gpcp pentad precipitation analyses: an experimental

1 JULY 2003 2197X I E E T A L .

q 2003 American Meteorological Society

GPCP Pentad Precipitation Analyses: An Experimental Dataset Based on GaugeObservations and Satellite Estimates

PINGPING XIE,* JOHN E. JANOWIAK,* PHILLIP A. ARKIN,1 ROBERT ADLER,# ARNOLD GRUBER,@

RALPH FERRARO,@ GEORGE J. HUFFMAN,#& AND SCOTT CURTIS#**

*NOAA/NWS/NCEP Climate Prediction Center, Camp Springs, Maryland1NOAA/OAR Office of Global Programs, Silver Spring, Maryland

#Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, Maryland@NOAA/NESDIS Office of Research and Application, Camp Springs, Maryland

&Science Systems and Applications, Inc., Lanham, Maryland**Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, Maryland

(Manuscript received 16 May 2002, in final form 2 December 2002)

ABSTRACT

As part of the Global Precipitation Climatology Project (GPCP), analyses of pentad precipitation have beenconstructed on a 2.58 latitude–longitude grid over the globe for a 23-yr period from 1979 to 2001 by adjustingthe pentad Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) against the monthlyGPCP-merged analyses. This adjustment is essential because the precipitation magnitude in the pentad CMAPis not consistent with that in the monthly CMAP or monthly GPCP datasets primarily due to the differences inthe input data sources and merging algorithms, causing problems in applications where joint use of the pentadand monthly datasets is necessary. First, pentad CMAP-merged analyses are created by merging several kindsof individual data sources including gauge-based analyses of pentad precipitation, and estimates inferred fromsatellite observations. The pentad CMAP dataset is then adjusted by the monthly GPCP-merged analyses so thatthe adjusted pentad analyses match the monthly GPCP in magnitude while the high-frequency components inthe pentad CMAP are retained. The adjusted analyses, called the GPCP-merged analyses of pentad precipitation,are compared to several gauge-based datasets. The results show that the pentad GPCP analyses reproducedspatial distribution patterns of total precipitation and temporal variations of submonthly scales with relativelyhigh quality especially over land. Simple applications of the 23-yr dataset demonstrate that it is useful inmonitoring and diagnosing intraseasonal variability. The Pentad GPCP has been accepted by the GPCP as oneof its official products and is being updated on a quasi-real-time basis.

1. Introduction

Significant progress has been made in the last twodecades in quantitatively documenting global precipi-tation variations, thanks to the advent and continuousoperation of satellite observations with advanced infra-red (IR) and microwave (MW) instruments. Various al-gorithms have been developed and applied to deriveprecipitation estimates over both land and ocean fromthese observations. Among many other products, pre-cipitation estimates have been produced on an opera-tional and experimental basis by the IR-based Geosta-tionary Operational Environmental Satellite (GOES)Precipitation Index (GPI; Arkin and Meisner 1987), theSpecial Sensor Microwave Imager (SSM/I) scattering-based algorithm of Ferraro (1997), the SSM/I emission-based technique of Wilheit et al. (1991), the Microwave

Corresponding author address: Dr. Pingping Xie, NOAA/NWS/NCEP Climate Prediction Center, 5200 Auth Rd., #605, CampSprings, MD 20746.E-mail: [email protected]

Sounding Unit (MSU) emission-based method of Spen-cer (1993), the outgoing longwave radiation (OLR)-based Precipitation Index (OPI; Xie and Arkin 1998),and the Television Infrared Observational Satellite(TIROS) Operational Vertical Sounder (TOVS) basedapproach of Susskind et al. (1997). Together with gaugeobservations and precipitation fields produced by var-ious numerical models, these satellite estimates provideimportant information about precipitation especiallyover the global oceanic areas. Several intercomparisonshave been conducted among various individual datasources of precipitation, including gauge observations,satellite estimates, and model outputs. The results showthat all individual data sources present similar distri-bution patterns of overall structures of global precipi-tation including rainbands associated with the ITCZ, theSouth Pacific convergenze zone (SPCZ), and major con-vection centers over the Tropics and storm tracks overthe extratropics. But differences exist in smaller-scalefeatures and in magnitude. At least three major defi-ciencies exist in the individual data sources: 1) incom-

2198 VOLUME 16J O U R N A L O F C L I M A T E

plete global coverage, 2) significant random error, and3) non-negligible bias (Janowiak 1992; Arkin and Xie1994, Xie and Arkin 1995; Ebert and Manton 1998;Adler et al. 2001).

Acknowledgment of the limitations inherent in theindividual datasets has led to the development of al-gorithms to merge them so as to take advantage of thestrengths of each to produce the best possible analysesof global precipitation. One such algorithm was devel-oped by a group at the National Aeronautics and SpaceAdministration (NASA) Goddard Space Flight Center(GSFC) by combining gauge observations with esti-mates derived from IR, OLR, SSM/I, and TOVS (Adleret al. 1993, 1994; Huffman et al. 1995). It has beenapplied successfully to construct the global monthly pre-cipitation analyses for the Global Precipitation Clima-tology Project (GPCP) for the period from 1979 to thepresent (Huffman et al. 1997; Adler et al. 2002, man-uscript submitted to J. Hydrometer., hereafter ADL).Another merging algorithm was developed by Xie andArkin (1996) that takes the gauge observations, satelliteestimates derived from IR, OLR, MSU, and SSM/I, andthe precipitation distributions from the National Centersfor Environmental Prediction–National Center for At-mospheric Research (NCEP–NCAR) reanalysis as in-puts. Using this algorithm, a global monthly precipi-tation dataset, called the Climate Prediction Center(CPC) Merged Analysis of Precipitation (CMAP; Xieand Arkin 1997a), has been created for the same periodas the GPCP product. Both the GPCP and the CMAPmonthly precipitation datasets have been applied widelyin climate analysis (Curtis and Adler 2000; Trenberthand Caron 2000; Lau and Wu 2001), numerical modelverification (Stephenson et al. 1998; Janowiak et al.1998; Dai et al. 2001), hydrological studies (Trenberthand Guillemott 1998), and other investigations (Yang etal. 1999).

