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Remote Sensing of Environm

An analysis of the performance of hybrid infrared and microwave satellite

precipitation algorithms over India and adjacent regions

John E.M. Brown

University of Miami, Rosenstiel School of Marine and Atmospheric Science, Miami, FL, USA

Received 25 May 2005; received in revised form 30 November 2005; accepted 1 December 2005

Abstract

The measurement of precipitation is fundamental to our understanding of the hydrological cycle. Increasingly, there exists the capacity to

independently determine components of the hydrological cycle from remote sensing data. Developing techniques to combine effectively the

multiple streams of information required for a water budget assessment provides a difficult challenge, particularly given the disparities in spatial

and temporal scales between measurements and predictions. Two research groups, the Naval Research Laboratory Monterey (NRL) and the

University of Arizona (UA), are using a combination of geostationary infrared and polar-orbiting microwave satellite data to derive 6-hourly

precipitation estimates over a global 0.25- grid. We examine the performance of these two algorithms for estimating the 24-h rainfall accumulation

over India and Sri Lanka for the years 2002 and 2003. The derived values are compared with observations from a network of 39 national weather

stations. In addition, two locations, Minicoy in the Laccadive Islands and Port Blair in the Andaman Islands were selected as being representative

of the ocean environment to compare these satellite rainfall products against measurements from local rain gauges and Tropical Rainfall

Measuring Mission (TRMM) satellite data. The NRL technique was accurate to within 25% of observed precipitation for only 33% of station

locations, while the UA technique was accurate to within 25% of observed precipitation for about 47% of station locations.

D 2005 Elsevier Inc. All rights reserved.

Keywords: Satellite derived-precipitation; PERSIANN; Hybrid infrared-microwave algorithm; Indian monsoon; NRL Monterey GEO; TRMM

1. Introduction

Given the profound influence of the Global Water Cycle on

human activities, and the growing demand for water in the face

of a steadily increasing human population, it is no wonder that

research into all aspects of the water cycle is a high priority

(NASA Earth Science Enterprise Strategy, 2003; USGCRP

Water Cycle Study Group, 2001). Having studied the evidence

of recent increases in natural disasters and the climate model

projections of such trends, the World Meteorological Organi-

zation (WMO)/United Nations Environment Programme

(UNEP) Inter-governmental Panel on Climate Change (IPCC)

in 2001 concluded that more intense precipitation events were

very likely in the future over many areas and would thus cause

increased flash-floods, landslides, soil erosion and avalanches.

The IPCC also concluded it was likely (66–90% probability)

that there would be an increase in summer drying over most

mid-latitude continental interiors with an associated risk of

0034-4257/$ - see front matter D 2005 Elsevier Inc. All rights reserved.

doi:10.1016/j.rse.2005.12.005

E-mail address: [email protected].

drought and an increase in intensity (but not frequency) of the

strongest tropical cyclones (IPCC, 2001).

From the conditions outlined in the IPCC report, we find that

a logical candidate for the study of the hydrological cycle is the

Indian Subcontinent. For the purposes of this study wewill focus

specifically on the Bay of Bengal, the Andaman Sea, and their

respective catchment areas. The Bay of Bengal is characteris-

tically different from other tropical ocean basins of the world.

Although the geographical setting is very similar to the Arabian

Sea, the Bay of Bengal is vastly different from the Arabian Sea in

its physical, chemical, and biological features. The prime reason

for this is the immense quantities of fresh water runoff and

associated sediment load it brings into the basin. In fact, the

Brahmaputra, Ganges and Irrawaddy Rivers discharge approx-

imately 1.43�1013 tonnes yr�1 of fresh water into the Bay of

Bengal and Andaman Sea (Martin et al., 1981), exceeded only

by three other rivers, the Amazon, Congo and Orinoco. In

addition, the Bay receives annually about 70�1010 m 3 of net

freshwater influx (precipitation minus evaporation) at the

surface (north of 15-N) (Shetye & Gouveia, 1998). Defining

ent 101 (2006) 63 – 81

w

http://www.ngdc.noaa.gov/mgg/image/2minrelief.html

http://www.cpc.ncep.noaa.gov/products/global_monitoring/precipitation/global_precip_accum.shtml

Fig. 1. Basic concept for a hybrid algorithm, combining the superio

sampling of GEO IR measurements with the more physically based

measurements of LEO PMW sensors. Some averaging is necessary to

account for spatial and temporal offsets (fallout rate� time=vertical height

when aligning these data on a per-pixel basis (adapted from Turk et al.

2002a,b).

J.E.M. Brown / Remote Sensing of Environment 101 (2006) 63–8164

the surface area of the Bay of Bengal to be 2,173,010 km2

(Encyclopedia of Oceanography, 1966) this amounts to an

equivalent layer thickness of about 32 cm.

The dominant force in this region driving the water cycle is

the Indian Monsoon System (IMS). The IMS is a large-scale

atmospheric circulation that results from complex ocean,

atmosphere, and land interactions. From June to October,

IMS precipitation provides much needed water for this semi-

arid region. The timing and intensity of the summer monsoon is

critical to human health, agriculture and the economy. Extreme

hydrological hazards such as flood or drought are common-

place. Indeed, in already arid and semi-arid regions like India, a

small decrease in rainfall and an increase in evaporation can

result in significant declines in runoff (WMO, 2004). To cope

with floods on large rivers, international collaboration is

necessary to provide adequate forecasts and warnings, espe-

cially in large river basins shared by two or more countries

(WMO, 2004) as is the case with Brahmaputra and Ganges

basins. India’s hydrologic forecasters need better tools to issue

warnings of floods and to more effectively manage their water

assets. Prediction and validation of IMS rainfall, therefore,

becomes very important.

Finer-scale rainfall data are required tomake further significant

progresswith hydrologicalmodels. Streamflowmodels need to be

validated by forcing them with observed data that can resolve

individual storms and catchment basins (Huffman et al., 2001).

NASA’s Earth Science Enterprise Strategy (2003) envisions that

by 2014 seasonal forecasts of precipitation could be made with

75% accuracy at order 10 km resolution. At present the water

budget can be balanced over global scales and long-time frames to

within 20%with large uncertainties on regional to local scales and

seasonal to annual time frames. Precipitation itself is measured

remotely from space only over the tropics while other important

variables such as soil moisture and snow water equivalent are

largely unknown globally.

The fundamental barrier to finer-scale estimates is the lack

of accurate, dense regional data, either from in situ or remote

sensors. Some regions do have the possibility of detailed

precipitation estimates thanks to local networks of sensors

(Huffman et al., 2001). Rain gauge networks are costly to

establish and maintain and there may be non-uniform standards

for collection and reporting. Finally, an additional barrier is the

need to work with several international partners to obtain

administrative permissions and maintain routine data deliver-

ies. Thus, for those locations around the world without the

resources to establish extensive ground networks or where the

process of accessing data is cumbersome, the best potential for

meeting these fine scale requirements will depend on satellite-

based sensors (Huffman et al., 2001). The Indian Subcontinent

is one region where the use of satellite-derived precipitation

could be most beneficial.

