Satellite Precipitation Measurements for Water Resource Monitoring

SATELLITE PRECIPITATION MEASUREMENTS FOR WATER RESOURCE MONITORING1

Chris Kidd, Vincenzo Levizzani, Joe Turk, and Ralph Ferraro2

ABSTRACT: Satellites offer an unrivaled vantage point to observe and measure Earth system processes andparameters. Observations of meteorological phenomena permit a more holistic view of the weather and climatethat is not possible through conventional surface observations. Precipitation (rain and snow) in particular, bene-fit from such observations since precipitation is spatially and temporally highly variable: conventional gaugeand radar measurements tend to be land-based with variable coverage. This paper provides an overview of thesatellite systems that provide the observations, the techniques used to derive precipitation from the observa-tions, and examples of the precipitation products available for users to access.

(KEY TERMS: precipitation; remote sensing; hydrologic cycle; meteorology.)

Kidd, Chris, Vincenzo Levizzani, Joe Turk, and Ralph Ferraro, 2009. Satellite Precipitation Measurementsfor Water Resource Monitoring. Journal of the American Water Resources Association (JAWRA) 45(3):567-579.DOI: 10.1111 ⁄ j.1752-1688.2009.00326.x

INTRODUCTION

The measurement and monitoring of precipitationis critical to the management of water resources.Conventional measurements using networks of raingauges and, where available, weather radar systemsare common. However, the distribution of these overthe globe is very much limited to the land masseswith few measurements collected over the world’soceans. Even over land areas distribution and densityof gauges is highly variable: while some regions havewhat could be described as ‘‘adequate’’ coverage,many regions have few or no gauges. In addition tothe spatial distribution of gauges, the availability ofthese measurements over time can be problematic.

Historical precipitation datasets vary in the availabil-ity, completeness, and consistency of the data record,and near real-time availability. Although rainfall esti-mates derived from satellite observations are notwithout their own problems, they do provide observa-tions over large areas at regular intervals.

In April 1960, the Television InfraRed ObservationSatellite (TIROS-1) was launched, providing the firstmeteorological images of the Earth. Since these initialimages, more frequent observations and greater infor-mation has been captured from many satellite sys-tems enabling more accurate, detailed, and timelymeasurements to be made. Archives of precipitationdatasets start in 1979 due to the availability of well-calibrated satellite infrared (IR) datasets, although itis not until 1987 that the main archive satellite

1Paper No. JAWRA-08-0123-P of the Journal of the American Water Resources Association (JAWRA). Received June 27, 2008; acceptedSeptember 30, 2008. ª 2009 American Water Resources Association. No claim to original U.S. government works. Discussions are openuntil six months from print publication.

2Respectively Senior Lecturer, School of Geography, Earth and Environmental Sciences, University of Birmingham, United Kingdom;Director of Research, Istituto di Scienze dell’Atmosfera e del Clima, Consiglio Nazionale delle Ricerche, Bologna, Italy; Physical Scientist,NRL, Naval Research Laboratory, Monterey, California; and Chief, Satellite Climate Studies Branch, NOAA and Visiting Scientist, Univer-sity of Maryland, College Park, Maryland (E-mail ⁄ Kidd: [email protected]).

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JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION

Vol. 45, No. 3 AMERICAN WATER RESOURCES ASSOCIATION June 2009

derived precipitation products starts due to the lack ofavailability of instrumentation specifically designedfor quantitative precipitation estimation.

This paper provides an overall review of satelliterainfall estimation, covering the basis of the satellitesystems used in the observation of precipitation andthe dissemination of this data, the processing of thesemeasurements to generate the rainfall estimates andthe availability, verification, and validation of theseprecipitation estimates.

