Gruber.et.al.2000.Bull.gpcp.pdf

7/28/2019 Gruber.et.al.2000.Bull.gpcp.pdf

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2631Bulletin of the American Meteorological Society

1. Introduction

The Global Precipitation Climatology Project

(GPCP) produced monthly mean precipitation data ona global 2.5 2.5 latitudelongitude grid for the

period of July 1987December 1998 (now available)

by combining gauge measurement and satellite esti-

mates (Huffman et al. 1997). Independently, Xie and

Arkin (1997) of the National Weather Service, Climate

Prediction Center, developed a merged satellitegauge

dataset with the same grid resolution. This dataset,

named for the Climate Prediction Center Merged

Analysis of Precipitation (CMAP) has its heritage in

the GPCP in that it shares some of the same input data.

CMAP, however, utilizes a different analysis proce-dure and adds other satellite data. Since both datasets

will ultimately be used in a variety of scientific stud-

ies such as seasonal to interannual variability studies

and model validation (e.g., Kondragunta and Gruber

1997; Janowiak et al. 1998), it is important to under-stand the differences between them. This paper repre-

sents such an attempt emphasizing the large time- and

space scales and using the error characteristics of the

monthly mean rainfall estimates as reported by the

developers to assess the significance of any differ-

ences. The study will compare the period July 1987

December 1998, which is a common period between

the two datasets. It should be noted, however, that the

datasets are continually being extended and that

CMAP actually begins in 1979. Also, while the two

datasets share much of the same input data, the inclu-sion of additional satellite data and the different analy-

sis methodology used by CMAP means that we may

not be able to unequivocally identify the cause of the

differences in all cases. Nevertheless we hope that one

of the results of the comparison may give some feed-

back for improving the methodology in merging gauge

and satellite measurements. Keeping these goals in

mind, the paper focuses on the spatial and temporal

distribution comparison and causes of the difference

between the two datasets.

The Comparison of Two MergedRain GaugeSatellite Precipitation

Datasets

Arnold Gruber,* Xiujuan Su,+ M. Kanamitsu,+ and J. Schemm+

*NOAA/NESDIS Office of Research and Applications, Camp

Springs, Maryland.+NOAA/NWS, National Centers for Environmental Prediction,

Camp Springs, Maryland.

Corresponding author address: Dr. Arnold Gruber, NOAA/

NESDIS, Office of Research and Applications, Camp Springs,

MD 20746-4304.

In final form 21 April 2000.

ABSTRACT

Two large-scale precipitation datasets, one produced by the Global Precipitation Climatology Project (GPCP) and

the other by the Climate Prediction Center of the National Weather Service, and called Climate Prediction Center Merged

Analysis of Precipitation (CMAP), were compared. Both datasets blend satellite and gauge estimates of precipitation.

And while the latter has its heritage in the GPCP, different analysis procedures and some additional types of input data

used by CMAP yielded different values. This study used the error characteristics of the data to assess the significance of

the observed differences. Despite good spatial and temporal correlations between the two fields some of the observed

differences were significant at the 95% level. These were traced to the use of some different input data such as the use

by CMAP of atoll gauges in the tropical Pacific and gauges uncorrected for wetting evaporation and aerodynamic ef-

fects. The former impacts the tropical ocean rain amounts and the latter is particularly noticeable in the Northern Hemi-

sphere land areas. Also, the application of these datasets to the validation of atmospheric general circulation models is

discussed.


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2632 Vol. 81, No. 11, November 2000

2. The data and merge methods inGPCP and CMAP

a. Data

The gauge and satellite estimates used in GPCP-

and CMAP-merged precipitation datasets are summa-

rized in Table 1 adapted in part from Janowiak et al.

(1998). The gauge data that was used by both GPCPand CMAP from 1987 to 1998 are from the Global Pre-

cipitation Climatology Center (GPCC; Rudolf et al.

1996). The GPCC collected 6700 rain gauge stations

worldwide, subjected them to a rigid quality control,

and prepared rainfall analyses on a 2.5 2.5 latitude

longitude grid. However, the GPCP uses the version

of the GPCC gauge analysis that is corrected for sys-

tematic errors caused by wetting, evaporation, and

aerodynamic effects, while CMAP used a version of

the analyses that is uncorrected. The climatological

estimate of systematic error is from Legates, which isbased on Sevruks method (Huffman et al. 1997). The

correction factor is between 1 and 1.5 and between

60S and 60N for most areas. In addition, the CMAP-

merged analysis uses more than 100 gauges located

on atolls and small islands (Morrissey and Greene

1991) for correction of merged satellite precipitation

estimates over the tropical ocean (Xie and Arkin

1997), but the GPCP does not. The impacts of those

100 stations and the use of uncorrected gauge data willbe discussed later.

