Download Satellite remote sensing of precipitation: progress

Remots Sensing and Hydrology 2000 (Proceedings of a s y m p o s i u m held at Santa Fe, N e w M e x i c o , U S A ,
April 2000). I A H S Publ. no. 267, 2 0 0 1 .
3
Satellite remote sensing of precipitation:
progress and problems
ERIC C. B A R R E T T
Centre for Remote Sensing,
Bristol BS8 1SS, UK
School
of Geographical
Sciences,
University
of
Bristol,
e - m a i l : c.c,barrett(5ibris.ac,uk
Abstract Thirty years after satellite data were first used for assessing rainfall
it is appropriate to critically review what has been achieved in this field to
date, and what has not. Good progress has been made in developing and
understanding the capabilities of satellite sensing systems, the physics of the
atmosphere and land surfaces of the Earth, and in the development of
computers capable of multiple, high-speed operations for near real-time data
processing. However, all types of satellite monitoring of falling precipitation
are subject to limitations, and progress is still relatively poor in several
regards. To make matters worse, there has been a global trend towards the
collection of less, not more, in situ data on precipitation over at least half of
the last century. Foci for further research and operations are proposed. Some
problems will require concentrated effort and significant expenditure if the
recent rate of progress in this field is even to be maintained.
K e y w o r d s a p p l i c a t i o n s ; infrared; m i c r o w a v e ; o p e r a t i o n s ; p r e c i p i t a t i o n ; p r o g r e s s ;
r e m o t e s e n s i n g ; r e s e a r c h ; satellite; v i s i b l e
INTRODUCTION
Precipitation is a notoriously difficult parameter to evaluate, first and foremost because
of its high spatial and temporal variability. Although most locations on the Earth's
surface experience precipitation some time in the course of any one year, and some
locations on more days than not, instantaneous precipitation is a rare phenomenon. The
area of the globe covered at any one time by falling precipitation has been estimated
from surface evidence as being probably below 1% (Barrett & Martin, 1981), and
"typically occurs only a very small fraction of the time over any given location ..."
(Theon, 1992), though satellite-based studies increasingly suggest that the true
frequency of occurrence may be a few percentage points higher then earlier thought, at
maybe even about 3 - 4 % in the spatial domain. Meanwhile, at any one instant,
precipitation intensities around the globe range from zero to >125 m m h" .
Further complicating the measurement problem, rainfall can vary by orders of
magnitude of intensity in both space and time over distances of no more than tens of
metres, and in a matter of minutes or even seconds. Simultaneously, towards the low
end of the rain rate spectrum, gradients of intensity can be quite shallow, and it is
never easy by any means to determine no-rain boundaries simply and with confidence.
For global weather forecasting purposes rainfall is measured by raingauges, some
of whose data are circulated in near real-time via the Global Telecommunication
System (GTS) of the World Meteorological Organisation (WMO). For climate analysis
purposes supplementary raingauge networks containing several times more gauges are
1
4
E. C. Barrett
operated in most countries, though typically these are managed by more than one (and
perhaps even several) environmental monitoring agencies within any one country, this
often causing severe problems when attempts are made to collect data for "best
possible" rainfall studies.
As if all this were not difficult enough, still further problems are encountered by
anyone seeking to use raingauge data to evaluate rainfall over wide regions and/or the
globe as a whole. These include the following (Barrett & Beaumont, 1994):
There are many different raingauge types.
Different classes or families of raingauges record rainfall for widely different
periods of time, ranging from very short intervals (even as short as seconds) in the
case of some "hydrographs" (continuously-recording raingauges), up to very long
intervals (even as long as months or seasons) in the case of some "accumulating"
precipitation gauges.
The shapes and sizes of raindrops or other precipitation particles and their
behaviour in the vicinity of raingauges, and in relation to local surface conditions,
are many and varied.
Deficiencies in rainfall station management and data quality control are common­
place, also seriously affecting both the rainfall data supply and its subsequent utility.
Of special significance in the context of extreme events, it may not be possible to
obtain "real-time" data related to very high intensity rainfall because of storm
disruption of gauges, radar or supporting infrastructures.
