navy, 1955. Marine Climatic Atlas of the World.Chief of Naval Operations,
Washington, District of Columbia, U.S.A.
van Schalkwyk, L., Dyson, L., 2013. Climatological characteristics of fog
at Cape Town International Airport. Weather Forecast. 28 (3),
631–646.
Vasey, M.C., Loik, M.E., Parker, V.T., 2012. Influence of summer marine fog
and low cloud stratus on water relations of evergreen woody shrubs
(Arctostaphylos: Ericaceae) in the chaparral of central California.
Oecologia 170 (2), 325–337.
Von Ficker, H., 1936. Die Passatinversion. Veroffentl. D. Meteorol. Inst. D.
Univ. Berlin, Bd., I, Heft, 4.
Wang, B., 1985. Sea Fog.China Ocean Press (330 pp.).
Watanabe, K., Ishizaka, Y., Takenaka, C., 2001. Chemical characteristics of
cloud water over the Japan Sea and the Northwestern Pacific Ocean near
the central part of Japan: airborne measurements. Atmos. Environ. 35
(4), 645–655.
Weathers, K.C., Likens, G.E., 1996. Clouds in southern Chile: an important
source of nitrogen to nitrogen-limited ecosystems? Environ. Sci.
Technol. 31 (1), 210–213.
Weathers, K.C., Likens, G.E., Bormann, F.H., Eaton, J.S., Bowden, W.B., Andersen,
J.L., Smiley, D., 1986. A Regional Acidic Cloud/Fog Water Event in the
Eastern United States.
Weathers, K.C., Lovett, G.M., Likens, G.E., Caraco, N.F., 2000. Cloudwater
inputs of nitrogen to forest ecosystems in southern Chile: forms, fluxes,
and sources. Ecosystems 3 (6), 590–595.
175
Weiss Penzias, P.S., Ortiz, C., Acosta, R.P., Heim, W., Ryan, J.P., Fernandez, D.,
Flegal, A.R., 2012. Total and monomethyl mercury in fog water from the
central California coast. Geophys. Res. Lett. 39 (3).
Wells, L.A., 2007. Marine Layer Stratus Study. Fog and Boundary Layer
Clouds: Fog Visibility and Forecasting Pageoph Topical Volumes 2007.
1397–1421.
Wetzel, M.A., Borys, R.D., Xu, L.E., 1996. Satellite microphysical retrievals for
land-based fog with validation by balloon profiling. J. Appl. Meteorol. 35,
810–829.
Wheeler, J.A., Ford, K.W., 1998. Geons, Black Holes, and Quantum Foam (A
Life in Physics).W.W. Norton & Co.(380 pp.).
Willett, H.C., 1928. Fog and haze, their causes, distribution and forecasting.
Mon. Weather Rev. 56, 435–468.
Williams, A.P., Still, C.J., Fischer, D.T., Leavitt, S.L., 2008. The influence of
summertime fog and overcast clouds on the growth of a coastal
California pine: a tree-ring study. Oecologia 156, 601–611.
Wurl, O., Obbard, J.P., 2004. A review of pollutants in the sea-surface
microlayer (SML): a unique habitat for marine organisms. Mar. Pollut.
Bull. 48 (11), 1016–1030.
Yang, D., Ritchie, H., Desjardins, S., Pearson, G., MacAfee, A., 2010. Highresolution GEM-LAM application in marine fog prediction: evaluation
and diagnosis. Weather Forecast. 25, 727–748.
Yue, Y., Niu, S., Zhao, L., Zhang, Y., Xu, F., 2012. Chemical composition of sea fog
water along the South China Sea. Pure Appl. Geophys. 169 (12), 2231–2249.
Zdunkowski, W.G., Barr, A.E., 1972. A radiative conductive model for the
prediction of radiation fog. Bound. Layer Meteor. 2, 152–177.
Zhang, S., Yi, L., 2013. A comprehensive dynamic threshold algorithm for
daytime sea fog retrieval over the Chinese adjacent seas. Pure Appl.
Geophys. 170, 1931–1944.
Zhang, S.-P., Xie, S.-P., Liu, Q.-L., Yang, Y.-Q., Wang, X.-G., Zhao-Peng Ren, Z.P., 2009. Seasonal variations of Yellow Sea fog: observations and
mechanisms. J. Clim. 22 (24), 6758–6772.
Zhou, B., Du, J., 2010. Fog prediction from a multimodel mesoscale ensemble
prediction system. Weather Forecast. 25, 303–322.
Zhou, B., Du, J., Ferrier, B., McQueen, J., Dimego, G., 2007. Numerical forecast
of fog — central solutions. 18th Conference on Numerical Weather
Prediction, AMS, 25–29 June 2007, Park City, UT.