NASA Technical Memorandum 104606, Vol. 12

Technical Report Series on

Global Modeling and Data Assimilation

Max J. Suarez, Editor

Goddard Space Flight Center

Greenbelt, Maryland

Volume 12

Comparison of Satellite Global Rainfall

Algorithms

Alfred T. C. Chang

Long S. Chiu

Goddard Space Flight Center

Greenbelt, Maryland

National Aeronautics and

Space Administration

Goddard Space Flight Center

Greenbelt, Maryland

1997
This publication is available from the NASA Center for AeroSpace Information, [

800 Elkridge Landing Road, Linthicum Heights, MD 21090-2934, (301) 621-0390. I
Abstract

Seventeen months of rainfall data (August 1987-December 1988) from nine satellite rainfall algorithms (Adler, Chang, Kummerow, Prabhakara, Huffman, Spencer,

Susskind, and Wu) were analyzed to examine the uncertainty of satellite-derived rainfall estimates. The variability among algorithms, measured as the standard deviation

computed from the ensemble of algorithms, shows regions of high algorithm variability

tend to coincide with regions of high rain rates. The ratio of the annual mean algorithm variability to the annual mean (August 1987-July 1988) rain rate is generally less

than 0.5 over most of the regions between 50°N and 50°S. Exceptions are found in the

oceanic dry zones and the Himalayan region. High algorithm variability in the oceanic

dry zones points to the different approaches in the treatment of low rain rates. Arkin's

algorithm, which uses cloud top temperature, tend to over-estimate in the mountainous

Himalayas and in the Indian monsoon region. The over-estimate over the Himalayas

is attributed to the cold surface. Over the Indian Ocean, the frequent occurrence of

non-raining high clouds contributed to the high estimates. The global annual rainfall

(over the latitude belts +50 o and for August 1987-July 1988) ranges from 2.5 to 3.5

mm/day, with higher variability over land than over ocean. The variability of zonal

averaged rain rates is in the range of about 0.5 mm/day in the subtropical dry zones

and increases to 2 mm/day in the tropical rain belts. Histograms of pattern correlation

(PC) between algorithms suggest a bimodal distribution, with separation at a PC value

of about 0.85. Applying this threshold as a criteria for similarity, our analyses show

that algorithms using the same sensor or satellite input tend to be similar, suggesting

the dominance of sampling errors in these satellite estimates.

The sensitivity of the algorithms to the 1986-87 El Nifio Southern Oscillation event

was examined using paired-t tests and PC analyses. Paired-t statistics results indicate

that the algorithms of Chang, Prabhakara, and Spencer show significant difference

between August 1987 and 1988, assuming the difference field is spatially uncorrelated.

This assumption needs to be further examined.

.°.

in

Contents

List of Figures xi

List of Tables

ooo

Xlll

1 INTRODUCTION 1

2 ALGORITHMS 2

3

2.1 GSFC Scattering Algorithm, Adler, Huffman and Negri (Adler) .......

2.2 Calibrating Geostationary IR Data with Microwave Data, Adler, Huffman

and Negri (Huffman) ............................... 4

2.3 GOES Precipitation Index (GPI) Technique (Arkin) ............. 4

2.4 Probability Distribution Functions Algorithm, Chang, Chiu and Wilheit (Chang) 4

2.5 Linear Regression Algorithm (Kummerow) ................... 5

2.6 Rain Area Dependent Algorithm (Prabhakara) ................ 6

2.7 Oceanic Rainfall from MSU (Spencer) ..................... 6

2.8 Global Precipitation from TOVS (Susskind) .................. 6

2.9 Multi-spectral Rainfall Algorithm (Wu) .................... 7

3 DATA PRE-PROCESSING

4 COMPARISON RESULTS 8

4.1 Annual mean (August 1987- July 1988) .................... 8

4.2 Monthly Comparison ............................... 9

4.3 Zonal Means ................................... 11

4.4 Regional Comparison ............................... 12

v
5 RESPONSE TO 1986-87 ENSO 13

6 SUMMARY AND DISCUSSIONS 14

vi
List of Figures

2

3

4.1

4.2

4.3

4.4

4.5

4.6

4.7

4.8

4.9

4.10

4.11

4.12

5.1

5.2

6

7

Land and sea mask used in this study. The oceans are further divided into

the Atlantic, Pacific, and Indian ocean sectors, and the land into north and

south America, Eurasia, Africa, and Australia sectors ............. 23

Mean (upper panel), standard deviation(SD, middle panel), and the ratio of

the SD to the mean (lower panel) for the annual mean (August 1987 - July

1988). The ensemble average is taken over all nine algorithms ........ 24

Algorithm with maximum (upper panel) and minimum (lower panel) rain

rates for the annual mean ............................. 25

Same as Figure 2, except for August 1987 .................. 26

Same as Figure 2, except for September 1987 ................ 27

Same as Figure 2, except for October 1987 .................. 28

Same as Figure 2, except for November 1987 ................. 29

Same as Figure 2, except for December 1987 ................. 30

Same as Figure 2, except for January 1988 .................. 31

Same as Figure 2, except for February 1988 ................. 32

Same as Figure 2, except for March 1988 ................... 33

Same as Figure 2, except for April 1988 ................... 34

Same as Figure 2, except for May 1988 ................... 35

Same as Figure 2, except for June 1988 ................... 36

Same as Figure 2, except for July 1988 ................... 37

Same as Figure 3, except for August 1987 .................. 38

Same as Figure 3, except for May 1988 .................... 39

Monthly rainfall for September 1988 for all algorithms ............ 40

Histogram of pattern correlation coefficients among algorithms for August

1987 and May 1988. The bin 0.9 includes PC coefficients in the interval (0.90,

0.95) ........................................ 43

vii
8 Monthly rainfall forSeptember1988for all algorithms ........... 44

9 Zonal meanrain ratesoverthe oceans(upperpanel),land (middlepanel)

and over both land and ocean(lowerpanel)for the annualmean(August

1987-July1988).................................. 47

10 SameasFigure9, exceptfor August1987................... 48

11 SameasFigure9,exceptfor May 1988..................... 49

12 Time seriesof theglobal(50°N- 50°S)averagefor all algorithmsoverthe

oceans(upperpanel),land (middlepanel),and overboth ocean and land

(lower panel) from August 1987 to December 1988 ............... 50

13.1 Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle

panel), and Indian ocean sector (lower panel) for August 1987 ........ 51

13.2 Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle

panel), and Indian ocean sector (lower panel) for September 1987 ...... 52

13.3 Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle

panel), and Indian ocean sector (lower panel) for October 1987 ....... 53

13.4 Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle

panel), and Indian ocean sector (lower panel) for November 1987 ...... 54

13.5 Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle

panel), and Indian ocean sector (lower panel) for December 1987 ...... 55

13.6 Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle

panel), and Indian ocean sector (lower panel) for January 1988 ....... 56

13.7 Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle

panel), and Indian ocean sector (lower panel) for February 1988 ....... 57

13.8 Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle

panel), and Indian ocean sector (lower panel) for March 1988 ........ 58

13.9 Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle

panel), and Indian ocean sector (lower panel) for April 1988 ......... 59

13.10 Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle

panel), and Indian ocean sector (lower panel) for May 1988 ......... 60

13.11 Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle

panel), and Indian ocean sector (lower panel) for June 1988 ......... 61

°°°

Vlll
13.12 Zonal mean ram rates for the Pacific (upper panel), Atlantic (middle

panel), and Indian ocean sector (lower panel) for July 1988 .........

13.13 Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle

panel), and Indian ocean sector (lower panel) for August 1988 ........

13.14 Zonal mean ram rates for the Pacific (upper panel), Atlantic (middle

panel), and Indian ocean sector (lower panel) for September 1988 ......

13.15 Zonal mean ram rates for the Pacific (upper panel), Atlantic (middle

panel), and Indian ocean sector (lower panel) for October 1988 .......

13.16 Zonal mean ram rates for the Pacific (upper panel), Atlantic (middle

panel), and Indian ocean sector (lower panel) for November 1988 ......

13.17 Zonal mean ram rates for the Pacific (upper panel), Atlantic (middle

panel), and Indian ocean sector (lower panel) for December 1988 ......

14.1 Zonal mean ram rate for the north and south American (upper panel),

Australia and Eurasia (middle panel), and Africa land sector (lower panel)for

August 1987 ....................................

14.2 Zonal mean ram rate for the north and south American (upper panel),

Australia and Eurasia (middle panel), and Africa land sector (lower panel)

for September 1987 ................................

14.3 Zonal mean ram rate for the north and south American (upper panel),

Australia and Eurasia (middle panel), and Africa land sector (lower panel)for

October 1987 ...................................

14.4 Zonal mean ram rate for the north and south American (upper panel),

Australia and Eurasia (middle panel), and Africa land sector (lower panel)for

November 1987 ..................................

14.5 Zonal mean ram rate for the north and south American (upper panel),

Australia and Eurasia (middle panel), and Africa land sector (lower panel)for

December 1987 ..................................

14.6 Zonal mean ram rate for the north and south American (upper panel),

Australia and Eurasia (middle panel), and Africa land sector (lower panel)for

January 1988 ...................................

14.7 Zonal mean ram rate for the north and south American (upper panel),

Australia and Eurasia (middle panel), and Africa land sector (lower panel)for

February 1988 ...................................

62

63

64

65

66

67

68

69

7O

71

72

73

74

ix
14.8 Zonal mean rain rate for the north and south Amerman (upper panel),

Australia and Eurasia (middle panel), and Africa land sector (lower panel)for

March 1988 ....................................

14.9 Zonal mean rain rate for the north and south American (upper panel),

Australia and Eurasia (middle panel), and Africa land sector (lower panel)for

April 1988 .....................................

14.10 Zonal mean rain rate for the north and south American (upper panel),

Australia and Eurasia (middle panel), and Africa land sector (lower panel)for

May 1988 .....................................

14.11 Zonal mean ram rate for the north and south American (upper panel),

Australia and Eurasia (middle panel), and Africa land sector (lower panel)for

June 1988 .....................................

14.12 Zonal mean r_n rate for the north and south American (upper panel),

Australia and Eurasia (middle panel), and Africa land sector (lower panel)for

July 1988 .....................................

14.13 Zonal mean ram rate for the north and south American (upper panel),

Australia and Eurasia (middle panel), and Africa land sector (lower panel)for

August 1988 ....................................

14.14 Zonal mean ram rate for the north and south American (upper panel),

Australia and Eurasia (middle panel), and Africa land sector (lower panel)for

September 1988 ..................................

14.15 Zonal mean ram rate for the north and south American (upper panel),

Australia and Eurasia (middle panel), and Africa land sector (lower panel)for

October 1988 ...................................

14.16 Zonal mean ram rate for the north and south American (upper panel),

Australia and Eurasia (middle panel), and Africa land sector (lower panel)for

November 1988 ..................................

14.17 Zonal mean ram rate for the north and south American (upper panel),

Australia and Eurasia (middle panel), and Africa land sector (lower panel)for

December 1988 ..................................

15.1 Rain rate distribution for August 1987 (upper panel), August 1988 (middle

panel), and the normalized difference (difference between August 1987 and

August 1988 divided by the average of these two months) (lower panel) for

the algorithms of Adler .............................

75

76

77

78

79

8O

81

82

83

84

85
15.2

15.3

15.4

15.5

15.6

15.7

15.8

16

RainratedistributionforAugust1987(upperpanel),August1988(middle

panel),and the normalizeddifference(differencebetweenAugust1987and

August1988dividedby the averageof thesetwo months)(lowerpanel)for

the algorithmsof Chang .............................

RainratedistributionforAugust1987(upperpanel),August1988(middle

panel),and the normalizeddifference(differencebetween August 1987 and

August 1988 divided by the average of these two months) (lower panel) for

the algorithms of Prabhakara ..........................

Rain rate distribution for August 1987 (upper panel), August 1988 (middle

panel), and the normalized difference (difference between August 1987 and

August 1988 divided by the average of these two months) (lower panel) for

the algorithms of Arkin .............................

Rain rate distribution for August 1987 (upper panel), August 1988 (middle

panel), and the normalized difference (difference between August 1987 and

August 1988 divided by the average of these two months) (lower panel) for

the algorithms of Spencer ............................

Rain rate distribution for August 1987 (upper panel), August 1988 (middle

panel), and the normalized difference (difference between August 1987 and

August 1988 divided by the average of these two months) (lower panel) for

the algorithms of Susskind ............................

Rain rate distribution for August 1987 (upper panel), August 1988 (middle

panel), and the normalized difference (difference between August 1987 and

August 1988 divided by the average of these two months) (lower panel) for

the algorithms of Wu ...............................

Rain rate distribution for August 1987 (upper panel), August 1988 (middle

panel), and the normalized difference (difference between August 1987 and

August 1988 divided by the average of these two months) (lower panel) for

the algorithms of GPCC .............................

Histograms of the normalized difference for Adler, Chang, Prabhakara,

Arkin, Spencer, Susskind, Wu, and GPCC ...................

86

87

88

89

9O

91

92

93

xi

List of Tables

Characteristic of Satellite Rainfall Algorithms Included in this Study .... 20

Available Data from the Nine Algorithms for this Study ........... 21

Pattern Correlation Coefficients for August 1987 Between Different Algorithms 21

Same as Table 2 except for May 1988 ...................... 21

Paired-t Statistics and Pattern Correlation Coefficients Between August 1987

and 1988 ...................................... 22

xiii

1 INTRODUCTION

The reality of Global Circulation Models (GCMs) to predict future climate changes hinges

on their ability to realistically reproduce current climate. Although most of the GCMs are

based on the same laws of thermodynamics and dynamics, they emphasize different physical

processes or used different parameterization schemes for many of the important climate

components. The Atmospheric Model Intercomparison Project (AMIP) was established to

evaluate the operational GCMs and to diagnostically compare products of the GCMs. Ten

years (1979-1988) of GCM runs were performed in a controlled situation for the cases with

and without actual sea surface temperature forcing.

Precipitation is an important climate parameter. Theoretical studies have shown the importance of the release of latent heat of condensation associated with precipitation in driving atmospheric circulation. Observational studies have also demonstrated the association

between precipitation pattern changes and climate variations. However, the spatial and

temporal variability of rainfall at all scales poses a great challenge in estimating space/time

rainfall.

In response to this challenge, the National Aeronautics and Space Administration (NASA)

of the United States (U.S.) and the National Space Development Agency (NASDA) of Japan

jointly sponsored the Tropical Rainfall Measuring Mission (TRMM). With TRMM well underway, the science community saw the opportunity for improved global rainfall estimation.

Techniques for estimating rainfall from space-borne measurements flourished. To compare

and calibrate these techniques, a number of projects to intercompare satellite rain retrieval

techniques have been conducted. The First Algorithm Intercomparison Project (AIP-1) was

organized by the Global Precipitation Climatology Project (GPCP). AIP-1 compared a variety of rainfall estimates derived from geostationary meteorological satellite visible and IR

observations and Special Sensor Microwave/Imager Microwave (SSM/I) observations with

rainfall derived from a combination of surface radar and rain gage data over the Japan and

the adjacent sea region during June and mid-July through mid-August of 1989 (Lee et al.,

1991). Subsequently, AIP-2 and AIP-3 were organized to evaluate the rainfall over England

and the Tropical Ocean-Global Atmosphere Coupled Ocean Atmosphere Response Experiment (TOGA-COARE) region respectively. Under the NASA- led WetNet Project, a series

of Precipitation Intercomparison Projects (PIPs) were organized to compare rainfall derived

from a common SSM/I microwave data set. PIP-1 was undertaken in 1992 and 1993, with

global rainfall for the months of August through November 1987 as its subject (Barrett,

1994). PIP-2 (Smith et al., 1995) and PIP-3 (Ebert, 1996) were organized to evaluate and

compare SSM/I derived rainfall for pre-selected rainfall cases and global patterns. These

intercomparison activities were designed to fulfill specific goals and were usually restricted

to a limited domain.

There are several ongoing satellite rain estimation activities at NASAs Goddard Space

Flight Center (GSFC). At the request of GSFC management, an in-house a£ hoc committee
wasformedin October1993. The main chartersof this committeewereto: (1) review

and evaluatethe GSFC satellite rain estimation efforts, (2) discuss duplications and/or

need for diversity, and (3) develop a plan for coordinating the GSFC efforts, including an

intercomparison of the algorithms at various space and time scales. This study extended

the comparison effort to include other operational or semi-operational rain rate algorithms.

Algorithm developers were requested to submit monthly data for the comparison effort. Two

types of comparison were conducted: snapshot comparison and time series comparison. The

snapshot comparison focuses on the global rain estimates for the months of August 1987 and

May 1988. The time series comparison focused on the time period during which the SSM/I

measurements overlap the AMIP period (July 1987 through December 1988). During this

period, the algorithms went through at least one seasonal cycle, allowing examination of

the seasonal differences of the algorithms. This period also saw the demise of the 1986-

1987 El Nifio Southern Oscillation (ENSO) episode and the return of the atmosphere to its

relatively normal state. This provided the opportunity to intercompare the sensitivity of

the algorithms to the same external forcing.

The remainder of this report is divided into 5 sections. Section 2 describes the algorithms.

Section 3 describes the preprocessing of these data sets. Results of the comparison of the

annual mean, seasonal and regional differences, and for individual months (August 1987 -

July 1988) are described in Sections 4. Section 5 examines the responses of the algorithms to

the 1986-87 ENSO event. Section 6 summarizes the results and discusses possible mutually

beneficial avenues of collaborations between the algorithms.