The common satellite precipitation estimates used

by both GPCP and CMAP are based on the Geosta-

tionary Operational Environmental Satellite (GOES)

operated by the United States; the Geostationary Me-

teorological Satellite operated by Japan; the Meteoro-

logical Satellite, Meteosat, operated by the European

Community; and the National Oceanic and Atmo-

spheric Administrations (NOAA) polar-orbiter infra-

red (IR) measurements in the region 40N40S, and

microwave scattering and emission measurementsfrom the Special Sensor Microwaver/Imager (SSM/I)

Geostationary infrared Eight times per day 40N40S GPCP Arkin and Meisner (1987)

(GPI) (land/ocean) CMAP

Polar infrared Four times per day 40N40S GPCP Janowiak and Arkin (1991)(land/ocean) CMAP

SSM/I microwave Up to two times 60N60S GPCP Wilheit et al. (1991)

emission per day (ocean only) CMAP

SSM/I microwave Up to two times 60N60S GPCP Ferraro et al. (1996);

scattering per day (land only) Ferraro and Marks (1995)

SSM/I microwave Up to two times 60N60S CMAP Ferraro et al. (1996);

scattering per day (land, ocean) Ferraro and Marks (1995)

OLR Precipitation Four times per day 90N90S CMAP Xie and Arkin (1998)

Index (OPI) (land/ocean)

Microwave Sounding Four times per day 60N60S CMAP Spencer (1993)

Unit (MSU) (ocean only)

Rain gauge Continuous at Global land GPCP Rudolf et al. (1996)

stations (corrected)

CMAP

(uncorrected)

Rain gauge Continuous at Tropical atolls CMAP Morrissey and Greene

stations (1991)

TABLE 1. Some characteristics of the satellite and rain gauge data used by the GPCP and CMAP analyses.

Sensor Sampling Coverage Usage References


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on the polar-orbiting Defense Meteorological Satellite

Program satellites. The emission estimates are from

Wilheit et al. (1991) and are available over oceans

only. The scattering estimates are from Ferraro et al.

(1996) and Ferraro and Marks (1995) and are avail-

able over land and ocean; however, they are used by

the GPCP over land only, while CMAP uses the scat-

tering estimates over both land and ocean. The geo-stationary infrared precipitation estimates are obtained

from the GOES Precipitation Index (GPI; Arkin and

Meisner 1987), which has been adapted for use on all

the geostationary satellites. It is applicable only be-

tween 40N and 40S where it is most reliable. This

technique is also applied to histograms of the polar-

orbiting data or when histograms are not available to

GPI-calibrated estimates of the outgoing longwave

radiation (OLR). Each of the satellite estimates used

in the GPCP and CMAP analyses has its advantages

and disadvantages. For example, the geostationary GPIestimates, which are based on cloud-top temperatures

equal to or colder than fixed threshold temperature

(235 K), have the most frequent sampling, but may

suffer from inaccuracy in estimating precipitation

amounts. Clearly this is the case for rain clouds with

tops warmer than 235 K and nonraining clouds with

cold top clouds such as cirrus. Microwave estimates

from the SSM/I are more closely related to precipita-

tion processes in clouds than the IR and provide more

accurate instantaneous rain estimates but are available

only up to two times per day for most areas. Infraredpolar satellite data are used only when the GPI is not

available. In addition to using SSM/I-scattering esti-

mates over the ocean, the CMAP-merged analysis uses

Microwave Sounding Unit (MSU)-based precipitation

estimates from Spencer (1993) and OLR-based pre-

cipitation index (OPI) data from Xie and Arkin (1998).

b. Merging methods

In general, rain gauge measurements provide rela-

tively accurate point estimates of precipitation but

suffer from sampling errors in representing area meansand are not available over most oceans and remote

areas. Satellite measurements can cover most of the

globe; however, they suffer from errors due to lack of

a direct relationship between observation parameters

and precipitation. The major goal of merging gauge

measurements and satellite estimates is to retain each

datasets advantage while reducing overall errors. As

described above, the GPCP and CMAP analyses use

some common datasets; however, the merging proce-

dures are quite different.

Huffman et al. (1995, 1997) described the GPCP

merging procedure. The first step is to merge the

SSM/I microwave emission estimates used over wa-

ter with the SSM/I microwave scattering estimates

used over land. Then the magnitudes of IR-based es-

timates obtained using the GPI (Arkin and Meisner

1987), which is used for all the geostationary IR sat-

ellites, are adjusted by the ratio of SSM/I- and GPI-based estimates that are roughly coincident in space

and time. Polar-orbiting IR estimates are adjusted using

a smoothly varying interpolation of the SSM/IGPI

adjustment ratio where the GPI is not available. Adjusted

GPI and polar IR estimates are limited to the area be-

tween 40N and 40S. The SSM/I estimates are used

alone outside the 40N40S region. The combination

of adjusted GPI with SSM/I estimates forms a

multisatellite estimate. Over land the bias of the

multisatellite estimate is adjusted according to the large-

scale (five grid boxes five grid boxes) average gaugeanalysis. Random errors are estimated for each dataset:

multisatellite and gauge estimate. The final step is to

merge multisatellite estimates and gauge estimates us-

ing a maximum likelihood estimate in which both val-

ues are combined with inverse error-variance weighting.