THE SATELLITE GLOBAL OBSERVING SYSTEM

Operational meteorological satellites are dividedinto two main categories: geostationary satellites andlow-Earth orbiting satellites (which include polar-orbiting satellites) (see Figure 1). Geostationary satel-lites are stationed at about 35,800 km above theEarth’s surface over specific points above the Equator(see Table 1). The orbits of these satellites are suchthat they rotate at the same speed as the Earth andtherefore appear to be stationary relative to theEarth. This characteristic enables each of these satel-lites to view about one third of the Earth’s surface ona frequent and regular basis. Imagery obtained from

these satellites is primarily visible (VIS) and IR atresolutions of about 4 km, allowing information onclouds to be collected nominally once every 30 min.However, geostationary satellites such as the Geosta-tionary Operational Environmental Satellite (GOES)and the Meteosat Second Generation (MSG) satellite(see Schmetz et al., 2002) provide imagery every15 min and rapid scanning permits sampling as fre-quent as 1 min, albeit over a reduced area of cover-age. Although the rapid-scan ability is useful formonitoring cloud evolution, it is rarely useful forrainfall estimation due to the temporal dampening ofthe cloud-surface rainfall relationship.

Geostationary orbits provide an unrivaled platformfor continual observation over much of the Earth.However, they are limited by the range and resolu-tion of the imagery they can obtain, and the paucityof their coverage of the polar regions. Low-Earthorbiting satellites (LEO: see Table 2) provide observa-tions that complement those taken by the geostation-ary instrumentation: the orbits of these are such thatthey cross the Equator at the same local time oneach orbit, providing about two overpasses each day.These satellites carry a range of instruments capableof precipitation retrievals. In addition to the multi-channel VIS and IR sensors, microwave (MW) sound-ers and imagers are included that are capable ofmore direct measurement of precipitation.

FIGURE 1. The Global Observing System of Meteorological Satellites. The geostationary satellites, provide frequent VIS and IR imageryfrom an orbit of about 35,800 km altitude, while the low-Earth orbiting satellites orbit the Earth at about 850 km.

KIDD, LEVIZZANI, TURK, AND FERRARO

JAWRA 568 JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION

In addition to the operational LEO satellites, sev-eral ‘‘research and development’’ satellite missionshave relevance to precipitation monitoring (seeTable 3). At the forefront of these is the TropicalRainfall Measuring Mission (TRMM), the first dedi-cated precipitation satellite, launched in 1997. Thissatellite marks a key milestone in the development ofsatellite rainfall estimation techniques due primarilyto the suite of instruments that it carries which allowdirect comparisons to be made between VIS, IR, pas-sive MW, and active MW observations: the latter (thePrecipitation Radar) is the first—and currently theonly – spaceborne radar for precipitation measure-ment. In addition to the instrumentation, the non-

sun-synchronous nature of its orbit allows thesampling of the full diurnal cycle to be made (see Fig-ure 2). Other satellites of note include the Aqua satel-lite which carries the Advanced Microwave ScanningRadiometer (AMSR) passive MW instrument,together with the MODerate-resolution ImagingSpectroradiometer (MODIS). The latter is also carriedon the Terra satellite and provides 36 measurementsacross the VIS and IR spectrum enabling greaterinformation on cloud properties (such as cloud dropradii and phase) to be retrieved.

Data collected by these satellite observations isavailable in two main forms. First, data can be col-lected directly through satellite data transmissions,either as low-resolution data, or as digital high-reso-lution data. The low-Earth orbiting satellites havetraditionally transmitted low-resolution data as Auto-matic Picture Transmission (see Figure 3): the quali-tative nature of the images is compensated to someextent by the ease of reception (via a simple FMreceiver) and the real-time nature of the imagery. Inaddition, High Resolution Picture Transmission pro-vides quantitative data from low-Earth orbiting satel-lites, but requires specialized reception equipment fordirect reception. The main form of data receptionfrom geostationary satellites is via the Low-RateInformation Transmission or High-Rate InformationTransmission (see Figure 4), and reflects the process-ing requirements. In addition to the availability of

TABLE 1. Current Operational Geostationary Meteorological Satellites (as of November 23, 2007).

Region Satellite Position Operator Notes

Western PacificJapan, Australia

MTSAT-1R 140�E Japan

Eastern PacificHawaii, Western U.S.

GOES-11 135�W U.S. Operational GOES-W

Western AtlanticEastern U.S. and S. America

GOES-10 60�W U.S. ⁄ NOAA S. America coverageGOES-12 75�W U.S. ⁄ NOAA Operational GOES-E

Eastern AtlanticEurope, Africa

Meteosat-9 0� EUMETSAT

Indian OceanMiddle East, India, SE Asia

Meteosat-7 57½�E EUMETSAT Indian Ocean data coverageFY-2C 105�E China ⁄ CMAKalpana-1 74�E IndiaINSAT-3A 93½�E India

TABLE 2. Current Operational Low-Earth Orbit Meteorological Satellites (as of May 27, 2008).