2. Satellite-derived precipitation

We can quantitatively and qualitatively describe the

hydrological cycle using various satellite remote sensing data

sets from either polar orbiting platforms or geostationary

satellites (Kumar & Schulz, 2002). The first set of techniques

for deriving precipitation are based on using visible and

infrared (VIS/IR) imagery to discriminate the brightness of

the cloud in the visible spectrum and/or the low temperature

of the cloud top as seen in the thermal spectrum (e.g., Arkin

& Meisner, 1987; Barrett & Martin, 1981; Lovejoy & Austin,

1979).Other VIS/IR techniques focus on adding sophisticated

criteria such as cloud area extent, time history, and textural

features (e.g., Adler & Negri, 1988; Scofield, 1987; Wu et al.,

1985). The VIS/IR techniques, however, are inherently

indirect depending only in a statistical sense on the presence

of rain below the cloud top (Petty, 1995).

Another set of techniques rely on passive microwave

(MW) sensors onboard low-Earth orbit (LEO) satellites

operated by government agencies, such as the US Department

of Defense-Special Sensor Microwave Imager (SSMI) on

Defense Meteorological Satellite Program (DMSP) platforms

(Ferraro, 1997); the National Oceanic and Atmospheric

Administration (NOAA)-Advanced Microwave Sounding

Unit (AMSU-B) (Ferraro et al., 2000); and the National

Aeronautics and Space Administration (NASA)-Advanced

Microwave Scanning Radiometer (AMSR-E) on the Earth

Observing Satellite (EOS) Aqua (Wilheit et al., 2003) and the

TRMM Microwave Imager (TMI) and Precipitation Radar

(PR) mentioned above.

Microwave instruments respond in a more physically direct

way than infrared sensors to the presence of precipitation-size

water and/or ice particles within clouds while remaining

relatively insensitive to non-precipitating clouds. Atmospheric

transmittance windows below 20 GHz, from 30 to 40 GHz, and

at 90 GHz are used for rainfall monitoring. Below 20 GHz,

rainfall absorption and emission are predominant, and ocean

surfaces are warmer than the background radiation. Above

60 GHz, evidence of rainfall is primarily from scattering, where

areas of heavy rainfall are colder than their backgrounds.

r

)

,

2 NRL’s Experimental Geostationary Rain Estimation (GEO) can be found at

Fig. 2. Topography and bathymetry of the Indian Subcontinent. Our Area of Study covers from 0-N to 30-N and from 70-E to 100-E and focuses on the Bay of

Bengal, the Andaman Sea, and their adjacent watershed catchments. Image combined from NOAA’s National Geophysical Data Center Surface of the Earth, 2

minute color relief images online at: http://www.ngdc.noaa.gov/mgg/image/2minrelief.html.

J.E.M. Brown / Remote Sensing of Environment 101 (2006) 63–81 65

Between 20–60 GHz, a combination of absorption and

scattering is present.1

Besides considering sensor measurement techniques, we also

need to take into account sensor sampling styles. Microwave

radiometers on polar-orbiting satellites may produce reasonably

accurate instantaneous estimates, but their sparse temporal

sampling (generally twice a day) constrains the time/space

averaging needed to reduce random errors in a regional or global

precipitation dataset. Sun-synchronous polar-orbiters can intro-

duce biases in the mean precipitation intensity because they

cannot sample the complete diurnal cycle of rainfall with a single

instrument. The TRMM satellite helps overcome this difficulty

by having a LEO that is not sun-synchronous. Its low inclination

(35-) orbit will pass over a given location at progressively later

local sun times. However, for numerical weather prediction

(NWP) and nowcasting applications requiring a rapid-update

global precipitation analysis on time scales of 3 to 6 h, the current

and near-future LEO satellite systems still leave significant

temporal and spatial coverage gaps. Geostationary IR methods

offer the spatial and temporal sampling required for our

purposes, but they also have a few drawbacks such as diurnal

biases due to changing solar illumination on the satellite (Wick et

al., 2002) and requiring very high angular resolution in order to

achieve a reasonable surface footprint size. Unfortunately, it is

1 AMSR-E algorithm website -http://nsidc.org/data/docs/daac/ae_rain_l2b.

gd.html.

this same high angular resolution constraint that effectively

precludes the use of current-generation MW radiometers on

geostationary platforms (Petty, 1995).

Land is very different from oceans in terms of the emitted

microwave radiation, appearing to have a surface emissivity of

90%. Thus, there is little contrast to detect the ‘‘warm’’

raindrops. Also, the background signal from a land surface may

not be as spatially uniform as an ocean surface. These factors

make determining rainfall intensity over land more difficult

than over the ocean.

One area of focused study is the effort by some research

groups to combine MW and VIS/IR techniques into hybrid

algorithms in order to exploit both the physical directness of the

microwave observations and the superior sampling and resolu-

tion of the infrared data (Fig. 1). Research groups at the Naval

Research Laboratory Monterey (NRL) and the University of

Arizona (UA), are using a combination of geostationary infrared

and polar-orbiting microwave satellite data to derive near real-

time 6-hourly precipitation estimates over a 0.25- grid between

60-N and 60-S. These estimates are produced using separate and

unique algorithms that are described below. Both research

teams’ products are posted on their respective Internet websites.2

http://www.nrlmry.navy.mil/sat-bin/rain.cgi covering 60-N to 60-S, and the

UA’s Hydrological Data and Information System (HyDIS) Precipitation can be

found at http://hydis.hwr.arizona.edu/precip/index.html covering 50-N to 50-S.

http://nsidc.org/data/docs/daac/ae_rain_l2b.gd.html

http://www.nrlmry.navy.mil/sat-bin/rain.cgi

http://hydis.hwr.arizona.edu/precip/index.html

http://www.nrlmry.navy.mil/sat-bin/rain.cgi

http://hydis.hwr.arizona.edu/precip/index.html

http://www.ngdc.noaa.gov/mgg/image/2minrelief.html

http://nsidc.org/data/docs/daac/ae_rain_l2b.gd.html


The purpose of this study is to compare the performance of

these two algorithms for 2002 and 2003 over the Indian

Subcontinent against surface weather station observations

within the study area of 0-N to 30-N and 70-E to 100-E(Fig. 2). We also examine the performance of these algorithms

over the ocean by comparing them against a standard level-3

NASA TRMM product. Finally, we examine the suitability of

the derived rainfall data as input into regional hydrological

models such as the University of New Hampshire’s Water

Balance Model/Water Transport Model (Vorosmarty et al.,

1989) or the University of Washington’s Variable Infiltration

Capacity (VIC) macroscale hydrologic model (Liang et al.,

1994) (Fig. 3) using a simulated river network (Fekete et al.,

2000) (Fig. 4) to generate river discharge estimates.