The CMAP merging analysis is summarized in Xie

and Arkin (1996, 1997). The first step is to produce a

base period of merged data. The GPI, SSM/I-scattering,

and the SSM/I emission precipitation estimates; the

OPI; and the ocean precipitation estimates based on

the MSU are combined in the first step by using amaximum likelihood estimate in which the weighting

coefficients are inversely proportional to the squares

of the individual random errors. The errors of each

satellite estimate are determined by comparing with

GPCC gauge measurements over the land and with

atoll gauge measurements (Morrissey and Greene

1991) for an estimate of the errors over the ocean. The

second step is the removal of possible biases by com-

bining the results of the first step with gauge analy-

ses. Over the land the blend with gauge measurements

uses the methods developed by Reynolds (1988). Inthis blending method, the assumption is made that the

combined satellite estimate can represent the structure

of precipitation distribution and there is no bias in

gauge estimates. The structure of precipitation can be

described by a Poisson equation in which the bound-

ary conditions are determined by gauge estimates.

Then the merged base products are obtained by solv-

ing the Poisson equation. Over the ocean the bias re-

moval is accomplished by comparing with atoll gauges

over the Tropics and by subjective assumptions about


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2634 Vol. 81, No. 11, November 2000

the bias structure in the extratropics. Since the atolls

are in the tropical western Pacific it is recognized that

the oceanic bias adjustment may differ in other parts

of the oceans (Xie and Arkin 1997).

3. The comparison of the GPCP and the

CMAP datasets and discussion

In the comparisons to follow we have tried to as-

sess the statistical significance of some of the differ-

ences by using the random error estimates provided

in the datasets. The procedures for estimating the num-

ber of degrees of freedom of monthly mean precipita-

tion estimates that are further averaged in space and

time were adapted from Janowiak et al. (1998). That

procedure calculates the number of independent

samples of space- and time-averaged data by account-

ing for the serial correlation typically found formonthly mean precipitation estimates. This informa-

tion is used to perform a ttest of significance of the

differences at the 95% confidence level. The null hy-

pothesis that the differences are not significantly dif-

ferent from zero at the 95% level is given as

P P tN N

i i

1 2 0 95

1

2

1

2

2

2

1 2

+

.

,

(1)

whereP1

andP2

are the sample mean, 1

and 2

are

the mean standard errors, andNi1

andNi2

are the inde-

pendent numbers of sample 1 and 2, respectively. For

large samples t0.95

1.96 at the 95% level.

a. Spatial pattern

The spatial pattern of the GPCP and the CMAP

annual mean precipitation for the period January

1988December 1998 (Fig. 1) are nearly identical

over the domain 60N60S. Indeed the spatial cor-

relation between them is 0.92. Both datasets depict theintense precipitation in the intertropical convergence

zone (ITCZ) over the Indian, Pacific, and Atlantic

Oceans, the South Pacific convergence zone (SPCZ),

the South Atlantic convergence zone, the primary

storm tracks over the North Pacific Ocean along the

Asian coast and North Atlantic Ocean along the North

American coast, and the active convective areas over

the continents of South America, Africa, and South-

east Asia. Also, both portray the dry areas with annual

mean precipitation less than 0.5 mm day1 in the

subtropical subsidence regions of the South Pacific

along the coast of Peru and Chile, the South Atlantic

between Brazil and South Africa, and the Sahara

Desert.

The differences in the annual means are shown in

Fig. 2; the upper panel is GPCP CMAP and the lower

panel is the percent difference. Looking at the upper

panel it is seen that the differences are mostly between0.5 mm day1 over most of the domain especially

over land where gauge measurements play an impor-

tant role in determining the final magnitude of the

merged precipitation estimates. Notable exceptions are

in the equatorial Tropics where CMAP is 0.5

2.5 mm day1 higher than GPCP and in the North At-

lantic storm tracks where GPCP is higher than CMAP

by up to 2.5 mm day1.

The relative difference (lower panel) provides a

more sensitive measure of the differences. It is calcu-

lated as the difference between the GPCP and CMAPestimates divided by their average,

FIG. 1. Annual mean precipitation in mm day1 for the period

Jan 1988Dec 1998 for (top) CMAP and (bottom) GPCP.


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(GPCP CMAP)/(GPCP + CMAP)/2.

