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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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    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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    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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    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.

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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.

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