The GPCP and the CMAP analyses described aboveare constructed for monthly precipitation with spatialresolution of 2.58 latitude–longitude. Many applications,including climate diagnosis of intraseasonal variability,surface water budget analysis, and verification of re-gional and mesoscale models, however, require a timeseries of precipitation on finer temporal and/or spatialresolution. To meet this requirement, Huffman et al.(2001) developed a satellite-based technique and ap-plied it successfully to construct analyses of daily pre-cipitation on a 18 latitude–longitude grid over the globe.Called the One-Degree Daily (1DD) technique, it firstdefines an all-satellite product of daily precipitation bycombined use of IR, MW, and TOVS satellite obser-vations. It then adjusts the daily values month by monthso that the local monthly accumulation of the 1DD anal-yses matches the local monthly GPCP analysis value.The 1DD precipitation analyses have been produced forthe period from 1997 to the present and have been ap-proved by the GPCP as one of its official products. Raingauge information is not used in the 1DD analyses pri-

marily because the 24-h period over which rain gaugedata are collected varies widely among countries, andthe availability of subdaily reports is very limited.

Another approach toward submonthly temporal res-olution is that of Xie and Arkin (1997b). Adopting thealgorithm used to create the monthly CMAP, analysesof pentad precipitation are defined on a 2.58 latitude–longitude grid over the globe by merging gauge obser-vations, satellite estimates, and, optionally, precipitationfields from the NCEP–NCAR reanalysis. As of April2002, the pentad CMAP analyses have been constructedfor the 23-yr period from 1979 to 2001. The dataset hasbeen used by many scientists as a useful informationsource for applications associated with weather, climate,and hydrological variations on submonthly scales (Ebi-suzaki et al. 1998; Zhou and Lau 1999; Qian and Yang2000; Roads et al. 2001). The precipitation magnitudein the pentad CMAP, however, is not consistent withthat in the monthly CMAP or GPCP datasets, primarilydue to the differences in the input data sources and themerging algorithms. This inconsistency may cause prob-lems in applications where joint use of the pentad andmonthly datasets is necessary. For example, a thoroughverification of a climate model would require datasetsof monthly and pentad precipitation with consistentmagnitude to examine its ability to represent the vari-ability of different temporal scales (decadal, interannual,and intraseasonal).

The objective of this work is to create analyses ofpentad precipitation that are consistent with the monthlyGPCP product. The basic notion of the work is to adjustthe original pentad CMAP analyses by the monthlyGPCP so that the magnitude of the adjusted pentad anal-yses are close to that of the monthly GPCP while thehigh-frequency components in the pentad CMAP areretained. This work is done as part of the GPCP andthe adjusted pentad analyses, called the pentad GPCPanalyses, have been accepted by the project as its officialproduct for pentad precipitation. The pentad CMAP isadjusted to the monthly GPCP instead of the monthlyCMAP because the adjusted pentad analyses are goingto be used as part of the GPCP product suite that includemonthly GPCP analyses of ADL and the GPCP 1DDdataset of Huffman et al. (2001).

Section 2 of this paper gives a brief description ofthe merging algorithm and input data sources used todefine the pentad CMAP dataset; section 3 presents pro-cedures to define the pentad GPCP analyses by adjustingthe pentad CMAP; section 4 shows validation resultsfor the pentad GPCP, section 5 illustrates some of itsapplications in analysis of intraseasonal variations; anda summary is given in section 6.

2. Defining the pentad CMAP by mergingindividual data sources

The pentad CMAP analyses are created by mergingseveral kinds of individual data sources of precipitation,

1 JULY 2003 2199X I E E T A L .

including gauge observations, satellite estimates, and,optionally, precipitation fields from the NCEP–NCARreanalysis. The algorithm used to define the pentad anal-yses is a modification of that for the monthly CMAP(Xie and Arkin 1996, 1997a), while the inputs are thesame products as or similar replacements to those usedin the monthly CMAP. Two versions of the pentadCMAP datasets are created. In the first version (CMAP/A), all available input datasets are used to define themerged analyses to ensure complete spatial coveragewith reasonable quality over the entire globe. In thesecond version (CMAP/O), only observation-based in-put datasets are used so that the resulting merged anal-yses are clear of any influences from numerical modelsused in creating the reanalysis precipitation fields. Inthis work, the observation-only version of the pentadCMAP (CMAP/O) is utilized to create the adjusted anal-yses, in accordance with the GPCP policy of avoidingnumerical model influence in its product suite. Briefdescriptions of the merging algorithm and the variousinputs to define the CMAP/O are given below.

a. Merging algorithm

The algorithm used to define the pentad CMAP/Odataset is basically the same as that used for the monthlyCMAP/O (Xie and Arkin 1996, 1997a). The mergingof the individual input data sources is conducted in twosteps. First, to reduce the random error, the satelliteestimates are combined linearly through the maximumlikelihood estimation method, in which the linear com-bination coefficients are inversely proportional to thesquares of local random error of the individual datasources. Over the global land areas, the individual ran-dom error is defined for each grid and for each pentadby comparing the data sources with the concurrentgauge-based analysis over the surrounding areas. Overglobal oceanic areas, it is defined by comparison withatoll gauge data (Morrissey et al. 1995) over the Tropicsand by subjective assumptions regarding the error struc-tures over the extratropics (see Xie and Arkin 1997a fordetails).

Since the output of the first step contains a bias thatis passed through from the individual input data sources,a second step is included to remove it. For that purpose,the gauge-based analyses are combined with the outputof the first step. Over land areas, the gauge data andthe output of the first step are blended through the meth-od of Reynolds (1988), in which the first-step outputand the gauge data are used to define the relative dis-tribution (or ‘‘shape’’) and the magnitude of the pre-cipitation fields, respectively. Over the oceans, the biasin the first-step output is removed by comparison withatoll gauge data over the Tropics and by subjective as-sumptions regarding the bias over the extratropics. Inthe process of defining the pentad CMAP, the gaugedata are used twice, first as ‘‘ground truth’’ to definethe random error for each satellite estimates and then

as ‘‘anchors’’ to determine the magnitude of the pre-cipitation. By doing this, the algorithm is able to bettertake advantage of the quantitative accuracy of the gaugeobservations.

b. Input data sources

In creating the monthly CMAP/O dataset, six kindsof individual data sources are used as inputs to the merg-ing process. These are the gauge data (the gauge-basedanalyses over land and the atoll gauge observations overocean) and five sets of satellite estimates of monthlyprecipitation derived from 1) the IR-based GPI (Arkinand Meisner 1987), 2) the SSM/I scattering-basedALG85 (Ferraro 1997), 3) the SSM/I emission-basedalgorithm (Wilheit et al. 1991), 4) the MSU-based meth-od (Spencer 1993), and 5) the OLR-based OPI (Xie andArkin 1998). Among these individual datasets, thegauge data, the satellite estimates of the GPI, ALG85,MSU, and OPI are available for pentad temporal res-olution and are utilized as inputs to define the pentadCMAP/O dataset. Pentad precipitation estimates fromthe SSM/I emission-based algorithm of Wilheit et al.(1991), however, are not available as of April 2002 anda replacement has to be used to fill in the gap. A de-scription of this replacement product is presented laterin this section.