Note that in the very dry areas such as the subtropical

dry zones and deserts it is not very meaningful since

small differences have a relatively large signal. The

distribution of the relative difference is similar to the

upper panel; however, an important feature that now

shows more clearly is the large positive differences of

about 10%50% over central North America and Eu-

rope and Asia in mid- and high latitudes. Since both

merge procedures utilize gauge data in setting the

magnitude, this broad pattern might be considered

puzzling except that the GPCP uses the correctedgauge data while the CMAP does not, as mentioned

earlier. Since the corrections always increase the rain-

fall the larger values for GPCP are quite reasonable and

in fact agree very well with a map of the relative differ-

ences of the corrected and uncorrected gauges (Fig. 3).

Also noticeable in the relative difference field are

large negative differences along coastal areasmost

noticeably the west coastal oceans of South America,

Africa, and North America. There are several factors

that may influence these differences. First, in ocean

coastal areas the GPCP assigns the gauge analyses pro-

portionally to the area of ocean and land in the grid boxwhereas CMAP considers the grid as either water or land

depending on a threshold value. Second, the GPCP

uses only SSM/I emission estimates over the oceans

with a fairly broad boundary near the coast in order to

avoid land contamination in the estimate. CMAP uses

both SSM/I scattering and emission over oceans and

is thus less influenced by land/water boundaries.

Finally over land areas another factor that may

contribute to the observed differences is the method-

ology used when there is no gauge measurement in a

grid box. In the GPCP analysis, the precipitation val-ues in the grid boxes where no gauge measurements

are available are determined by interpolating the

gauge measurements from surrounding areas. In the

FIG. 2. Difference maps of annual mean precipitation: (top)

GPCP CMAP in mm day1; (bottom) the relative difference,

(GPCP CMAP)/(CMAP+ GPCP)/2, in percent.

FIG. 3. The relative difference between corrected gauge (CG)

and uncorrected gauge (UC) analyses from the GPCC. The rela-

tive difference is (CG UG)/(CG + UG)/2 and is expressed in

percent.

FIG. 4. The frequency of disagreement (%) between CMAP and

GPCP. The fields are considered in disagreement if they exceeded

1.96 times the average standard errors of monthly precipitation

(the 95% confidence interval).


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2636 Vol. 81, No. 11, November 2000

CMAP-merged analysis, the values of precipitation

are determined by the modified satellite estimates.

This difference in analysis methods can contribute to

some of the observed differences. One example is over

central equatorial Africa where the relative difference

approaches 50%. Recently, McCollum et al. (2000)

examined the difference between the GPCP-merged

and multisatellite estimates (GPI and SSM/I) in equa-

torial central Africa and demonstrated that both GPI

and SSM/I overestimate the monthly precipitation in

central Africa relative to the gauge and the GPCP-

merged analysis. This is an area where the gauge den-

sity is very low. The GPCP-merged analysis, which

utilizes surrounding gauge information from a large

area, is more influenced by the gauges whereas theCMAP is more influenced by the satellite estimates

in areas with low-gauge density. This may be why

CMAP is up to 50% higher than GPCP in that area.

Clearly this affects primarily land areas and may not

be a factor in coastal regions except in mixed land/

water grids.

Over the oceans the differences may be explained

in large measure by the different data sources used in

each climatology. In the Tropics the biggest influence

comes from the use of the atoll gauge dataset that is

used to adjust the final values in CMAP but is not usedin GPCP because of the very low density of gauges.

The adjustment used in CMAP is applied throughout

the Tropics with decreasing weight with latitude phas-

ing out to zero at 40N,S. GPCP uses only the GPI and

SSM/I emission estimates and further adjusts the IR

to the SSM/I estimates. Studies have shown that all sat-

ellite estimates are lower than the atoll gauge measure-

ments (Xie and Arkin 1995). Thus, precipitation from

CMAP with the atoll gauge correction probably results

in higher values than from GPCP over the tropical

oceans.At higher latitudes over the oceans (4060S,

4060N), CMAP merges SSM/I scattering and

SSM/I emission while GPCP depends only on SSM/I

emission estimates. The SSM/I emission precipitation

estimates are almost 6 times higher than SSM/I-scattering

estimates according to Xie and Arkin (1995). This

may explain in part why the precipitation from CMAP

is less than from GPCP there.

FREQUENCYOFDISAGREEMENT

The previous discussion looked at the average dif-ferences over the entire period. Clearly as one aver-

ages over space and time the random error reduces

significantly and the differences can become signifi-

cant, as will be shown later. However, it is of interest

to see how well individual monthly mean estimates

agree or disagree at each grid point. We have utilized

the error estimates provided in the datasets to calcu-

late the frequency of disagreement between the two

datasets at each grid point for the period 198897. The

estimates were judged to disagree if the monthly mean

FIG. 5. Zonal average profiles of annual mean precipitation:

(top) CMAP and GPCP (CMAP is solid, GPCP dashed), their dif-

ference; (middle) CMAP GPCP; and (bottom) significant dif-

ferences at the 95% level, shaded black. Units are in mm day1.