Rainfall radars are designedly better able to reveal continuous pictures of the
spatial distribution of rainfall than the average gauge network, and for the most part
provide data used qualitatively, and locally or regionally, for very short-term
forecasting ("nowcasting") activities and related research. Collier (1989) has
concluded that there are still "... few examples of (radar) systems which produce
quantitative (precipitation) data operationally" and that important "... sources of error
in radar measurements arise from the characteristics of the precipitation particularly in
the vertical". Proportionately more rainfall radars are located in middle latitudes than
in the tropics.
Existing raingauge and radar densities are not nearly adequate either for global
rainfall monitoring, even when quite sophisticated statistical or mathematical proc­
edures are applied to the resulting scatters of point or local area data in order to
extrapolate from them across intervening conventional data remote regions, and so
provide spatially-complete inventories of this key variable, or for regional rainfall
monitoring in most areas.
Theon (1992) commented that "It is remarkable that as important as rain is to us,
we really do not know its geographical distribution to within a factor of two over much
of the Earth." Rasmusson & Arkin (1992) concluded that "Continuous global
monitoring of this variable is a challenging task. Surface-based observations are totally
inadequate for estimating rainfall over the ocean areas of the tropics, where surface
observations are essentially non-existent except for potentially non-representative
island stations, and over most of the continental areas as well". Making the problem of
variability much worse, especially where mountainous landfalls are involved, both the
temporal and spatial variations of rainfall may approach, and often establish, new
global record levels.
Satellite remote sensing ofprecipitation:
progress and problems
5
Thus, estimation and prediction of rainfall represents one of the biggest scientific
challenges in satellite meteorology and climatology, not least because it involves both
the upper and lower extremities of the statistical distributions of rainfall rates, totals
and variabilities.
P R O G R E S S IN SATELLITE R E M O T E SENSING OF PRECIPITATION
General comments
Historically, satellite meteorology may be said to have begun in April 1960 along with
the launch of the first Television and InfraRed Observation Satellite (TIROS-1)
designed primarily for global weather monitoring. Since then many "environmental"
satellites have been operated for such purposes, providing relatively low spatial
resolution data (mostly between 0.5 km and 50 km at the Earth's surface), relatively
frequently (mostly between successive images at a given location, between 30-min and
3-day intervals). Through the same period, great advances have been made both in the
capacities of the satellite "buses" available to carry Earth-observing instrument
payloads into space, and in the capabilities of instruments to provide data of the types,
qualities and quantities required by precipitation information end-users.
Three types of electromagnetic radiation from the target have proven particularly
valuable for the estimation of rainfall from environmental satellites, namely VIS
(visible), IR (thermal) and M W (microwave) radiation. To the satellite meteorologist
VIS includes short-wave radiation (sunlight) reflected back towards space from the
tops of clouds mostly within the 0.4-0.7 urn region of the electromagnetic spectrum;
characteristically, the spatial resolution of such data is from about 0.5-2.5 um.
Thermal IR is longer wavelength radiation, mostly measured within the 10.6-12.6 um
region, the result of absorption of some incoming solar radiation by the Earth and its
atmosphere, plus radiation subsequently re-emitted towards space as heat energy;
spatial resolutions of IR data are characteristically similar to those of VIS data. M W is
even longer wavelength energy, re-emitted or reflected towards space, and mainly
exploited for rainfall measurements between wavelengths of about 0.3-3 cm
(frequencies ranging from 90-10 GHz); here the spatial resolutions of sensors are lower
than with either the VIS or the IR, mostly from about 10 km to 50 km. The principal
reason for the relatively poor spatial resolutions of the M W sensors is not the signal-tonoise ratio, as might be supposed, but diffraction. In this case there is a modest
additional decrease in resolution owing to the time-integration of the received power.
