Download Analyzing and forecasting of atmospheric fronts development and

Scientific Journals
Zeszyty Naukowe
Maritime University of Szczecin
Akademia Morska w Szczecinie
2008, 13(85) pp. 29‐32
2008, 13(85) s. 29‐32
Analyzing and forecasting of atmospheric fronts
development and dissipation areas for meteorological
support of maritime navigation
Wyznaczanie i prognozowanie obszarów powstawania
i rozpadu frontów atmosferycznych dla potrzeb
meteorologicznego zabezpieczenia żeglugi
Janusz M. Jasiński, Dariusz Chaładyniak
Wojskowa Akademia Techniczna, Wydział Inżynierii Lądowej i Geodezji
00-908 Warszawa, ul. S. Kaliskiego 2, tel. 022 683 99 22,
e-mail: [email protected], [email protected]
Key words: frontogenesis, frontolysis, satellite images, meteorological elements
Abstract
Efficiency and safety of maritime navigation depends very much on weather conditions. Since, in majority of
cases, severe weather conditions are related with atmospheric fronts, it is crucial for meteorological support
to provide a profound analysis and precise forecast of atmospheric fronts development and dissipation areas.
The paper presents a method of determining the areas of frontogenetic and frontolytic zones worked out with
the use of the results of research concerning the frontogenesis and frontolysis processes. The method is based
on extensive analysis of the values of the frontogenetic function and the Q-vectors’ divergence.
Słowa kluczowe: frontogeneza, frontoliza, zdjęcia satelitarne, elementy meteorologiczne
Abstrakt
Skuteczność i bezpieczeństwo żeglugi morskiej zależy w dużym stopniu od warunków atmosferycznych.
W większości przypadków niebezpieczne zjawiska pogody są związane z frontami atmosferycznymi, stąd dla
prawidłowego wsparcia meteorologicznego niezwykle istotnym jest zapewnienie głębokiej analizy i precyzyjnej prognozy obszarów rozwoju i rozpadu frontów atmosferycznych. Artykuł przedstawia metodę wyznaczania obszarów występowania stref frontogenetycznych i frontolitycznych opracowaną z wykorzystaniem
wyników badań dotyczących procesów frontogenezy i frontolizy. Metoda opiera się na kompleksowej analizie wartości funkcji frontogenetycznej i dywergencji Q-wektorów.
Introduction
of information and methods of processing. Comprehensive diagnosing and forecasting of frontogenetic and frontolytic areas uses both remote sensing
and numeric methods.
Among remote sensing methods applied in meteorology, photo-interpretation of cloud systems
plays a special role. The importance of the method
results from the fact that the satellite images enable
to observe the evolution of organized structures and
systems of clouds at the same time and they are
a scene for a standard synoptic analysis using conventional meteorological data. Numerical methods
The issue of atmospheric fronts forecasting is
still open despite many years of research. It is no
longer strange when we observe satellite imagery
and the miscellaneous forms of clouds. That is why
all results of investigating atmospheric states are
of considerable value and are basis for better
understanding of the processes that determine the
weather.
Innovation of contemporary atmospheric research consists in using numerous available sources
Zeszyty Naukowe 13(85)
29
Janusz M. Jasiński, Dariusz Chaładyniak
the trough, the Q-vector direction is westerly because the wind direction changes from north-west
to south-west along the isotherm. However, across
the wedge the appropriate changes of wind are reversed and the Q-vector’s direction is easterly. We
can conclude that ascending currents occur downstream the trough while descending currents exist in
the downstream part of the wedge, which is also
confirmed by analysis based on vorticity advection.
using mathematical approach to analysis and assessment of large-scale weather processes enable
objective determination of the probable areas of
initiating and developing of cyclones, atmospheric
fronts and weather phenomena [1].
Traditional methods of determining the frontogenetic and frontolytic areas used so far are not
a fully reliable source of information for research or
practical operational applications, even if requirements concerning precision are not high.
Ascending
currents
Cold air
Application of the frontogenetic function
and Q-vectors divergence to diagnosing
and forecasting of the frontogenesis and
frontolysis processes
The diagnosis of frontogenesis and frontolysis
processes consists in determining zones with appropriate values of the frontogenetic function and
Q-vectors divergence and analyzing them along
with existing fronts observed in satellite images
[2, 3].