Period Satellite Equatorial Crossing Time Operator Instrumentation

Morning NOAA-17 10:24 (D) U.S. ⁄ NOAA Vis ⁄ IR, soundingDMSP-F16 20:13 (A) U.S. ⁄ NOAA Passive Microwave (SSMIS)FY-1D 08:20 (D) China ⁄ CMA Vis ⁄ IRMETOP-A 21:30 (A) EUMETSAT Vis ⁄ IR, sounding

Afternoon NOAA-18 13:55 (A) U.S. ⁄ NOAA Vis ⁄ IR, soundingEarly morning DMSP-F13 18:33 (A) U.S. ⁄ NOAA Passive Microwave (SSMI)

DMSP-F17 17:31 (A) U.S. ⁄ NOAA Passive Microwave (SSMIS)

TABLE 3. Current Research and Development Satellites Relevantto Precipitation (as of November 26, 2007).

Satellite Operator Notes

TRMM JAXA ⁄ NASA Various precipitation-related sensors,including first precipitation radar.Non-sun-synchronous orbit;38�N–38�S coverage

AQUA NASA AMSR-E passive microwave sensor,MODIS 36-channel Vis ⁄ IR sensor(also deployed on TERRA satellite)

CLOUDSAT NASA ⁄ CSA Cloud profiling radar: greatersensitivity than TRMM radar,with greater latitudinal coverage,although nadir only.

SATELLITE PRECIPITATION MEASUREMENTS FOR WATER RESOURCE MONITORING


the standard imagery in these transmissions, ‘‘valueadded’’ products are also usually available.

Due to the growth of the internet coupled with thenecessity to archive the satellite datasets, most satel-lite datasets are now available on-line (see Table 4).Although data may not be available in real-time,

most sites will provide data within a few hours ofreception, with the added ability of being able to sub-set regions and datasets ⁄ products as required, and toprovide mosaics of complete orbits and collections oforbits. Examples of these archives include the NOAAComprehensive Large Array data Stewardship

FIGURE 2. Imagery Derived From the Passive Microwave TRMM Microwave Imager Showing theLow-Latitude Coverage of This Satellite. The brighter areas in the imagery are the regions of scattering

caused by precipitation (Data source: TRMM Science and Data Information Service; TSDIS).

FIGURE 3. Automatic Picture Transmission (APT) Imagery (complete with telemetry data) From the NOAA-15Advanced Very High Resolution Radiometer (AVHRR) at 16:42Z on June 10, 2008 Over Western Europe. Note thedifferences between the clouds observed in the VIS (channel 2, left) and IR (channel 4, right) imagery. The bright

clouds in the VIS and IR are indicative of precipitating clouds (Source: University of Birmingham).



System (CLASS) archive which provides data from arange of satellites including the NOAA ⁄ AVHRR andGOES; EUMETSAT provides access to data fromMSG (and other satellites) through its Unified Meteo-rological ARchive Facility (UMARF).

DERIVATION OF QUANTITATIVEPRECIPITATION ESTIMATES

Estimates of precipitation (snow or rain) can bederived from a range of observations from many dif-ferent satellite sensors. The key to any accuratesatellite-derived precipitation product is a combina-tion of the frequency of the observations and thedirectness of the observation-to-rainfall relationship.

VIS ⁄ IR Methods

Observations of clouds in the visible part ofthe spectrum provide the most directly-comparable

imagery to what we would observe with our eyes:from above clouds appear relatively bright againstthe surface of the Earth due to the reflection of sun-light. The cloud brightness is dependent upon theirthickness: thick clouds are more likely to be associ-ated with surface rainfall. Cloud-top texture can alsobe derived from VIS imagery, providing useful infor-mation on the type of clouds being observed: smoothcloud tops are typical of stratus (either high-levelcirro-stratus, or low-level stratus), while ‘‘lumpy’’cloud tops are typical of clouds with strong up-draughts. Near IR channels are able to provide infor-mation on the properties of the cloud-top particles,such as the size and whether the particles are liquidor ice. However, the main drawback of these channelsis that they are limited by solar illumination, restrict-ing their usage to full daylight operations.