2.1. NRL Monterey GEO rain algorithm

The central core process of this hybrid IR–MW technique

involves localized, accurate space and time alignments of all

MW and IR pixels as each MW-based sensor passes over the

various geostationary satellite coverage regions. The newly

acquired MW rain rate estimates are paired with their time and

space co-located (within 10-km), frequently acquired geosta-

tionary IR data (10-min maximum allowed time offset between

the pixel observation times). The idea is to keep track of the

latest MW-based rain rate observations over 2.5- box regions,

and align the probability density functions (PDFs) of these rain

Fig. 3. The University of Washington’s Variable Infiltration Capacity (VIC) macrosc

used as input P into the model to derive R.

rates with the PDFs of the IR observations from whatever

geostationary satellite is applicable. The geostationary config-

uration used for the study period consists of GOES-8 (western

Atlantic), GOES-10 (eastern Pacific), GMS-5/GOES-9 Back-

up (western Pacific), Meteosat-5 (Indian Ocean), and Meteosat-

8 (eastern Atlantic).

The statistics of these observations are then updated as soon

as the next MW-based sensor over pass occurs. To accomplish

the updating process, histograms of the IR-temperature and

MW-rain rate pairs are built for a 0.25- grid box and are

accumulated until the percent coverage of a given box exceeds

a threshold, currently set to 90%. The overall age of the data

used typically ranges from 2–10 h. Probability matching is

then performed on the histograms in each box, which

adaptively tunes subsequent geostationary satellite data into

rain rate estimates. Thus, the ‘‘adjustment’’ of the IR-

temperatures to a corresponding MW-rain rate is dynamic,

adapting to the rain conditions observed by the blend of MW-

based sensors. So the technique statistically adapts itself to

MW-based rain rates. To work as accurately as possible on an

operational basis, the technique requires a real-time or near-to-

real-time set of data from a constellation of 11 satellite sensors

(3 SSMI, TMI, 2 AMSU-B and the 5 geostationary satellites).

Lastly, forecast model winds are used along with a topographic

database to apply a correction in regions of likely orographic

enhancement. The method is completely autonomous, self-

adapting (as long as satellite data latency time is under 2–3 h),

ale hydrologic model (Liang et al., 1994). Satellite-derived precipitation will be

Fig. 4. Simulated topological river network at 30-min spatial resolution (STN-30p) developed by the University of New Hampshire (Fekete et al., 2000).


and requires no user intervention. Further details are given in

Turk et al. (2002a,b, 2000). Fig. 5 shows how the NRL

algorithm process works.

2.2. University of Arizona PERSIANN algorithm

The fundamental algorithm is based on a neural network and

can therefore be easily adapted to incorporate relevant new

information as it becomes available. An adaptive training

feature facilitates rapid updating of the network parameters

whenever independent estimates of rainfall are available. The

independent estimates are provided by the TRMM 2A12

instantaneous rain rate product, which is derived by matching

the nine-channel brightness temperatures measured by the

TRMM microwave imager (TMI) with model-based Bayesian

estimates of these temperatures (Kummerow et al., 1998,

1996).

PERSIANN (Precipitation Estimation from Remotely

Sensed Information using Artificial Neural Networks) uses a

neural network function approximation procedure to compute

an estimate of rainfall rate at each 0.25-�0.25- pixel of the

infrared brightness temperature image provided by a geosta-

tionary satellite every 30 min. The system scans the infrared

pixel array with a 5�5 moving window surrounding the

central pixel, and five features (pixel temperature, 3�3 mean

temperature, 5�5 mean temperature, 3�3 standard deviation,

5�5 standard deviation), are extracted from this window.

Next, a neural network is used to classify these five features

into groups associated with different cloud spatial character-

istics. For each group, a multivariate linear function mapping is

developed that relates the values of the input features to the

output rain rate using available satellite rainfall data. The

rainfall rate at each pixel is averaged to 6-h resolution. The

University of Arizona PERSIANN method is described in

detail in Soroosh et al. (2000). Fig. 6 is a schematic of the

PERSIANN algorithm.

3. Observational data and methods

3.1. Hybrid algorithm data

Daily rainfall accumulation data for the years 2002 and

2003 were computed from each group’s satellite precipitation

product. The UA PERSIANN 6-hourly precipitation files

have units of mm/6 h and are labeled according to the start of

their respective six hour period in Greenwich Mean Time

(GMT). So to calculate 24-h rainfall accumulation for any

given day one would add up each 6-h file: 0000Z, 0600Z,

Fig. 5. The NRL GEO product uses whatever MW sensor happens to pass over a given location and compares its rain product against those from the geostationary

product (here SSM/I is shown). Statistical coefficients are then updated until the next MW sensor passes over the same location. (Turk, 2003 personal communication).


1200Z, and 1800Z. Fig. 7 shows an example of a 6-h PERSIANN

precipitation product. The NRL 6-hourly files, on the other hand,

have units of average mm/hour per 6 h and are labeled according

PERSIA

Fig. 6. The University of Arizona’s PERSIANN product uses a 5�5 moving windo

features into groups associated with different cloud surface characteristics. For each g

of the input features to the output rain rate using available rainfall data. The rainfal

calibrated neural network mapping functions generate error statistics for pixels with

adjust the parameters of the associated mapping function. (Soroosh et al., 2000).

to the end of their respective six hour period in GMT. So to

calculate 24-h rainfall accumulation for a certain day one needs

to add up each 6-h file: 0600Z, 1200Z, 1800Z, and 0000Z, and

NN

w to capture five features. Next, a neural network is used to classify these five

roup, a multivariate linear function mapping is developed that relates the values

l rate at each pixel is averaged to 6-h resolution. Simulations using previously

TRMM instantaneous rainfall estimates. These error statistics are then used to

Fig. 7. The UA PERSIANN 6-h product for November 7, 2003 0000Z.


multiply that total by 24 to be comparable to PERSIANN.

Fig. 8 shows an example of a 6-h NRL GEO precipitation

product. These daily rainfall accumulations were then

compared to their respective daily observed precipitation

reported by surface weather stations within the area of study.

3.2. Weather station observations

Historically, both ground based radar and rain gauge data

have been used for ‘‘ground truth data’’ for precipitation

(AMSR Rainfall Validation Implementation Strategy 2001–

2005 draft of January 11, 2002). However, few wide-area

networks exist with a dense, homogeneous spatial coverage

and time sampling fine enough to permit meaningful compar-

ison with the fundamental ‘‘instantaneous snapshot’’ nature of

moving platform satellite measurements. A dense, rapid-time-

update rain gauge network can reduce (but not eliminate) some

of the intrinsic sampling offsets typical of intermittent and

sporadic rain events, e.g., the so-called ‘‘rain-sneaking-

between-the-gauges’’ effect (Turk, 2003). While the rain gauge

network used for this validation study is not very dense or

rapid, we expect these shortcomings will be compensated by

the continuous nature and large area extent of the Indian

Summer Monsoon.