The best spatial resolution cited above for an environmental satellite sensor
(0.5 km) relates to a VIS system (part of the Operational Line Scan (OLS) sensor on
current U S Defense Meteorological Satellite Program (DMSP) spacecraft), whilst the
lowest (about 50 km) relates to a M W sensor (the 19.35 GHz channel on the D M S P SSM/I (Special Sensor Microwave Imager instrument)). VIS and IR data are obtain­
able from both high-orbiting geostationary satellites ("GEOs") and low-orbiting Earth
satellites ("LEOs"). The former are capable of providing very frequent information
(every few minutes in some cases) from fixed positions in relation to the Earth, centred
on the equator. Meanwhile, since the latter orbit the Earth many times a day, each of
6
E. C. Barrett
these satellites is able to provide at best two good observations of any point on the
surface during that period. For any particular application the frequency of coverage of
the target, the spatial resolution of the data, and the type of spectral information
provided, are all co-equally important. For rainfall monitoring, the physical charact­
eristics and behaviour of cloud particles (including raindrops), the structures and lifecycles of rain cells, and the nature and influence of local precipitation enhancement
processes (e.g. convergence, and orography), are important too.
Although most satellite rainfall monitoring to date has been based on "passive"
remote sensing of the target, i.e. on the collection and measurement of radiation
naturally reflected or emitted from the target, it is also possible to use "active" remote
sensing for this application, i.e. in respect of radiation artificially generated and
propagated towards the target. The first active microwave (AMW) system for rainfall
assessment from satellites is the rainfall radar on the recently-launched joint
Japanese/US Tropical Rainfall Measuring Mission (TRMM) satellite. This satellite is
providing a substantial stimulus to satellite rainfall monitoring not only because of its
rainfall radar, but because of the relative ease with which it is possible to intercompare and combine (co-located) data from its Precipitation Radar (PR), Passive
Microwave (PMW) (TMI), VIS and ER (VIRS) imaging systems.
Analytical approaches
Since the early 1970s, much progress has been made with the development of
analytical approaches to extract rainfall estimates from satellite and collateral data sets
over most regions of the world. Increasingly these methods have become "multisource", and "mature": over the global oceans satellite-based rainfall estimation
techniques are now the leading methods for rainfall measurements and monitoring;
over land areas, as we shall see later, the challenges are more difficult and demanding.
In general, satellite rainfall algorithms seek to:
(a) Identify areas of probable precipitation, through recognition of the most likely
rain/no-rain boundaries (sometimes referred to as "screening" for no-rain areas).
(b) Evaluate associated rain rates (sometimes referred to as "rain rate conversion").
(c) Combine (a) and (b) through time where estimates of total accumulated rainfall are
required. This is most reliable where the satellite data collection return period is
shortest, i.e. from GEOs rather than from LEOs.
Variations on these themes have seen satellite rainfall estimation and monitoring
techniques developed for a wide range of applications, from the climatological,
through meteorological monitoring, to short period, high-intensity rainstorm identi­
fication and prediction.
In order to achieve reliable results, analyses are often made of the satellite data initially
using surface (raingauge and/or radar) data for algorithm "calibration". Often the results
will later be compared with independent sets of surface data for algorithm "validation" or
"verification". We will go on to see that the object of present day research involves the
blending of perhaps even several different types of data from both satellite and nonsatellite sources into a final "best possible" rainfall product. Such activity in respect to
multiple-source algorithms is expected to increase further in the foreseeable future.
Satellite remote sensing ofprecipitation:
progress and problems
7
While three types of electromagnetic radiation have proved particularly valuable for
such purposes, namely the visible (VIS), thermal infra-red (IR) and passive microwave
(PMW), the Tropical Rainfall Measuring Mission (TRMM), launched in November
1997, carries for the first time an active microwave (AMW) instrument, whose data have
been eagerly awaited by scientists interested especially in rainfall processes.
In order to achieve best possible results, satellite rainfall estimates are often made
in conjunction with surface (raingauge and/or radar) data, meteorological analyses, and
increasingly with outputs from numerical weather prediction models (NWPs). Most of
today's operational techniques routinely implemented by the Global Precipitation
Climatology Centre at monthly, 2 A° lat./long. scales, are relatively simple, deploying
fixed rain/no-rain IR temperature thresholds, and constant rain rates for all parts of the
globe (Arkin et al., 1994). Others, e.g. the B4 technique (Barrett, 1993) applied to
daily estimation over 25 km grid squares or smaller, are much more complicated,
deploying spatially and temporally variable IR temperature thresholds and rain rates,
related as closely as possible to local morphoclimatic influences, and seasonal weather
cycles. Still others are even more complex and take account of very detailed cycles of
cloud growth and development, particularly where severe storms are involved.