IR imagery provides measurements of temperaturesand is consequently much more useful, being availableboth day and night. Heavier rainfall is associated withlarger, taller clouds, which mean colder cloud tops.Thus, by observing the cloud top temperatures it ispossible to produce a reasonable rainfall estimatethrough simple cloud top temperature–rainfall

FIGURE 4. A False-Color Composite Image From the Meteosat Second Generation Satellite Taken onJune 19, 2005. The synoptic nature of the geostationary imagery is clearly evident: each HRIT image is

3712 · 3712 pixels, amounting to some 230MB every 15 min (Data source: EUMETSAT).



relationships. However, the relationship betweenthe cloud top temperature and rainfall is not ideal,with significant variations in the relationship occur-ring during the lifetime of a rainfall event, betweenrain systems, and between climatological regimes.Despite this, IR-based techniques benefit from adegree of simplicity coupled with the availability ofthe data in near real-time using relatively cheapreceiving equipment.

Techniques have been developed to exploit one ormore channels of these VIS and IR observations.One of the simplest techniques is the Global Precipi-tation Index (GPI; Arkin and Meisner, 1987) thatderives rainfall fields from thermal IR observationsby assigning a constant rain-rate (3 mm h)1) to thefraction of clouds below a set threshold (235 K or)38�C) over a period of time, usually one month.This cold-cloud duration technique, although notwithout its problems, provides a useful benchmarkagainst which to compare other techniques. Byincorporating information on the background clima-tological rainfall and the occurrence of cold cloudsthis type of methodology can be tuned to local andregional scale rainfall retrievals, thus providing thepotential to enhance rainfall information in datasparse regions. The GOES Multi-Spectral RainfallAlgorithm (GMSRA; Ba and Gruber, 2001) utilizesdata from the GOES observations: five channels cov-ering the VIS, near IR, water vapor and two ther-mal channels are interrogated to extract informationon the cloud and rain extent, derive rainfall and

apply corrections depending upon the type ofcloud ⁄ precipitation regime.

Passive Microwave Methods

The main drawback of VIS ⁄ IR techniques, despitethe frequency of their observations, is that the rela-tionship between the cloud top temperature and thesurface rainfall is indirect. This is often manifested inthin, high-cloud (such as cirrus) appearing as rain-bearing cloud, while warm, low-level rain cloud (suchas stratus) is omitted. These shortfalls can beaddressed through the use of passive MW observa-tions. In the MW part of the spectrum precipitation-sized particles are the main source of atmosphericattenuation; this allows a more direct measurement ofprecipitation to be made. Over water bodies the mainsignal comes from the increased emission of radiationfrom the rain droplets, making rain areas appear warm-er over the radiometrically ‘‘cold’’ ocean background.Over land areas the rainfall is associated with reducedamounts of radiation due to scattering of the upwellingsurface radiation by precipitation-related ice particles.It should be noted that with either scheme, the obser-vations made by the satellite sensors is that of the totalatmospheric column, not just the rainfall close to thesurface. The main drawback of these passive-MWbased techniques is that the instruments are currentlyonly available on low-Earth orbiting satellites, conse-quently observations can only be made (at best) twice

TABLE 4. Examples of Useful Precipitation-Related Web-Sites and Sources of Rainfall Estimates.

Informationhttp://www.wmo.ch/pages/prog/sat/CGMS/CGMS_home.html Coordination Group for Meteorological Satellites (CGMS)http://www.isac.cnr.it/~ipwg/ International Precipitation Working Group (IPWG)http://precip.gsfc.nasa.gov/http://www.gewex.org/gpcp.html

Global Precipitation Climatology project

http://www.dwd.de/ (follow link under climate +environment and climate centers)

The Global Precipitation Climatology Centre

http://essic.umd.edu/~Emsapiano/PEHRPP/ Program to Evaluate High-Resolution PrecipitationProducts (PEHRPP)

Satellite datasetshttp://www.class.ncdc.noaa.gov/ Comprehensive Large Array data Stewardship System