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difference between them exceeded1.96

times the average standard errors of

monthly precipitation at a grid point

from both datasets. This is the range of

values for the 95% confidence interval

for the null hypothesis that these two

datasets are from the same population. If

it exceeds this interval the null hypoth-esis is rejected and disagreement is sig-

nificant. This is based on the assumption

that the monthly precipitation estimates

fall in a normal distribution. Although

the distribution of the population from

which the monthly precipitation is cal-

culated is not known (daily or hourly

precipitation), it is assumed normal

based on the central limit theorem.

Another assumption related to the test is

that both monthly precipitation estimatesare unbiased and the difference between

them is caused by random errors, esti-

mated by Huffman et al. (1997) and Xie

and Arkin (1996). However, as discussed

by Janowiak et al. (1998) these assump-

tions may not always be assured. Thus,

the confidence limits should not be considered abso-

lute but the test is a useful diagnostic that can provide

insights into the comparison of the datasets.

The spatial distribution of the frequency of dis-

agreement is shown in Fig. 4. Over the study period,the frequencies of disagreement between two datasets

are less than 10% over most of the area between 60N

and 60S. Over the equatorial tropical oceans frequen-

cies as high as 50%60% are observed in isolated ar-

eas, with the values typically being 10%30% in areas

where the mean differences (Fig. 2) were large. The

largest frequency of disagreement between the two

datasets occurs over the coast line of northern South

America, Southeast Asia, Africa, and over the north

European land (20%80%) with the higher values oc-

curring at isolated locations.This pattern is reflected in the profile of zonal av-

erages of the annual mean values (Fig. 5). The zonal

means are in the upper panel where it is seen in the

region 15S to about 20N that the GPCP is less than

CMAP and in the region from 35N,S to about 60N,S

GPCP is greater than CMAP. The largest difference,

about 1 mm day1, is located at 5N. The lower panel

shows in black where the differences are significant

at the 95% confidence level. In between the differences

are statistically negligible.

b. Temporal variability

The mean annual cycle of both CMAP and GPCP

are displayed in Fig. 6 for land and ocean for the North-

ern Hemisphere (060N), Southern Hemisphere (0

60S), and the globe (60N60S). For a morecomplete assessment of the significance of the differ-

ences, reference should be made to Table 2. There the

differences and their 95% confidence intervals

grouped by season for land and ocean for each hemi-

sphere and for the globe are displayed. The seasons are

DecemberJanuaryFebruary (DJF), MarchApril

May (MAM), JuneJulyAugust (JJA), and Septem-

berOctoberNovember (SON). If the magnitude of

the confidence interval is less than the magnitude of

the difference the differences are judged to be signifi-

cant at the 95% level. Looking first at the time seriesof the land areas (Fig. 6) it is seen that both CMAP

and GPCP show nearly identical seasonal variability

in each hemisphere, as should be expected in view of

the gauge influence. An interesting feature is the large-

amplitude variation in the Southern Hemisphere, but

because of its limited area the 60N60S average

(plotted in the figure and distinguished by its low

amplitude) is dominated by the Northern Hemisphere

values. It is also seen that GPCP tends to be slightly

higher than CMAP, especially in the Northern Hemi-

FIG. 6. Average seasonal cycle for land, ocean, and combined land and ocean

for the Northern Hemisphere, 060N, and the Southern Hemisphere, 060S,

and 60N60S. The middle group of low amplitude lines is the 60N60S av-

erage for each month: land, ocean, and combined. CMAP is solid, GPCP is dashed.

Units are in mm day1.


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2638 Vol. 81, No. 11, November 2000

sphere, which is consistent with our previous discus-

sion on the use of corrected and uncorrected gauges.

Looking at Table 2 it is interesting to note that the sig-

nificant differences between GPCP and CMAP are

during the JJA and SON season in the Northern Hemi-

sphere and for the 60N60S area and for the annual

average 060N and 60N60S.

The oceans present a somewhat different picture.The variabilities between the two datasets are quite

similar however; as seen in Table 2 there are signifi-

cant magnitude differences between them in the an-

nual averages and all seasons except for SON in the

Southern Hemisphere and 60N60S in DJF. Further

note that in the Northern Hemisphere CMAP is greater

than GPCP from April to November and less from

about December to March, undoubtedly the influence

of the tropical differences during the summer months

and the storm track differences in the winter months.

The Southern Hemisphere shows a reverse differenceto the Northern Hemisphere, that is, CMAP greater

than GPCP in JanuaryApril and slightly less than

GPCP JuneOctober, but not as prominently.

The combined land and ocean for 60N60S show

vanishing small differences with the MAM and JJA

seasons exhibiting a barely statistically significant

difference.

c. Temporal correlations

A good way to examine how the two datasets agree

in time is to look at the temporal correlations betweenthem at each grid point. We have calculated correla-

tions for both the mean fields and the anomaly fields.