Together, techniques are available to provide a range of results from long-term, broadarea statistics to small basin or even locally-specific statistics. Whilst most operational
or quasi-operational techniques today are automatic (or objective), intervention by
analysts is still necessary for some special applications, including severe storm
monitoring and flash flood forecasting, e.g. the US Weather Service's Interactive Flash
Flood Analysis (Scofield, 1987).
In the past decade, much effort has also been put into the development of rainfall
techniques based on P M W data. Whereas in the VIS/IR regions of the spectrum most
rain-related information comes from cloud tops, microwave emissions or reflections
received by the satellite emanate from hydrometeors within the clouds themselves.
Below about 20 GHz evidence of rain is provided mainly by rainfall absorption/
emission processes, through which areas of active rainfall over water surfaces (e.g.
oceans and seas) are warmer than the background radiation; above about 60 GHz
evidence of rainfall comes predominately from scattering processes, whereby areas of
heavy rainfall are much colder than their backgrounds; meanwhile between about 20
and 60 GHz some combinations of absorption/emission and scattering processes
dominate in different situations. Whilst most existing P M W techniques are empirical
in the sense that they seek to exploit observed relationships between P M W radiation
characteristics and rainfall data from other sources (e.g. raingauge radar) it seems
likely that best results may eventually be obtained by physical techniques, through
which the observed P M W variations are interpreted in terms of detailed cloud models
and understanding of related rainfall processes and patterns.
l
Application achievements
Results of satellite precipitation monitoring operations can therefore be provided to the
hydrological user community to meet a wide variety of different needs. Whilst the
satellite may provide little additional information of a sufficiently reliable nature in
8
E. C. Barrett
areas which are well monitored by more conventional means, satellite-derived
estimates may be the only ones available for some types of hydrological activities over
large areas of the globe. Increasing opportunities are arising for satellite precipitation
estimates to be used for the following hydrometrical applications, amongst others:
The incorporation of satellite-derived rainfall estimates into hydrological datasets.
The generation of independent sets of precipitation estimates for comparison with
existing data types.
In support of both point-location and area-wide monitoring and modelling.
The estimation and prediction of runoff and stream hydrographs.
In these situations, satellites represent a still relatively under-exploited source of
data of great potential value in hydrometry, especially in the developing world.
However, even in the relatively developed world proper cognizance of the strengths
and weaknesses of raingauges, radar and satellites would clearly bring many practical
and financial benefits.
P R O B L E M S IN SATELLITE R E M O T E SENSING O F P R E C I P I T A T I O N
After three decades of research and growing operational use of satellite remote sensing
data on precipitation it is clear that limitations on the quality and utility of such data
remain, due to numerous factors, including the following:
The temporal, spatial and spectral characteristics of the satellite observing
systems—which are less than optimal for such a variable phenomena.
The varied, and variable, interactions between electromagnetic radiation and its
environment—particularly over land areas.
The availability and suitability of collateral data for calibration and validation—
which are still generally deteriorating.
The provision of appropriate infra-structures, systems, personnel and policies to
ensure optimum use of all relevant data sources, which are becoming more difficult to
sustain as analytical approaches continue to become more complex and demanding.
In view of the above, it is unsurprising that progress has been relatively poor in a
number of areas, the most significant of which for overland hydrology relate to the
following:
Convergence of satellite-derived estimates with those from independent (in situ)
sources: for example PEP-3 P M W estimates over the western equatorial Pacific and
ground-based radar, which initially differed b y approximately a factor of two; and
convergence between satellite estimates of different kinds, e.g. IR and P M W
estimates over West Africa, which seem to show under-sampling effects from the
latter even over unit periods of one month, and different algorithms applied to
high-intensity rainfalls.
Areas of signal ambiguity remain, e.g. from tropical cyclones in Central America;
in respect of cold clouds in the IR which m a y or may not have enough depth to
rain heavily; recognition and assessment of low intensity rains over dry, very dry,
or frozen land surfaces which may give "false rain" signals in the P M W ; retrieval
of "warm rain" by both IR and PMW; and effects of ice in the atmosphere and/or
at the surface, which are difficult to discriminate in the P M W or A M W .