The gauge-based analyses of pentad precipitation areconstructed by interpolating gauge observations fromover 6000 Global Telecommunication System (GTS)stations over the global land areas. First, station obser-vations of pentad precipitation are defined for each GTSstation by accumulating daily reports for the corre-sponding period. Analyses of pentad precipitation arethen created on a 2.58 latitude–longitude over globalland areas by interpolating the station observations us-ing the algorithm of Shepard (1968).

The atoll gauge rainfall data of Morrissey et al. (1995)are used to define the error structure of the individualinput precipitation fields over tropical oceanic areas.The atoll gauge dataset used here consists of stationobservations of daily precipitation from over 100 gaugeslocated on atolls and small islands without high terrain.These atoll gauges are located mainly in the central andwestern tropical Pacific Ocean along a northwest tosoutheast axis extending from 108N and 1408E to 208Sand 1408W (see Fig. 1 of Morrissey et al. 1995). In thisstudy, pentad precipitation is first defined for each atollgauge station by accumulating corresponding daily ob-servations. Areal mean precipitation is then calculatedfor 2.58 latitude–longitude grid boxes with one or moreatoll gauges by taking the arithmetic mean of the cor-responding stations.

The GPI technique estimates area mean precipitationfrom fractional coverage of clouds colder than 235 Kin IR images using an empirical linear equation. Cov-ering 408S–408N over both land and ocean, the GPIestimates are available in pentad and monthly accu-


mulations for a period from 1986 to the present. Here,the pentad GPI estimates are used as input to create themerged analyses.

The SSM/I scattering-based precipitation estimatesused here are those produced by the ALG85 algorithm(Ferraro 1997). First, rain rates are calculated from MW-scattering signals of ice particles and large water drop-lets using an empirical relation derived by comparisonwith radar observations. An additional rainfall retrievalis then made over oceanic areas using the 19 and 39-GHz components of the liquid water emission technique(Weng and Grody 1994) to pick up rainfall unidentifiedin the first step. The pentad precipitation estimates ofALG85 are available from 608S to 608N over both landand ocean and cover a period from July 1987 to thepresent with data for December 1987 missing.

Since the SSM/I emission-based precipitation esti-mates of Wilheit et al. (1991) are not available at pentadresolution, the oceanic components of ALG37 (Ferraro1997) are used as an alternative product. The ALG37retrieves oceanic precipitation from brightness temper-atures observed from the 19- and 37-GHz channels ofthe SSM/I using a liquid water emission technique de-veloped by Weng and Grody (1994). A simple adjust-ment was conducted for the original ALG37 estimatesto ensure that the monthly climatology of the ALG37matches that of the Wilheit et al. (1991). This was doneby comparing the original pentad ALG37 and the Wil-heit et al. (1991) estimates for a 8-yr period from July1987 to June 1995. First, monthly fields of ALG37 werecomputed for the 8-yr period by accumulating the cor-responding pentad estimates, and monthly climatologieswere calculated for both the ALG37 and the Wilheit etal. (1991) estimates. The adjustment factor was thendefined for each grid box and for each calendar monthas the ratio between the local mean value of the esti-mates of Wilheit et al. (1991) to that of the originalALG37 over a 9 3 9 array of grid boxes centered atthe target. Finally, this adjustment factor was interpo-lated back to pentad intervals and used to modify theoriginal ALG37 estimates for the period from July 1987to the present.

The MSU-based precipitation data used here are de-fined from daily estimates of Spencer (1993), whichcover the global ocean from 608S to 608N and extendfrom January 1979 to May 1994. Following the pro-cedures in producing the monthly CMAP (Xie and Arkin1997a), the original MSU estimates are adjusted by a‘‘base product’’ of pentad precipitation defined by merg-ing gauge observations and satellite estimates of GPI,SSM/I scattering, and SSM/I emission (see section 2 ofXie and Arkin 1997a for details). The objective of thisadjustment procedure is to reduce the systematic dif-ferences in spatial distribution observed between theMSU and the base product (Janowiak et al. 1995).

The OPI technique derives pentad precipitation inthree steps. First, the mean annual cycle of pentad pre-cipitation is defined by averaging the pentad base prod-

uct for an 8-yr period from July 1987 to June 1995. Thepentad anomaly of precipitation is then calculated fromthe pentad OLR anomaly using proportional constantsthat are linear functions of local pentad climatology.The OPI estimates of total precipitation are finally ob-tained by adding the anomaly to the pentad climatology(Xie and Arkin 1998). The pentad OPI estimates areavailable over most of the globe and for a period fromJanuary 1979 to the present.

c. Pentad CMAP merged analyses

The CMAP/O analyses of pentad precipitation areconstructed on a 2.58 latitude–longitude over the globefor the 23-yr period from 1979 to 2001 by merging thesix kinds of individual data sources, whenever available,using the algorithm described in section 2a. Figure 1shows an example of precipitation distribution for pen-tad 41 (20–24 July) of 1988 as obtained from the in-dividual inputs and the merged analyses of CMAP/O.In general, all of the satellite estimates present similarlarge-scale distribution patterns, characterized by rainbands associated with the ITCZ, the SPCZ over theTropics, and storm tracks extending from the Tropics tothe midlatitudes. The GPI and the OPI exhibit broaderand smoother distributions of raining areas comparedto those in the SSM/I-based products. Over land, theGPI shows overestimates compared to the gauge-basedanalyses, especially over the extratropics. The mergedanalyses of CMAP/O present spatial distribution pat-terns similar to those in the individual satellite estimates,while their magnitude over land is close to that of thegauge-based analyses, indicating that the bias presentin the individual satellite estimates has been reducedsubstantially.