The anomalies are calculated for each month of the

060S 0.19 0.30 0.15 0.24 0.03 0.14 0.03 0.22 0.10 0.12

Land 060N 0.11 0.31 0.10 0.17 0.15 0.10 0.13 0.10 0.12 0.10

60S60N 0.10 0.25 0.11 0.14 0.12 0.08 0.10 0.09 0.11 0.08

060S 0.12 0.06 0.11 0.05 0.07 0.04 0.03 0.04 0.03 0.02

Ocean 060N 0.09 0.07 0.13 0.07 0.40 0.10 0.28 0.09 0.18 0.04

6060N 0.03 0.05 0.11 0.05 0.12 0.05 0.09 0.04 0.08 0.03

Land and 60N60S 0.01 0.06 0.06 0.05 0.06 0.05 0.04 0.05 0.03 0.03

ocean

TABLE 2. The mean precipitation difference (mm day1) between CMAP and GPCP (CMAP GPCP) and the 95% confidence

intervals for the period 198794. The entries are stratified by region: 060N, 060S, 60N60S, and for land and ocean sepa-

rately and combined and for seasons as defined in the text. Differences judged to be significant at the 95% level are in bold type.

Latitude range DJF MAM JJA SON Annual

FIG. 7. Temporal correlation between CMAP and GPCP for

(top) monthly precipitation and (bottom) anomaly fields. The

anomalies are calculated based on the period Jul 1987Dec 1998for both datasets with the annual cycle removed. Units are in

mm day1.


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dataset (annual cycle removed) for the

period July 1987December 1998 for

both the GPCP and CMAP datasets.

They are shown in Fig. 7. For the mean

fields (upper panel) correlation coeffi-

cients are greater than 0.9 for most re-

gions over land where the gauge data

plays a dominant role and for the tropi-cal oceans where satellite estimates are

more accurate. Correlation coefficients

less than 0.5 are evident for small areas

in the southeast Pacific and Atlantic sub-

sidence region and the Sahara Desert

where the monthly precipitation is less

than 0.5 mm day1.

The anomaly correlations show some

striking differences. There is consider-

ably less area of greater than 0.9 correla-

tion than for the mean fields, mostnotably over the tropical and subtropical

land areas and in the high-precipitation

areas of the ITCZ, SPCZ, and the Indian

Ocean, although those areas still exhibit

correlations greater than 0.7.

To further examine the time series of these two

datasets, we examined time series of mean precipita-

tion and monthly anomaly for six zones; 020N,S;

2040N,S; and 4060N,S, over land and ocean

separately. Figures 8 and 9 contain the time series of

monthly means for land and ocean re-spectively, and Figs. 10 and 11 show the

anomalies for land and ocean, respec-

tively. The monthly mean and anomaly

correlations of the time series of CMAP

and GPCP are summarized in Table 3.

For the monthly mean the two datasets

fit each other better over land than over

ocean although the correlations over

both land and ocean are mostly greater

than 0.9, the exception being the ocean

areas 4060N and 4060S. The bestfit occurs over the land areas of 0

40N,S (correlation coefficients are be-

tween 0.9960.999), and the poorest fit

occurs over 4060S ocean (correlation

coefficient 0.680). The largest difference

(< 0.5 mm day1) between two time se-

ries over land occurs between 4060N

in each winter, which further confirms

the influence of the corrected and uncor-

rected gauge data. Over the oceans high

FIG. 8. Comparison of CMAP and GPCP time series of monthly mean precipi-

tation over land for 20-wide latitude belts from 60N to 60S. CMAP is solid,

GPCP is dashed. A single solid line indicates overlap. Units are mm day1.

FIG. 9. Same as Fig. 8 but for ocean areas.

values of CMAP, as seen on the average maps, are

evident in the 020N,S regions where the differ-

ences are largest in the respective summer months

with better agreement in the winter season. In the

ocean zone 4060N,S the GPCP is systematically


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2640 Vol. 81, No. 11, November 2000

higher than CMAP in most of the months. There are

small or no differences between CMAP and GPCP

over the 2040N,S ocean.

For the monthly anomaly, the correlations between

the GPCP and the CMAP resemble those of the monthly

precipitation time series in that they are higher over

land than over oceans. However, over the oceans the

anomaly correlation is less than 0.7 in the

three Northern Hemisphere zones and is

somewhat higher in the Southern Hemi-

sphere. This suggests that the two datasets

may exhibit some differences in the oce-

anic anomaly structures, although it may

be somewhat subtle since the correlations

are relatively high. In fact we comparedthe anomaly structures for the major

El Nio warm event of 1997/98 (Fig. 12)

and indeed find for all practical purposes

there is no significant difference in the

anomaly pattern and only subtle differ-

ences in magnitude. This was also reflected

in anomaly time series (not shown) over

Nio-4 (5N5S, 160E150W) and

Nio-3.4 (5N5S, 170120W),

which exhibited excellent agreement de-

picting not only the 1997/98 warm epi-sode but also the 1991/92 warm and

1988/89 and 1995/96 cold events, with

only minor differences in magnitude.

