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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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2633Bulletin of the American Meteorological Society
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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2635Bulletin of the American Meteorological Society
(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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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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2637Bulletin of the American Meteorological Society
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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2639Bulletin of the American Meteorological Society
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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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.
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2641Bulletin of the American Meteorological Society
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.
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2643Bulletin of the American Meteorological Society
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.
References
Arkin, P. A., and B. N. Meisner, 1987: The relationship between
large-scale convective rainfall and cold cloud over the west-
ern hemisphere during 198284.Mon. Wea. Rev.,115, 5174.
Ferraro, R., and G. Marks, 1995: The development of SSM/I rain-
rate retrieval algorithms using ground-based radar measure-
ments.J. Atmos. Oceanic Technol.,12, 755770.
, F. Weng, N. C. Grody, and A. Basist, 1996: An eight-year
(19871994) time series of rainfall, clouds, water vapor, snow
cover, and sea ice derived from SSM/I measurements. Bull.
Amer. Meteor. Soc.,77, 891905.
Huffman, G. J., R. F. Adler, B. Rudolf, U. Schneider, and P. R.
Keehn, 1995: Global precipitation estimates based on a tech-
nique for combining satellite-based estimates, rain gauge
analysis, and NWP model precipitation information.J. Climate,
8, 28102823.
, and Coauthors, 1997: The Global Precipitation Climatol-
ogy Project (GPCP) combined precipitation dataset. Bull.
Amer. Meteor. Soc., 78, 520.
Janowiak, J. E., and P. A. Arkin, 1991: Rainfall variations in the
tropics during 19861989 as estimated from observations of
cloud top temperature. J. Geophy. Res.,96 (Suppl.), 3359
3373.
, A. Gruber, C. R. Kondragunta, R. E. Livezey, and G. J.
Huffman, 1998: A comparison of the NCEPNCAR reanaly-
sis precipitation and the GPCP rain gaugesatellite combined
dataset with observational error considerations.J. Climate,11,
29602979.
Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year
Reanalysis Project.Bull. Amer. Meteor. Soc.,77, 437471.
Kondragunta, C., and A. Gruber, 1997: Intercomparison of spa-
tial and temporal variability of various precipitation estimates.
Adv. Space. Sci.,19, 457460.
McCollum, J., A. Gruber, and M. Ba, 2000: Discrepancy between
gauges and satellite estimates of rainfall in equatorial Africa.
J. Appl. Meteor.,39, 666679.
Morrissey, M. L., and J. S. Greene, 1991: The Pacific atoll rain
gage data set. Tech. Rep. 648, University of Hawaii at Manoa,
Honolulu, Hawaii, 9 pp. [Available from University of Hawaii
at Manoa, 2444 Dole St., Honolulu, HI 96822.]
Reynolds, R. W., 1988: A real time global sea surface tempera-
ture analysis.J. Climate,1, 7586.
Rudolf, B., H. Hauschild, W. Ruth, and U. Schneider, 1996: Com-
parison of raingauge analysis, satellite-based precipitation es-
timates, and forecast model results.Adv. Space Res.,18, 5362.
Shukla, J., and M. J. Fennessy, 1988: Prediction of time-mean
atmospheric circulation and rainfall: Influence of Pacific sea
surface temperature anomaly.J. Atmos. Sci.,45, 928.
Spencer, R. W., 1993: Global oceanic precipitation from the MSU
during 197991 and comparison to climatologies.J. Climate,
6, 13011326.
Sperber, K. R., and T. N. Palmer, 1995: Interannual tropical rain-
fall variability in general circulation model simulations asso-
ciated wth the atmospheric model intercomparison project.
PCMDI Rep. 28, Lawrence Livermore National Laboratory,
Livermore, CA, 79 pp. [Available from Lawrence Livermore
National Laboratory, 7000 East Ave., Livermore, CA 94550-
9234.]
Wilheit, T. J., A. T. C. Chang, and L. S. Chiu, 1991: Retrieval of
monthly rainfall indices from microwave radiometric measure-
ments using probability distribution functions.J. Atmos. Oce-
anic Technol.,8, 118136.
Xie, P. P., and P. A. Arkin, 1995: An intercomparison of gauge
observations and satellite estimates of monthly precipitation.
J. Appl. Meteor.,34, 11431160.
, and , 1996: Analyses of global monthly precipitation
using gauge observations, satellite estimates, and numerical
model predictions.J. Climate,9, 840858.
, and , 1997: Global precipitation: A 17-year monthly
analysis based on gauge observations, satellite estimates, and
numerical model outputs.Bull. Amer. Meteor. Soc.,78, 2539
2558.
, and , 1998: Global monthly precipitation estimates
from satellite-observed outgoing longwave radiation.J. Cli-
mate, 11, 137164.