3. Defining the Pentad GPCP by adjusting thepentad CMAP

a. Discrepancies between the pentad and monthlymerged analyses

The pentad CMAP/O analyses contain useful infor-mation of precipitation variations with submonthlytimescales. Their magnitude, however, is inconsistentwith that in the merged analyses of monthly precipi-tation. Figure 2 shows the distribution of annual meanprecipitation for a 20-yr period from 1979 to 1998 asdefined from the version 2 dataset of the monthly GPCP-merged analyses (Fig. 2, top; ADL 3), the observation-only version of the pentad CMAP (CMAP/O, Fig. 2,middle), and the differences between them (Fig. 2, bot-tom). The two datasets present very similar spatial dis-tribution patterns in annual mean precipitation, char-acterized by rain bands associated with the ITCZ andSPCZ in the Tropics and storm tracks in the extratropics.The temporal correlation between the monthly GPCPand the monthly precipitation accumulated from the

1 JULY 2003 2201X I E E T A L .

FIG. 1. Precipitation (mm day21) for pentad 41 (20–24 Jul) of 1988 as observed in the satellite estimates of GPI,SSM/I scattering (SCT), SSM/I emission (EMS), OPI, and MSU; the gauge-based analyses; the merged analyses ofpentad CMAP (observation-only version); and the pentad GPCP.

pentad CMAP/O is very high over most of the globe(figures not shown here), indicating good agreements intemporal variation patterns of monthly and longer time-scales between the two datasets.

Systematic differences, however, are observed in theoverall magnitude of the precipitation (Fig. 2, bottom).Over the ocean, the pentad CMAP/O is wetter over the

Tropics and drier over the mid- and high latitudes com-pared to the monthly GPCP analyses. In addition, thepentad CMAP/O tends to have smaller amounts of pre-cipitation over some of the global land areas. Thesedifferences are caused primarily by the differences inthe algorithms used to define the merged analyses andin the input data sources (Gruber et al. 2000). While


FIG. 2. Distribution of mean precipitation (mm day21) for a 20-yrperiod from 1979 to 1998 as defined from (top) the monthly GPCPanalyses version 2 dataset, (middle) that from the observation-onlyversion of pentad CMAP, and (bottom) the difference between thetwo.

the GPCP merged analyses of monthly precipitation de-rive their magnitude from the SSM/I-based precipitationestimates over the oceans, the pentad CMAP, as de-scribed in section 2a, determines the oceanic precipi-tation magnitude by comparison with observations madeby gauges located over atolls and small islands over thewestern Pacific Ocean. Differences over land are caused

primarily by discrepancies between gauge observationsfrom monthly reports and those from the accumulationof daily reports. The accumulation of daily reports tendsto underestimate the precipitation over some of the glob-al land areas, especially over North and South Americas.In general, the quality of monthly reports is better be-cause higher-quality observations are available atmonthly time resolution (‘‘CLIMAT’’ reports) and be-cause daily errors tend to average out over time. Thecomparison results between the monthly GPCP and themonthly accumulation of pentad CMAP/O show thatwhile the two sets of data are in good agreement intemporal variation patterns, systematic differences doexist and may cause problems in some applications.While there are uncertainties in the magnitude of theanalyses over oceanic areas, the differences over landare caused mostly by some gauge observations with theless desirable quality in the pentad dataset. Adjustingthe pentad CMAP/O against the monthly GPCP will notonly create a pentad precipitation analysis with a mag-nitude consistent with that of the monthly GPCP butalso will improve the quantitative accuracy of the ad-justed product, at least over land.

b. Selection of the adjustment method

To create a pentad precipitation dataset consistentwith the monthly-merged analyses, we decided to adjustthe original pentad CMAP by the monthly GPCP-merged analyses so that the magnitude of the adjustedanalyses is close to that of the monthly GPCP whilecomponents of high-frequency variations in the originalpentad CMAP are retained. In accordance with theGPCP policy of avoiding numerical model influence inits product suite, the observation-only version of thepentad CMAP (CMAP/O) is used to define the adjustedanalyses. The resulting pentad analyses therefore willhave missing values over high-latitude oceanic areas.The version 2 dataset of the GPCP-merged analyses ofprecipitation is used here as the reference to determinethe magnitude in the adjustment. As described in detailin ADL, the monthly GPCP version 2 dataset is con-structed by combining gauge observations and satelliteestimates of GPI, SSM/I, OPI, and TOVS and coversthe period from 1979 to the present.

While other methods might have been used, a simpleand straightforward approach was adopted here; namely,to adjust the pentad CMAP/O at each grid box and foreach pentad by multiplying the original analysis valuesby an adjustment factor. The adjustment factor is definedas the ratio between the local mean value of the monthlyGPCP and that of the pentad CMAP/O averaged overa time–space domain centered at the target grid box andthe target pentad. Experiments were conducted to de-termine the best combination of the time- and space-averaging scales over which the mean values of themonthly GPCP and pentad CMAP/O are used to definethe adjustment factor. In the following discussions,

1 JULY 2003 2203X I E E T A L .

TABLE 1. Comparison between the monthly GPCP analyses andmonthly accumulations of original pentad CMAP and those adjustedwith factors calculated over various spatial and temporal averagingscales.

Adjusted temporalscale (month)

Spatial scale (grid box)

1 3 5 7

CorrelationOriginal pentad CMAP: 0.871

1357

0.9970.9750.9690.965

0.9530.9410.9370.934

0.9320.9230.9200.918

0.9190.9120.9100.908

Bias (%)Original pentad CMAP: 22.9

1357

20.721.221.321.4

21.121.221.321.3

21.121.121.221.2

21.221.121.321.3

RMS error (%)Original pentad CMAP: 54.9

1357

7.420.823.524.9

28.932.533.634.3

35.237.438.238.7

38.740.140.741.0

time–space averaging means the process used to cal-culate the local mean values of the monthly GPCP andthe monthly accumulations of the pentad CMAP/O. Nosmoothing is applied to the ratio between the localmeans.

First, monthly values for the CMAP/O were calcu-lated for a 20-yr period from 1979 to 1998 by accu-mulating corresponding pentad analyses. Ratios be-tween the mean value of the monthly GPCP and that ofthe accumulated monthly CMAP/O were then computedfor each grid box and for each pentad over 16 combi-nations of time–space-averaging domains. These do-mains include time averaging of 1, 3, 5, and 7 monthsaround the target pentad and space averaging of 1, 3,5, and 7 grid boxes of 2.58 latitude–longitude in boththe north–south and east–west directions centered at thetarget grid box. We denote the averaging scale of 1monthly–1 grid box as no temporal–spatial averaging,meaning that only the monthly GPCP and monthly ac-cumulation of CMAP/O for the month including thetarget pentad over the target grid box is included in thecalculation. The ratio is limited to a range of 0.2–5.0to avoid unrealistic adjustments.

As expected, noticeable discontinuities are observedin the ratios over the monthly boundaries when no tem-poral averaging is applied in calculating the local meansfor the monthly GPCP and monthly accumulations ofpentad CMAP/O (not shown here). Adding temporalaveraging, meanwhile, results in smooth variations inthe time series. This implies that adjustment based ononly the spatial averaging may alias the high-frequencytemporal variation components in the pentad precipi-tation analyses.