(CLASS)http://archive.eumetsat.org/umarf/ Unified Meteorological Archive FacilityPrecipitation productshttp://nsidc.org/ National Snow and Ice Data Centerhttp://www.radar.aero.osakafu-u.ac.jp/~gsmap/ GSMaP datasetshttp://www.hoaps.org/ Hamburg Ocean Atmosphere Parameters and Fluxes

from Satellite Data (HOAPS)Inter-comparison siteshttp://www.bom.gov.au/bmrc/SatRainVal/sat_val_aus.html Australiahttp://www.cpc.ncep.noaa.gov/products/janowiak/us_web.shtml U.S.http://kermit.bham.ac.uk/~ipwgeu/ Europehttp://cics.umd.edu/~dvila/web/SatRainVal/dailyval.html South Americahttp://www.radar.aero.osakafu-u.ac.jp/~gsmap/IPWG/sat_val_Japan.html Japan



per day per satellite. Another problem is that the reso-lutions of the supporting measurements are not ideal:ocean rainfall product resolution is of the order of50 · 50 km2, while over land resolutions are typicallyno better than 10 · 10 km2.

Rainfall estimation techniques based upon passiveMW observations can be broadly split into two maingroups: empirical techniques that calibrate the obser-vations with surface datasets, and physical techniquesthat minimize the difference between a modeled atmo-spheric rainfall event and the observation (see Kiddet al., 1998). The empirical techniques are relativelysimple to implement and incorporate many artifacts(such as beam-filling ⁄ inhomogeneous field-of-view,absolute calibration issues, resolution differences)associated with passive MW observations, however,regional calibration may be necessary due to varia-tions in the physical nature of precipitation systems.An example of the physical techniques is the GoddardProfiling technique (GPROF; Kummerow et al., 2001):this technique uses a database of model-generatedatmospheric profiles to which the observed satellitemeasurements are compared, and the best profilematch is then selected. The advantage of such a tech-nique is that it provides more information about theprecipitation system than just the surface rainfall.

Multi-Sensor Techniques

The estimation of rainfall from VIS ⁄ IR and passiveMW observations both have advantages (frequency ofobservation and directness) and disadvantages (indi-rectness and infrequent observations, respectively).Therefore, it is logical to try and combine the twotypes of observations to provide (more) frequent and(more) direct measurements.

Blended techniques can be classified into two mainmethodologies, adjustment techniques and motion-based techniques. Adjustment techniques involve thecorrection or adjustment of IR cloud-top temperatureinformation by some other dataset, be this radar,gauge of another satellite dataset. In many ways, allIR-derived estimates can be attributed to this classsince the rain-rate used in the simple GPI techniquewas originally determined through the comparison ofthresholded cold cloud duration against surface data.More recent advances in these techniques include theTRMM Multi-Satellite Precipitation Algorithm(TMPA; Huffman et al., 2007): the goal of the TMPAis to use all available data with traceable calibrationto generate a precipitation product at 3-hourly, 0.25degree resolution. The current algorithm ingests datafrom passive MW imaging and sounding sensors andgeostationary IR data: adjustments are made for thedifferent satellite retrievals before these are com-

bined into the single precipitation product. Othertechniques include the Passive Microwave InfraRedtechnique (Kidd et al., 2003), the Microwave-adjustedIR Algorithm (MIRA; Todd et al., 2001), the NRLtechnique (Turk et al., 2000), and the GSMaP (Kubotaet al., 2007). This category can also include the neu-ral-network derived estimates since they combine theinformation from IR and MW observations to providea rainfall product (e.g., PERSIANN; Hsu et al., 1997).

Increased computing power has enabled the evolu-tion of practical motion-based (or advection) tech-niques. The premise for these algorithms is that theIR observations provide a less accurate representa-tion of surface rainfall, but are capable of capturingthe movement of the clouds and hence, the precipita-tion falling from them. The passive MW observations,handicapped by their infrequent nature, do howeverprovide a more direct measurement of the precipita-tion. Therefore, by using the rain area and intensitiesderived from passive MW observations, together withthe movement information derived from the IR hightemporal and (potentially) high spatial resolutionrainfall estimates can be derived. Examples ofthese current state-of-the-art methodologies are theClimate Prediction Center Morphing technique(CMORPH; Joyce et al., 2004). The main drawback ofthis methodology is that the motion of the cloudsmight not represent the true motion of the precipita-tion at the surface. Also, changes in the surface pre-cipitation between the infrequent MW observationsmight not be truly reflected in the final product.