Forecasting of frontogenesis and frontolysis
processes, however, consists in application of the
frontogenetic function and Q-vectors divergence
values calculated with the use of forecast values of
meteorological elements. These were obtained from
GRID data. Numerical values of forecasted charts
are available every 6 hours (from Bracknell) or
every 12 hours (from Offenbach). Calculated values
of the frontogenetic function and Q-vectors divergence are forecasts for 6, 12, 18 etc. hours. The
calculated values are used to determine frontogenesis and frontolysis areas for comparison with satellite images of cloud systems [3, 4, 5].
Fields of measured values of geopotential Φ and
temperature T on the same isobaric surface, obtained from GRID data sets, have been used for
analysis. The following figures present examples of
cases in which air temperature has decreased in the
direction of the North.
Figure 1 presents a model field of geopotential
with two highs and a low in a slightly disturbed
westerly thermal wind. In the vicinity of the low
center, the change of the geostrophic wind direction
to east along isotherms takes place from north to
south through cold air in accordance with the circulation in the low. Expression ∇Q describes descending currents in the area of cold air advection
on the western side of the trough and ascending
currents in the area of warm air advection on the
eastern side [6].
The upper trough and wedge system in figure 2a
indicates that, in this case, there is no temperature
advection (parallel isotherms and isobars). Across
Descending currents
Warm air
Fig. 1. Distribution of Q-vectors in case of a model field
of geopotential (solid lines) and temperature (dashed lines);
W – high, N – low [6]
Rys. 1. Rozkład Q-wektorów w modelowym przypadku pola
geopotencjału (linie ciągłe) i temperatury (linie przerywane);
W – wyż, N – niż [6]
A model case of frontogenesis process in a convergent flow zone corresponding to an area of entrance to a jet stream in upper layers of the atmosphere is presented in figures 2b and 3a. Q-vectors
a)
Cold air
Descending
currents
Ascending
currents
Warm air
b)
Cold air
Descending
currents
Ascending
currents
Warm air
Fig. 2. a) A model case of Q-vectors layout in the fields of
isobars (solid lines) and isotherms (dashed lines) on upper
levels of the atmosphere for a system of troughs and wedges;
b) a model case of frontogenesis process in a convergent flow
[6]
Rys. 2. a) Modelowy przypadek rozkładu Q-wektorów w polach izobar (linie ciągłe) i izoterm (linie przerywane) na górnych poziomach atmosfery dla systemu zatok i klinów;
b) modelowy przypadek procesu frontogenezy w zbieżnym
przepływie [6]
30
Scientific Journals 13(85)
Analyzing and forecasting of atmospheric fronts development and dissipation areas...
esses is a result of the research using remote sensing and numerical methods. It is composed of the
following phases [3, 7, 8]:
are directed across the isotherms to the warm air
with ascending currents while the cold air descends.
This situation is in harmony with traditional observations of relative vertical movements of air masses
on an atmospheric front. An important observation
here is that it is rather the frontogenetic forcing than
the front itself that causes such a layout of vertical
currents. Various parts of the baroclinic zone may
have various forms of frontogenetic or frontolytic
forcing [6].
– acquisition, archiving and analysis of satellite
images of cloud systems in various spectral
channels (IR, VIS, WV) and in various areas
(D1, D2, D3);
– decoding numerical data from GRID data sets,
which comprise meteorological elements charts
(geopotential, air temperature, relative humidity,
wind seed and direction) in regular angular geographical grid;
– projecting satellite images contents and GRID
data to a common cartographic system for parallel analysis of remote sensing and numerical data;
– superimposing meteorological elements information (GRID data) above satellite images;
– determining the frontogenetic function and
Q-vectors divergence values in meteorological
elements fields, gradient fields and in satellite
images;
– analysis of the frontogenetic function and
Q-vectors divergence values in the fields of
geopotential, air temperature, relative humidity,
wind speed and direction as well as in the fields
of air temperature advection, relative vorticity
and in satellite images;
– determining the areas of frontogenesis (positive
values of the frontogenetic function and
Q-vectors divergence) and frontolysis (negative
values), as well as forecasting of creation, evolution and dissipation of atmospheric fronts;
– verifying the forecasted areas of frontogenesis
and frontolysis by comparing them with satellite
images analysis.