4. Validation of GCM precipitationoutputs

One important use of these climate-scale estimates

of precipitation is to validate the tropical precipitation

produced by atmospheric general circu-lation models (AGCMs). It has been

known that although the long-range pre-

dictability is the greatest in the Tropics

(Shukla and Fennessy 1988), AGCMs in

general do not simulate precipitation

very well because of imperfectness in the

model, particularly the convective pa-

rameterization (Sperber and Palmer

1995). Improvements in tropical precipi-

tation is an imperative task of numerical

modelers for providing credible guidancefor short- and seasonal-scale weather pre-

dictions. In this study, the CMAP and

GPCP precipitation analyses have been

utilized to verify tropical precipitation in

two atmospheric general circulation

model simulations and in the National

Centers for Environmental Prediction

National Center for Atmospheric Re-

search (NCEPNCAR) reanalysis. As

was discussed earlier, these estimates

FIG. 10. Same as Fig. 8 but for anomalies over land.

FIG. 11. Same as Fig. 10 but for anomalies over ocean.


11/14


while capturing the mean structures quite well have

somewhat lower anomaly correlation with each other

in oceanic areas. In an attempt to utilize these datasets

for validating the large-scale precipitation structure of

AGCMs, both analyses and AGCM-simulated precipi-

tation were filtered to capture only large-scale features.

The filtering of the small scale is a well-known pro-

cedure for verification of forecasts, since simulationof the large scale is the primary interest of modelers

(and simulation of the small scale is much more diffi-

cult). The spectral smoothing of triangular wavenum-

ber 20 (T20) is commonly used to verify 500-hPa

geopotential height field for comparison of various

short- and medium-range forecast models. The vali-

dation of forecasts using raw data produces misleading

results since errors in small-scale features contaminate

the verification of large scales. Following the common

procedures used in geopotential height verifications,

and considering that the precipitation has larger spa-tial variability, the following smoothing procedures are

employed to the precipitation field. First, the logarithm

of base 2 was applied to precipitation amount to tem-

per spatial gradients and then spectral truncation was

imposed. Figure 13 illustrates an example of a filtered

precipitation field compared to the original. It dem-

onstrates that the logarithmic manipulation does not

distort the spatial distribution and the spectral trunca-

tion retains the large-scale features without abnormal

expansion of precipitation area.

When the same filter was applied to the two analy-ses, the difference between them diminishes consid-

erably and they correlate much better to each other.

This is shown in Fig. 14, which compares the weighted

anomaly correlation (AC) between the two analyses

over the Tropics (20S20N) for the 198897 period.

The average AC over the period increases to 0.94 from

0.86 with filtered analyses. The variability of the AC

is much smaller for filtered analyses. In computing

weighted AC, the anomalies are weighted proportional

to the inverse of the climatological root-mean-square

difference (rmsd) between the filtered analyses. This

procedure is introduced to maximize the AC between

the two analyses by incorporating uncertainties in

measurements. Therefore, AC of 0.94 might be the

upper bound of agreement between the two analyses

over the Tropics.

Monthly Land 0.999 0.997 0.980 0.996 0.998 0.958

Ocean 0.964 0.905 0.865 0.976 0.922 0.680

Anomaly Land 0.967 0.969 0.978 0.946 0.987 0.823

Ocean 0.680 0.672 0.631 0.817 0.824 0.708

TABLE 3. The correlation between the GPCP and the CMAP for the indicated latitude zones for anomaly and monthly mean pre-

cipitation and separated by land and ocean.

020N 2040N 4060N 020S 2040S 4060S

FIG. 12. Depiction of the precipitation anomaly for El Nio

conditions during Dec 19971998: (top) GPCP and (bottom)

CMAP. Units are mm day1

.


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2642 Vol. 81, No. 11, November 2000

The filtered analyses were used to evaluate the

tropical precipitation simulations with two AGCMs.

One is an experimental version of the Medium Range

Forecast (MRF) AGCM of NCEP and the other is

ECHAM AGCM of the Max Plank Institute, Ham-

burg, Germany. Both of the AGCM simulations were

performed in the horizontal spatial resolution of T40.