Perhaps, the ultimate rainfall products for waterresource management are the satellite-surface mergedproducts that combine all available datasets: productsderived from the various satellite observations, gaugedatasets and, where available, surface radar data.Various combination schemes can be used, most takingaccount of the expected errors inherent in each of theproducts. There is some experimentation with the useof Kalman filters to blend products derived from obser-vations taken from different sources at different timesto better account for the errors in the component esti-mates. One satellite rainfall product that is widelyused is that of the Global Precipitation and Clima-tology Project (Huffman et al., 1997), similar to theTMPA product, but incorporates surface datasets,where available, to produce a largely homogenousglobal precipitation product.

INTER-COMPARISONS AND VALIDATION

Several projects have taken place to inter-compareand evaluate the performance of precipitation



estimates derived from satellite observations andthose from models. During the 1990s a series of Pre-cipitation Inter-comparison Projects (PIP; Dodge andGoodman, 1994; Barrett et al., 1994; Kniveton et al.,1994; Smith et al., 1998; Adler et al., 2001) assessedthe estimates from the then new SSM ⁄ I sensor. Thefirst and third projects concentrated upon estimatesderived at monthly, 0.25 degree resolutions, whilstthe second investigated individual instantaneous casestudies over a variety of different meteorological situ-ations and geographically diverse regions. Parallel tothese projects was the Algorithm Inter-comparisonProgram (Arkin and Xie, 1994; AIP; Ebert et al.,1996; Ebert and Manton, 1998). These encompassed arange of precipitation estimates from various satelliteobservation sources as well as model output for spe-cific coordinated observation periods.

The main result from the PIP and AIP series ofinter-comparisons was that for instantaneous esti-mates of precipitation the passive MW techniqueswere clearly better than IR techniques due to theirmore direct observation of the rainfall. However, theadvantage of these techniques quickly deterioratedover longer time scales due to poorer sampling com-pared with the IR-based techniques. Although com-bined IR-passive MW techniques held great promise,they did not show a clear advantage at this stageover the IR-alone ⁄ IR-blended techniques. All satellitealgorithms, however, were very much dependentupon common underlying factors, such as surfacebackground conditions, seasons ⁄ latitude, and meteo-rological conditions.

All of these validation ⁄ inter-comparison exercisesdepended upon surface datasets for ‘‘truth’’ such asgauge data or radar depending upon the scales overwhich the estimates were being evaluated. The AIP-3project, in particular, utilized data specifically col-lected for the project from the TOGA-COARE regionof the Pacific Ocean (see Ebert and Manton, 1998).This type of ground-truth data collection (or groundvalidation) has become a major component of mostvalidation ⁄ inter-comparisons of satellite products. Forexample, TRMM ground validation campaigns (e.g.,Wolff et al., 2005) identified specific locations for thecollection of (primarily) radar and gauge datasets forthe physical validation of the satellite estimates.However, more recent and on-going validation relyupon mainly statistical inter-comparisons with sur-face data sourced from routinely available datasets,such as the United States (U.S.) NEXRAD radarproduct, or the European Nimrod radar product.

As part of the International Precipitation WorkingGroup (IPWG) activities (see Turk and Bauer, 2006;Ebert et al., 2007), a series of inter-comparison regionshave been established to provide algorithm developersand the wider user community information on the

performance of satellite rainfall products in near real-time. The primary IPWG inter-comparison sites coverthe U.S. (see Figure 5), Europe, Australia (see Figure6), and South America, using radar and ⁄ or gauge dataas their surface ground truth. Comparisons of satelliteand model precipitation estimates are provided in nearreal-time for daily rainfall estimates at a resolution ofabout 0.25� resolution. These sites form a frameworkfor other validation efforts across the globe, such asSouth Korea, China, South Africa, Ethiopia, etc. Anoffshoot of the IPWG activities is the Program of theEvaluation of High-Resolution Precipitation Products(PEHRPP). The goal of PEHRPP is to characterize theerrors in high-resolution precipitation products(<0.25� resolution and three-hourly or less) over differ-ent spatial, temporal, regional, and climate scales.