Satellite remote sensing of precipitation: progress and problems
-
9
H o w to calibrate satellite-only techniques in areas whence little or no reliable in
situ data are forthcoming (e.g. Nigeria), and how best to combine data from
different sources where they are (typically) fragmentary and incomplete.
As time has progressed, it seems that local access to satellite data and abilities to
use the same have gone full circle: in the early days of satellite meteorology (1960s to
mid-1970s) reception facilities were rare, and competence in satellite rainfall retrieval
limited to a few global centres; from the mid-1970s to the late 1980s both were more
widespread; over the last decade key data have become unavailable to many users, and
better techniques have grown more sophisticated and demanding. Thus, state-of-the-art
use of satellite remote sensing of precipitation has become restricted, once again, to a
relatively small number of centres, in the more developed countries where they can be
government supported.
FOCI F O R F U T U R E R E S E A R C H AND OPERATIONS
Addressing problems of the types listed in the previous section will require
concentrated effort, and significant resource expenditure if progress in this field is to
be even maintained in relation to the last decade or two.
Of paramount importance in research will be the refinement of procedures
to
retrieve precipitation
estimates over drier and colder land surfaces, and from
short-period, high-intensity storm situations.
Since much precipitation is organized on meso- and local-scales, in operations
more attention must be given to rainfall retrieval at space and time scales
adequate for effective local use, whether in hydrology or weather and crop
forecasting. In this regard, the Global Precipitation Mission (GPM) is promising,
though probably still not adequate to give the coverage required.
Both research and operations will be challenged particularly b y the overwhelming
logic of "multi-sourcing" the solution to the general problem, within flexible
systems simultaneously involving gauge and radar data, plus satellite data from
different satellites and sensors, and both should be advanced by the development
of the Internet, through which—given appropriate international initiatives and
encouragement—outputs from advanced methods could be m a d e accessible to
all would-be users, maybe preferably in some kind of "proto-results" form,
which could then be calibrated better locally, say using datasets not usually
disseminated (quickly) outside a particular country, authority or agency.
Last but not least, better progress must be made with hydrometric design, to ensure
that each available observation unit—whether surface-based or space-borne—can
contribute optimally to the monitoring system as a whole.
To achieve all this, more interchange is urgently needed between scientists,
engineers, planners and politicians, plus the customers for rainfall and related data, and
potential sponsors for schemes appropriate to less developed regions of the world and
from which the potential benefits of improved rainfall data could be greatest. Perhaps
the IAHS might accept the challenge this represents, e.g. through the organization of a
colloquium designed to bring together such a wide range of parties with interest in, or
responsibility for, the use of water.
10
E.
C.
Barrett
REFERENCES
Arkin, P. A. Joyce, R. & J a n o w i a k , J. E. (1994) T h e estimation of global m o n t h l y m e a n rainfall using infrared satellite
data: the G O E S Precipitation Index (GPI). Remote
Sens. Reviews
11, 619-628
Barrett, E. C. (1993) Satellite rainfall monitoring for agrometeorology: operational p r o b l e m s , practices and prospects.
EARSeL
Adv. Remote
Sens. 2(2-VI), 6 6 - 7 2 .
Barrett, E. C. & B e a u m o n t , M . J . (1994) Satellite rainfall monitoring: an o v e r v i e w . Remote
Barrett, E. C. & Martin, D . W . (1981) The Use of Satellite
Collier, C. G. (1989) Applications
Data in Rainfall
of Weather Radar Systems.
Monitoring.
Sens. Reviews
11, 23-48.
A c a d e m i c Press, L o n d o n , U K .
Ellis H o r w o o d , Chichester, U K .
R a s m u s s o n , E. M. & Arkin, P. A. (1992) Observing tropical rainfall from space: a review. In: The Global Role of
Tropical
Rain (ed. by J. S. T h e o n ) , 1 0 5 - 1 1 8 . Hampton, Virginia, U S A .
Scofield, R. A. (1987) T h e N E S D I S operational convective precipitation estimation t e c h n i q u e . Mon. Weath. Rev. 115,
1773-1792.
T h e o n , J. S. (1992) T h e tropical rainfall measuring mission ( T R M M ) . In: The Global Role of Tropical Rain (ed. b y
J. S. T h e o n ) , 2 0 2 - 2 2 2 . H a m p t o n , Virginia, U S A .