Cold air
a)
Descending
currents
Ascending
currents
Warm air
b)
Cold air
Descending currents
Frontogenesis
Ascending currents
Stable state
Frontolysis
Descending currents
Ascending currents
Warm air
Fig. 3. a) Q-vectors layout in a convergent flow; b) a model
case of baroclinic zone with frontogenesis and frontolysis
processes and a stable state [6]
Rys. 3. a) Rozkład Q-wektorów w polu zbieżności; b) modelowy przypadek strefy baroklinowej z procesami frontogenezy,
frontolizy i stanem stabilnym [6]
In figure 3b the area on the left shows the same
orientation of isotherms and Q-vectors as in figure
2b which may lead to the development of an intensive frontal surface. However, the area on the right
side shows something totally different, simething
that would occur if the location of highs and lows
in figure 2b had been exchanged. Q-vectors are
now directed to the cold air which indicates a frontolysis process. Between the areas there is a zone
with parallel Q-vectors and isotherms. In this case
the baroclinic zone is not active and the vertical
currents are not intensive. These are areas of stable
atmosphere [6].
Summary of results
The research indicates that the best results of
frontogenesis forecasting are obtained in high value
fields of horizontal geopotential and air temperature
gradients. In these cases, both the frontogenetic
function and the Q-vectors divergence have positive values.
Creation and development of atmospheric fronts
are also observed in fields of significant values of
wind speed and convergence which is accompanied
by positive values of the frontogenetic function and
the Q-vectors divergence. A slightly weaker conformity of the frontogenetic function and the
Q-vectors divergence is observed in the fields of
relative humidity, air temperature advection and
relative vorticity. Forecasting of frontolysis yields
the best results in low-gradient fields of pressure
Methodology of analyzing and forecasting
the frontogenesis and frontolysis
processes
The following methodology of analyzing and
forecasting the frontogenesis and frontolysis procZeszyty Naukowe 13(85)
31
Janusz M. Jasiński, Dariusz Chaładyniak
3. CHAŁADYNIAK D.: Analysis of cloud systems by means of Qvector fields and satellite images. 31st Conference on Broadcast Meteorology, Williamsburg, Virginia, 24–28.06.2002.
4. CHAŁADYNIAK D., WINNICKI I. A.: Application of numerical
data and satellite images to analysis of frontogenesis and
frontolysis processes. 18th Conference on Weather Analysis
and Forecasting, Fort Lauderdale, Florida, 30.07–2.08.2001.
5. JASIŃSKI J., KROSZCZYŃSKI K., PIETREK S.: Non-standard
synoptic materials preparation based on mesoscale models
data and satellite imagery. V European Conference on
Applications of Meteorology ECAM 2001, Budapest, 24–
28.09.2001.
6. SANDERS F., HOSKINS B. J.: An easy method for estimation
of Q-vectors from weather maps. Weather Forecasting, 1990,
5, 2, 346–353.
7. CHAŁADYNIAK D.: Analysis and forecasting of frontogenesis
and frontolysis by means of Q-vector diagnostics combined
with satellite images. P. J. Envir. Stud. 2006, 15, 3c.
8. JASIŃSKI J., KROSZCZYŃSKI K., PIETREK S.: Nowcasting of
temporal and spatial distributions of meteorological elements
values in severe weather. VI European Conference on Applications of Meteorology ECAM 2003, Rome, 15–19.09.2003.
9. CHAŁADYNIAK D.: Analysis and forecasting of frontogenesis
and frontolysis processes by means of Q-vectors method.
XIV International Scientific and Technical Conference, The
Part of Navigation in Support of Human Activity on the Sea,
Gdynia 2004.
and air temperature as well as in zones of wind
divergence. The frontogenetic function and the
Q-vectors divergence values are negative here. The
research indicates also that forecasting of the frontogenesis and frontolysis processes is more accurate
when more meteorological elements and characteristics are considered (temperature advection, relative vorticity) [3, 7, 9].
Some of the analyzed synoptic situations have
given slightly different results from the general
observations concerning forecasting of the frontogenesis and frontolysis processes. Due to variety of
processes in the atmosphere, in some cases the
frontogenetic function and the Q-vectors divergence values have opposite signs. The proposed
methodology still requires modification and consideration of the new sources of information and new
achievements in data processing [3, 7, 9].
References
1. JASIŃSKI J., PIETREK S.: Numerical models results application
to mesoscale weather forecasting. 18th Conference on
Weather Analysis and Forecasting, Fort Lauderdale, USA,
30.07–02.08 2001.
2. CHAŁADYNIAK D.: Combined analysis of satellite image and
GRID data to forecasting the frontogenesis and frontolysis
areas. 18th International Conference on Interactive information processing Systems for Meteorology, Oceanography,
and Hydrology, Orlando, Florida, 13–17.01.2002.
Recenzent:
prof. dr hab. inż. Bernard Wiśniewski
Akademia Morska w Szczecinie
32
Scientific Journals 13(85)