Results from the IPWG inter-comparisons (seeEbert et al., 2007) show that the numerical weatherprediction (NWP) models tend to outperform satelliteestimates of precipitation in cold-season environ-ments (e.g., mid-latitude winters): in these situationsthe motion-based techniques also appear to outper-form the single-sensor and blended algorithms. How-ever, warm-season performance studies tend to favorthe satellite techniques since these can capture theconvective nature of the precipitation better than theexisting NWP models.

WATER RESOURCE APPLICATIONS

The ability of satellite precipitation estimates tocontribute to hydrological and water resource applica-tions is very much dependent upon the type of appli-cation and the accuracy, spatial resolution, temporalresolution, and latency of the precipitation product:different applications will have different data require-ments. Table 5 outlines the requirements for precipi-tation products as defined by several internationalorganizations. Terrestrial climate is particularlytough on the accuracy of the derived precipitationproducts: the implication of an ideal accuracy of0.05 mm ⁄ h is that the full spectrum of rainfall inten-sities needs to be faithfully captured by the satelliteobservations: while this might be conceivable fromdual-frequency radar measurements [such as that ofthe future Global Precipitation Measurement (GPM)core satellite], the observation cycle would not be eas-ily fulfilled. However, the spatial resolution of theproducts is reasonably achievable.

The hydrological requirements for precipitation esti-mates can be broken down into two main categoriesbased upon scale: high resolution ⁄ short-duration sam-pling and lower-resolution ⁄ longer-term estimates.



Flash flood events, with rapid catchment response pro-viding little time to react, necessitate fine spatial andtemporal scales as well as data availability and timeli-ness. Satellite estimates of rainfall in these situations

could be provided at spatial resolutions of about 4 kmevery 15 min and in real-time assuming the use of geo-stationary IR data from satellites such as MSG andGOES: there is the potential for 1 km estimates every

FIGURE 5. Precipitation Products Derived From the CPC CMORPH Technique for May 7, 2008 Over theContinental U.S. Note the idiosyncrasies within each estimate: individual gauge locations can be discerned in the gauge estimate,

the satellite technique provides a relatively smooth rainfall field, while in the radar estimate individual radar locations are betrayedby ground clutter (U.S. daily inter-comparisons are available from http://www.cpc.ncep.noaa.gov/products/janowiak/us_web.shtml).



TABLE 5. Observational Requirements for WMO Programs (after WMO, 2003).

Application

HorizontalResolution (km)

ObservationCycle (hours) Delay (hours)

Accuracy(mm ⁄ h · mm ⁄ day)

Confidence SourceMinimum Ideal Minimum Ideal Minimum Ideal Minimum Ideal

Agri. Met. 50 10 72 24 48 24 10* 2* Reasonable WMOAOPC 500 100 6 3 12 3 2 0.6 Firm GCOSGEWEX 250 50 12 1 1,440 720 5* 0.5* Reasonable WCRPGlobal NWP 100 50 12 1 4 1 1 0.1 Tentative WMOHydrologyliquid 50 5 1 0.08 0.5 0.08 1 0.1 Firm WMOHydrologysolid 50 5 1 0.25 0.5 0.5 1 0.1 Firm WMORegional NWPdaily 250 10 12 0.5 720 24 5 0.5 Reasonable WMORegional NWPliquid 50 10 6 0.5 2 0.5 1 0.1 Tentative WMORegional NWPsolid 100 10 12 0.5 2 0.5 1 0.1 Tentative WMOSynoptic meteorology 100 20 6 3 6 0.25 1 0.1 Firm WMOTerrestrial climate 10 1 6 3 120 24 0.1 0.05 Tentative GTOS

GCOS

*Measurements are in mmd)1.

FIGURE 6. Precipitation Estimates Over Australia on June 12, 2008. Gauge analysis is shown at the top left: to the rightof this are the 3B40, 3B41, and 3B42 real-time satellite product. The center row are the (left to right) geostationary, passive

microwave, hydro-estimator, and PMIR products. The bottom row (left to right) are the GPI infrared satellite product,ECMWF, USAVN, and NOGAPS model outputs. The nominal resolution for the products is 0.25�, daily estimates.