2 ALGORITHMS

The satellite rain algorithms included in this study are:

(1) The Goddard Scattering Algorithm by Adler, Huffman and Negri (Adler),

(2) Calibrating GOES Precipitation Index (GPI) infrared (IR) data with microwave data

by Adler, Huffman and Negri (Huffman),

(3) Monthly oceanic rainfall using SSM/I brightness temperature histogram by Chang,

Chiu and Wilheit (Chang),

(4) Theoretical regression method by Kummerow (Kummerow),

(5) Precipitation area dependent technique by Prabhakara (Prabhakara),

(6) GOES Precipitation Index (GPI) Technique by Arkin (Arkin),

(7) Oceanic rainfall from Microwave Sounding Unit (MSU) by Spencer (Spencer),
(8) Globalprecipitationfrom TIROSOperationalVerticalSounder(TOVS) by Susskind

(Susskind)and

(9) Multi-spectralrainfall algorithmbyWu (Wu).

In addition,the griddedgaugerainfalldataset preparedby the GlobalPrecipitationClimatologyCenter(GPCC)(Rudolfet al., 1994)wasincludedfor validationoverland.

The characteristicsof thesealgorithmsaresummarizedin Table 1. The algorithmsare

describedbelow.

2.1 GSFC Scattering Algorithm, Adler, Huffman and Negri (Adler)

This algorithm is based on the technique described by Adler et al. (1993). The first step

is the screening of SSM/I data for missing and physically unrealistic data and jumps in

scan-average values. The SSM/I brightness temperatures are then interpolated to 12.5

km resolution pixels. Pixels that are unlikely to precipitate are identified and screened

out. The remaining pixels are tested for the scattering signal at 85.5 GHz due to frozen

hydrometeors. A precipitation rate is calculated based on radiative computation using

numerical cloud model results (Adler et al. 1991). The relationship they derived is:

Tb(85.5V) = 251.0- 4.19R, (1)

where Tb(85.5V) is the SSM/I brightness temperature of the vertically polarized 85.5 GHz

channel, and R is the rain rate in mm/hr. A rain/no-rain cutoff was set at 1 mm/hr

(Tb = 247K). The cutoff is applied to each individual SSM/I pixels. However, no cutoff

is applied to subsequent averages. Comparison with rain gage data collected over the

Pacific atolls (Morrissey, 1991) showed that the rain rate derived from (1) consistently

underestimated precipitation by a factor of two over the atolls, so the slope in (1) was

adjusted for oceanic regions to yield:

Tb(85.5V) = 251.0 - 2.09R (2)

while keeping the same rain/no-rain cutoff. In coastal regions, the slope is an average of

the land and ocean value. In the areas where sea ice is possible, land value is used.
2.2 Calibrating Geostationary IR Data with Microwave Data, Adler,

Huffman and Negri (Huffman)

This method takes advantage of the frequent temporal sampling by the geostationary satellite (8 times per day) and the physically based microwave rain signal by the Defense Meteorological Satellite Program (DMSP) SSM/I (twice per day) (Huffman et al., 1993). Coincident SSM/I and geostationary IR data are compared to derive an IR-rain rate relationship

consistent with the microwave-inferred rain rate. It is assumed that these microwave calibrated IR-rain rate relationships vary slowly in space and time. The calibrated IR-rain rate

relationships are then applied to all the geostationary satellite data to take advantage of

the superior IR time sampling. At present, the microwave rain estimates are based on the

85.5 GHz measurements of the SSM/I. The algorithm can, however, accommodate any rain

estimates derived from microwave techniques if they are deemed to be superior.

2.3 GOES Precipitation Index (GPI) Technique (Arkin)

The GPI technique (described in detail in Arkin and Meisner, 1987) was developed over the

Global Atmospheric Research Program Atlantic Tropical Experiment (GATE) domain in

the Atlantic Inter-tropical Convergence Zone (ITCZ) region west of the African continent.

From linear regression analyses between cloud-top temperature as inferred from satellite IR

data, and radar derived rainfall, a linear relationship between the fraction of high cloud as

inferred from IR histogram and rain rate is established. The method is a simple cloud-top

temperature thresholding algorithm that assumes that satellite pixels with IR equivalent

blackbody temperatures at or below 235K precipitate at a constant rainfall rate of 3 mm/hr,

and pixels warmer than 235K do not precipitate. Although the GPI was developed exclusively over the GATE region, the technique has been applied to satellite data over the entire

global tropics (40°N-40°S).

2.4 Probability Distribution Functions Algorithm, Chang, Chiu and Wilheit (Chang)

This algorithm is used to derive the oceanic monthly SSM/I rainfall estimates for the GPCP.

It is based on a plane parallel radiative transfer model for the 19 and 22 GHz radiance

(Wilheit et al., 1991). A Marshall-Palmer rain drop distribution was assumed, and water

vapor and non-raining cloud are also included in the model. However, frozen hydrometeor

and surface wind speed are excluded. An analytic function of the rain rate and brightness

temperature (R-Tb relationship) is obtained based on radiative transfer calculations.

Monthly histograms of brightness temperatures and linear combinations of brightness temperatures are collected over 5 ° x 5° grid boxes. The probability distribution of the, rainfall
is assumedto follow a mixedlog-normaldistribution. Usingthe R - Tb relationship, the

mixed log-normal rainfall distribution is transformed into an expected brightness temperature histogram. The parameters of the log-normal distribution are obtained by adjusting

the fitting parameters of the expected histogram to the observed histogram. The freezing

height, which is needed for converting the brightness temperature to rain rates, is estimated

from the Tb (19V) and Tb (22V) histograms by an objective technique using the R - Tb

relationships.

No empirical calibration has been applied to this data set. The principal parameter that

needs to be studied is a beam-filling correction factor. The beam filling correction factor

depends on the R - Tb relationship and the spatial variance of the rain fields (Chiu et al.,

1990). At present, an empirically derived correction factor of 1.5, estimated from observed

radar rain rates, is applied to the entire data set.

2.5 Linear Regression Algorithm (Kummerow)

This algorithm makes use of an inversion technique based on theoretically calculated relationship between rainfall rates and SSM/I brightness temperatures. The algorithm uses

18 convective and 9 stratiform distinct rain structures. These structures, which are based

primarily on dynamic cloud model output, consist of five vertical layers. Hydrometeor

distributions in each of the layers are specified based on the surface rainfall rate. Potential errors introduced into the theoretical calculation by the unknown vertical distribution

of hydrometeors are minimized by explicitly accounting for the diverse hydrometeor profiles. Detail descriptions of this algorithm can be found in Kummerow et al. (1989) and

Kummerow and Giglio (1994).

One unique feature of the cloud structures is the fact tfiat hydrometeor contents are not

assumed uniform over a given cloud structure. This is necessary to avoid the classical beamfilling problem. To account for the variability within each retrieved pixel (50 km), the 85.5

GHz variability is used as a proxy indicator for the true variability. The proxy indicator

is defined as the standard deviation of the 85.5 GHz measurements divided by the mean

rainfall within a given pixel. Rainfall is then assumed to be log-normally distributed with

a standard deviation that is consistent with the 85.5 GHz measurements.

Due to the strong dependence of the upwelling radiance on the vertical distribution of

hydrometeors, the retrieval algorithm selects the optimal structure from among the 27

cloud structures. This is accomplished by comparing the observed and calculated brightness

temperatures at all frequencies and polarizations. The vertical structure and rainfall rate

which produces the smallest brightness temperature deviations in the root mean square

(rms) sense is selected as the optimal solution for any given pixel.

5
2.6 Rain Area Dependent Algorithm (Prabhakara)

SSM/I data were used to develop a model to discriminate heavy convective and light stratiform rain in mesoscale convective systems. Weather radar data from Japan and Florida

were used to calibrate this model. This discrimination is done on the basis of the packing

of rain cells in the field-of-view of the radiometer. From the SSM/I data, it is inferred

that strong convective rain cells normally associated with downdrafts and/or entrainment

produce relatively large "holes" or clearances between cells. This leads to a loose packing

of rain cells of a few kilometers in size in a large radiometer footprint. Clouds with diffused

cell structure appear to produce uniform light rain. This model is used to determine the

effective rain area over oceans. The effective rain areas are then related to the area averaged rain intensity empirically. Monthly rainfall maps using the Scanning Multi-channel

Microwave Radiometer (SMMR) and SSM/I data have been produced at a resolution of 3°

latitude by 5 ° longitude.

2.7 Oceanic Rainfall from MSU (Spencer)

The Spencer algorithm foroceanicrainfallisbased on observationsofthe Microwave Sounding Unit (MSU) onboard the TIROS-N seriesofNOAA polarorbitingsatellites.The MSU

measures the microwave radianceat4 frequenciesinthe50-70GHz oxygen absorptionband.

Although the instrument was designed primarilyto measure atmospherictemperature profiles,rain and cloud signalswithin thesefrequencieswere exploited.The 50.3 GHz channel

has the leastatmospheric absorptionand approaches a window channel measurement such

as the 37 GHz channel of the SSM/I. The rain rate isassumed to be proportionalto the

brightnesstemperature warming above a threshold.The algorithmiscalibratedby comparing 7 to 10 years of monthly rainfallrecordsat 122 islandand coastallocationsat both

high and low latitudesand in both hemispheres.Detaileddescriptionsofthisalgorithmare

given by Spencer (1993).

2.8 Global Precipitation from TOVS (Susskind)

This technique is primarily based on a relationship between cloud volume and rain rate

(Susskind et al., 1984). Products from the TOVS sounding retrievals are used. The retrieval

provides global coverage of geophysical parameters at 60 km spatial resolution twice per

day. The retrieved data is potentially useful for initialization of general circulation models as

well as climate studies. Currently the algorithm estimates precipitation based on the cloud

top pressure, effective cloud fraction, and temperature-humidity profile with a nominal

60 km resolution. Empirically obtained proportionality constants have been developed for

different seasons and different surface types (land or water) (Susskind and Pfaendtner, 1989)

by comparisons with rain gage data for 1979. Production of a 16 year TOVS global data
set hasbegunaspart of the Pathfinderactivity. Differentempiricalcoefficientsmay have

to begeneratedto relateprecipitationto soundingdatafromdifferentsatellites.

2.9 Multi-spectral Rainfall Algorithm (Wu)

Wu's (1991)rainfallalgorithmis basedoncloudtop altitude,emissivity,cloudamount,and

diurnal variationof surfacetemperaturederivedfrom TOVSsoundingdata. It useslongwavecloudradiativeforcing (LCRF) togetherwith the differencebetweenday-time and

night-timeclearsky outgoinglong-waveradiation(DCLR). A linearrelationshipbetween

rainfall (rain gagedata overland) and LCRF and DCLR is established.The correlation

coefficientbetweenthe rainfallestimatesandgagedatafor 4°latitudeby 5° longitudeboxes

is 0.8. Global ten-day and monthly mean rainfall maps have been produced for 1979 and

1980. Multi-year (1979-1995) rainfall estimates will be produced in the coming years using

results generated from the TOVS pathfinder activities.

As part of the GPCP, the Global Precipitation Climatology Center (GPCC) is responsible

for assembling rain gauge data over land (Rudolf et al., 1994). The GPCC data were

included for comparison over land.

3 DATA PRE-PROCESSING

Table 2 shows the data available from each algorithm. There are substantial missing data

in the SSM/I radiance for December 1987, hence that month is missing for algorithms using

SSM/I as input. Due to the evolutionary nature of some of these algorithms, data sets are

often updated after they are first acquired for our analysis. For example, a revised version

of Spencers data set came into existence in the middle of our analysis. The newer version

has been used in comparison with GCM results (Lau et al., 1996) and was used in our

comparison study. Since this change, the data sets for our comparison study have been

frozen.

These rain rate data sets have different spatial and temporal resolutions. Some of the algorithms have produced pentad (five day) data and monthly means were derived by averaging

the pentad data. There are six pentads for all the months except August when the monthly

mean is the average of seven pentads. Other algorithms have used calendar months. For

comparison purpose, the data are resampled to the lowest spatial resolution of the data

sets (5 ° by 5 °) without area weighting. For example, Prabhakara's data has a resolution

of 3 ° latitude by 5 ° longitude, hence latitudinal weights of [3,2], [1,3,1], or [2,3] were used

where appropriate. All units are converted to mm/day by dividing the monthly mean by

the appropriate number of days in the month. This reduces the mismatch for data sets

using pentad and calendar month.

7
While mostof the algorithmsareapplicableoverboth land and ocean,someareoceanspecificalgorithms. To comparethe performanceoverland and ocean,a land-seamask,

with 5° by 5° resolution,wascreated.Statisticsarecomputedfor oceanicandland areas,

respectively.Theoceanis further dividedinto threeoceansectors:Atlantic, Pacific,and

IndianOcean.Landareasaredividedintofivelandmasks:NorthAmerica,SouthAmerica,

Eurasia,Africa, andAustralia. Figure1showsthe land-seamaskusedin this study.

Wefirst pre-screenedthe databy computingthe histogramsof the rain rates.Datapoints

of unrealisticallyhigh valuesoccurin mostof the SSM/I algorithms. Inspectionshowed

that thesepixelsmaybecontaminatedbylandor islands.Hence,all rain rateswith values

greaterthan 30mm/dayarescreenedout andassignedamissingvalue.The maximumand

minimumrain rate,therainratehistograms,andthenumberofdatapointsscreenedout are

listedin AppendixA. In general,oneto twodatapointsarescreenedout by this procedure

eachmonthfor mostSSM/I-basedalgorithms.Thenumberof datapointsscreenedout is

muchlessfor otheralgorithms.In examiningthemeanfields,a largejump acrossthe 50°S

latitude wasnoted.This maybedueto the50° boundary for most algorithms.

The number of data points screened out from Kummerow's algorithm is generally higher

than average. We also noted that unrealistically high rain rates occur over the Himalayas.

Only oceanic rain rates from Kummerow's algorithm were included in our comparison.

4 COMPARISON RESULTS

4.1 Annual mean (August 1987 - July 1988)

The annual mean (August 1987 - July 1988) rain rate distributions for each algorithms were

computed. The upper panel of Figure 2 shows the annual mean rain rate averaged over all

algorithms. The annual mean map shows all the major features of the global precipitation

field: Inter-tropical Convergence Zone (ITCZ), South Pacific Convergence Zone (SPCZ),

South Atlantic Convergence Zone (SACZ), Monsoon regions, mid-latitude storm tracks,

and major rain areas in the Amazon and equatorial Africa. The oceanic dry zones in the

Atlantic and Pacific are also evident. There is a rain band in the southern mid-latitudes

(30°-60°S), and a sudden decrease at about 50°S which is due to the difference in the number

of algorithms across this boundary.

The standard deviation (SD) averaged over the algorithms for the annual mean is depicted

in the middle panel of Figure 2. Areas of high SD tend to coincide with high rain rate

areas. There is a region of high SD in the Himalayas region. The high SD in this region

is attributed to high rain rate estimates from Arkins algorithm during the northern winter

months, as demonstrated later in Section 4.4.

The ratio of SD to the mean is shown in the lower panel of Figure 2. Over the oceans, this
ratio is lessthan 0.5,exceptin theoceanicdry zones.Thereis a jump in the ratio across

the 50°Sboundary,whichis probablydueto the suddendecreasein the meanacrossthe

boundary(seeupperpanel).

Figure 3 showsthe algorithmwith the maximum(upperpanel)andthe minimum(lower

panel)rain rates.Algorithmswith the maximumrainfalltendto orientin thezonaldirection. Arkins algorithmdisplaythe largestrain rate in mostland areasand in the Indian

Ocean,whilePrabhakara'srain rateis the highestin mostof the oceanicregions.Wu's

rain rate is the highestoverthe oceanicdry regions.The lowerpanelof Figure 3 shows

the algorithmsof minimumrain rate. Kummerow'sis the lowestin most of the tropical

oceanarea. Prabhakara'salsorevealsthe minimumin the 40°-50°Slatitude band. Polewardof 50°, whereonly algorithmsofAdler,Kummerow,Susskind,andWu havecoverage,

Adler andWu'salgorithmdisplaythe minimumin thesouthernand northernhemisphere,

respectively.

4.2 Monthly Comparison

Figures4.1-4.17showthe mean(averagedoverall algorithms),SD,andthe ratio of SDto

the meanforAugust1987to December1988.

InAugust1987(Figure4.1),all algorithmsshowthemajorpatternsof precipitation:ITCZ,

SPCZ,andtheoceanicdry areasin thewesternnorthAtlanticandsouthPacific.Overland,

themaximumovertheAmazon,equatorialAfrica,andovertheIndiancontinentareevident.

Themaximumandminimumintensitiesat the locationof the maximumandminimumare

quite varied,however.The dry oceanicregionsshowthe largestratios. This is probably

due to the small rain ratesovertheseregionsand the deficiencyin somealgorithms in

estimatingthe low rain rates.Again,asin the annualmeancase,the regionsof high SD

correspondgenerallyto regionsof highmeanrain rate. The ratio in the rangeof 0.5. in

mostareas.