The ratios calculated over various time–space-aver-aging domains were applied to the original CMAP/O,creating 16 sets of adjusted pentad precipitation analysesfor the 20-yr period from 1979 to 1998. These adjustedanalyses were then compared to the monthly GPCP andthe original pentad CMAP/O to examine their perfor-mance.

Two sets of comparisons were conducted for the 16sets of the adjusted pentad analyses to determine thebest combination of time–space-averaging domain. Inthe first set, the monthly accumulations of the variousadjusted pentad analyses were compared to the monthlyGPCP to examine how well their magnitudes match.Table 1 presents the comparison results over the entireglobe from 608S to 608N and for the 20-yr period from1979 to 1998. Overall, all of the 16 sets of the adjustedpentad analyses based on various combinations of thetime–space-averaging domains yield good agreement inboth the magnitude and variation patterns. The corre-lation is higher than 0.9 and the bias is only 20.7% to21.4% for the various adjusted analyses. The bestagreement with the monthly GPCP is observed for theadjusted analyses with no temporal and spatial aver-aging, for which the correlation reaches 0.997, the biasis as low as 20.7%, and the random error is only 7.4%

(Table 1). Averaging in both space and time degradesthe agreements between the resulting adjusted analysesand the monthly GPCP. The correlation, bias, and ran-dom error are 0.908, 21.3%, and 41.0%, respectively,for time–space averaging of 7 months and 7 grid boxes.While good agreement is expected for the adjusted anal-ysis with no temporal and spatial averaging, high cor-relation for the pentad analyses adjusted with variousaveraging scales implies a reasonable match in the tem-poral–spatial patterns in the monthly GPCP and the orig-inal pentad CMAP/O over the averaging domains.

Figures 3 and 4 show the spatial distribution of thetemporal correlation and bias for eight selected sets ofadjusted pentad analyses. Almost perfect agreement isobserved between the monthly GPCP and the adjustedanalyses based on ratios calculated with no time–spaceaveraging. The correlation is close to 1.0 (Fig. 3), thebias is nearly 0 (Fig. 4), and the random error is verysmall (not shown) over most of the global areas. Al-though the agreement becomes worse as the averagingscale increases, the correlation is higher than 0.7 overmost of the globe for adjusted analyses based on variouscombination of averaging scales (Fig. 3). Particularlynoticeable is the spatial distribution of the bias for thevarious adjusted pentad analyses (Fig. 4). Bands of biaswith alternating signs are observed around major pre-cipitation systems, indicating that systematic under- andoverestimation of precipitation occur in the adjustedanalyses if spatial averaging is included in calculatingthe adjustment factors.

The following two things are clear from the com-parison results described above: 1) the adjustment based


FIG. 3. Correlation between the monthly GPCP analyses and the monthly accumulation of pentad analyses defined by adjusting the originalpentad CMAP/O by a ratio calculated over various time–space-averaging domains for a 20-yr period from 1979 to 1998.

on less averaging yields better agreement with themonthly GPCP; and 2) spatial averaging is not desirablein calculating the adjustment factor.

The procedures described above examined the bestaveraging scales to ensure magnitude agreement withthe monthly GPCP. However, it is equally important toensure that the high-frequency variations inherent in thepentad CMAP/O are retained. To this end, 20–100-daybandpass filtering was performed for the time series ofthe original pentad CMAP/O and for the 16 sets of theadjusted pentad analyses. Comparisons were then con-ducted between the bandpass-filtered components in theoriginal pentad CMAP/O and those in the 16 sets of theadjusted analyses. Table 2 presents the correlation co-efficients between the bandpass-filtered components ofthe original pentad CMAP/O and those of the variousadjusted analyses calculated over the global areas from608S to 608N and for a time period from 1979 to 1998.

In general, the agreement in high-frequency com-ponents is very good for all of the 16 adjusted analyses(Table 2). The correlation is the lowest for the analyses

adjusted with no time and space averaging. The cor-relation improves with increasing averaging scale inboth time and space and reaches the highest for spaceaveraging of seven grid boxes and time averaging ofseven months. Applying averaging in either the spaceor time direction results in substantial improvements inthe correlation compared to that for the adjustment withno averaging at all. The correlation jumps from 0.914for the nonaveraging option to 0.950/0.953 with a one-step averaging in time–space.

Overall, in considering the agreement in componentsof high-frequency temporal variations, it is desirable toadjust the original CMAP/O by a ratio between the localmean values of the monthly GPCP and that of themonthly accumulation of the pentad CMAP/O averagedover a larger time–space domain. Especially, temporalaveraging is necessary to avoid aliasing of the temporalvariability.

The following four things are clear from examinationof both the magnitude agreement with the monthly

1 JULY 2003 2205X I E E T A L .

FIG. 4. Same as in Fig. 3, except for bias (%) relative to the mean value of the monthly GPCP.

TABLE 2. Correlation between the bandpassed components of theoriginal pentad CMAP and those adjusted by the monthly GPCP withfactors calculated over various spatial and temporal averaging scales.

Temporalscale

(month)

Spatial scale (grid box)

1 3 5 7

1357

0.9140.9500.9550.958

0.9530.9700.9730.974

0.9650.9770.9790.980

0.9720.9800.9820.983

GPCP and the agreement in high-frequency componentswith the original pentad CMAP/O:

1) Averaging on a smaller time–space domain resultsin better magnitude agreement with the monthlyGPCP;

2) Improved agreement in high-frequency componentsis achieved when the adjustment factor is calculatedon a larger/longer averaging domain;

3) Spatial averaging yields undesirable artificial biaspatterns; and

4) Temporal averaging is necessary to avoid aliasing inhigh-frequency variability.

These four conclusions point to an option to definethe adjustment factor with temporal averaging of someextent but with no space averaging. Since defining theadjustment factor over time-averaging domains of 3 or7 months would yield only minor differences in theperformance (Table 2), we chose the time averaging of3 months, or 61 month around the target pentad, as thebest averaging domain for calculation of the adjustmentfactor.

c. Construction of the adjusted pentad analyses

We applied the adjustment factor calculated in thismanner to adjust the original pentad CMAP/O for a 23-yr period from 1979 to 2001. We call this adjusted pen-tad analyses the pentad GPCP merged analyses. An ex-ample of the adjusted pentad analyses for pentad 41(20–24 July) of 1988 is shown in Fig. 1. As expected,the pentad GPCP exhibits very similar spatial distri-


TABLE 3. Comparison of the pentad GPCP analyses withgauge observations.