Indian weather station data came from the National Oceanic

and Atmospheric Administration (NOAA) Climate Prediction

Center (NCPC) and NOAA’s National Climatic Data Center

(NCDC). There are 79 stations covering the Indian Subcon-

tinent available online at the NCPC Global Precipitation Time

Series website.3 The data are divided into two regions

Pakistan/Northern & Central India (Fig. 9a), and Southern

India/Sri Lanka (Fig. 9b). We will consider only reporting

stations from India (56) and Sri Lanka (5) within the area of

study, thus we will not include station 10 Khanpur, Pakistan

shown in Fig. 9a. Indian stations 14–17 (Srinagar, Amritsar,

Patiala, and Dehra Dun) are just north of the area of study and

station 31 (Bhuj) is just west. This leaves a total of 61 stations

to consider.

3 NCPC website: http://www.cpc.noaa.gov/products/global_monitoring/

precipitation/global_precip_accum.html.

The NCPC’s Global Precipitation Time Series are available

only in graphical form, but they show clearly the yearly

progression of rainfall. Fig. 10a and b show the daily rainfall

for Port Blair, in the Andaman Islands for 2002 and 2003. The

accumulated actual rainfall (thick line) can be compared easily

against the accumulated climatological mean rainfall (thin dashed

line). Regions shaded green show precipitation surpluses; regions

shaded brown show precipitation deficits. Blank areas indicate

missing data. Since these images are not archived by NCPC they

must be downloaded within a limited window of opportunity.

Daily rainfall accumulation data in numerical form were

downloaded from the NCDC Climate Visualization (CLIM-

VIS) Global Summary of the Day version 6 dataset (Example

data not shown). According to the NCDC website, 4 version 6

data (available from January 1994 onward) incorporate

enhancements to the precipitation data (not publicly listed).

These data are referenced to GMT similar to the satellite data.

There are a total of 103 stations listed on the CLIMVIS

website covering the Indian Subcontinent, 92 for India, and

11 for Sri Lanka. Only 86 stations listed for India are within

the area of study. Twelve stations (11 India, 1 Sri Lanka) did

not have the minimum of 5 days of data required for display

and data download, leaving a maximum of 85 stations

available to consider. The two sets of weather station

observations provide a useful quality control check on one

another, so we use only stations that are common to both sets.

All stations except two (Calicut and Trivandrum, India) listed

with the NCPC website are also listed on the NCDC website.

Of the 57 remaining common NCPC/NCDC stations (52

India, 5 Sri Lanka) 4 stations are missing NCPC charts for

either 2002 or 2003 Jabalpur, Indore, East Akola and

Sholapur. No NCDC data exist for the Sri Lankan station

Mannar. This leaves 49 Indian stations and 4 in Sri Lanka.

The final quality control check is to consider only those

remaining stations that have 60 days or less of missing

NCDC data in each year. This cuts another 9 Indian stations

from the data set and all 4 Sri Lankan stations (each missing

between 212 and 228 days of NCDC data). After applying

4 NCDC CLIMVIS website: http://lwf.ncdc.noaa.gov/oa/climate/onlineprod/

drought/xmgr.html.

http://www.cpc.noaa.gov/products/global_monitoring/precipitation/global_precip_accum.html

http://lwf.ncdc.noaa.gov/oa/climate/onlineprod/drought/xmgr.html

http://www.cpc.noaa.gov/products/global_monitoring/precipitation/global_precip_accum.html

http://lwf.ncdc.noaa.gov/oa/climate/onlineprod/drought/xmgr.html

Fig. 8. The NRL GEO 6-h product for November 7, 2003 0000Z.


each of these criteria we are left with 40 Indian stations for

testing the performance of the NRL and UA satellite

precipitation algorithms. There is one station (Nellore) that

has no recorded rainfall in the NCDC numerical data but has

a regular seasonal rainfall pattern in the NCPC graphical data.

Removing Nellore leaves 39 stations.

Fig. 9. a, b. Northern and Central India and Pakistan station locations (left) as w

NOAA Climate Prediction Center global precipitation monitoring website. h

precip_accum.shtml.

The NRL product had no missing data for 2002 and several

days of missing data for 2003, while the PERSIANN product

had only a few days of missing data in each year. This study

will focus only on days having data common to all three data

sets (i.e., only those days that have rain data from each source

will be compared).

ell as the Southern India and Sri Lanka station locations (right) listed on the

ttp://www.cpc.ncep.noaa.gov/products/global_monitoring/precipitation/global_

http://www.cpc.ncep.noaa.gov/products/global_monitoring/precipitation/global_precip_accum.shtml

Fig. 10. a, b. Yearly rainfall accumulation for Port Blair, India for 2002 (left) and 2003 (right). From NOAA CPC Global Precipitation Time Series. Accumulated

‘‘Normal’’ represents the climatological mean seasonal progression of rainfall.


3.3. Performance characterization methodology

Performance by each of the satellite algorithms were

characterized into three basic types: underestimation, overes-

timation and approximately equal (within 10%), and a given

station could have various combinations. The data were

characterized into 9 groups of algorithm performance for the

years 2002 and 2003. For each station a series of operations

were conducted: (1) raw daily rainfall accumulations, (2) raw

accumulated rainfall through the year, (3) common data daily

rainfall accumulations, (4) common data accumulated rainfall

through the year, (5) histogram of common data rainfall

intensities, (6) common data daily satellite algorithm rainfall

minus daily observed rainfall (common daily difference

error), and (7) accumulated common daily difference error

through the year. Figs. 11a–d and 12a–c show these seven

characteristics for WMO station #428090 Calcutta, India.

Table 1 shows the results for the 39 criteria stations described

in Section 3.2 based on the year-end common data

accumulated rainfall (item #4).

Examples of stations in groups 1, 2, 8 and 9 are shown in

Fig. 13a–d.

3.4. Oceanic rainfall data

To continue our study of the hydrological cycle for the Bay

of Bengal and Andaman Sea, we need also to quantify the

contributions of rain to the ocean. The satellite-derived

precipitation products from NRL and UA can also be used

for this purpose. We examine the performance of these

algorithms by comparing them against the standard NASA

TRMM level-3 product, 3B42.

The TRMM satellite, launched in 1997, was designed

specifically to provide improved observations of rainfall over

the oceans. A visible/infrared instrument (Visible and Infrared

ScannerVIRS) was incorporated to establish a connection

between TRMM and operational geostationary platforms, thus

allowing TRMM to serve as a ‘‘flying rain gauge’’ (Kummerow

et al., 2000).