Theupperpanelof Figure5.1showsthe algorithmwhichproducesthe maximumrain rate

at eachgrid point for August1987.Arkins algorithmdisplaysthe largestrain rate over

the Indiancontinentand northernIndianOcean.This is consistentwith earlierresultsof

Chiu et al. (1993) who attributed the high rain rates to the presence of non-precipitating

high cirrus in these areas. Arkin's rain rate is also the maximum in Africa between .the

latitudes of 10°S to 20°N, and in most of the latitude band between 35°S to 40°S. In the

oceanic dry regions, such as the eastern north and south Pacific, and western south Atlantic,

Wu's algorithm shows the maximum. The high variability may be due to the use of cloud

radiative forcing in Wu's algorithm and the low cutoff threshold imposed in some microwave

algorithms. Most of the algorithms are confined to latitudes of 50 ° -60 ° latitude. Poleward

of 60 ° latitude, algorithms of Adler, Susskind, and Huffman show the maximum rain rate.

The lower panel of Figure 5.1 shows the algorithm that produces the minimum rain rate for
August 1987.Adler andKummerowsalgorithmsarethe minimumin mostof the regions

between60°N and 60°S.At the 45°-50°Slatitude band,Prabhakara'sis the minimum.

Polewardof 60° latitude, Wu's algorithm is the minimum.

In September 1987 (Figure 4.2), streaks of large rainfall areas, oriented in a north-south

direction, are seen in the north Pacific. These large rainfall areas are associated with

typhoon tracks. The corresponding SD is also high. Figure 6 shows the rainfall distribution

for all nine algorithms. SSM/I-based algorithms generally have the largest rain rates in

the typhoon track areas, followed by the algorithms of Arkin and Spencer. The algorithms

of Susskind and Wu also show streaks of high rain rate areas associated with the typhoon

tracks, but the rainfall intensity are lower than other algorithms. The lower panel of Figure

6 shows the ratio of theSD to the mean rain rate. Large ratios appear south of the equatorial

rain band and in the oceanic dry zones.

Over the Himalayan region the ratio is high in general. The ratio reaches a value above 2

in October 1987 (Figure 4.3) and remains high till June 1988.

We examined the similarity between the different algorithms. Table 3 shows the pattern

correlation coefficients (PCs) between the different algorithms for August 1987. All coefficients greater than 0.85 were highlighted. The PCs range from 0.6 to 0.95. Figure 7 shows

the histogram of the PCs for August 1987. The histograms suggests a bi-modal distribution, with peaks around 0.75 and 0.90. If we consider a PC value of 0.85 as a threshold

for similarity, i.e., algorithms with PC higher than 0.85 are similar, then the algorithms

tend to cluster. For example, there is similarity between the algorithms of Huffman, Adler,

Chang, and Kummerow, all of which use SSM/I data as input. The highest correlation of

0.94 is found between Susskind and Wu, which are based on TOVS retrieved parameters.

There is also a high PC between Arkin's and Huffman's algorithm, which may be due to the

incorporation of GPI data in Huffman's algorithm. Spencer's algorithm, which is the only

one using MSU data, has no PC values greater than 0.85 with any of the other algorithms.

This pattern suggests that algorithms that use the same sensor data (SSM/I or MSU) or

retrieved parameters (TOVS) tend to be similar. The reason for this similarity may be

due to the error inherent in these algorithms. There are essentially two kinds of errors in

satellite retrieval algorithms: retrieval and sampling error(Wilheit, 1988). The above result

indicates that the rainfall pattern derived from these retrieval algorithms are extremely

dependent on the sampling of the sensors.

To ascertain that our results are not fortuitous, we repeated the computation for May 1988.

Table 4 shows the PCs between the algorithms for May 1988. The histogram of correlation

coefficients are also shown in figure 7. If we again impose a PC threshold of 0.85 as a criteria

for similarity, similar conclusion to the August 1987 analysis can be reached.

There is a general degradation of the pattern correlation between Huffmans algorithm and

the other algorithms in 1988. Figure 8 shows the rain fields for all nine algorithms for April

1988. Huffmans algorithm shows a very strong precipitating region in the equatorial Indian

10
oceanwhichis not presentin otheralgorithmsexceptthat of Arkin.

4.3 Zonal Means

Zonalmeanrain ratefor all algorithmswerecomputedfor theannualmean,August1987,

and May 1988.Theresultsarepresentedin Figures9-11.Figure9 showsthe annualzonal

meansoverocean(upperpanel),overland (middlepanel),and overboth land and ocean

(lowerpanel).Thevariabilityoveroceansiscomparableto that overland. Overtheocean,

Kummerow'srain rateis the minimumthroughoutmostof the tropicsandin the northern

extra-tropics.Prabhakara'sis the highestin thelatitudesbetween10°N- 25°N. Over land,

Arkin's rain rate is highest in most of the latitude bands, with large departure from the

mean particularly in the latitudes between 300-40 °. P_in rates over land compiled by the

GPCC were plotted on the same figure. Most of the algorithms, except Arkin's in latitudes

north of 20°N, are very close to the GPCC estimate.

Figure 10 shows the zonal average rain rate for August 1987. The range of oceanic zonal

mean is about 3 mm/day near the equator and about 1.5 mm/day in the subtropics around

20 ° latitudes. All oceanic algorithms show zonal maximum at latitude 5°N-10°N, except

that of Prabhakara, which shows a maximum further north in the latitude 10°N-15°N. At

the zonal mean rain rate minimum (20°S), the algorithms tend to cluster into two groups.

The first group, consisting of Chang, Prabhakara, Wu, and Spencer has a zonal average of

about 2 mm/day. The second group, consisting of Adler, Huffman, Kummerow, Arkin, and

Susskind has a zonal average of less than 1 mm/day.

The variability over land (middle panel) is higher than that over the oceans. At the rain

rate peak between 10°-15°N, the algorithms range from about 6 to 11 mm/day. Arkin's

rain rates is the highest at this latitude. All algorithms peak at 10°N-15°N, whereas the

GPCC data show a maximum at a latitude south of 10°N-15°N.

When averaged over ocean and land, the global zonal averages for all algorithms track each

other well (lower panel). At the global zonal peak (5°-10°N), the algorithms range from

about 7 to 10 mm/day. Arkin's rain rate is much higher than the rest at 35°-40°S.

Figure 11 shows the same statistics as those depicted in Figure 10 for May 1988. The

peak in the oceanic zonal mean (4-7 mm/hr) is much smaller than that in August 1987

(7-10 mm/hr). There is also more variability among algorithms, with a range (maximumminimum) of 3-4 mm/day for most of the latitude bands (upper panel). Over land (middle

panel), the latitude of maximum rain shifted southward, and peaks between 50°S - 50°N.

The high rain rate of Arkin's algorithm at the 350-40 ° latitudes is also quite discernible.

Figure 12 shows the time series (August 1987- December 1988) of the global average rain

rate over ocean (upper panel), over land (middle panel), and over both land and ocean

(lower panel). Zonal averages derived from the GPCC are also included. The algorithm

11
variability overthe oceansis of the order of 1.5 mm/day. Overland, the variability is

higher,with Arkins rain ratesdistinctly higherthantheaverage,especiallyduringJanuary

- April 1988.If Arkin's dataisexcludedfrom thecomparison,therangeofgloballyaveraged

rainfall overland is comparableto that overtheocean.The globaloceanaverageis about

3 mm/day,whereasthat overlandis about 2- 2.5mm/day. Thecombinedaverage(land

andocean)arefairly stablefor mostalgorithms,with a medianof about 3 mm/day.

4.4 Regional Comparison

To investigate regional discrepancies, we partitioned the globe into three ocean sectors:

Pacific, Atlantic, and Indian Ocean, and five land masses: North and South America,

Eurasia, Australia, and Africa. Monthly zonal means for these ocean and land sectors were

computed and their temporal variability examined.

Figures 13.1-13.17 shows the monthly zonal means for the Atlantic, Pacific, and Indian

Ocean sectors for the period August 1987 through December 1988. In August 1987, the

mean position of the ITCZ is located at 5°N-10°N. A secondary peak appears at 5°S-10°S.

The secondary zonal peak strengthens in time. By December 1987, the intensity of the

secondary peak is comparable to that of the ITCZ. The two zonal peaks then weakens.

By March 1988, these peaks are no longer the prominent features in the zonal mean. In

May 1988, the ITCZ re-establishes itself and continues to be the dominant zonal peak until

November 1988. By December 1988, the peak in the southern hemisphere (10°S-15°S)

re-emerges. All algorithms show this change.

The dominant feature in the Atlantic is the SACZ which shows a very pronounced seasonal

displacement. The zonal maximum is situated at 5°N-10°N from August 1987 to November

1987. Its southward migration took place in January 1988, and a band of maximum rainfall

is found in 5°S-5°N from February to April 1988. The return of the zonal maximum to its

northern position started in May 1988. By June 1988, the zonal maximum is again seen at

5°N-10°N. Most of the algorithms follow this trend.

The main feature in the Indian Ocean is the Monsoon. The range of rain rates is highest in

the equatorial and northern Indian Ocean (5°S-20°N). A band of maximum zonal rainfall is

situated in the north Indian Ocean from August 1987 - October 1987. In November 1988,

the maximum zonal rainfall is seem in the southern hemisphere. By December 1988, the

zonal peak in the south equatorial Indian Ocean peak is clearly established. The zonal peak

maintains its position till April 1988 when its northward excursion takes place. Arkin's rain

rate is the largest, followed by Wu's for all the summer months at the zonal peak. The only

exception is Huffman's rain rate in April 1988- it shows a peak at 10°S-15°S.

Figure 14.1-14.17 shows the zonal average for the land sectors. High variability are seen

over South America (especially in the Amazon basin region), at the low latitudes of Eurasia,

12
and in the tropicsin Africa. In SouthAmericathe variability of algorithmsseemsto show

seasonaldependency.For example,the rain rate rangeat 0°-5°Nis 3 mm/dayin August

1987,andreachesa rangeof 7mm/dayin February1988.Arkin's rain ratesismuchhigher

then the rest of the algorithmsat latitudesnorth of 25°N in Eurasiafrom October1987

to May 1988.This periodcorrespondsto the snowseasonin theselatitudes,and IR rain

estimatesmaybecontaminatedby the coldgroundsurface.

5 RESPONSE TO 1986-87 ENSO

Our study period saw the demise of the 1986-1987 (El Nifio Southern Oscillation) (ENSO)

episode and the return of the atmosphere to a relatively normal state. It therefore provides

an opportunity to compare the responses of the algorithms to the same climate (ENSO)

signal.

Figure 15 shows the rain rate distribution for August 1987 (upper panel), August 1988 (middle panel), and the normalized difference between August 1987 and August 1988 (difference

divided by the mean, lower panel). The normalized difference is an index that ranges from

-2 to +2. The extreme values occur when the rain rate in a pixel changes from zero in one

month to a non-zero rain rate in the other month. Visual inspection shows the high spatial

variability in the normalized index of Adler's algorithm which is contrasted with the low

spatial variability of the algorithm's of Wu and Susskind.

Figure 16 shows the histograms of the normalized difference for all seven algorithms. Most

of the histograms show bell-shape distributions. The histograms of Adler and Spencer's

algorithm show secondary peaks at a normalized difference values of 2, whereas the GPCC

histogram shows a secondary peak at a value of-2.

To quantify the difference, we define a paired-t statistics between these two months. Let

X(x) and Y(x) denote the rain field in August 1987 and August 1988, respectively, where

x is the position vector. The difference field Z(x) is defined as

Z(x) - X(x) - Y(x) (3)

We want to test the null hypothesis H0 : E(Z) = O, where E(Z) is the expected value of Z;

i.e., there is no difference between the rain field in August 1987 and August 1988. Taking

the expected value of the above equation, we get

E(Z) = E(X)- E(Y) (4)

13
Let

to- n_l/_S D (5)

where [Z] and SD are the sample mean and standard deviation of Z (see Chang et al., 1995)

Since to has a t distribution with n - 1 degree of freedom, H0 is rejected if abs(t) > t_,,_-l.

H0 is accepted otherwise. For a = 0.025, ta,_-i = 1.96 if n > 30.

Table 5 shows the paired-t statistics and PC between August 1987 and August 1988 for all

the algorithms. Only the algorithms of Chang, Prabhakara, and Spencer show t-statistics

greater than 1.96, hence H0 can be rejected at the 95% confidence level, if all grid boxes

can be assumed independent. Since rain fields are spatially correlated, the number of

independent samples may effectively be less than n, even for the difference field. We will

address this issue in a separate study. PCs between these two months were aJso computed.

High paired t-statistics tend to be associated with low PC. Adler's algorithm is an exception.

His algorithm shows the least paired-t statistics and the lowest PC.

6 SUMMARY AND DISCUSSIONS

Annual mean and monthly rain rates from nine algorithms were compared. Results from the

annual (August 1987-July 1988) and two monthly (August, 1987 and May, 1988) comparison

showed that there are substantial differences in the amount of rainfall estimated by different

algorithms. The global mean rainfall estimated from the algorithms ranges from 2.7 to 3.5

mm/day. In comparison, the range of the GCM estimates (Figure 3 of Chahline, 1992) in

the tropical region vary from 2 ram/day to 9 mm/day, and a more recent comparison shows

a range from 2 to 3.7 ram/day (Lau et al., 1996). These global estimates can be compared

to past estimates. From water budget analysis, the global annual mean rainfall is estimated

to be in the range of 2.6 to 3.2 mm/day (950 mm to 1130 mm) (Chahine, 1993; Eagleson,

1960).

The variability among algorithms tends to be high in regions of high rain rates, such as in

the major rain bands (ITCZ, SPCZ, SACZ), monsoon regions, and in the Amazon. The

ratio of the variability to the mean is in general less than 0.5, except in the oceanic dry

regions. This high ratio is due to the difference in treatment of the low rain rates.

High PCs were found between algorithms that use the same satellite/sensor data as input. This points to sampling error as the dominant contributor to the errors in space/time

14
satelliterain algorithms. Future algorithms must address the sampling issue to improve

the accuracy of satellite rain algorithms.The algorithms of Adler, Chang, Kummerow, and

Prabhakara all used SSM/I data. Similarities between these algorithms are minimal since

each algorithm is focused on different physics. Adler's algorithm is mainly based on the

scattering signatures from ice particles. Chang's algorithm is based on the relationship

between hydrometeors and microwave emission. The strength of Kummerow's algorithm is

the capability to retrieve vertical profiles of precipitation and hydrometoers. Prabhakara's

algorithm emphasizes the characterization of convective and stratiform rainfalls. These efforts are complementary. By combining their strength, an improved rainfall product can

be provided to the community. For example, by combining Adlers scattering and Chang's

emission algorithms, a global data set over land and oceans can be achieved. A beam-filling

correction factor is required for Chang's algorithm which depends on the rain type and structure. Prabhakara's scheme for discriminating heavy convective and light stratiform rain can

potentially contribute to Chang's algorithm. The convective and stratiform classification

can also be valuable to Adler's scattering algorithm to account for the varying distribution

of warm rain and stratiform rain, when the scattering signal is small. Kummerow's theoretical model should provides a better understanding of the instantaneous rainfall information

in the footprint scale. This knowledge can be used by other investigators to further understanding the effect of non-raining cloud, thus improving the brightness temperature-rainfall

rate (Tb--R) relationship.

Algorithms of Susskind and Wu are based on the parameters retrieved from TOVS sounding

data. The major differences between these two algorithms are the variables used in their

regression analyses. These products are derived primarily from TOVS observations and are

complementary to those derived from SSM/I.

To address the sampling issue, techniques are being developed that combine microwave

and IR data. These techniques take advantage of the superior time sampling of IR sensors

onboard geostationary satellites and the physically based retrieval from microwave measurements. The unique feature of the combined algorithm is that the IR data can be calibrated

against any microwave estimates, if such estimates are deemed superior.

tL_infall retrieval algorithms using SSM/I data are typically based on the differences in

multi-spectral responses. General meteorological conditions associated with the satellite

overpasses are usually treated as additional degrees of freedom in the retrieval While satellite information has been used for data assimilation in GCMs, it might be advantageous to

incorporate GCM parameters and rain gage measurements to the satellite rainfall retrievals,

as described by Huffman et al. (1997).

Rainfall information from ground observations is needed for algorithm validation and comparison. At present, comparison data are available from only a few sources. GPCC and

the Surface Reference Data Center (SRDC) of the GPCP are preparing global and regional

(test-sites) rain gage data sets. Over the oceans, a 20 year monthly rainfall data set covering the western Pacific region have been compiled by Morrisey (1991). During the recent

15
TOGA-COAREIntensiveObservingPeriod,shipboardradar and gage data have been collected. This radar rain rate data set has been used to validate satellite based rain algorithms

for AIP-3. Over land_ the network of weather radar can potentially provide continuous spatial coverage. Looking ahead, the TRMM project is acquiring data from various ground

radar sites and field campaigns are planned as part of the validation program for TRMM

algorithms. These rainfall data sets will prove to be of great value to the satellite rainfall

community. However, a major effort is required to manage and distribute these data sets.

It is hoped that the Earth Observing System Data and Information System (EOSDIS) will

be able to fill this role.

Acknowledgments

We thank R. F. Adler, P. Arkin, G. Huffman, J. Janowiak, C. Kummerow, A. Negri, C.

Prabhakara, R. Spencer, J. Susskind, and M. L. Wu for supplying us with their data and

comments for this study, and K. M. Lau for his continued interest. J. Meng of SAIC/Generai

Sciences Corporation provided programming and graphics support. This work is supported

by NASA through an EOS Interdisciplinary Project Climate and Hydrospheric Processes.