Quantity Correlation Bias (%)Rms error

(%)

United StatesTotal precipitationAnomalyIntraseasonal comparison

0.8730.8420.865

26.7——

68.2——

BrazilTotal precipitationAnomalyIntraseasonal comparison

0.7760.6600.688

0.7——

70.7——

Central and western PacificTotal precipitationAnomalyIntraseasonal comparison

0.6670.6370.661

221.6——

95.0——

FIG. 5. (top) Correlation, (middle) relative bias (%), and (bottom)relative rms error (%) between the total precipitation in the pentadGPCP and that in the guage-based analyses of Higgins et al. (2000)over the United States for an 18-yr period from 1979 to 1996.

bution patterns with that of the original pentadCMAP/O while differences in magnitude are observed.

4. Validation of the pentad GPCP analysis

The pentad GPCP merged analyses of precipitationwere compared to three gauge-based datasets to examinetheir ability to represent temporal and spatial variationsin several regions over the globe. The three gauge-baseddatasets used here are those of Higgins et al. (2000)over the United States, Shi et al. (2001) over Brazil,and the atoll gauge data of Morrissey et al. (1995) overthe central and western Pacific Ocean.

The dataset of Higgins et al. (2000) consists of anal-yses of daily precipitation on a 0.258 latitude–longitudegrid over the continental United States covering a 51-yr period from 1948 to 1998. The analyses are definedby interpolating quality-controlled gauge observationsat over 8000 stations collected from multiple sources.Pentad accumulation of precipitation over 2.58 latitude–longitude grid boxes was calculated from the daily anal-yses and compared to our pentad GPCP-merged anal-yses. Although gauge observations at many GTS sta-tions used as part of the inputs to the pentad CMAP/Oand therefore the pentad GPCP-merged analyses are alsoincluded in creating the daily analyses of Higgins et al.(2000), the latter contains at least 10 times more gaugesthan those in the GTS over the United States. The twodatasets therefore can be considered largely indepen-dent.

Table 3 (top) presents the comparison results with thegauge-based analysis of Higgins et al. (2000) over theentire continental United States and for the 18-yr periodfrom 1979 to 1996. Overall, the pentad GPCP mergedanalyses compare very well with the gauge-based anal-yses of Higgins et al. (2000). The correlation for thetotal precipitation, pentad anomaly, and intraseasonalcomponents (defined as the 20–100-day bandpass-fil-tered components) are 0.873, 0.842, and 0.865, respec-tively, indicating that the pentad GPCP analyses are

capable of representing the spatial distribution and tem-poral variation of total precipitation as well as its com-ponents of submonthly timescales with good accuracy.A negative bias of 26.7% is reported over the combinedtime–space domain, suggesting a slight underestimationof the pentad product compared to Higgins et al. (2000).

Figure 5 shows the spatial distribution of the temporalcorrelation, bias, and random error between the totalprecipitation of the pentad GPCP and that of the Higginset al. (2000) for the 18-yr period from 1979 to 1996.The correlation for the total precipitation (Fig. 5, top)is higher than 0.8 and the random error is less than 60%over most of the United States. The best agreement isobserved over the central United States where precip-

1 JULY 2003 2207X I E E T A L .

FIG. 6. Same as in Fig. 5, except for comparison with Shi et al.(2001) over Brazil.

itation with less spatial variation is observed by a rel-atively dense network of GTS gauges. The worst per-formance of the pentad GPCP, meanwhile, is seen overthe western mountainous areas where the GTS gaugenetwork is sparse and the satellite estimates are lessaccurate. Negative bias in the pentad GPCP is observedover most of the United States, especially over the west-ern mountainous and the coastal regions. This negativebias is caused primarily by the underestimation of thegauge-based analyses of the Global Precipitation Cli-matology Centre (GPCC; Schneider 1993) and Xie atal. (1996), which dominate the land portion of themonthly GPCP merged analyses that in turn control themagnitude of precipitation in our pentad GPCP analy-ses. A preliminary examination showed that the gauge-based analyses of GPCC and Xie at al. (1996), definedby interpolating station observations of total precipi-tation, may contain bias over areas where systematicdifferences exist between the total precipitation over thetarget grid points and that over the reporting gauge sta-tions (Chen et al. 2002). Over the United States, theGTS stations tend to be located over flat areas with lessprecipitation and the resulting gauge-based analysestherefore may underestimate precipitation. The datasetof Higgins et al. (2000), meanwhile, is created usingstation observations from many more gauges that arebetter representative of precipitation distribution overthe region.

Similar comparisons of the pentad GPCP analyseswere conducted with the gauge-based analyses of Shiet al. (2001) over Brazil for the 18-yr period from 1979to 1996. Like Higgins et al. (2000), the dataset of Shiet al. (2001) also comprises analyses of daily precipi-tation created by interpolating gauge observations fromup to 1000 stations over the nation. The original dailyanalyses were created on a 1.08 latitude–longitude gridover the domain and cover a 38-yr period from 1960to 1997. Pentad accumulation of precipitation was com-puted on a 2.58 latitude–longitude grid over the domainfor the 18-yr period from 1979 to 1996. They were thencompared to our pentad GPCP dataset. Since the numberof gauges used to define the daily analyses of Shi et al.(2001) is at least an order of magnitude more than thatfrom the GTS for the period of comparison, the twodatasets are largely independent.

Table 3 (middle) presents the comparison results be-tween the pentad GPCP analyses and the pentad accu-mulation of Shi et al. (2001) over the entire region ofBrazil and for the entire 18-yr period from 1979 to 1996.Good agreement is observed between the pentad GPCPand the gauge-based dataset of Shi et al. (2001) overthe combined space–time domain. The correlation is0.776, 0.660, and 0.688, respectively, for the total value,anomaly, and intraseasonal components of the pentadprecipitation. The bias is only 0.7% and the randomerror is 70.7% relative to the gauge-based analyses overthe combined space–time domain. The spatial distri-bution of the comparison statistics for total precipitation


(Fig. 6), however, exhibits regional differences in theperformance of the pentad GPCP analyses. While ex-cellent agreement is observed over the eastern half ofthe Brazil where reasonable GTS coverage is availableto define the pentad GPCP, the agreement is degradedover the central portion of the Amazon basin whereheavy rainfall is observed by relatively sparsely dis-tributed GTS networks. The correlation is over 0.8 overthe eastern portion, while it is around 0.6–0.7 over thecentral Amazon basin. Particularly interesting is the spa-tial distribution patterns of the bias in the pentad GPCPmerged analyses. While no significant bias exists overthe combined space–time domain, over-and underesti-mation of precipitation were reported over the northerncoastal regions and the central Amazon basin, respec-tively. As discussed for the comparison over the UnitedStates, most of this bias is likely attributed to the sys-tematic differences in the gauge-based analyses ofmonthly precipitation over the region.