Comparison of TRMM-based estimates of rainfall with

independent, surface-based measurements over both ocean and

land is a way of understanding the validity of the TRMM

retrievals. For oceanic validation data, the TRMM Science

Team used monthly atoll rain gauge data from the Compre-

hensive Pacific Rainfall Data Base (Morrissey et al., 1995) and

ground-based radar estimates from the Kwajalein primary

validation site (Schumacher & Houze, 2000). Kwajalein is the

only permanent site where ground-based radar coverage is

almost entirely over water. A key component to the TRMM

validation effort was the comparison of TRMM rainfall

products to the pre-TRMM state-of-the-art global precipitation

analysis of the Global Precipitation Climatology Project

(GPCP; Huffman et al., 1997). These atoll rain gauge data

were analyzed in 2.5-�2.5- boxes similar to the GPCP

analysis grid. TRMM 1-�1- rainfall products were smoothed

to 2.5- for comparison (Adler et al., 2000). The TRMM

a b

d

50 100 150 200 250 300 3500

50

100

150

200

Year Day

24 H

our

Pre

cip

itation (

mm

)24 H

our

Pre

cip

itation (

mm

)

428090 -- CALCUTTA/DUM DUM -- INDIA -- YEAR: 2002

NRL Monterey

U.Arizona

NOAA NCDC

o NRL Missing Data:0 days

+ U.A Missing Data:1 days

x NCDC Missing Data:26 days

All Missing Data:27 days Overlaping missing days:0

50 100 150 200 250 300 3500

500

1000

1500

2000

2500 NRL Monterey

U.Arizona

NOAA NCDC





428090 -- CALCUTTA/DUM DUM -- INDIA -- YEAR: 2002

428090 -- CALCUTTA/DUM DUM -- INDIA -- YEAR: 2002 428090 -- CALCUTTA/DUM DUM -- INDIA -- YEAR: 2002

Year DayA

ccum

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recip

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mm

)

c

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50

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Year Day

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50 100 150 200 250 300 3500

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o All Missing Data:27 days

Overlaping missing days:0

Year Day

Accum

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ted O

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recip

itation (

mm

)

Fig. 11. a, b. Raw daily rainfall accumulation (top left). Raw accumulated rainfall through the year (top right). c, d. Common data daily rainfall accumulation (bottom

left). Common data accumulated rainfall through the year — used in Table 1 and Fig. 14a,b (bottom right).


product and GPCP estimates showed significant scatter in

comparison with the atoll rain gauge analysis with a near-

zero positive difference (1 mm month�1) for the TRMM

analysis and a larger, negative difference for the GPCP

analysis (�26 mm month�1). Adler et al. (2000) stated that if

the atoll rain gauge estimates are representative of the open

ocean surrounding the atolls, then their statistics would lead to

a tentative conclusion that the TRMM estimates were very

good in terms of absolute magnitude. However, the Kwajalein

radar observations (adjusted by gauges) suggested that the

TRMM estimates were high by 21%. The GPCP estimates, on

the other hand, were low compared to the atolls and roughly

(within 6%) matched the Kwajalein results (Adler et al., 2000).

In a similar fashion, we selected two locations—Minicoy

station in the Laccadive Islands and Port Blair station in the

Andaman Islands as being representative of the ocean

environment to compare the NRL and UA satellite algorithms

against TRMM. Despite a lingering question on how repre-

sentative atoll/island reports are of the open ocean (Adler et al.,

2000), we feel these two stations should still provide a more

valid ocean example than any continental land station. While

the Port Blair station is nestled near some mountains, the

a b

0 10 20 30 40 50 60 700

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Rain Intensity (mm/Day)

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428090--CALCUTTA/DUM DUM--INDIA--YEAR: 2002

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o NRL Missing Data:0 days+ U.A Missing Data:1 days

x NCDC Missing Data:26 days Overlaping missing days:0

c

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U.Arizona NOAA NCDC




Year Day

Num

ber

of O

ccur

renc

es o

f Rai

n In

tenc

ityA

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emot

e S

ensi

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erve

d D

aily

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cipi

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n

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ote

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sing

– O

bser

ved

Dai

ly D

iffer

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Pre

cipi

tatio

n (m

m)

Fig. 12. a, b. Histogram of common data rainfall intensities (top left). Common data daily satellite algorithm rainfall minus daily observed rainfall (common daily

difference error) (top right). c. Accumulated common daily difference error through the year (bottom left).


Minicoy station is on an atoll similar to the Kwajalein

validation site.

The TRMM data set chosen was the standard level-3 3B42

product obtained from NASA’s TRMM Online and Visuali-

zation and Analysis System (TOVAS) website.5 Descriptions

of each algorithm used to create TRMM products are

available from the TRMM Data and Information System

(TSDIS) and the TRMM website.6 The 3B42 product is based

5 NASA TOVAS website: http://lake.nascom.nasa.gov/tovas.6 TSDS website: http://trmm.gsfc.nasa.gov/data dir/ProductStatus.html.

on the Adjusted Geostationary Operational Environmental

Satellite Precipitation Index technique described by Adler et

al. (1994).

The online data are available for latitudes from 40-S to

40-N (the latitudinal range of the TRMM satellite) with

spatial resolution of 1-�1- and 24-h temporal resolution.

Daily accumulated rainfall in ASCII format was down-

loaded and formatted in the same fashion as the rainfall

observations from the weather stations. There were no

missing days of TRMM data from this website (example

data not shown).

http://lake.nascom.nasa.gov/tovas

http://trmm.gsfc.nasa.gov/data%20dir/ProductStatus.html

http://lake.nascom.nasa.gov/tovas

http://trmm.gsfc.nasa.gov/data%20dir/ProductStatus.html


4. Results

4.1. Hybrid algorithms versus observations

Fig. 14a,b show the number of stations meeting the selection

criteria described in Section 3.2 where the hybrid algorithms

either underestimated, overestimated, or were approximately

equal to the observed daily rainfall (T10% — dotted red, T25%— dashed magenta).The performance for the satellite algo-

rithms is summarized in Fig. 14a for calendar year 2002 and in

Fig. 14b for calendar year 2003.

The NRL GEO product behaves similarly for both years.

For the 39 land stations the ratio of overestimation to

underestimation is about 1 :1 with a slight bias to overestima-

tion. The NRL product is accurate to within 10% of the land

stations only about 17% of the time. By expanding our

definition of acceptable accuracy to be T25% of observed

precipitation, we find the NRL product to be accurate for only

33% of station locations.

In 2002 PERSIANN was approximately equal for 31% of

the stations while for the remainder of other stations, overes-

timation was more common than underestimation by a factor of

¨3 :2. In 2003, PERSIANN was approximately equal for only

8% of the stations. The remainder of the stations showed a shift

in the other direction with the UA product overestimating by a

ratio of about 5 :4. For both years combined the PERSIANN

product is accurate to within 10% of the land stations only 19%

of the time. By expanding our definition of acceptable accuracy

to be T25% of observed precipitation, we find the UA product

to be accurate for about 47% of station locations.