16
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o

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0,1

X

o

¢'q

"0

..¢: ..c:

0 0

_'_

°_

<

o

+ o

0

X _

_

• 0

tn

©

bJO

tO

°_

r_

_ -_ '--_

+_+ -_'-_

00 0 0

g,

_ x

x _

:o

°_

°_

0

_ X

_ 0

r,_

0 "_--

I:=I 0

0 tO

:>_ 0 0 0 0 :>_

0 t= '_ _ _ _ 0

_0

2O
Table 2: Available Data from the Nine Algorithms for this Study

Algorithm

Adler x x x x

Huffman x x x x

Chang x x x x

Prabhakara x x x x

Kummerow x x x x

Spencer

Arkin

Suskind

Wu

X X X X X X X X X X X X

X X X X X

X X X X X X X X X X X X

X X X X X X X X X X X X

X X X X X X

X X X X X X X X X X X X X X X X X

X X X X X X X X X X X X X X X X X

X X X X X X X X X X X X X X X X X

X X X X X X X X X X X X X X X X X

Table 3: Pattern Correlation Coefficients for August 1987 Between Different A]._orithms

Huffman Prabhakara Chang Kummerow Spencer I Arkin I Susskind ] Wu

Adler 0.9 0.66 0.88 0.92 0.74

Huffman 0.65 0.83 0.83 0.71

Prabhakara 0.70 0.68 0.76

Chang

Kummerow

Spencer

Arkin

Susskind

0.90 0.75

0.76

0.82 0.83 0.79

0.88 0.82 0.79

0.60 0.68 0.70

0.78 0.79 0.78

0.79 0.79 0.77

0.72 0.81 0.79

0.91 0.87

0.94

Table 4: Same as Table 2 except for May 1988

Huffman Prabhakara Chang Kummerow Spencer I Arkin I Susskind I Wu

Adler 0.45 0.54 0.82 0.90

Huffman 0.23 0.38 0.42

Prabhakara 0.65 0.53

Chang 0.85

0.63 0.77 0.77 0.70

0.33 0.47 0.44 0.42

0.63 0.58 0.56 0.61

0.69 0.75 0.73 0.69

0.74 0.81 0.78 0.71

0.76 0.79 0.75

0.93 0.89

0.93

Kummerow

Spencer

Arkin

Susskind

21
Table5: Paired-tStatisticsandPatternCorrelationCoefficientsBetweenAugust1987and

1988

Algorithm I Adler

Aug 87mean 2.76

Aug 87S.D. 3.40

Aug 88mean 2.71

Aug 88S.D. 3.25

paired-t 0.63

Corr. coeff. 0.55

Chang

3.56 3.18

3.59 3.09

3.29 2.94

3.41 2.84

2.77 2.73

0.60 0.58

Spencer[Arkin Susskind[

3.10 3.48 2.68

2.69 4.38 2.46

2.93 3.35 2.60

2.82 4.26 2.49

2.29 1.07 1.59

0.59 0.70 0.73

Prabhakara Wu

3.34

2.68

3.41

2.90

-1.13

0.75

22
¢:)

to

x

tO

O

O

to

I

"O

tO

o

0

Figure 1: Land and sea mask used in this study. The oceans are further divided into the

Atlantic, Pacific, and Indian ocean sectors, and the land into north and south America,

Eurasia, Africa, and Australia sectors.

23
60N

30N

EQ

30S

60S

0

60N

30N

EQ

30S.

60S.

6ON.

30NEQ30S

60S

RR (mm/day) of 9 Algo (8/87 to 7/88)

60E 120E 180 120W 60W

0 2 4 8 10 12

SD (mm/day) of 9 Algo (8/87 to 7/88)

0

60E 120E 180 120W 60W

_ I

0 1 2 3 4 5

SD / Meon of 9 Algo (8/87 to 7/88)

0 60E 120E 180 120W 60W 0

_,_

0 0.5 1 1.5 2

Figure 2: Mean (upper panel), standard deviation(SD, middle panel), and the ratio of the

SD to the mean (lower panel) for the annual mean (August 1987 - July 1988). The ensemble

average is taken over all nine algorithms.

24
Algorithms With Maximum Rain Rate -- 8/87 to 7/88

60N

30N

EQ

30S

60S :

60E 120E 180 120W 60W

Algorithms With Minimum Rain Rate -- 8/87 to 7/88

0

6ON"

30N

EQ

30S

60S

0 60E 120E 180 120W 60W

Adler Huff WCC Kum Prab Suss Wu Arkin Spen

1 2 3 4 5 6 7 8 9

Figure 3: Algorithm with maximum (upper panel) and minimum (lower panel) rain rates

for the annual mean.

25
Mean RR (ram/day) of 9 Algorithms - Aug 1987

6ON"

3ON"

EQ"

30S"

60S"

60N

30N

EQ

30S

60S

60N

30N

EQ

30S

60S'

60E 120E 180 120W 60W

0 2 4 8 10 12 16

SD (ram/day) of 9 Algorithms - Aug 1987

0 60E 120E 180 120W 60W

.... I I

0 1 2 3 4 5 6

SD / Mean of 9 Algorithms - Aug 1987

60E 120E 180 120W 60W

0 0.5 1 1.5 2

0

Figure 4.1: Same as Figure 2, except for August 1987.

0

26
60N

30N

EQ

30S60S.

0

60N

30N

EQ30S.

60S.

60N

30N

EQ

30S60SRR (ram/day) of 9 Algorithms - Sep 1987

60E 120E 180 120W 60W

0 2 4 8 10 12

SD (ram/day) of 9 Algorithms - Sep 1987

0 60E 120E 180 120W 60W 0

0 1 2 3 4 5

Mean RR (ram/day) of 9 Algorithms - Sep 1987

0 60E 120E 180 120W 60W 0

0 0.5 1 1.5 2

Figure 4.2: Same as Figure 2, except for September 1987.

27
60N

30N

EQ.

30S.

60S.

0

6ON'

3ON'

EQ'

30S'

60S'

RR (mm/doy) of 9 Algorithms - Oct 1987

60E 120E 180 120W 60W

0 2 4 8 10 12

SD (ram/day) of 9 Algorithms - Oct 1987

0 60E 120E 180 120W 60W 0

I

0 1 2 3 4 5

Mean RR (ram/day) of 9 Algorithms - Oct 1987

6ON.

3ON,

EQ,

30S'

60S,

0 60E 120E 180 120W 60W 0

0 0.5 1 1.5 2

Figure 4.3: S_me as Figure 2, except for October 1987.

28
60N

30N

EQ

30S

60S

0

60N

30N

EQ

30S.

60S _

60N30N

EQ

30S

60S

RR (mm/doy) of 9 Algorithms - Nov 1987

60E 120E 180 120W 60W

0 2 4 8 10 12

SD (mm/doy) of 9 Algorithms - Nov 1987

0 60E 120E 180 120W 60W 0

0 1 2 3 4 5

Meon RR (mm/doy) of 9 Algorithms - Nov 1987

0 60E 120E 180 120W 60W 0

0 0.5 1 1.5 2

Figure 4.4: Same as Figure 2, except for November 1987.

29
60N

30N

EQ

30S

60S

0

60N

30N

EQ30S60SRR (mm/day) of 9 Algorithms - Dec 1987

60E 120E 180 120W 60W

0 2 4 8 10 12

SD (ram/day) of 9 Algorithms - Dec 1987

0 60E 120E 180 120W 60W 0

0 1 2 3 4 5

Mean RR (mm/doy) of 9 Algorithms - Dec 1987

60N -

30N -

EQ30S -

60S,

0 60E 120E 180 120W 60W 0

0 0.5 1 1.5 2

Figure 4.5: Same as Figure 2, except for December 1987.

30
60N"

30N

EQ

30S

60S

0

60N

30N

EQ30S60S -

6ON'

3ON'

EQ'

30S"

60S"

RR (mm/doy) of 9 Algorithms - Jon 1988

60E 120E 180 120W 60W

0 2 4 8 10 12

SD (mm/doy) of 9 Algorithms - Jon 1988

0

0 60E 120E 180 120W 60W 0

_%_ I

0 1 2 3 4 5

Meon RR (mm/doy) of 9 Algorithms - Jon 1988

0 60E 120E 180 120W 60W 0

0 0.5 1 1.5 2

Figure 4.6: Same as Figure 2, except for January 1988.

31
60N"

30N"

EQ"

30S"

60S"

60N"

30N"

EQ"

30S"

60S"

RR (ram/day) of 9 Algorithms - Feb 1988

60E 120E 180 120W 60W

i i

0 2 4 8 10 12

SD (ram/day) of 9 Algorithms - Feb 1988

60E 120E 180 120W 60W

0 1 2 3 4 5

Mean RR (mm/day) of 9 Algorithms - Feb 1988

6ON"

3ON"

EQ"

30S

60S

0 60E 120E 180 120W

0 0.5 1 1.5

Figure 4.7: Same as Figure 2, except for February 1988.

60W 0

2

32
60N "

3ON"

EQ30S.

60S

60N

30N

EQ

30S

60S

RR (mm/day) of 9 Algorithms - March 1988

0 60E 120E 180 120W 60W

0 2 4 8 10 12

SD (mm/doy) of 9 Algorithms - March 1988

0 60E 120E 180 120W 60W 0

L I' I

0 1 2 3 4 5

Mean RR (mm/doy) of 9 Algorithms - March 1988

60N "

3ON

EQ"

30S"

60S

0 60E 120E

0 0.5 1 1.5

Figure 4.8: Same as Figure 2, except for March 1988.

180 120W 60W 0

2

33
6ON'

30N

EQ

30S'

60S'

6ON'

3ON'

EQ'

30S'

605'

RR (ram/day) of 9 Algorithms - Apr 1988

60E 120E 180 120W 60W

,,

0 2 4 8 10 12

SD (mm/doy) of 9 Algorithms - Apr 1988

60E 120E 180 120W 60W

0 1 2 3 4 5

Mean RR (ram/day) of 9 Algorithms - Apr 1988

60N30N o

EQ30S60S60E 120E 180 120W

I

0 0.5 1 1.5

Figure 4.9: Same as Figure 2, except for April 1988.

60W 0

2

34
Mean RR(mm/day) of 9 Algorithms- May 1988

60N

30N

EQ

30S

60S,

0 60E 120E 180 120W 60W 0

_

0 2 4 8 10 12 16

SD (ram/day) of 9 Algorithms - May 1988

60N

3ON

EQ30S.

60S.

6ON"

30NEQ30S'

60S

0 60E 120E 180 120W 60W

0 I 2 3 4 5 6

SD / Mean of 9 Algorithms - May 1988

0 60E 120E 180 120W 60W 0

0 0.5 1 1.5 2

Figure 4.10: Same as Figure 2, except for May 1988.

35
6ON'

3ON"

EQ"

30S"

60S"

RR (mm/day) of 9 Algorithms - Jun 1988

60N

30N

EQ

30S

60S

0 60E 120E 180 120W 60W 0

0 2 4 8 10 12

SD (ram/day) of 9 Algorithms - Jun 1988

0 60E 120E 180 120W 60W 0

0 1 2 3 4. 5

Mean RR (mm/doy) of 9 Algorithms - Jun 1988

6ON.

30NEQ30S60S0 60E 120E 180 120W 60W 0

0 0.5 1 1.5 2

Figure 4.11: Same as Figure 2, except for June 1988.

36
6ON"

3ON'

EQ'

30S'

60S'

0

60N

30N

EQ

30S

60S

RR (mm/doy) of 9 Algorithms - July 1988

60N

30N

EQ

30S

60S

60E 120E 180 120W 60W

_,

0 2 4 8 10 12

SD (ram/day) of 9 Algorithms - July 1988

0

0 60E 120E 180 120W 60W 0

0 1 2 3 4 5

Mean RR (mm/doy) of 9 Algorithms - July 1988

60E 120E 180 120W

0 0.5 1 1.5

0

Figure 4.12: Same as Figure 2, except for July 1988.

60W 0

2

37
AlgorithmsWith MaximumRain Rate -- Aug 1987

60N

30N

EQ

30S

60S

0 60E 120E 180 120W 60W

Algorithms With Minimum Rain Rote -- Aug 1987

0

60N

30N

EQ

30S

60S

0 60E 120E 180 120W 60W

Adler Huff WCC Kum Prab Suss Wu Arkin Spen

1 2 3 4 5 6 7 8 9

Figure 5.1: Same as Figure 3, except for August 1987.

38
Algorithms With Maximum Rain Rate -- May 1988

60N

30N

EQ

30S'

60S

0 60E 120E 180 120W 60W

Algorithms With Minimum Rain Rate -- May 1988

60N

30N

EQ

30S

60S

0 60E 120E 180 120W 60W

Adler Huff WCC Kum Prab Suss Wu Arkin Spen

1 2 3 4- 5 6 7 8 9

0

Figure 5.2: Same as Figure 3, except for May 1988.

39
60N

30N

EQ

30S.

60S ..........

0

60N30NEQ

30S

60S

Mean RR (ram/day) of Adler - Sep 1987

60N

30N

EQ

30S.

60S,

6()E 12'0E 180 12'0W 6()W

_

0 2 4 8 10 12

Mean RR (mm/doy) of Huffmon - Sep 1987

60E 120E 180 120W 60W

0 2 4 8 10 12

Mean RR (ram/day) of WCC - Sep 1987

60E 12'0E 180 12bW 60W

0 2 4 8 10 12

Figure 6: Monthly rainfall for September 1987 for all algorithms.

0

0

4O
Mean RR (mm/day) of Kummerow - Sep 1987

60N

30N

EQ

30S

60S .........................................

0 60E 12'0E 180 120W 60W 0

0 2 4 8 10 12

Mean RR (mm/doy) of Prabhokora - Sep 1987

6ON"

3ON"

EQ"

30S"

60S .....................................

0 60E 12'0E 180 120W 60W

0 2 4 8 10 12

Mean RR (mm/day) of Susskind - Sep 1987

0

60N

30N

EQ

30S

60S

0 60E 120E 180 120W 60W 0

0 2 4 8 10 12

Figure 6-continued 1: Monthly rainfall for September 1988 for all algorithms.

41
6ON'

3ON'

EQ'

30S'

60S'

Mean RR (mm/day) of Wu - Sep 1987

60N

30N

EQ

30S

0 60E 120E 180 120W 60W 0

0 2 4 8 10 12

Mean RR (ram/day) of GPI - Sep 1987

60S .................................... "

60N

30N

EQ

30S

60S

0 60E 120E 180 120W 60W 0

:::: _ I II I

0 2 4 8 10 12

Mean RR (ram/day) of MSU - Sep 1987

0 60E 120E 180 120W 6(3W 0

..... I

0 2 4 8 10 12

Figure 6-continued 2: Monthly rainfall for September 1987 for all algorithms.

42
O

Number of Coefficients

0

¢31

0

0

0

b

m,

Figure 7: Histogram of pattern correlation coefficients among algorithms for August 1987

and May 1988. The bin 0.9 includes PC coefficients in the interval (0.90, 0.95).

43
Meon RR (mm/doy) of Kummerow - Apr 1988

60N

30N

EQ

30S

60S .........................................

0 60E 120E 180 120W 60W 0

0 2 4 8 10 12

Meon RR (ram/day) of Prabhokoro - Apt 1988

60N

30N

EQ

30S

60S ..........................................

0 60E 120E 180 120W 60W

_ : _!!_r[l'llllllllllll IIII

o 2 4 8 lO 12

Meon RR (mm/doy) of Susskind - Apt 1988

BON

30N

EQ

30S

60S

0 60E 120E 180 120W 60W

_,_, II

0 2 4 8 10 12

Figure 8: Monthly rainfall for April 1988 for all algorithms.

0

44
6ON'

3ON'

EQ'

30S

60S

6ON'

3ON'

EQ

30S

60S

Mean RR (mm/day) of Adler - Apr 1988

60E 12'0E 180 120W 60W

0 2 4 8 10 12

Mean RR (ram/day) of Huffman - Apr 1988

0

0 60E 120E 180 120W 60W 0

I I

0 2 4 8 10 12

Meon RR (mm/doy) of WCC - Apr 1988

6ON"

3ON'

EQ

30S

60S ................................... "....

0 60E 12'0E 180 120W 60W 0

0 2 4 8 10 12

Figure 8 continued 1: Monthly rainfall for April 1988 for all algorithms.

45
6ON"

3ON"

EQ"

30S"

60S"

6ON"

30N"

EQ"

30S"

60S"

6ON"

3ON"

EQ"

30S"

60S"

Meon RR (mm/doy) of Wu - Apr 1988

60E 120E 180 120W 60W

0 2 4 8 10 12

Meon RR (mm/doy) of GPI - Apr 1988

0

6OF" 12'0E 180 120W 60W

_ ,

0 2 4 8 10 12

Meon RR (mm/doy) of MSU - Apr 1988

0

0 60E 120E 180 120W 60W 0

0 2 4 S 10 12

Figure 8 continued 2: Monthly rainfall for April 1988 for all algorithms.

46
15

10

h

5 m

o

-60

15

IO--

5--

o

-6o

15

1o _

5

Oceanic Annual Zonal Mean Rain Rate --

; l _ i i

Adler Susskind

............ Huffman Wu

_ _ _ WCC Arkin

/_, A A /_ Kummerow Spencer

[] [] [] [] Prabhakara

Aug 1987 - July 1988

._/'" [2

" ---:_ -.-_-_=A 2_

f ] ] i i

-40 -20 0 20 40

Latitude

Land Annual Zonal Mean Rain Rate -- Aug 1987 - July 1988

i i i i

Adler Susskind

............ Huff man ....... Ifu

_> _> _ _ GPCC Arkin

Spencer

/ "0" -'-- _'\"._ .'"