1 min if visible rapid scan imagery is considered.However, due to the indirect nature of the could-top tosurface rainfall relationship such estimates would beconsidered to be at best ‘‘experimental’’ with a signifi-cant degree of error, something that would not be idealfor flash-flood estimation. Techniques such as theHydro-Estimator (Scofield and Kuligowski, 2003)designed for operational use over the U.S., are basedupon the GOES IR observations.

Although direct assessment of rainfall for flashflood events is challenging, satellite observations canprovide information on the occurrence of rainfallextremes and characteristics of the local meteorologi-cal situation help to improve short-term forecastingof such events. For example, precipitation data fromthe TRMM satellite allows climatological maps to be

generated of occurrence and rainfall intensity to begenerated that can be used to produce intensity-dis-tribution models, thus aiding hydrologists. In particu-lar, it allows data to be collected and assessed inregions where no existing conventional data exists(see Figure 7).

For larger-scale events, such as large river floodingor water availability, which are characterized by rela-tively long lead times, satellite derived precipitationproducts can be of great benefit. Static surface parame-ters (e.g., geology, soil type, relief), dynamic surfaceparameters (e.g., soil moisture, vegetation, groundwater), as well as the precipitation (satellite and sur-face) and meteorological conditions can be broughttogether on a global scale to provide a comprehensivehydro-meteorological data base. An example of this

A

B

FIGURE 7. Distribution of the Total Precipitation and Precipitation Occurrence Derived from 10 Years (1998-2007)of TRMM Precipitation Radar Data Over the Southern U.S. and Northern South America. Note the maximum rainfall

totals (>6,000 mm per annum) and precipitation occurrence (>15%) in the Gulf of Panama.



combined approach is the estimation of natural hazardpotential whereby information on precipitation ‘‘his-tory’’ together with surface parameters can be used tohighlight regions of the globe that could experiencelandslides or floods (Adler et al., 2008). Such informa-tion can be invaluable in emergency situations wherein situ measurements may be disrupted by such events.

CONCLUSION

Precipitation products derived from satellite obser-vations have reached a good level of maturity overthe last decade. Ongoing research and development iscontinuing to address the accuracy and the resolution(temporal and spatial) of these products, along withthe generation of error estimates vital to hydrologicalmodeling and water resource assessment. At present,there is a relatively data-rich environment for satel-lite rainfall estimates. VIS and IR data derived fromgeostationary satellites is available nominally every30 min, while a good number of passive MW instru-ments are currently available, both imaging instru-ments and cross-track sounders. However, the dataavailability from passive MW imagers is likely todwindle somewhat in the near future placing morereliance upon data from the MW sounders which donot have the same sensitivity to precipitation as theimagers, particularly over land surfaces. The use ofhigh frequencies, such as 183 GHz, for new retrievalmethods seem to help in estimating precipitation overland by reducing the emission problems of lower reso-lution channels (e.g., Surussavadee and Staelin,2008). Research into geostationary passive MW sen-sors is also currently underway (Bizzarri et al., 2007).

A major international project, the GPM mission,lead by the National Aeronautics and Space Adminis-tration (NASA) and the Japan Aerospace ExplorationAgency (JAXA) will start with the launch of a coresatellite in 2013. This satellite, similar to the TRMMsatellite, will form the center of a constellation of sat-ellites aimed at providing precipitation estimates overthe globe with a temporal sampling period of 3 hoursor less. Coupled with the satellite effort, new tech-niques are being developed to combine satellite obser-vations into standard and comparable measurementsto ensure consistency across the different constella-tion sensors.

ACKNOWLEDGMENTS

The authors would like to acknowledge the various members ofthe satellite precipitation community that contribute material for

the furtherance of precipitation research. We would also like tothank data providers, particularly EUMETSAT and NASA ⁄ TSDISfor the provision of image data.

The page publication support provided by the Ivanhoe Founda-tion (http://www.theivanhoefoundation.com) is gratefully acknowl-edged. The Ivanhoe Foundation grants Fellowships to studentsfrom developing countries. These students must be studying at aU.S. university for a Master of Science degree, with an emphasison water resources.

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Satellite Precipitation Measurements for Water Resource Monitoring

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