4.2. Hybrid algorithms versus TRMM 3B42

For 2002 at Port Blair (Fig. 15a) the TRMM 3B42 product

begins to diverge around yearday 195 underestimating the

rainfall as does the PERSIANN product. The NRL product

Table 1

Satellite algorithm performance characterization grouping

Group 1 Group 2 Group 3

NRL underestimates NRL approx. equal NRL overestimates

PERSIANN

underestimates

PERSIANN

underestimates

PERSIANN

underestimates

Year 2002. . .8 Year 2002. . .4 Year 2002. . .7

Year 2003. . .13 Year 2003. . .0 Year 2003. . .0



PERSIANN

approx. equal

PERSIANN

approx. equal

PERSIANN

approx. equal

Year 2002. . .4 Year 2002. . .3 Year 2002. . .5

Year 2003. . .4 Year 2003. . .0 Year 2003. . .0



PERSIANN

overestimates

PERSIANN

overestimates

PERSIANN

overestimates

Year 2002. . .0 Year 2002. . .5 Year 2002. . .6Year 2003. . .15 Year 2003. . .0 Year 2003. . .4

closely matches the station observations until about yearday 260

when it starts to underestimate the rainfall. All products show

divergence around yeardays 260 and 325. For 2003 (Fig. 15b)

the NRL product grossly underestimates the rainfall for most of

the year showing the largest divergence on yeardays 85, 150, and

260. The PERSIANN product more closely matches the

observed rainfall than the NRL product but it also shows large

divergence the same yeardays. The TRMM 3B42 product does

the best in matching the weather station rainfall observations but

also misses the heavier rainfall on yearday 85, 150, and 260. Its

performance is better through the end of the year.

For 2002 at Minicoy (Fig. 15c) both the NRL product and

the PERSIANN product perform remarkably well in compar-

ison to the observed rain fall and in comparison to each other.

The TRMM 3B42 product on the other hand greatly over-

estimates the rainfall showing large divergence on yeardays

160, 215, 280, and 310. For 2003 (Fig. 15d) all the satellite

derived rainfall products do relatively well with the PER-

SIANN product just slightly underestimating due mainly to

missing the heavy rainfall around yearday 85.

4.3. Effects of geography and elevation

Although rain gauge data are taken here as the reference

standard, they have sources of inaccuracy. This includes effects

of local orography. Given the overall performance results from

our study, we wanted to see if there were any localized effects

that could explain some of the distribution. The relationships

between the NRL and UA hybrid algorithm performance and

their geographical location and elevation are shown in Figs.

16a–d and 17a–d. The hybrid algorithms underestimate

significantly the observed rainfall for those stations located

on the Indian west coast; one clear example is that of Bombay

(Mumbai) (Fig. 13a). In the opposite case the hybrid algorithms

overestimate the observed rainfall for those stations located on

the Indian east coast. Some of these stations (Kakinada,

Nellore, and Cuddalore, India) show no recorded precipitation

in the NCDC CLIMVIS database for either 2002 or 2003 and

were not included in the group of criteria stations listed above.

However, the NCPC graphical data shows each station having

a yearly accumulation of rainfall of 675 & 1075 mm (Kaki-

nada), 1050 & 890 mm (Nellore), and 1125 & 1000 mm

(Cuddalore) for 2002 & 2003, respectively. When we take

these observed values and compare the hybrid algorithms again

we find they perform as shown in Table 2.

Using the graphical NCPC data we see that there is a

systematic difference between the years 2002 and 2003. The

three stations show the NRL and UA algorithms estimating the

accumulated rainfall in a similar fashion for 2002, but behaving

very differently for 2003. Indian Meteorological Department

records indicate that the Summer Monsoon rainfall over India

during July 2002 was the lowest since rainfall records have

been available with the 2002 summer seasonal rainfall 19%

below normal (Srivastava et al., 2004). While for July 2003,

climate monitoring products from NCDC show large positive

precipitation anomalies (¨80–100 mm) for the southeast coast

of India. Thus, it appears that the NRL and UA algorithms

a

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430030 -- BOMBAY/SANTACRUZ -- INDIA -- YEAR: 2002

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424150 -- TEZPUR -- INDIA -- YEAR: 2002

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423390 -- JODHPUR (INAFB) -- INDIA -- YEAR: 2002

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1000NRL Monterey

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NOAA NCDC



433210 -- COIMBATORE/PEELAME -- INDIA -- YEAR: 2002

Year Day

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ted O

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recip

itation (

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)

Fig. 13. a, b. Type 1 — both NRL and UA underestimate (top left). Type 2 — NRL approximately equal, UA underestimates (top right). c, d. Type 8 — NRL

approximately equal, UA overestimates (bottom left). Type 9 — Both NRL and UA overestimate (bottom right).


exacerbate the actual rainfall trend. The algorithms could be

picking up on the decreased amount of cloud cover and then

decreasing further the amount of rainfall produced by these

clouds by using inappropriate assumptions of the cloud

microphysics. Another possible explanation could be that the

constellation of available LEO MW sensors used to dynami-

cally adjust the GEO IR rainfall estimates was different in 2002

then it was in 2003, making the algorithm performance

characterization between the two years less valid.

These values above do not account for any missing data

from the hybrid algorithms as this would be impossible to find

the corresponding days in the NCPC graphical data. By using

the NCPC data in locations where the NCDC data were

suspect, the performances of the hybrid algorithms did not

improve in absolute accuracy (i.e., increase the number of

stations within 25%), but did improve relative accuracy by

reducing the numbers of stations being excessively over-

estimated to zero. The three examples listed above also

illustrate one of the problems associated with surface observa-

tions — whether they can be relied upon being reported,

archived or even shared within institutions completely and

accurately. Indeed, the NCPC data are not subjected to the

a b

0 500 1000 1500 2000 2500 30000

500

1000

1500

2000

2500

3000

U. Arizona PERSIANN & NRL Monterey GEO Satellite– Precip Algorithm Performance: 2002

U. Arizona PERSIANN & NRL Monterey GEO Satellite– Precip Algorithm Performance: 2003

NCDC year-end accumulated precip observation (mm) NCDC year-end accumulated precip observation (mm)

1 km 800 m 600 m 400 m 200 m 100 m 50 mNRL GEO

UA PERSIANN

0 200 400 600 800 1000 1200 1400 1600 1800 20000

200

400

600

800

1000

1200

1400

1600

1800

2000 1 km 800 m 600 m 400 m 200 m 100 m 50 mNRL GEO

UA PERSIANN

Sat

ellit

e al

gorit

hm y

ear-

end

accu

mul

ated

pre

cip

valu

e (m

m)

Sat

ellit

e al

gorit

hm y

ear-

end

accu

mul

ated

pre

cip

valu

e (m

m)

Fig. 14. a, b. Scatter plot showing satellite algorithm performance versus NCDC observation for 2002 (left) and 2003 (right). T10% is shown by the dotted red line

and T25% is shown by the dashed magenta line.


same quality controls as the NCDC data (John J. Bates, NCDC,

2005 pers. comm.).

Another local effect apparent in the data can be seen with

Coimbatore, India (Fig. 13d). This station is located in what

looks like a ‘‘rain shadow’’ caused by the nearby mountains.