Since no independent gauge observations of pentadprecipitation are available over an extended area overthe global oceanic areas and for an extended time period,here we tried to use the atoll rain gauge observationsto examine the performance of the pentad GPCP in rep-resenting oceanic precipitation. Although, as describedin section 2, the atoll gauge data are used to determinethe error structure of the individual input data sourcesin constructing the pentad CMAP and therefore the com-parison of the pentad GPCP with the atoll data is nottruly independent, we hope that this comparison is stillbe able to provide us with some information about theperformance of the pentad GPCP merged analyses.

The correlation is 0.667, 0.637, and 0.661 for the totalvalue, anomaly, and intraseasonal components of thepentad precipitation, respectively, over the combinedspace–time domain compared to the atoll gauge data(Table 3c), indicating that the pentad GPCP analysesrepresent precipitation variations reasonably well overthe oceanic areas examined here. Part of the degradationof the agreement is due to the limited number of atollgauges available to define the grid box mean of precip-itation. Previous work by Xie and Arkin (1995) revealedthat the correlation between the satellite estimates andatoll gauge data improves for grid boxes with more atollgauges. It is not surprising that the pentad GPCP mergedanalyses exhibit a negative bias of 21.6% compared tothe atoll gauge observations. As described in section 3,the magnitude of the pentad GPCP analyses over theglobal ocean is adjusted against the monthly GPCPwhose magnitude is dominated by the satellite estimatesof Wilheit et al. (1991). Negative bias of the Wilheit etal. (1991) against the atoll gauge data has been reportedby several intercomparison projects (e.g., Adler et al.2001). The real magnitude accuracy of Wilheit et al.(1991), however, is unknown due to the lack of reliablein situ observations over the open ocean. Although onlygauge observations over stations located over atolls andsmall islands are included in the comparison, they may

not be representative of precipitation over surroundingopen oceans due to local circulations induced by thetopography.

It is clear from the comparisons with the three gauge-based datasets that the pentad GPCP analyses are ableto represent precipitation variations of intraseasonal andlonger timescales with good accuracy over both landand oceanic areas examined here. Biases, however, existin the magnitude of precipitation over some of the landareas and the quantitative accuracy of the pentad GPCPis uncertain over oceanic areas.

5. Applications of the Pentad GPCP analyses

The annual, interannual, and intraseasonal variabilityof global precipitation, as observed in the Pentad GPCPdataset, is examined for the 22-yr period from 1979 to2000 and compared with that in the NOAA pentad OLRdataset (Gruber and Krueger 1984).

First, components associated with the mean annualcycle, interannual and intraseasonal variability are de-fined for the precipitation and OLR, respectively. Forthis purpose, the mean values of precipitation and OLRare calculated for each of the 73 pentads and for each2.58 latitude–longitude grid box over the globe from the22-yr pentad datasets of precipitation and OLR. Har-monic analysis is then applied to the time series of 73mean values and the accumulation of the first 6 har-monics is used to approximate the annual cycle of theprecipitation and OLR. The components associated withinterannual and intraseasonal variability, meanwhile, aredefined by applying bandpass filtering to the time seriesof pentad anomaly defined by subtracting the annualcycle from the original total precipitation and OLR. Thebandwidth used to extract the interannual and intrasea-sonal components is 73–365 pentads (1–5 yr) and 4–20 pentads (20–100 days), respectively.

Shown in Fig. 7 are latitudinal profiles of the annualcycle of mean precipitation averaged over ocean (top),land (middle), and the entire globe (bottom). The evo-lution of mean precipitation is dominated by the mi-gration of rainbands associated with the ITCZ, SPCZ,convergence zones over South America and Africa, andstorm tracks over the extratropics. The center of heavyrain is located south of the equator during boreal winter,moves northward during spring and reaches ;108N inboreal summer. Over ocean, the magnitude of precipi-tation is the maximum during boreal summer when anintensified ITCZ extends across the Pacific basin. Theprecipitation over land, however, is stronger from De-cember to May when enhanced convection is presentover tropical Africa, the Maritime Continent, and theAmazon basin. A band of precipitation is observed overthe midlatitude on both hemispheres throughout theyear. Over land, this midlatitude rainband reaches max-imum intensity in summer, while over ocean, the max-imum appears in winter when the storm tracks arestrong.

1 JULY 2003 2209X I E E T A L .

FIG. 7. Longitudinal profiles of mean annual cycle of precipitation (mm day21) averaged over(top) the ocean, (middle) land, and (bottom) the entire globe.

One of the major components of the global climate,the intraseasonal variability has long been examined us-ing pentad averages of the OLR observed by the Na-tional Oceanic and Atmospheric Administration(NOAA) polar-orbiting satellites (e.g., Weickmann et al.1985; Lau and Chan 1986; Waliser et al. 1999). Whilethe OLR data are thought to be a good index of tropicalconvection and a reasonable proxy for latent heat releaseover the Tropics, a dataset of pentad precipitation ispreferable for its direct and quantitative relation to latentheating.

Fig. 8 shows the time–longitude sections of the 20–

100-day bandpass-filtered precipitation (mm day21, left)and OLR (W m22, middle) averaged over 108S–108Nfor a period from October 1996 to May 1997. Alsoplotted in Fig. 8 (right) is the time series of an indexfor tropical intraseasonal oscillation (TISO) defined asthe 20–100-day bandpass-filtered velocity potential inthe NCEP–NCAR reanalysis averaged over a domainof 108S–108N, 1008–1408E (Sperber et al. 1997). East-ward propagation of the anomaly fields is apparent inboth the precipitation and OLR with a period of about40 days. The correspondence between anomaly in pre-cipitation and that in OLR (convection) is generally very


FIG. 8. Time–longitude sections of (left) 20–100-day bandpass-filtered precipitation (mm day 21) and (middle) OLR (W m22) averagedover 108S–108N for the period from Oct 1996 to May 1997. (right) The time series of an index associated with the TISO defined as bandpass-filtered velocity potential averaged over 108S–108N, 1008–1408E.

good and both of them are in phase with changes in theTISO index. In general, a positive TISO index (con-vergence at 200 mb) is accompanied by depressed con-vection (enhanced OLR) and weakened precipitationover the Maritime Continent area. Compared to that ofthe OLR, the bandpass-filtered anomaly of precipitationcontains many small-scale features and some breaksover the Maritime Continent area.