The simulation with the NCEP MRF was for the 1979

98 period and that of ECHAM was for a

much longer period of 195094. The

observed sea surface temperature (SST)

was given as the lower boundary condi-

tion for both simulations. The simulated

precipitation underwent the same filter-

ing procedure as in the analyses. Because

of the longer overlapping data period, theskill estimates of the AGCM simulations

shown in this paper are with the CMAP

analysis. Both models exhibit the same

level of skill in simulating the tropical

precipitation despite several differences

in the model configurations. The mean

AC over the 197994 period is 0.19 for

MRF and 0.21 for ECHAM (Figs. 15 and

16). Mean AC scores using unfiltered

simulation and analysis are 0.010.02

lower as expected. Close inspection ofthe figures reveals that the AC scores are

high in some years when strong El Nio/

La Nia events are observed (e.g., years 1983, 1987,

1989, and 1992). The filtered scores for those events

are much higher than the unfiltered ones, and compari-

son of the two models becomes much more meaning-

ful with these scores. In comparison, the mean AC of

the NCEPNCAR reanalysis (Kalnay et al. 1996) pre-

cipitation is at the level of 0.54 for the same period

(Fig. 17), and the score with unfiltered data is nearly

FIG. 14. Anomaly correlation coefficient between the CMAP

and GPCP analyses over the Tropics (20N20S). Solid line cor-

responds to AC with the original analyses and dashed line to the

filtered analyses. Solid and dashed straight lines indicate average

AC for the 198897 period.

FIG. 15. Tropical (20N20S) anomaly correlation coefficients

of the MRF GCM simulations against the CMAP analysis. Solid

line corresponds to AC between the original analysis and simula-

tion and the dashed line to that of the filtered analysis and simu-

lation. Solid and dashed straight lines indicate average AC for the

197994 period.

FIG. 13. Comparison of (left) original and (right) filtered precipitation for Jun

1998 from the CMAP analysis. (top) The total precipitation and (bottom) anoma-lous precipitation, mm day1.


13/14


0.1 lower. The tendency for the higher scores to bemagnified with filtered data is also apparent. The same

AC scores were computed using GPCP analysis (for

shorter period) and comparable skill levels for both

AGCMs and reanalysis precipitation were observed

(not shown).

These examples indicate that smoothed analysis

provides excellent means to compare skill of differ-

ent models, but model simulation itself is still far from

perfect and the choice of analysis is still not an im-

portant issue for model verifications.

5. Conclusions

The two observation datasets, the GPCP1 and the

CMAP, derived from combining several satellite es-

timates and gauge measurements are compared. Both

datasets are compatible in overall space and temporal

pattern and in depicting important events such as the

El NioSouthern Oscillation (ENSO) episodes. The

intensity patterns of annual global distribution from

both datasets match each other, as evidenced by themean spatial correlation of annual global distribution

being 0.92. The position of important intensive pre-

cipitation zones as the intertropical convergence zone,the South Pacific convergence zone, the South Atlan-

tic convergence zone, and storm tracks generally co-

incide with each other. The seasonal variations from

both datasets have similar phase and amplitude. The

correlation coefficients of monthly precipitation are

mostly greater than 0.90 except in the 4060S

oceans. Anomaly correlations are lower, especially

over oceans where they range from 0.63 to 0.82.

Nevertheless the spatial pattern of cold and warm

ENSO episodes agree quite well.

However there exist differences in magnitude inmean values, global distribution, and zonal average

time series. The mean values over land from GPCP are

greater than from CMAP and which is larger over other

areas depends on the seasons of the year. The magni-

tude of the difference of global distribution tends to

be larger for more intensive precipitation. The varia-

tion of magnitude differences between monthly pre-

cipitation of the two datasets causes the lower

correlation between the monthly anomaly. The major

difference between the two datasets over land is caused

by the different treatment of the gauge measurements.Over tropical oceans the incorporation of the tropical

atoll data by CMAP in their analysis is the main cause

of the differences between CMAP and GPCP, which

does not use the atoll data. Over higher-latitude oceans

the differences may be caused in part by the different

satellite input data, as previously discussed. Clearly,

over the oceans, where there are essentially no gauges

to anchor the magnitudes, the estimates are very sen-

sitive to the satellite input data or even scattered gauge

data such as available from atolls.

FIG. 16. Same as Fig. 15 but for the ECHAM GCM.

1The GPCP and CMAP data are available online. For GPCP the

address is http://www.ncdc.noaa.gov/pub/data/gpcp/. The com-

plete merged dataset as well as individual components as well as

some experimental datasets are available. The CMAP data can

be obtained via anonymous ftp: ftp.ncep.noaa.gov cdpub/precip/

cmap.

FIG. 17. Same as Fig. 15 but for reanalysis vs CMAP.


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2644 Vol 81 No 11 November 2000

A filtering technique that retains the large-scale

characteristics of the precipitation fields was shown

as a good way to compensate for the differences of the

two analyses especially for use in validating precipi-

tation produced by AGCMs.

Acknowledgments. The authors wish to thank Bob Adler,

George Huffman, Pingping Xie, Robert Livezey, and John

Janowiak for many fruitful discussions and other help. Also, we

wish to acknowledge the NOAA Office of Global Programs,

which provided support to two of the authors, AG and XS.

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