This could explain why the hybrid algorithms both signifi-

cantly overestimate the observed rainfall.

The influence of elevation showed no clear patterns with

both algorithms having about the same distribution of under

and overestimation throughout the elevation range of the

criteria stations (Fig. 17a–d).

5. Discussion and conclusions

The NRL and UA precipitation products provide greater

spatial and temporal resolution than the standard level-3 NASA

TRMM products (1-�1- daily or monthly) that are currently

available. Indeed, the 0.25-�0.25- resolution allows individ-

ual pixels to be matched with individual land reporting stations

and will fit within the 0.5 grid domain of a simulated river

network. There is a fine balance between grid size and time

scale as the correlation between satellite rainfall intensities and

rain gauge observations falls off rapidly below 24-h and 1-.Turk et al. (2002b) found that the correlation drops below 0.5

close to these time-grid size pairs: 12-h at 0.25-, 6-h at 0.5-,3-h at 0.75-, and 1-h at 1-.

Problems occur when comparing 0.1- individual satellite

observations to �1 m2 rain-gauge measurements (e.g.,

sporadic rain, location of rain gauge, topography, etc). Despite

careful efforts to provide climate quality validation data,

ground-based measurements themselves have limitations. The

greatest shortcoming is in the treatment of uncertainties,

particularly when applied to relatively few satellite overpasses

containing rainfall in any given month. In our case study this

shortcoming is alleviated during the Indian Summer Monsoon

and for the near year-round rainfall found over Sri Lanka and

the southern tip of India. The ground validation (GV) paradigm

of radars and individual rain gauges has been effective at

verifying to first order that satellite precipitation algorithms are

not making any egregious errors. Indeed, validation studies in

the late 1980s and early 1990s proved the GV paradigm could

differentiate between products that often differed by more than

a factor of two (AMSR Rainfall Validation Implementation

Strategy 2001–2005 draft of January 11, 2002).

However, the TRMM validation program found that more

subtle biases, in the 10–20% range, are very difficult to detect

in this manner due to the problems inherent with these ground-

based observations. If only the area over the rain gauges is

considered, the TRMM team estimated that 2–3 years of data

would be required in even the rainiest locations before random

errors reduce to less than 10%. Even subtle topography or

urban influences can cause systematic differences in excess of

20% (AMSR Rainfall Validation Implementation Strategy

2001–2005 draft of January 11, 2002).

Other errors can come from missing data from individual

satellite passes. Even with a full constellation of LEO satellites

a three-hour latency period can exist before these data are

manifested in the hybrid algorithms (Turk, 2003). Light rain

conditions pose another type of problem due to the MW rain/

no-rain screening techniques used (Turk et al., 2002a). Under

certain conditions, light rain can be misidentified over land

surfaces that scatter radiation similarly to a precipitating cloud.

Likewise an opposite effect can occur when the screen fails to

identify regions of light rain.

a b

c d

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433330--PORT BLAIR--INDIA--YEAR: 2002 433330--PORT BLAIR--INDIA--YEAR: 2003

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433690--MINICOY ISLAND--INDIA--YEAR: 2002 433690--MINICOY ISLAND--INDIA--YEAR: 2003

Year Day

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TRMM 3B42



Year Day

Accu

mu

late

d O

bse

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ita

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n (

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)

Fig. 15. a, b. NRL, UA, and TRMM 3B42 for Port Blair, India in 2002 (top left) and 2003 (top right). c, d. NRL, UA, and TRMM 3B42 for Minicoy, India in 2002

(bottom left) and 2003 (bottom right).


One type of rain that produces relatively few, if any, large

ice particles is the persistent orographic rain encountered on

sloping terrain in the tropics and subtropics, especially over

parts of India and elsewhere in tropical Asia during the

Summer Monsoon. In these situations, collision–coalescence is

thought to be an important mode of precipitation formation,

and ice particles may occasionally be absent altogether (Petty,

1995). Petty (1999; J. Climate) has also undertaken an initial

survey of the apparent prevalence of rain from warm-topped

clouds and found it to be non-negligible in at least certain parts

of east Asia and the western Pacific. This applies directly to the

stations on India’s west coast. However, for our current

purposes, these same coastal stations lie outside the watershed

basins that drain into the Bay of Bengal. While the NRL

algorithm makes an orographic correction, the method used is

ad hoc and under corrects in steep small-scale topography

(Turk, 2003). In general, though, this study shows that rainfall

of this type can be observed at least somewhat reliably over

large portions of the Indian Subcontinent using hybrid IR/

passive MW (PMW) techniques.

While hybrid algorithms reduce sampling errors, other

sources of uncertainty in rainfall retrievals associated with

a

6000

4000

2000

0

2000

4000

6000

Ele

vatio

n (

m)

NRL Monterey GEO Satellite–Precip Algorithm Performance: 2002% Greater or Less than its Weather Station Observation

NRL Monterey GEO Satellite–Precip Algorithm Performance: 2003% Greater or Less than its Weather Station Observation

Latit

ude

72°E 78°E 84°E 90°E 96°E

72°E 78°E 84°E 90°E 96°E 72°E 78°E 84°E 90°E 96°E

72°E 78°E 84°E 90°E 96°E 0°

6°N

12°N

18°N

24°N

30°N

0°

6°N

12°N

18°N

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30°N

0°

6°N

12°N

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0°

6°N

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30°N

1 km800 m600 m400 m200 m100 m 50 m>250%+250%+100% +50% +25% +10% 0% 10% 25% 50% 75%

100%

6000

4000

2000

0

2000

4000

6000

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vatio

n (

m)

Latit

ude

1 km800 m600 m400 m200 m100 m 50 m>250%+250%+100% +50% +25% +10% 0% 10% 25% 50% 75% 100%

c

b

d

6000

4000

2000

0

2000

4000

6000

Ele

vatio

n (

m)

U. Arizona PERSIANN Satellite–Precip Algorithm Performance: 2002% Greater or Less than its Weather Station Observation

LongitudeInset: (top) Elevation range of Weather Stations

(bottom) Performance % Greater or Less Than Obs







Latit

ude

1 km800 m600 m400 m200 m100 m 50 m>250%+250%+100% +50% +25% +10% 0% 10% 25% 50% 75%

100%

6000

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0

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6000

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m)

U. Arizona PERSIANN Satellite–Precip Algorithm Performance: 2003% Greater or Less than its Weather Station Observation

Latit

ude

1 km800 m600 m400 m200 m100 m 50 m>250%+250%+100% +50% +25% +10% 0% 10% 25% 50% 75%

100%

Fig. 16. a, b. NRL performance versus geographical distribution for 2002 (top left) and 2003 (top right). c, d. UA performance versus geographical distribution for

2002 (bottom left) and 2003 (bottom right).