." /" I_-.--.'%. -. /

v _ "-. . v/5__- _'-_ --_'_9_.. _ x, ,'

<2

, l I I , I , , , l ,

-40 -20 0 20 40

Latitude

Global Annual Zonal Mean Rain Rate -- Aug 1987 - July 1988

i , _ i f

Adler Susskind

............ Huffman Wu

Arkin

Spencer

0

-60

Figure 9: Zonal mean rain rates over the oceans (upper panel), land (middle panel) and

over both land and ocean (lower panel) for the annual mean (August 1987-July 1988).

60

60

60

47
10--

L

5 _-

i

o

-60

1o

5

oL

-60

15  ̧

10--

5_

o

-80

Oceanic Zonal Mean Rain -- Aug 1987

Adler Susskind

............ Huffman ....... Wu

_ _ _ WCC ........ Arkin

/% A A /_ Kummerow Spencer

[] [] [] [] Prabhakara ,_

m

d g: :'.

I f , , I ,

-40 -20 0 20 40

Latitude

Land Zonal Mean Rain -- Aug 1987

' Adler ' ' " Susskind

............ Huff man ....... Wu

O O O GPCC ........ Axkin

J., Spencer

\

/'

60

,/ • ,,...

,

_-

. i

__--

.

.

,

. "_-.

8 "

.-"

.

i.

. i , , . i , , ,

___._-

I , , , I

-40 -20 0 20 40 60

Latitude

Global Zonal Mean Rain -- Aug 1987

' Adler ' ' ' Susskind

............ Huffmen ....... Wu

........ Arkin

Spencer

_--oC._.,_",_ , "_ .._..._. __t-z__.,,_._- "7 "" :-:-": ""_

-40 -20 0 20 40

Latitude

6O

Figure 10: Same as Figure 9, except for August 1987.

48
E

E

E

15

I0

-60

15

10--

5--

o

-60

15

lO--

5--

o

-60

Oceanic Zonal Mean Rain -- May 1988

' u _ r

Adler

u

Susskind

............ Huffman ....... Wu

_ _ _ _ WCC ........ Arkin

_ A A /k Kummerow Spencer

[]["7[][-I Prabhakara

[] ..... ._ ,_--_ --,_- _ -

h I , , , I , , , I , h , I , , , I i

-40 -20 0 20 40

Latitude

Land Zonal Mean Rain -- May 1988

i u n u F

Adler Susskind

............ Huffman ....... Wu

_ _ _ GPCC Arkin

Spencer

..':/..>.- -. ..\. /

\,, ..-"../_\ ,/"

_ ...-_/ "'.?%,, ,,.

"-_-----_'_0 .... -_:_'_-_. ..-'"./":>'/ -_\\ 0___._" _

.. _---._. . ....

, , I i , i i , , , i , , , I , , , i

-40 -20 0 20 40

Latitude

Global Zonal Mean Rain -- May 1988

, i _ u ,

Adler Susskind

............ Huffman Wu

Arkin

Spencer

i

._-.-':_-_ .-.../--/ _ \_. / ____

.........,_.._: --:_.:_ ,\,,'---:.._ .-_. _ _

-......--

-40 -20 0 20 40

Latitude

60

6O

60

Figure 11: Same as Figure 9, except for May 1988.

49
e$

_3

E

Oceanic Monthly Mean Rain Rate, (50N - 50S) -- Aug 1987 - Dec 1988

: ' ' _dler ' ' _ _ _ _CC .... _usskir_d

........... Huffman /_ /k A /% Kummerow ....... Wu

[] [] [] ['7 Prabhakara ....... Arkin

Spencer

A A A

1 1 I I I I I I I I I I I I I I

8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11

Month

4__

e$

-u

_3

Land Monthly Mean Rain Rate, (50N - 50S) -- Aug 1987 - Dec 1988

. i i i , i i

' ' 'Adler ' ' . "I ' "_ ' I", _usskil_d

Huff man I/ "-_" Wu

_ _ O GPCC _-" \ ....... Arkin

. -" Spencer

. /" \ o_-''°_

_3

2

1

8 9

_'- _ .... _" --_"'-<_- -.. c,//_/ x-_ "_"

.----.....

""- 0 _ <_ " _ -"..... _:- --"_ -

l I I I I I I I I I I I I

8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11

Month

Global Monthly Mean Rain Rate, (50N - 50S) -- Aug 1987 - Dec 1988

5 _- ' ' 'Adler .......... _ uss kind

: ........... Huffman ....... Wu

- Arkin

- Spencer

4

12

I I I I I I I I I f I I I I

10 11 12 1 2 3 4 5 6 7 8 9 10 11

Month

12

12

Figure 12: Time series of the global (50°N - 50°S) average for all algorithms over the oceans

(upper panel), land (middle panel), and over both ocean and land (lower panel) from August

1987 to December 1988.

5O
E

E

E

E

15

1o

5--

o

-60

15

10

5

0

-60

15

Pacific Ocean Zonal Mean Rain -- Aug 1987

Adler Susskind

I0 -

............ Huff man Wu

_ _ _ WCC _ ........ Arkin

/_ /_ /k A Kummerow Spencer

[] [] [] [] Prabhakara ,_, [--]

-40 -20 0 20 40

Latitude

Atlantic Ocean Zonal Mean Rain -- Aug 1987

i i i t

Adler Susskind

............ Huff man '........ Wu

_ _ _ WCC ,' '. ........ Arkin

A /_ /h A Kummerow ...'_, ',, Spencer

[] [] [] [] Prabhakara ._'.

-_,--_"-'_-e__"%, /:{ _,.... [] ..--_-"m-.._

-40 -20 0 20 40

Latitude

5

o

-6o

Indian Ocean Zonal Mean Rain -- Aug 1987

' Adler ' ' ' Susskind

............ Huff man ....... Wu

_ _t_ _ WCC ........ Arkin

/% /h /h /h Kummerow Spencer

[] [] [] [-7 Prabhakara ,"\ /

•_-I_-'4...:. ',

_':... _L,- .__ .,_

, ...... - _ -, ,,

-40 -20 0 20 40

Latitude

Figure 13.1: Zonal mean rain r_tes for the Pacific (upper panel), Atlantic (middle panel),

and Indian ocean sector (lower panel) for August 1987.

6o

60

60

51
i0

5 -

o

-60

' ' Adler

............ Huffmsn

_ _ )_ WCC

a A a a Kummerow

[] [] [] [] Prabhakara

Pacific Ocean Zonal Mean Rain -- Sep 1987

' ' Susskind

Wu

........ Arkin

:., Spencer

-40 -20 0 20 40

Latitude

15

10

5 -

o

-61

Atlantic Ocean Zonal Mean Rain -- Sep 1987

' Adler ' _ Susskind

............ Huffmsn _u

_ _ _ WCC ........ Arkin

A A A A Kummerow ,.... Spencer

[-] [] [] [] Prabhakara .' ,

"', :' "X", []

!f_'--"_'_"_'- _/_ __' ,,.//" __"- .-....... ._. • _'_

. , -,m , , ,_'-_-_-'-__<_, . , .-. , , , . , ,

-40 -20 0 20 40

Latitude

15

10

5 -

o

-60

Indian Ocean Zonal Mean Rain -- Sep 1987

' ' Adler ' ' ' Susskind

............ Huffman Wu

_ _ _ WCC ........ Arkin

A A A A Kummerow Spencer

[] [] [] [] Prabhakara

A

,° ,° ° .. *_." _.

>_._ ... ,._., -_.-_::_.,,

-40 -20 0 20 40

Latitude

Figure 13.2: Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle panel),

and Indian ocean sector (lower panel) for September 1987.

6o

6o

1

6O

52
E

15

I0

5 -

o

-60

15

._

I0--

5

0

-60

15

10 -

0

-60

Pacific OceanZonal Mean Rain -- Oct 1987

' 'Adler ' ' Susskind

............ Huff man ....... Wu

_ _I_ _ WCC Arkin

Z_ /_ A /_ Kummerow Spencer

[] [] [] [] Prabhakara ,_,

""'"" 13 "SI_/'-_'\_ "'" [] [] _v.

-40 -20 0 20 40

Latitude

Atlantic Ocean Zonal Mean Rain -- Oct 1987

' Adler _ _ Susskind

............ Huff man Wu

_ _ _#_ WCC ........ Arkin

/_ A /_ A Kummerow Spencer

[] [] [] [] Prabhakara

// ii, • t_

.. _ _._.-_.. _ ._. --.

, ---.-_-_. __2_ = ......

-40 -20 0 20 40

Latitude

Indian Ocean Zonal Mean Rain -- Oct 1987

' Adl-'r ' ' ' Susskind

............ Huff man Wu

_ _ _ WCC ........ Arkin

/_ /_ /X A Kummerow Spencer

[] [] [] [] Prabhakara

j' . _

-40 -20 0 20 40

Latitude

Figure 13.3: Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle panel),

and Indian ocean sector (lower panel) for October 1987.

6o

6o

6O

53
E

15

I0

5 _

0

-60

Pacific Ocean Zonal Mean Rain -- Nov 1987

Adler Susskind

............ Huff•an Wu

_/__I_ )I__ WCC _' Ark'in

A A A /_ Kummerow ,/ "',_ Spencer

[] [-] I-] [] Prabhekara • ., II

.:_\_ /'

". z':" ":._ •

- - " "\4_-.-_ _'

-40 -20 0 20 40

Latitude

10

5

--61

Atlantic Ocean Zonal Mean Rain -- Nov 1987

' Adler ' ' Susskind

............ Huff men Wu

_ _ _#_ WCC ........ Arkin

A A A A Kummerow Spencer

[] [] l'-I [] Prabhakara

.".,_,_ [] ._-. __ _

... ...)=,_;!,---- _,..;,_..-_.:....... .-:.;

-40 -20 0 20 40

Latitude

15

10

5

ot

-60

Indian Ocean Zonal Mean Rain -- Nov 1987

Adler Susskind

............ Huff man Wu

_)__I_)_ _ WCC ........ Arkin

A A A A Kummerow Spencer

[] [] [] [] Prabhakara

J _ _. _,_,ts /"

- ._.-.4_,'-A . -_ , "_

-40 -20 0 20 40

Latitude

Figure 13.4: Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle panel),

and Indian ocean sector (lower panel) for November 1987.

60

6O

6O

54
15

10

5--

0

Pacific Ocean Zonal Mean Rain -- Dec 1987

' Adler ' ' Susskind

............ Huff man ....... Wu

_ _ _ WCC ........ Arkin

A h A A Kummerow Spencer

[] [] [] [] Prabhakara

i i i i I , I ,

,14

E

-60

15

IO--

L

5

o

-60

15

lO

-40 -20 0 20 40

Latitude

Atlantic Ocean Zonal Mean Rain -- Dec 1987

' Adler ' ' Sus'skind

............ Huffman ....... Wu

_ _ _ WCC ........ Krkin

A m A A Kummerow Spencer

[] [] [] [] Prabhakara

/\ /L _ _ _

t .'/ f

4:/ "\\\,. - ......

_i_-_.--_ .____--_'_-¢t2__=_.:, .' ". _...___..

l " I I I I ,

-40 -20 0 20 40

Latitude

Indian Ocean Zonal Mean Rain -- Dec 1987

' Adler ' ' ' Susskind

............ Huff man ....... Wu

)_ )_ )_ )_ WCC Arkin

A m A A Kummerow Spencer

[] [] [] [] Prabhakara

0

-60

/:_,.. /.

,.,7- -__ /

_/_---,,.... ,.#';' -%,,, ,

A I J • • i . •

-40 -20 0 20 40

Latitude

Figure 13.5: Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle panel),

and Indian ocean sector (lower panel) for December 1987.

6o

6o

60

55
15

10

5 -

0

-60

15,

10

0

-60

15i

10--

Pacific Ocean Zonal Mean Rain -- Jan 1988

.... Adler Susski nd

............ Huffman ....... Wu

_ _ _ WCC Arkin

/_ A /% /% Kummerow Spencer

[] [] [] [] Prabhakara

Atlantic Ocean Zonal Mean Rain -- Jan 1988

' " Adler_ ' _ Sus ski nd '

............ Huff man ........ Wu

_ _i_ _ WCC ........ Arkin

/% /% /% /% Kummerow Spencer

[] [] r-] [] Prabhakara

/

, ,-,-, -=, ,

-40 -20 0 20 40

Latitude

I

0

-60

Indian Ocean Zonal Mean Rain -- Jan 1988

' Adler ' ' Sus;kind

............ Huff man Wu

_)_ _ _/_ _ WCC ........ Arkin

/% A /% /% Kummerow _ Spencer

[] [] [] i--] Prabhakara M'_

"\'., I

'\_.. I,,,I"

--- )K-. [] [] / "_,_ ,. .'

, , . , "..;.."_ _.,.-':¢_a...-*j ,_.../, ,

-40 -20 0 20 40

Latitude

Figure 13.6: Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle p_nel),

and Indian ocean sector (lower panel) for January 1988.

6O

I

6O

60

56
E

15

lo

5

o

-60

15

lO

5

o

-60

15

10

Pacific Ocean Zonal Mean Rain -- Feb 1988

i , ) ) i , i

Adler Susskind

............ Huff man ....... Wu

_ _ _ WCC ........ Arkin

/k A /N /N Kummerow Spencer

[] [] [] [] Prabhakara

•-\ _ []

o._ ',_,

l I i L-x_ i

-40 -20 0 20 40

Latitude

Atlantic Ocean Zonal Mean Rain -- Feb 1988

' Adler ) ' Susskind

Huffman ....... Wu

_ _ _ WCC Arkin

A A A A Kummerow Spencer

[] [] [] [] Prabhakara

[] []

,, -____-_

-40 -20 0 20 40

Latitude

0

-60

Indian Ocean Zonal Mean Rain -- Feb 1988

Adler Susskind

............ Huff man ....... Wu

A

_ _ _ WCC ........ g_kin

/% /X /k /_ Kummerow Spencer

[] [] [] [] Prabhakara ,"'.

. .-_ __(' •

I I , I , -- _ "

-40 -20 0 20 40

Latitude

Figure 13.7: Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle panel),

and Indian ocean sector (lower panel) for February 1988.

6o

6O

16O

57
15

I0--

-60

Pacific Ocean Zonal Mean Rain -- Mar 1988

' '"Adler ...... _ ' Sus _skind

............ Huff man Wu

_ _ _ WCC ........ Arkin

/X A A A Kummerow Spencer

[] [] [] [] Prabhakara

-40 -20 0 20 40

Latitude

15

10--

0

-60

Atlantic Ocean Zonal Mean Rain -- Mar 1988

' 'Adler ' ' Susskind

............ Huffman ....... Wu

_ _ _ WCC ....... Arkin

A A A /_ Kummerow Spencer

[] [] [] [] Prabhakara

,510I

5--

0

-60

Indian Ocean Zonal Mean Rain -- Mar 1988

' ' Adler ..... Su,iskind'

............ Huff man ....... Wu

_l_ _ _I_ WCC ........ Arkin

A A A A Kummerow Spencer

[-'] ['-] [] [] Prabhakar_ _. / "" A

_"_._ _---..,. _ _E b_..-'" ', _.X,\\:\ /,.,,

_.-_--'_.-AI_-,t_,,_-, . -_L,,o ...-/\<.

...... _-_,._.._._, ... _- :.. -. ,',' -_., /..,:f."_\

, , , i....;...., , , ........ " -,;_: __ _,-N , ,

-40 -20 0 20 40

Latitude

Figure 13.8: Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle panel),

and Indian ocean sector (lower panel) for March 1988.

60

6O

6O

58
E

E

"O

15

I0

0

-60

15

10

0

-60

15

lO

Pacific Ocean Zonal Mean Rain -- Apr 1988

l J t L f

Adler Susskind

............ Huff man ....... Wu

_ _ _ WCC Arkin

A A A /k Kummerow Spencer

[] [] [] [] Prabhakara

[]

, , , , , , , ...... "')__,.._..-.:-- , , ,

-40 -20 0 20 40

Latitude

Atlantic Ocean Zonal Mean Rain -- Apr 1988

' Adler ' ' Susskind

............ Huff man ....... Wu

_ _ _ WCC Arkin

_ /_ A Kummerow Spencer

[] [] [] [] Prabhakara

__ _ ==__ .... "_a_' :\'_.\ ".... ___ "

, , i , , , I : - • , I , ,

-40 -20 0 20 40

Latitude

o

-60

Indian Ocean Zonal Mean Rain -- Apr 1988

Adler Susskind

............ Huff man ....... Wu

_ _ _ WCC Arkin

A A A _ Kummerow Spencer

[] [] [] [] Prabhakar.a...

",/.^ ,, /

-40 -20 0 20 40

Latitude

Figure 13.9: Zonal mean rain rates for the Pacific (upper p_nel), Atlantic (middle panel),

and Indian ocean sector (lower panel) for April 1988.

60

60

1

6O

59
P_

15

I0

15

I0

5 --

o

-60

o

-60

Pacific Ocean Zonal Mean Rain -- May 1988

' Adler ' ' ' Susskind

............ Huff man Wu

_ _ _ WCC ........ Arkin

A /X A A Kummerow Spencer

[] [] [] [] Prabhakara

.', []

,,." ",, _ /

I I I I I ,_

-40 -20 0 20 40

Latitude

Atlantic Ocean Zonal Mean Rain -- May 1988

Adler Sus nd

............ Huff man Wu

_ _ _ WCC ........ Arkin

A A A A Kummerow Spencer

[] [] [] [] Prabhakara A

..-__[]

[] ....,¢

.. ./. \__-- .-_. . --_

, [] _'___.'_ .%. _" ..... :

-40 -20 0 20 40

Latitude

15

10

5 -

o

-60

Indian Ocean Zonal Mean Rain -- May 1988

, • , , , , , ,,,

Adler Susskind

............ Huff man Wu

_1_ _ _ WCC Arkin

A A A Kummerow ," _ -,, Spencer

[] [] [] [] Prabhakara / \

...-

" " -'" [] . _,S._-"',_x\

-40 -20 0 20 40

Latitude

Figure 13.10: Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle panel),

and Indian ocean sector (lower panel) for May 1988.

60

60

1

60

6O
15

10

15

10

5--

oi

-60

5 -

0

-60

15

10

0

-60

Pacific Ocean Zonal Mean Rain -- Jun 1988

i • t i i i

Adler Susskind

............ Huff man Wu

_( _( _ _ WCC ....... Arkin

A A A _ Kummerow Spencer

[] [] [] [] Prabhakara ._,

-40 -20 0 20 40

Latitude

Atlantic Ocean Zonal Mean Rain -- Jun 1988

i t i i • i

Adler Susskind

............ Huff man ....... Wu

_ _ _ WCC ........ Arkin

/_ /_ L_ A Kummerow Spencer

[] [] [] [] Prabhakara ,',

', , .-- _-=-.. --<...p_._.__:_

-40 -20 0 20 40

Latitude

Indian Ocean Zonal Mean Rain -- Jun 1988

, , , i t i

Adler Susskind

............ Huff man Wu

_( _ _( _( WCC ........ Arkin

A A /_ A Zummerow _ Spencer

[] [] [] [] Prabhakara /'_.i_"_

.." _- -'"'_/ - "_'I__

-40 -20 0 20 40

Latitude

60

60

60

Figure 13.11: Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle panel),

and Indian ocean sector (lower panel) for June 1988.

61
E

E

as

15

10 m

i

5

L

o

-60

Pacific Ocean Zonal Mean Rain -- Jul 1988

• "Adler ' ' Susskind'

Huff man Wu

WCC ........ Arkin

Kummerow Spencer

Prabhakara

I I I I , b , I ,

-40 -20 0 20 40

Latitude

15f

10--

5

0

-60

Atlantic Ocean Zonal Mean Rain -- Jul 1988

Adler Susskind

............ Huffman ....... Wu

_ _ _ wgc ........ Arkin

A A A A Kummerow _ Spencer

[] [] f-] [] Prabhakara I_['-]

....

-40 -20 0 20 40

Latitude

15

10

-60

Indian Ocean Zonal Mean Rain -- Jul 1988

J • i i i i

Adler Susskind

. Huff man _- Wu

_ _ _ WCC / ,\ ........ Arkin

/% /_ A /% Kummerow . Spencer

[] [] [] f--] Prabhakara / ._-_

\

f

//- / _ ''

j. _ / \,',

-40 -20 0 20 40

Latitude

Figure 13.12: Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle panel),

and Indian ocean sector (lower panel) for July 1988.

J

6O

7

60

6O

62
15

10

oi

--61

15

10 -

Pacific Ocean Zonal Mean Rain -- Aug 1988

Adler Susskind

............ Huff man ....... Wu

_ _ _ WCC ....... Arkin

/X /k /X /X Kummerow Spencer

[] [] [] [] Prabhakara

-40 -20 0 20 40

Latitude

5 -

o

-60

Atlantic Ocean Zonal Mean Rain -- Aug 1968

i ' _ i i i Adl r Susskind

............ Huff man ....... Wu

_ _ _ WCC ....... Arkin

/_, A /k /k Kummerow _ Spencer

[] [] [] [] Prabhakara

'X

-40 -20 0 20 40

Latitude

15

10

o

-60

Indian Ocean Zonal Mean Rain -- Aug 1988

i i j i i *

Adler Susskind

............ Huff man ....... Wu

_( _ _ WCC ,' "" ......... hxkin

/k A A A Kummerow C''" "_" Spencer

[] [] [] [] Prabhakara _ _-. \

='1⁄2:4. []-=x<.., ,=

-40 -20 0 20 40

Latitude

Figure 13.13: Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle panel),

and Indian ocean sector (lower panel) for August 1988.

6o

60

m

6O

63
H

15

10

I

15

5--

o

-60

10 -

5 -

0

-60

15

10 -

* i

Pacific Ocean Zonal Mean Rain -- Sep 1988

' Adler ' ' ' Susskind

............ Huff man ....... Wu

_ _ _ WCC ........ Arkin

m A A A Kummerow Spencer

[] [] [] [] Prabhakara

Ill "_)_.", "/ / - i

i I * , , I I I * I i

-40 -20 O 20 40

Latitude

Atlantic Ocean Zonal Mean Rain -- Sep 1988

' " Adler ' ' Susskind

............ Huff man ....... Wu

_t_ _( _J_ _1_ WCC ........ Arkin

A A A A Kummerow Spencer

D [] [] [] Prabhakara

-40 -20 0 20 40

Latitude

5

0

-60

Indian Ocean Zonal Mean Rain -- Sep 1988

l ! i i l

Adler Susskind

............ Huff man ....... Wu

_ _ _ wcc ......... Arkin

A A A A Kummerow / "' , Spencer

[] [] [] [] Prabhakara ....

\

"-...

.,'/^ . IS, .

-40 -20 0 20 40

Latitude

Figure 13.14: Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle panel),

and Indian ocean sector (lower panel) for September 1988.

60

6O

t

6O

64
c_

E

E

E

15

10

5 I

0

-60

1510 I

tO

-60

15'

10

Pacific Ocean Zonal Mean Rain -- Oct 1988

' 'Adler ' ' Susskind

............ Huff man ....... Wu

_ _ _#_ WCC ........ Arkin

a A a a Kummerow Spencer

[] [] [] [] Prabhakara

i i , i , h , i , i i i

-40 -20 0 20 40

Latitude

Atlantic Ocean Zonal Mean Rain -- Oct 1988

' '_di_r ' ' Susskmd

............ Huffman ....... Wu

_ _ _ WCC ........ Arkin

A a A a Kummerow Spencer

r-] [] [] [] Prabhakara

-40 -20 0 20 40

Latitude

Indian Ocean Zonal Mean Rain -- Oct 1988

' ' Adler ' ' Sus _'kind

............ Huffman ....... Wu

)_ _ _ _ WCC ........ Arkin

a a a a Kummerow Spencer

[] [] [] [] Prabhakara

0

-60

./_'\

-40 -20 0 20 40

Latitude

Figure 13.15: Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle panel),

and Indian ocean sector (lower p_nel) for October 1988.

60

6O

1

6O

65
15

10

0

-60

L_ A A /X

D _ [::]O

Pacific Ocean Zonal Mean Rain -- Nov 1988

• i f I 1 '

Adler Susskind

Huffman ....... Wu

WCC ........ Arkin

Kummerow Spencer

Prabhakara

[]

[] " "_r-1

i I , * , I , , , l ] ,

-40 -20 0 20 40

Latitude

15

I0

0

-60

Atlantic Ocean Zonal Mean Rain -- Nov 1988

Adler Susskind

............ Huff man Wu

)I_ _ _ WCC ........ Arkin

A A A A Kummerow Spencer

[] [] [] [] Prabhakara

/',

a_N [] .....

i k , ,

-40 -20 0 20 40

Latitude

15

10

0

-60

Indian Ocean Zonal Mean Rain -- Nov 1988

i • ; ] " r • r '

Adler Susskind

............ Huftman ....... Wu

_ _ _ WCC ........ Arkin

A A A /_ Kummerow Spencer

[] [] [] [] Prabhakara /,

_: "<" "_'_L . _.'\

-40 -20 0 20 40

Latitude

Figure 13.16: Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle panel),

and Indian ocean sector (lower panel) for November 1988.

6O

1

I

60

4

6O

66
E

E

e_

E

15

10

15

10

5 -

0

-60

Pacific Ocean Zonal Mean Rain -- Dec 1988

i ' w i J _ ,

Adler Susskind

............ Huff man ....... Wu

_ _ _ WCC Arkin

/_ /_ A h, Kummerow Spencer

[] [] [] [] Prabhakara

"_ _'_--_. i./"

I I I , , I I

-40 -20 0 20 40

Latitude

0

-60

Atlantic Ocean Zonal Mean Rain -- Dec 1988

' Adler ' ' Susskind

............ Huff man ....... Wu

_ _?__ WCC ........ Arkin

/_ /_ /_ /_ Kummerow Spencer

[] [] [] [] Prabhakara

-40 -20 0 20 40

Latitude

15

10

0

-60

Indian Ocean Zonal Mean Rain -- Dec 1988

' Adler ' ' Susskind

............ Huff men ....... Wu

_ _ _ WCC ....... Arkin

A /_ A A Kummerow Spencer

[] [] [] [] Prabhakara

/:\ ,./

_:__--:_- ,/....:.;____,

[] • . .

, , ...... v _--_', , , \_

-40 -20 0 _0 40

Latitude

Figure 13.17: Zonal mean rain rates for the Pacific (upper panel), Atlantic (middle panel),

and Indian ocean sector (lower panel) for December 1988.

6O

6O

6O

67
E

E

15

10

5--

0

-60

15

10 -

5 -

0

-60

15:

10

N & S America Zonal Mean Rain -- Aug 1987

Adler

............ Huffman

0 0 0 0 cpcc

",, ._'I I

i , , , _'_ , i ,

-40 -20 0

Latitude

i

Susskind

....... Wu

........ Arkin

Spencer

L__. -,

_, \,,

20 40 60

-40 -20

Eurasia and Australia Zonal Mean Rain -- Aug 1987

' ' Adler ' ' Susskind

............ Huff man \ Wu

0 0 0 0 GPCC ,\ ........ Arkin

Spencer

"',,O_. \.

..0

i , I I

0 20 40

Latitude

5--

0

-60

I

-40

Africa Zonal Mean Rain -- Aug 1987

t • i J i l

Adler Susskind

............ Huff man Wu

° 0 0 _ GPCC Arkin

Spencer

.-o ,

/

'\

..-

, ,

-20 0 20 40

Latitude

Figure 14.1: Zonal mean rain rate for the north and south American (upper panel), Australia

and Eurasia (middle panel), and Africa land sector (lower panel) for August 1987.

6O

60

68
15

I0

5 -

0

-60

m

E

E

ri0 i--

5--

0

-60

15

I0

N & S America Zonal Mean Rain -- Sep 1987

I ' _ J l i

Adler Susskind

............ Huff man ........ Wu

O _ _ GPCC ........ Arkin

Spencer

/./i

\,, i .--.---(>><,_.

.\ ,, ,," _ yl

_ .- -- ? -_ .L;" . "i" .> <> "

I , t i

-40 -20 0

Latitude

\

", ' ,

20 40 60

Eurasia and Australia Zonal Mean Rain -- Sep 1987

l J i ,

Adler Susskind

............ Huffman

0 0 0 0 cPCC

t _--,_k_-_,_ ,

-40 -20

I i

0

Latitude

....... WU

Arkin

\ Spencer

,

'\

, ,_

20 40

0

--6i

Africa Zonal Mean Rain -- Sep 1987

i t _ t

Adler Susskind

............ Huff man Wu

_ _ _ GPCC ........ Arkin

Spencer

P-.

\

i

-40

f ,,

/" ./_\\..

' .------J," ",, ,

• .._. _.

"v ., , , ,

-20 0 20 40

Latitude

Figure 14.2: Zonal mean rain rate for the north and south American (upper panel), Australia

and Eurasia (middle panel), and Africa land sector (lower panel) for September 1987.

6O

6O

69
E

E

15

I0

o

-60

15

10--

5 -

0

-60

15

10

N & S America Zonal Mean Rain -- Oct 1987

i , i _ i , t ,

Adler Susskind

............ Huffman ....... Wu

_ _ _ GPCC ........ Arkin

Spencer

\ /,>,

--_.2 <>.-_ _;¢ :/I

-40 -20 0

Latitude

, _t . - f

20 40

Eurasia and Australia Zonal Mean Rain -- Oct 1987

I • i

Adler

............ Huffman

O _ _ GPCC

-40 -20

i i

o

Latitude

i

Susskind

Wu

Arkin

Spencer

i

\

'\

_.',. ./

l I L

6O

o

-60 -4o

20 40 60

Africa Zonal Mean Rain -- Oct 1987

' *dX_r ' ' ' S._kind

............ Huff man Wu

0 0 0 0 GPCC ......... Arkin

Spencer

.--°°

j" \

, _-._ ___,_ _. , _"_.-=_. "_.. --._._ , _ . .-.L__ i

-20 0 20 40

Latitude

Figure 14.3: Zonal mean rain rate for the north and south American (upper panel), Australia

and Eurasia (middle panel), and Africa land sector (lower panel) for October 1987.

6o

7O
15

10

o

-6o

15

10--

5

0

-60

15

I0 -

5--

N & S America Zonal Mean Rain -- Nov 1987

i • i

Adler

............ Huff man

<> <> <> <> cpcc

J',

\

/ /O----_;\,

-40 -20 0

Latitude

, i

Susskind

Wu

Arkin

Spencer

/

20 40

Eurasia and Australia Zonal Mean Rain -- Nov 1987

j , i i i i

Adler Susskind

............ Huff man ....... Wu

_ _ _ GPCC ........ Arkin

Spencer

-40 -20 0

Latitude

\

i\'\ ."

_. ../

20 40

Africa Zonal Mean Rain -- Nov 1987

Adl r Susskind

Wu ............ Huff man .......

_ _ <> GPCC Arkin

Spencer

60

o

-6(

i

-40

/"

," xx

." ._/k J _

.... .-_- 1 I,_./ -,.._,_

_-_ _ _ ..

-20 0 20 40

Latitude

Figure 14.4: Zonal mean rain rate for the north and south American (upper panel), Australia

and Eurasia (middle p_nel), and Africa land sector (lower panel) for November 1987.

6o

i

60

71
m

E

E

E

15

I0

0

-60

t5

10

5

oi

-60

15

10

N & S America Zonal Mean Rain -- Dec 1987

Adler

............ Huffman

0 0 0 0 cPCCr-..

/

/ <>./" -'.

/ /'/

-40 -20

' i

Susskind

Wu

Arkin

Spencer

/"_ /

/

0 20 40

Latitude

: i .

Adler

............ Huff man

<> <> 0 0 cpcc

Eurasia and Australia Zonal Mean Rain -- Dec 1987

i i • i

Susskind

Wu

........ Arkin

Spencer

/

/

-40 -20 0 20 40

Latitude

Africa Zonal Mean Rain -- Dec 1987

Adler Susskind

............ Huff man ....... Wu

O _ _ _ GPCC ......... Arkin

Spencer

0 , , , ,,i

-60 -40

.o°_

•' F_\

../o.j- o--

/ 1>/'- _.. \_: ./

-20 0 20 40

Latitude

Figure 14.5: Zonal mean rain rate for the north and south American (upper panel), Australia

and Eurasia (middle panel), and Africa land sector (lower panel) for December 1987.

6O

I

6O

i

i

60

72
c_

15

I0

o

-60

15

lO

o

-60

15

lO -

N & S America Zonal Mean Rain -- Jan 1988

l • i J i i

Adler Susskind

............ Huffman ....... Wu

O _ _ GPCC Arkin

Spencer

f-'" _. j./<_

.. /I _. \

,

-40 -20 0

Latitude

#

/

r -.-': - _ - -_"

•

20 40

Eurasia and Australia Zonal Mean Rain -- Jan 1988

' Adler

............ Huff man

O O O O GPCC

i i J

-40 -20 0

Latitude

i

Susskind

Wu

Arkin

Spencer

i

,

/

/

20 40

5 -

0 , , ,

-60

0000

i

-40

Africa Zonal Mean Rain -- Jan 1988

Adler ' ' Susskind

fluff man ....... Wu

GPCC ,'" _ ........ Arkin

/ Spencer

\ '\

-20 0 20 40

Latitude

Figure 14.6: Zonal mean rain rate for the north and south American (upper panel), Australia

and Eurasia (middle panel), and Africa land sector (lower panel) for January 1988.

6o

6o

6o

73
15

10

5--

0

-60

15

I0--

-60

15

I0

L

5--

0

-60

N & S America Zonal Mean Rain -- Feb 1988

i i i i

Adler

............ Huff man .......

O _ _ _ GPCC ........

/ .,/ ..\

• / \\ \

.,......... /.; ......... _.-''( •

... ..':::_.Lt _,',y>,,',,

-...- /_:" _...',_

"'" / '"', ,I

_'_ . _-, • , , i , , , i , l ,

-40 -20 0

Latitude

i

Susskind

_u

krkin

Spencer

//*"

,__>,

20 40

Eurasia and Australia Zonal Mean Rain -- Feb 1988

, i

Adler

............ Huffman

O (> O O cPcc

/

-40 -20

Susskind

....... W_

......... "Arkin

/ Spencer

/

/'

/

o'

/

0 20 40

Latitude

Adler

............ Huff man

<_ _ _ _ GPCC

/

/

Africa Zonal Mean Rain -- Feb 1988

i ! 1 •

Susskind

Wu

........ Arkin

Spencer

i

-40

.\

"\

......