PMW sensors also exist. These include beam-filling error

(sub-pixel inhomogeneity in the rainfall field), uncertainty in

the vertical distribution of hydrometeors, and errors in

estimating the freezing level. The determination of the

freezing level is important due to the inversion problem of

converting brightness temperatures into surface rain rates.

This involves two steps: 1) retrieving a profile of precipitating

hydrometeors from the measured brightness temperatures, and

2) relating the hydrometeor profile to a surface rain rate

(usually by applying an assumed fall speed to the hydrome-

teor profile). The inversion problem, however, is often under-

determined, meaning that the same set of brightness

temperatures may correspond to several different profiles of

precipitating water and ice (Wilcox, 2002). The hybrid

algorithms address these issues with a statistical approach in

the case of NRL and with the use of a neural network in the

case of UA PERSIANN.

In the future, as model and measurement resolution time

and space scales shrink, three-dimensional cloud effects will

need to be taken into account. At fine scales, cloud

morphology and satellite-view geometry become important.

Cloud-resolving radiative transfer models as well as high

a b

0 2 4 6 8 10 12

1

2

3

4

5

6

7

Performance Characterization

Ele

vatio

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ateg

ory

50m

10

0m

200

m

400m

60

0m

800

m

1km

Elevation vs Performance: NRL GEO 2002

-100%

-75%

-50%

-25%

-10%

-0%

-100%

-75%

-50%

-25%

-10%

-0%

-100%

-75%

-50%

-25%

-10%

-0%

-100%

-75%

-50%

-25%

-10%

-0%

+10%

+25%

+50%

+100%

+250%

0 2 4 6 8 10 12

1

2

3

4

5

6

7

Performance CharacterizationE

leva

tion

Cat

egor

y50

m

100m

2

00m

40

0m

600m

8

00m

1k

m

Elevation vs Performance: NRL GEO 2003

+10%

+25%

+50%

+100%

+250%

c d

0 2 4 6 8 10 12

1

2

3

4

5

6

7


Ele

vatio

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ateg

ory

50m

10

0m

200

m

400m

60

0m

800

m

1km

Elevation vs Performance: PERSIANN 2002

+10%

+25%

+50%

+100%

+250%

0 2 4 6 8 10 12

1

2

3

4

5

6

7


Ele

vatio

n C

ateg

ory

50m

10

0m

200

m

400m

60

0m

800

m

1km

Elevation vs Performance: PERSIANN 2003

+10%

+25%

+50%

+100%

+250%

Fig. 17. a, b. NRL elevation versus performance for 2002 (top left) and 2003 (top right). c, d. UA elevation versus performance for 2002 (bottom left) and 2003

(bottom right).


resolution, limited area numerical models of cloud dynamics

need to be developed or improved. From such models it is

possible to generate candidate profiles of precipitating hydro-

Table 2

India east coast station performance

(a) Kakinada, India NRL +3% in 2002, �44% in 2003

. . .(68 missing NCDC

days in 2002)

UA +7% in 2002, �22% in 2003

(b) Nellore, India NRL �36% in 2002, �11% in 2003

. . .(within criteria, but

not included)

UA �29% in 2002, +14% in 2003

(c) Cuddalore, India NRL �53% in 2002, �51% in 2003

. . .(61 missing NCDC

days in 2002)

UA �58% in 2002, �32% in 2003

meteors, and corresponding simulated brightness temperatures,

to compare with observed brightness temperatures (Wilcox,

2002).With the proposed international Global Precipitation

Mission (GPM), satellite view geometries will also be a factor.

For example, when the AMSR-E sensor passes over a given

location followed by a GPM sensor a few minutes later, these

sensors despite having the same zenith angle would have

different azimuth angles due to their different swath paths and

thus have different view geometries. This becomes important

with satellites using narrower beam width channels (¨85 GHz)

sensing higher in clouds (more horizontal variation) and

follow-on satellites using wider beamwidth channels (¨10,

19 GHz) sensing lower in clouds (less horizontal variation)

(Bidwell et al., 2002).


Despite the limitations mentioned above, the NRL Mon-

terey GEO and University of Arizona PERSIANN data sets

reproduce well the progression of the seasonal rainfall. They

also reproduce the natural variability of daily rainfall

accumulation. While these hybrid algorithms may not

reproduce each day’s rain-gauge recorded rainfall quantities

they are adequate on a watershed/catchment area scale where

time delays exist between a specific rainfall event and its

eventual drainage into a stream/river network as runoff. These

products should prove to be especially useful for estimating

precipitation in regions with no, or unreliable, surface

reporting stations and as ‘‘gap-fillers’’ in between radar

coverage.

Gottschalck et al. (2005) conducted a comparison study of

various precipitation data sets, including PERSIANN, to

determine the best precipitation forcing for Global Land Data

Assimilation System (GLDAS) simulations for the continen-

tal USA. They found that PERSIANN had the largest overall

errors during the March 2002–February 2003 study period.

PERSIANN typically underestimated rainfall on the west

coast and overestimated rainfall in the US central and eastern

regions. However, during the summertime, PERSIANN

performed better than NWP models, capturing the timing of

intra-daily and inter-daily precipitation associated with

mesoscale convective systems. They found that large

differences in precipitation forcing lead to large differences

in land surface states such as soil temperature and soil

moisture with the land surface models ‘‘dampening’’ the

impact somewhat. Unfortunately, Gottschalck et al. (2005)

did not generate surface runoff or river discharge estimates

from these simulation runs.

Given the mixed results from the Gottschalck et al.

(2005) study, we find that satellite-derived precipitation

products, such as those from NRL and UA, should still be

well-suited as input into hydrological models for the

purposes of generating river discharge estimates. This is

especially true for application during the Indian Summer

Monsoon season when the rainfall is of high intensity, long

duration and relatively uniform over large area extent.

Indeed, one of the main motivations for assessing satellite-

derived rainfall products is to provide a counter argument to

one view held by the Hydrology community — namely,

that since the accuracy of river discharge estimates are in

the range of 10–20% (Fekete et al., 2000) and that since

this level of accuracy is much higher than what can be

achieved in measuring precipitation (Hagemann & Dumenil,

1998), ‘‘it would be desirable to develop new techniques,

which could incorporate discharge estimates into the

estimation of distributed precipitation’’ (Fekete et al.,

2000). Instead of the traditional approach of estimating

runoff from precipitation data (Runoff, R =runoff coefficient,

w�Precipitation, P), it was suggested that the inverse

calculation be used to estimate precipitation from runoff

data (P=1 /w�R). The next step will be to take these

satellite rainfall products, enter them into hydrological

models and compare the resulting discharge estimates to

discharge observations.

Acknowledgements

The support of the sponsor, NASA for the Earth System

Science Fellowship (ESSF/03-0000-0053) is gratefully ac-

knowledged. Many thanks go to the author’s advisor, Dr Peter

J. Minnett and to Dr F. Joe Turk of Naval Research Laboratory

Monterey and the University of Arizona Department of

Hydrology and Water Resources PERSIANN team for the

use of their rainfall datasets.

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