, I

-20 0 20 40

Latitude

Figure 14.7: Zonal mean rain rate for the north and south American (upper panel), Australia

and Eurasia (middle panel), and Africa land sector (lower panel) for February 1988.

6O

60

1

6O

74
15

10 m

15

5m

o

-60

10 -

5 -

0

-60

15

N & S America Zonal Mean Rain -- Mar 1988

10 -

E i , i ,

Adler Susskind

............ Huffman Wu

O _ _ GPCC ........ Arkin

Spencer

j'.

\

./ pZ-:--%

/ o.'/ '_\_

,d'_..___-,4.': ', \

-40 -20 0

Latitude

]_

/ \ .... ---

I

/

20 40

, i

, i

Adler

............ Huff man

<Y 0 _ <> cPCC

Eurasia and Australia Zonal Mean Rain -- Mar 1988

Susskind

....... Wu

........ Arkin

.i - Spencer

/

/

.; /°

-40 -20 0 20 40

Latitude

Africa Zonal Mean Rain -- Mar 1988

' Adler ) ' Susskind

............ Huffman ....... Wu

_ _ 0 GPCC ........ Arkin

Spencer

J

-60 -40 -20 0 20 40

Latitude

Figure 14.8: Zonal mean rain rate for the north and south American (upper panel), Australia

and Eurasia (middle panel), and Africa land sector (lower panel) for March 1988.

m

60

I

60

m

i

60

75
15

I0

5 -

o

-60

15

10

0

-60

15

10

N & S America Zonal Mean Rain -- Apr 1988

Adler

............ Huffman .......

<> <> <> <> GPCC ........

r_--C" ." z _

........--, \,

-40 -20 o

Latitude

' i

Susskind

Wu

Arkin

Spencer

/

20 40

.....

-40 -20

Eurasia and Australia Zonal Mean Rain -- Apr 1988

Adler Sus nd

............ Huff man ....... Wu

0 O O O GPCC ........ Arkin

Spencer

/

/

/

\ ,"

r,'ii\ •I'

0 20 40

Latitude

o

-60

Africa Zonal Mean Rain -- Apr 1988

J t t t , l ,

Adler Susskind

............ Huff man Wu

_ _ 0 GPCC ........ Arkin

Spencer

..o_

/

/\ "\

/I'_ "\, "',

..",. ,,_\,,\

.-".

I , I , , I , , , I , , _ . ._'l_-_y'._-_'--_, - -I I ,

-40 -20 o 20 40

Latitude

Figure 14.9: Zonal mean rain rate for the north and south American (upper panel), Australia

and Eurasia (middle panel), and Africa land sector (lower panel) for April 1988.

6o

6o

6o

76
e$

E

15

1o

0

--60

15

10

o

-60

15

I0

N & S America Zonal Mean Rain -- May 1988

' Adler ' ' Susskind

............ Huff man ....... Wu

O _ O GPCC ........ Arkin

0 Spencer

(i4

\ .'" ,I >

_ _--_.. ..... ;- _ _.-- -.. _ " _ - ___

-40 -20 0 20 40

Latitude

Eurasia and Australia Zonal Mean Rain -- May 1988

i

Adler

............ Huff man

<> <> <> <> cPCC

i E

Susskind

Wu

Arkin

Spencer

\

I

J [ _, , , , ,

-40 -20 0

Latitude

_\ /

, i i , I

20 40

Africa Zonal Mean Rain -- May 1988

, i i , i

Adler Susskind

............ Huffmsn ....... Wu

_ _ _ GPCC Arkin

Spencer

\ ""'i ."/

_'-_. _.. ..'" ,' /_.

-60 -40 -20 0 20 40

Latitude

Figure 14.10: Zonal mean rain rate for the north and south American (upper panel), Australia and Eurasia (middle panel), and Africa land sector (lower panel) for May 1988.

60

60

60

77
a

15

10

15

5 -

0

-60

10 -

5 -

0

-60

15

10 -

N & S America Zonal Mean Rain -- Jun 1988

I , i u i • , n ,

Adler Susskind

............ Huff man ....... Wu

_ _ _ GPCC _ ........ Arkin

Spencer

-40 -20 0

Latitude

p • , , I

20 40

Adler

............ Huffman

_ _ _ GPCC

Eurasia and Australia Zonal Mean Rain -- Jun 1988

Susskind

....... Wu

........ Arkin

Spencer

0

Latitude

\

i\ \

I i

-40 -20

60

20 40 60

0 . 1

-60 -40

Africa Zonal Mean Rain -- Jun 1988

i • f ," , , , • i ' ,

Adler Susskind

............ Huff man WU

_ _ _ GPCC ........ Arkin

Spencer

-20 0 20 40

Latitude

Figure 14.11: Zonal mean rain rate for the north and south American (upper panel), Australia and Eurasia (middle panel), and Africa land sector (lower panel) for June 1988.

6O

78
ERRATA

Following is Figure 14.12, page 79 which was not printed.

E

E

E

15

tO

0

-60

N & S America Zonal Mean Rain -- Jul 1988

i

Adler

............ Huftman

0 _ 0 GPCC

//."

/ /

-40 -20 0

Latitude

Susskind

Wu

Arkin

Spencer

,\ '\

l , i , I

20 40 6O

15

10

5

ol

-60

Eurasia and Australia Zonal Mean Rain -- Jul 1988

' Adler ' \" ' Susskind

............ Huffmen ..... Wu

(> (> ¢_ 0 GPCC \ --" Arkin

\ "_ Spencer

| 0 \.\ "'\

-40 -20 o

Latitude

20 40 6O

15

I0 -

5 -

0

-60

• , l

-40

Africa Zonal Mean Rain -- Jul 1988

i f i i i

Adler Susskind

............ Huff man ....... Wu

0 0 0 0 GPCC ........ Arkin

Spencer

-20 0 20 40

Latitude

1

6O

Figure 14.12: Zonal mean rain rate for the north and south American (upper panel). Australia and Eurasia (middle panel), and Africa land sector (lower panel) for July 1988.

79
15

10

e_

5

III

0

-60

15

I0--

5 -

0

-60

15

I01

N & S America Zonal Mean Rain -- Aug 1988

Adler

............ Huffman

0 _ _ _ GPCC

/','q

//'

-40 -20 0

Latitude

k i ' ' ' '

Susskind

._ Wu

\,i_. .. Arkin Spencer

I I

20 40

Eurasia and Australia Zonal Mean Rain -- Aug 1988

Adler \ Susskind

............ Huff man ....... Wu

_ _ _) QPCC i\. \ Arkin

,\ .\ _._ __ __ Spencer

I \ "\ ",

I \ -,."\ \

@/\ x\

I "q"_\,,

l <> '_¢>_'\

i I

0 20 40

Latitude

Africa Zonal Mean Rain -- Aug 1988

i • i i i

Adler

............ Huff man

0 0 0 0 GPCC

Sussklnd

Wu

Arkin

Spencer

-60 -40 40

/'i'\

-20 0 20

Latitude

Figure 14.13: Zonal mean rain rate for the north and south American (upper panel), Australia and Eurasia (middle panel), and Africa land sector (lower panel) for August 1988.

w

I

6O

60

6O

8O
E

15

10

0

-60

15-

lO w

5--

0

-60

N & S America Zonal Mean Rain -- Sep 1988

• i

Adler

Huff man

OOOO GPCC

/

, I , , i 1 I ,

-40 -20 0

Latitude

Susskind

Wu

_ --'::_--::: sA_keinner

\

20 40

Adler

............ Iduffman

O _ _ GPCC

i J

60

Eurasia and Australia Zonal Mean Rain -- Sep 1988

\ ' Sus'skind

"_ ....... WRI

\ ........ Arkin

Spencer

, , i I I ,

0

Latitude

-40 -20

\'\*,

20 40

Africa Zonal Mean Rain -- Sep 1988

60

i J i 1 i i

15 .... Adler Susskind

............ Huff man Wit

O O <_ GPCC ........ Arkin

Spencer

10

F

I

O! .....

-60 -40 -20 0 20 40

Latitude

Figure 14.14: Zonal mean rain rate for the north and south American (upper panel), Australia and Eurasia (middle panel), and Africa land sector (lower panel) for September 1988.

16O

81
15

10

N & S America Zonal Mean Rain -- Oct 1988

i , , n L u '

Adler Susskind

............ Huffman ....... Wu

_> O _ O GPCC ........ Arkin

Spencer

o

-60

15

lO

/

-/,

/'" :."_.--'7 ,

j -/

i I i I I ,

-40 -20 0

Latitude

I I , I

20 40

Eurasia and Australia Zonal Mean Rain -- Oct 1988

' " Adler ' ' Susskind

............ Huff man Wu

_ _ _ GPCC ........ Arkin

\ Spencer

\

o , I.'>-_'='-=-'.--:-_ ,

-60 -40 -20 o

Latitude

\

I I

20 40

15

I0 m

@@@@

Africa Zonal Mean Rain -- Oct 1988

• Adl'er ' '

Huff man

GPCC

0 i , I

-60 -40 0 40

Latitude

/

,° '\

/

," /" \ \

• / %

-20 2O

i

Susskind

Wu

Arkin

Spencer

Figure 14.15: Zonal mean rain rate for the north and south American (upper panel), Australia and Eurasia (middle panel), and Africa land sector (lower panel) for October 1988.

60

6o

60

82
E

E

15

1o

15

lO

5 -

o

-60

15

lO

o

-6o

o

-60

, ' I

Adler

............ Huff man

<_ O O GPCC

N & S America Zonal Mean Rain -- Nov 1988

/'\

/

.----, \

f"/. \ "...

/ / J\'_l

/

i

\" -i

I I , , , 1 , I ,

-40 -20 0

Latitude

Susskind

....... Wu

........ Arkin

Spencer

20 40

Eurasia and Australia Zonal Mean Rain -- Nov 1988

i i 1 _ i

Adler Susskind

............ Huff man ....... Wu

0 <_ 0 _ GPCC ........ Arkin

Spencer

\

-40 -20 o

Latitude

_ ...i

-

20 40

Africa Zonal Mean Rain -- Nov 1988

' Adler ' ' ' Susskind

............ Huff man ....... Wu

O _ _ _ GPCC ........ Arkin

Spencer

/ "?\,

• / \', ,'

/ / "\\, ./

,. ./ _...._ ",_, ..

-40 -20 0 20 40

Latitude

Figure 14.16: Zonal mean rain rate for the north and south American (upper panel), Australia and Eurasia (middle panel), and Africa land sector (lower panel) for November 1988.

60

60

60

83
15

I0

5 -

0

-60

15

10 -

N & S America Zonal Mean Rain -- Dec 1988

' Adler ' ' Susskind

............ Huff man ....... Wu

O O O O GPCC ........ Arkin

• "I" "" Spencer

/.' / ,.. \

/ ,\

"/ / " ,

• , _ \\

.

-40 -20 0 20 40

Latitude

/

Eurasia and Australia Zonal Mean Rain -- Dec 1988

' Adler ' ' Susskind

............ Huff man Wu

000 _ GPCC ........ Arkin

Spencer

0

-60

/

-40 -20

/*

/

/

f

, , , , , . _.-__.__.:'_-:_-W,--2 _ . .

0 20 40

Latitude

15

I0 -

-60

Africa Zonal Mean Rain -- Dec 1988

l ] l 1 1

Adler Susskind

............ Huff man Wu

0 0 0 _ GPCC /'" ......... Arkin

• Spencer

/ \

/ ._'_-4

//.1" /_ "_

I

L "-....

, , , , , 'i, , , , , , , ,_--.---'-_-='=-_"'" - - -- , , , ,

-40 -20 0 20 40

Latitude

Figure 14.17: Zonal mean rain rate for the north and south American (upper panel), Australia and Eurasia (middle panel), and Africa land sector (lower panel) for December 1988.

6O

6O

6O

84
60N

50N40N30N20NIONEQlOS.

20S.

30S.

40S.

50S

60S

0

Adler SSMI Rain (mm/day) -- Aug 1987

60E 120E 180 120W 60W 0

60N

50N

40N

30N

20NIONEQIOS20S30S40S50S60S

0

60N

50N

40N

30N

20N

1ON

EQ

10S

20S

30S

40S

50S

60S

0

Adler SSMI Rain (mm/day) -- Aug 1988

60E 120E 180 120W 60W

0 4 8 12 16 20 24

Adler Normalized Difference: Aug 1987 - Aug 1988

60E 120E 180 120W 60W 0

-1.5 -0.5 0.5 1.5

Figure 15.1: Rain rate distribution for August 1987 (upper panel), August 1988 (middle

panel), and the normalized difference (difference between August 1987 and August 1988

divided by the average of these two months) (lower panel) for the algorithms of Adler.

85
60N

50N

40N

30N

20N

1ON

EQ

lOS

20S

30S40S50S60S

0

WCC SSMI Rain (mm/day) -- Aug 1987

J

60E 12'0E 180 120W 6()W 0

WCC SSMI Rain (mm/day) -- Aug 1988

60N

5ON- J

40N30N -

20N -

1ONEQlOS20S"

30S40S50S .....................................................

60S

0 60E 12'0E 180 120W 60W

IIII II IIII I

0 4 8 12 16 20 24

WCC Normalized Difference: Aug 1987 - Aug 1988

J

6ON'

50N'

40N

30N

20N

1ON ......

EQ

10S

20S

30S

40S

50S ....................................................

60S

0 60E 12'0E 180 120W

-1.5 -0.5 0.5 1.5

6()W 0

Figure 15.2: Rain rate distribution for August 1987 (upper panel), August 1988 (middle

panel), and the normalized difference (difference between August 1987 and August 1988

divided by the average of these two months) (lower panel) for the algorithms of Chang.

86
60N

50N

40N

30N20N1ON

EQ

10S

20S30S.

40S.

50S.

60S

0

Probkahara SSMI Rain (mm/doy) -- Aug 1987

J

60E 120E 180 120W 60W 0

Prabkohoro SSMI Rain (mm/doy) -- Aug 1988

60N

50N J

40N

30N

20N

1ON

EQ

10S

20S

30S

COS

50S ...................................................

60S

12[)W 60W

20 24

1987 -- Aug 1988

o B6E 12'0E 180

II II

0 4 8 12 16

Prabkahoro Normalized Difference: Aug

60N

5ON- : J :

0 _"

40N -

3ON"

20N -

1ON.......

EQ

10S.

20S.

30S.

40S'

50S .....................................................

60S

0 66E 12'0E 180 120W 60W 0

-1.5 -0.5 0.5 1.5

0

Figure 15.3: Rain rate distribution for August 1987 (upper panel), August 1988 (middle

panel), and the normalized difference (difference between August 1987 and August 1988

divided by the average of these two months) (lower panel) for the algorithms of Prabhakara.

87
Arkin IR Rain (mm/day) -- Aug 1987

J

60N

50N

40N

30N

20N

IONEQ10S20S30S40S ...........................

50S60S

0 60E 12'0E 180 12()W 66w o

Arkin IR Rain (mm/day) -- Aug 1988

60N , _- . .

50N_ ....... :........ J- .i. •.

40N3ON2ON1ONEQ10S20S30S40S ............................ ,_ •

50S ......... ; .......... : .......... ;' ......... :........ t

60S

0 60E 12'0E 180 120W 60W

Illl

0 4 8 12 16 20 2#

GPI Normalized Difference: Aug 1987 - Aug 1988

60N .

50N -_- --: ...... i........ J..:...

40N30N20N1ONEQ10S20S30S40S ...................................

, - . J

i

50S .... 1 ,...........

60S

0 60E 12'0E 180 120W

-1.5 -0.5 0.5 1.5

66w o

Figure 15.4: Rain rate distribution for August 1987 (upper panel), August 1988 (middle

panel), and the normalized difference (difference between August 1987 and August 1988

divided by the average of these two months) (lower panel) for the algorithms of Arkin.

88
60N

50N

40N

30N

2ON'

1ON,

EQ,

10S,

20S'

30S'

40S.

50S.

60S.

0

Spencer MSU Rain (mm/doy) -- Aug 1987

60E 120E 180 120W 60W 0

60N

50N

40N

30N

20N

ION

EQ

IOS.

20S.

30S.

40S.

50S.

60S

0

60N

50N

40N

30N

20N

ION

EQ.

IOS,

20S30S40S50S60S

0

Spencer MSU Rain (mm/day) -- Aug 1988

60E 120E 180 120W 60W

0 4 8 12 16 20 24

Spencer Normalized Difference: Aug 1987 - Aug 1988

60E 120E 180 120W 60W

-1.5 -0.5 0.5 1.5

0

Figure 15.5: Rain rate distribution for August 1987 (upper panel), August 1988 (middle

panel), and the normalized difference (difference between August 1987 and August 1988

divided by the average of these two months) (lower panel) for the algorithms of Spencer.

89
60N

50N

40N

30N

20N

ION

EQ

lOS

20S

30S

40S.

50S.

60S

0

Susskind TOVS Rain (mm/day) -- Aug 1987

60E 120E 180 120W 60W 0

60N

50N

40N

30N

2o. i

1ONEQ10S20S30S40S50S60S

0

60N50N40N30N20NIONEQ10S20S30SCOS

50S

60S

0

Susskind TOVS Rain (mm/doy) -- Aug 1988

60E 120E 180 120W 60W

r _'_'_:;_ III II

0 4" 8 12 16 20 24

Susskind Normalized Difference: Aug 1987 - Aug 1988

60E 120E 180 120W 60W

-1.5 -0.5 0.5 1.5

0

0

Figure 15.6: Rain rate distribution for August 1987 (upper panel), August 1988 (middle

panel), and the normalized difference (difference between August 1987 and August 1988

divided by the average of these two months) (lower panel) for the algorithms of Susskind.

9O
60N

50N -

40N _

20NIONEQ10S20S30S40S

50S.

60S

0

Wu TOVS Rain (mm/day) -- Aug 1987

60E 120E 180 120W 60W 0

60N

50N

40N

30N

20N

1ON

EQ

10S

20S

30S

40S

50S

60S

0

60N

50N

40N

30N

20N

1ON

EQ

lOS.

20S.

30S.

40S

Wu TOYS Rain (mm/day) -- Aug 1988

60E 120E 180 120W 60W

: III

0 4 8 12 16 20 24

Wu Normalized Difference: Aug 1987 - Aug 1988

60S

0 60E 120E 180 120W 60W 0

-1.5 -0.5 0.5 1.5

Figure 15.7: Rain rate distribution for August 1987 (upper panel), August 1988 (middle

panel), and the normalized difference (difference between August 1987 and August 1988

divided by the average of these two months) (lower panel) for the algorithms of Wu.

91
60N

50N

40N

30N

20N

1ON

EQ

lOS

20S

30S

40S ......................

50S

60S

GPCC Rein -- Aug 1987

0 60E 12'0E 180 120W 60W 0

GPCC Rein -- Aug 1988

60N

50N

40N

30N

20N

1ON

EQ

lOS

20S

30S

sos .......... i........... !.......... !_......... i........ 1

60S

60N

50N

40N

30N

20N

1ON

EQ •

10S

20S

30S

40S .......... i..... i v • _21 ....................

sos ......... ........... !.......... !'.......... i........ t _.........

60S

0 60E 12'0E 180 120W 60W 0

0 4 8 12 16 20 24

GPCC Normelized Difference: Aug 1987 - Aug 1988

Figure 15.8: Rain rate distribution for August 1987 (upper panel), August 1988 (middle

panel), and the normalized difference (difference between August 1987 and August 1988

divided by the average of these two months) (lower panel) for the algorithms of GPCC.

92
E

m

O

(9

100

8O

6O

4O

2O

Histogram of Normalized Difference, Aug 1987 - Aug 1988

Adler

......... i ......... i ......... J .........

0 ........ i ......... i ......... i ........ i

-2 -1 0 1

Normalized Difference

Susskind

200 t .................. i ..................

1SO

50

0 ......... i ......... i ......... i ........ ,

-2 -I 0 1 2

Normalized Difference

Spencer

25O

2o0

E

15o

100

5O

0

-2 -1 0 1 2

Normalized Difference

E

_4o

o

WCC

80 t ......... J ......... t ......... I .........

1

6O

2O

-2 -I 0 1

Normalized Difference

Wu

_0 ......... i ......... i ......... i .........

E

4OO

3O0

100

-2 -1 0 1 2

Normalized Difference

GPCC

0 ......... i ......... i ......... J,,, ......

-2 -I 0 1

NormcJized Difference

t-

_4o

Prabkahara

60 ......... i ......... i ......... _ .........

-2 -1 0 1 2

Normalized Difference

Arkin

100 ......... _......... , ......... , .........

4O

2O

0

O

¢)

8O

60

4O

0

-2

2O

....... i ......... i ......... i .........

-I 0 I 2

Normalized Difference

Figure 16: Histograms of the normalized difference for Adler, Chang, Prabhakara, Arkin,

Spencer, Susskind, Wu, and GPCC.

93
Appendix A: Histogram of Rain Rate for All Algorithms

Adler Max Min ntot

8708 21.9 0 2135

8709 22.1 0 20951

8710 18.3 0 1964

8711 24.9 0 1870!

8712 0 0 0_

8801 25.1 0 1874

8802 17.5 0 1880

8803 28.4 0 1901

8804 21 0 1919

8805 18.4 0 2005

8806 27,6 0 2084

8807 18.9 0 2112

8808 23.3 0 2122

8809 17.9 0 2101

8810 20.1 0 1954

8811 24.6 0 1843

8812 20,6 0 1846

Chang

8708 24.7 0 1033

8709 29.9 0 1029

8710 20.5 0 1014

8711 22.6 0 1029

8712 0 0 0

8801 28.6 0 998

8802 29.9 0 1026

8803 27.6 0 1026

8804 20.2 0 1033

8805 21.3 0 1026

8806 24.4 0 1023

8807 17.2 0 1027

8808 28 0 1031

8809 22.5 0 1007

8810 27 0 1016

8811 23.4 0 1016

8812 26.1 0 1015

Kummemw

8708 22.4 0 1440 0

8709 22.3 0 1440 0

8710 28.2; 0 1425 15

8711 25.7 0 1412 28

8712 0 0 0 0

8801 0_ 0 0 0

8802 28.6 0 1373 67

8803 28.4 0 1397 43

8804 28 0 1428 12

8805 271 0 1437 3

8806 28.21 0 1438 2

8807 21! 0 1438 2

8808 0! 0 0 0

8809 0 01 0 0

8810 0 0 0 0

8811 0 0 0 0

8812 0 0 0 0

nd Bin1 Bin2

0 1468 363 151 71

1 1435 378 155 62

0 1181 397 179 90

0 1312 319 117 69

0 0 0 0 0

1 1332 286 132 55

2 1156 341 155 94

0 1378 337 127 36

0 1207 371 171 92

0 1188 349 232 127

0 1467 374 157 52

0 1291 426 185 117

0 1463 390 152 67

0 1319 363 205 106

0 1216 364 203 84

0 1269 303 137 84

0 1190 303 174 78

0 522 256 126 67

1 585 279 87 36

0 420 257 157 72

0 475 259 145 60

0 0 0 0 0

1 590 214 107 45

1 609 232 99 37

0 524 281 133 45

1 436 279 148 91

0 421 282 147 76

0 480 300 124 54

0 408 242 142 96

0 579 279 98 35

1 492 259 105 79

0 577 215 114 58

0 513 228 131 85

2 535 250 124 63

939 244 145 52

941 269 128 49

962 286 101 45

878 323 117 40

0 0 0 0

0 0 0 0

884 289 114 38

866 324 116 46

904 304 142 32

913 323 131 47

980 302 109 28

887 282 144 68

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

o o o! o

94

Bin3 Bin4 Bin5 Bin6 Bin7 Bin8 Bin9 Bin 10 Bin 11

41 19 11 6 3 1 1

34 14 9 4 1; 2 1

54 28 14 13 3 4 1

25 16 61 5i 0 0 1

0 0 0 0! 0 0 0

36 16 10 2_ 2 2 1

53 32 19 17_ 11 1 1

14 6 2 0i 0 0 1

43 20 8 3; 2 1 1

49 30 17 4 7 1 1

21 8 1 1 1 1 1

48 16 15 8 3 2 1

23 14 7 3 2 0 1

48 29 15 7 6 2 1

45 21 12 4 4 01 1

26 15 7 1 0 0! 1

53 25 6 7 5 4j 1

22 19 13 6 0 1 1

31 5 2 0 1 2 1

36 28 24 10 6 3 1

43 22 9 7 6 2 1

0 0 0 0 0 0 0

18 10 6 5 1 1 1

27 17 3 1 0 0 1

23 12 3 1 3 0! 1

40 12 11 7 4 4 1

52 28 10 6 3 0 i 1

31 15 11 6 0 1 1

54 38 16 13 8 9 1

20 10 7 1 1 0 1

29 21 7 8 2 4 1

34 13 1 2 0 1 1

28 11 10 3 4 2 1

20 14 2 3 3 0 1

26 14 9 2 7 1 1

22 17 8 3 1 1 1

18 7 2 1 0 2 1

20!0 13 11 3 1 5 1 0 0 0 0 0 0

0; 0 0 0 0 0 0

191 7 8 4 6 3 1

12 _ 8 6 9 5 4 1

16_ 14 6 3 4 2 1

9 8 3 1 1 0 1

13 3 1 1 0 0 1

27 12 8 6 3 0 1

0 0 0 0 0 0 0

0 0 0 0 0 0 0

0 0 0 0 0 0 0

0 0 0 0 0 0 0

0 0 0 0 0 0 0
Huffman

8708

8709

8710

8711

8712

8801

8802

8803

8804

8805

8806

8807

8808

8809

8810

8811

8812

Prabhakara

8708

8709

8710

8711

8712

8801

8802

8803

8804

8805

8806

8807

8808

8809:

8810

8811

8812

Spencer

8708

8709

8710

8711

8712

8801

8802

8803

8804

8805

8806

8807

8808

8809

8810

8811

8812

23.9

22

27

27.7! 00

23.7

18.9

20.1

23.4

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19.9

26.4

21.1

20.40

28.3

28.6

24.7

26.3

19.8

22.9

19.4

23.6

20.4

27.2

29.6

25.6

15.4

18

16.5

18.3

15.4

14.8

17.8

19.5

16.8

14.8

17.7

15.6

18.1

15.7

16.1

17.4

17.91

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2592

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2591

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1899

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1968

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496

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461

548

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571

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1465

1511

1487

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1602

1632

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1521

1581

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1499

1421

1525

1554

1530

378

432

331

31300

326

337

382

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294

260

276

2880

283

263

299

304

293

273

2761

257

241

229

259

265

555

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578

433

449

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494

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531

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570

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480

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152

152

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150

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192

152

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128

115

138

164

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91

87

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120

137

117

112

109

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293

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45 29 16 14211

31 1863101

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34 25 113121

32 20 13 10331

41 32 206121

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31 1213001

29 1175001

24 103220 ! 1

46 3962411

28 11 811011

38 25 14=2111

21 1032001

56 1582311

20642001

15432101

24763211

85 49 20 18 113

69 40 24200

71 41 27630

69 349321

90 57 48 19 114

87 58 33 2195

68 48 28 1431

58 30 12101

71 26 18931

67 37 ! 29 1235

64_ 27 12752

92 44 21 18 117

55 19 1786 41

85 40 27 21 116

77 52 20851

76 24 19! 7:23

69 30 20411

95
Arkln

8708 24.5 0 1151 1 645 188

8709 24.3 0 1152 0 618 204

8710 24.4 0 1152 0 610 216!

8711 18.7 0 1152 0 474 227

8712 22.8 0; 1152 0 606 196

8801 22.3 0 1152 0 585 221

8802 21.5 0 1152 0 609 206

8803 22.5 0 1152 0 591 205

8804 18.6 0 1152 01 477 236

8805 26.8 0 1152 01 611 217

8806 25.2 0 1152 01 612 203

8807 23.3 0 1152 0 i 629 178

8808 21.3 0 1152 0 620 172

8809 23.9 0 11,52 0! 635 192

8810 26.4 0 1152 0 660 220

8811 ! 29.5 0 1150 2 683 258

8812; 25.7 0 1152 0 636 208

Susskind

8708 13.7 0 2592 0 1323 556

8709 11.9 0 2592 0 1179! 515

8710 13 0 2592 0 1256 495

8711 11 0 2592 0 1117 534

8712 15.1 0 2592 0 1359 645

8801 13.9 0 2592 0 1296 474

8802 14.1 0 2592 0 1337 511

8803 10.7 0 2592 0 1099 492

8804 12.2, 0 2592 0 1121 583

8805 14 0 2592 0 1287 517

8806 13.9 0 2592 0 1308 514

8807 13.3 0 2592 0 1266 545

8808 13.7 0 2592 0 1288 568

8809 12 0 2592 0 1249 486

8810 17.3 0 2592 0 1487 599

8811 15.2 0 2592 0 1404 579

8812 15.4 0 2592 0 1417 533

Wu

8708 16.5 0 2592 0 1361 727

8709 13.3 0 2592 0 1150 668

8710 13.1 0 2592 0 1172 738

8711 13.5 0 2592 0 1141 861

8712 15.6 0 2592 0 1395 793

8801 15 0 2592 0 1320 783

8802 14.3 0! 2592 0 1306 832

8803 14.7 0! 2592 0 1263 830

8804 13.1 0 2592 0 1159 801

8805 15.5 0 2592 0 1223 705

8806 13.6 0 2592 0 1283 642

8807 14.3 0 2592 0 1292 662!

8808! 16.7 0 2592 0 1370 741

8809 14.7 0 2592 0 1304 709

8810 16 0 2592 0 1366 817

8811 20 0 2592 0 1516 764

8812 18.7 0 2592 0 1572 641

111 76 64 36 19 7! 1 3

132 90 53 31 20 0 2 1

152 95 44 23 7 3 0 1

183 103 55 43 32 22 9 3

131 77 57 37 26 7 10 4

129 65 58 40 27 12 8 6

111 65 44 43 35 18 12 8

136 90 71 26 13 11 2 6

157 100 84 42 29 13 8 5

171 93 40 12 3 2 1 1

169 102 37 19 5 2 1 1

123 85 55 38 25 8 6 4

116 89 60 35 20 20 12 7

133 75 55 27 21 4 6 3

130 67 26 28 9 5 5 1

109 48 30 5 8 4 2 2

114 83 53 37 17 2 1 0

313 205 87 56 25 16 8 2

383 274 112 68 36 13 8 3

418 191 103 66 39 13 9 1

412 176 124 89 57 52 19 11i

322 158 105 50 26 18 6 2

401 188 108 63 26 23 10 2

342 176 98 48 36 21 18 4

312 261 209 102 53 31 22 10

362 255 142 79 31 14 4 0

389 217 105 53 14 7 1 1

395 194 87 54 21 15 2 1

444 132 75 59 36 18 10 6

362 188 67 57 27 21 8 5

378 234 85 58 43 28 23 7

289 126 44 23 13 6 3 1

296 147 93 30 20 7 9 6

321 146 72 46 28 22 4 2

258 76 57 55 36 13 6 2

465 131 60 471 52 _ 13 2 3

403 105 69 541 35 10 4 1

299 106 72 441 40 19 8 1

171 69 65 34! 28 18 10 8

238 79 65 36 28 28 12 2

178 101 58 32 29 20 26 9

235 102 81 34 22 10 11 3

332 113 56 54 34 27 13 2

370 135 77 54 14 8! 3 2

356 105 98 67 21 15 2 2

321 101 57 69 38 26 14 11

210 911 64: 39 28 23 20 5

321 71 62 51 34 21 9 9

208 84 44 34 16 12 6 4

147 66 38 27 14 11 3 5

172 74 44 39 23 11 13 2

First column shows the year and month yymm

Second column shows the ma)dmumfor the month in ram/day (max)

Third column shows the rdrimum rain rate in ram/day (rain) |

Fourth column shows the local number of pixels excluding pixels larger than 30 mm/day (ntot]

Fifth column shows the number of pixels in excess of 30 ram/day (nd)

Sixth -sixteenth columns show the histogram [ [ I

Bin I = number of pixels between rain and rain + (max-rain)/10, ...,

Bin 10 = number between max - (max-min)/lO and max I

96

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• AGENCY USE ONLY (Leave b/ank) 2. REPORT DATE 3. REPORT TY=I_EAND DATES COVERED

August 1997 Technical Memorandum

4. TITLE AND SUBTITLE 5. FUNDING NUMBERS

Technical Report Series on Global Modeling and Data Assimilation Volume 12-

Comparison of Satellite Global Rainfall Algorithms

6. AUTHOR(S)

Alfred T. C. Chang and Long S. Chiu

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS (ES)

Hydrological Sciences Branch

Climate and Radiation Branch

Goddard Space Flight Center

Greenbelt, Maryland

9. SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS (ES)

National Aeronautics and Space Administration

Washington, DC 20546-0001

11. SUPPLEMENTARY NOTES

C - NAS5-32332

Code 974/913

8. PEFORMING ORGANIZATION

REPORT NUMBER

97B00055

10. SPONSORINGIMONITORING

AGENCY REPORT NUMBER

TM-104606, Voi.12

12a. DISTRIBUTION / AVAILABILITY STATEMENT

Unclassified - Unlimited

Subject Category 46

12b. DISTRIBU_ON CODE

Availability: NASA CASI (301) 621-0390.

i i iii

:13. ABSTRACT (Maximum 200 words)

Seventeen months of rainfall data (August 1987-December 1988) from nine satellite rainfall algorithms (Adler, Chang,

Kummerow, Prabhakara, Huffman, Spencer, Susskind, and Wu) were analyzed to examine the uncertainty of satellitederived rainfall estimates. The variability among algorithms, measured as the standard deviation computed from the

ensemble of algorithms, shows regions of high algorithm variability tend to coincide with regions of high rain rates.

Histograms of pattern correlation (PC) between algorithms suggest a bimodal distribution, with separation at a PC-value

of about 0.85. Applying this threshold as a criteria for similarity, our analyses show that algorithms using the same

sensor or satellite input tend to be similar, suggesting the dominance of sampling errors in these satellite estimates.

ml

14. SUBJECT TERMS

Arkins algorithm, pattern correlation (PC), E1 Nino Southern Oscillation, Adlers algorithm,

t statistics

17. SECURITY CLASSlRCATION 18. SECURITY CLASSlRCATION 19. SECURITY CLASSlRCATION

OF REPORT OF THIS PAGE OF ABSTRACT

Unclassified Unclassified Unclassified

i

NSN 7540-01-280-5500

15. NUMBER OF PAGES

96

16. PRICE CODE

20. LIMITATION OF ABSTRAC'!

LTL

Standard Form 298 tRey. 2-89)