To further understand the behavior of the precipitationand OLR in response to TISO, precipitation and OLRcomposites were assembled for different phases of theTISO evolution during the December–January–Febru-ary (DJF) seasons. The entire life cycle of a TISO isdivided into four phases based on the bandpass-filteredTISO index. Phases 1 and 3 are assigned to the pentadperiods when the TISO index reaches maximum andminimum, respectively, while phases 2 and 4 are labeledto the periods in between. To make the resulting com-posites typical of the TISO evolution, only cases withmaximum/minimum index values greater/less than 0.75/20.75 standard deviation were included in defining thecomposites.

As shown in Fig. 9, during phase I, a weak positiveprecipitation anomaly appears over the central and west-ern Indian Ocean, while suppressed precipitation is ob-

served over the Maritime Continent area and its vicinity.As it propagates eastward, the positive precipitationanomaly intensifies and its extent widens (phase 2). Itthen reaches its maximum in phase 3 when the enhancedprecipitation is over the Maritime Continent. Upon pass-ing the landmass, the anomaly weakens as it movestoward the southeast. In general, the OLR (right panels)shows similar evolution processes to those for the pre-cipitation, with negative–positive anomaly in OLR cor-responding generally to enhanced–depressed precipita-tion. Mismatches between the precipitation and OLRanomalies, however, exist especially over some of theland areas. The negative OLR anomalies over tropicalAfrica in phase 1, over tropical Africa and south Aus-tralia in phase 2, and over the Sahara Desert in phase4 are not accompanied by enhanced precipitation. Abrief examination of both the precipitation and the OLRfields showed that there is no precipitation observed inthe DJF seasons over these land areas. The negativeOLR anomalies are therefore most likely attributed tochanges in surface features and clouds not associatedwith precipitation.

With the 22-yr dataset of pentad precipitation, it be-comes possible to compare the relative magnitude ofvariability with different timescales. To do this, the stan-

1 JULY 2003 2211X I E E T A L .

FIG. 9. Composites of (left) 20–100-day bandpass-filtered precipitation (mm day21) and (right) OLR (W m22) definedby dividing a cycle of intraseasonal oscillation into four phases based on the TISO index. Phases 1 and 3 denote timewhen the TISO index reaches max and min, respectively, while phases 2 and 4 are periods in between. Only cases withmax/min index values larger/smaller than 0.75/20.75 std dev are included in defining the composites.

dard deviation is calculated for the time series of com-ponents associated with annual, interannual, and intra-seasonal variability for precipitation and OLR, respec-tively. Presented in Figs. 10 and 11 are spatial distri-butions of the standard deviation of the mean annualcycle (top right), interannual (bottom left), and intra-seasonal components (bottom right) for the DJF seasonfor the 22-yr period from 1979 to 2000 for precipitationand OLR, respectively. The distribution of annual meanprecipitation and OLR (top left) is also plotted for com-parison purpose.

The magnitude of the annual cycle of precipitation(Fig. 10, top left) is large over the major rainbandsassociated with the ITCZ, SPCZ, and storm tracks, bothover land and over ocean. Especially the annual cycleis stronger over the eastern Pacific compared to the west-ern Pacific, although the opposite is observed in theseasonal mean precipitation. The interannual variabilityin precipitation (Fig. 10, bottom left) exhibits a largemagnitude over the entire tropical Pacific basin with itsmaxima centered at the central Pacific near the date line.Strong interannual variability in precipitation is also ob-

served over Brazil, the tropical Atlantic, and the regionsover and south of the storm tracks over the NorthernHemisphere.

Overall, the magnitude of the standard deviation inthe pentad precipitation anomaly components associatedwith the intraseasonal variability (Fig. 10, bottom right)is much larger than that of the interannual variabilityover most of the globe. During the DJF season, a largemagnitude is observed over the eastern Indian and west-ern Pacific Oceans where the maximum values are over6 mm day21, almost double of that for the interannualvariability over the central Pacific. This intraseasonablevariability is noticeable over South America where thestandard deviation of the bandpass-filtered pentad pre-cipitation anomaly is over 2 mm day21 over most of theregion.

The spatial distribution pattern of annual, interannual,and intraseasonal variations as observed in the NOAApentad OLR data (Fig. 11) is similar to those in theprecipitation over most of the Tropics as describedabove. Significant differences, however, exist over theextratropics and over some of the tropical areas. As


FIG. 10. GPCP pentad analysis global distribution of (top left) seasonal mean precipitation (mm day21) and std dev of precipitation (mmday21) for (top right) the mean annual cycle, and components associated with (bottom left) interannual and (bottom right) intraseasonalcomponents for the DJF season.

FIG. 11. Same as in Fig. 10, except for the OLR (W m22) observed by the NOAA satellites.

1 JULY 2003 2213X I E E T A L .

shown in Figs. 10 and 11, most of the extratropicalfeatures in the annual, interannual, and intraseasonalvariability of precipitation are missing in those of theOLR, attributed mostly to the fact that the OLR is de-termined by the surface temperatures and clouds thatmay or may not be precipitating. In particular, differ-ences in the magnitude in precipitation and OLR arenoted over tropical Africa and northern Australia. Whileour Pentad GPCP precipitation dataset shows little var-iance in precipitation over these regions (Fig. 10), theOLR data present relatively large variance over the sameregion. Comparisons with the distribution of the annualmean precipitation and OLR show that mean precipi-tation is very small and the OLR values are high, in-dicating that the variability in the OLR is caused mostlyto changes in surface temperature and nonprecipitatingclouds.

6. Summary

Analyses of pentad precipitation have been construct-ed on a 2.58 latitude–longitude grid over the globe forthe 23-yr period from 1979 to 2001 by adjusting theobservation-only version of the pentad CMAP(CMAP/O) against the monthly GPCP-merged analyses.First, pentad CMAP/O-merged analyses are created bymerging several kinds of individual data sources usingthe same algorithm as that for the monthly CMAP (Xieand Arkin 1997a). The individual data sources used asinputs to the merging process include the gridded fieldsof pentad precipitation derived by interpolating GTSgauge observations, and estimates inferred from satelliteobservations of GPI, SSM/I, MSU, and OPI. The pentadCMAP/O dataset is then adjusted by the monthly GPCP-merged analyses (ADL) so that the adjusted pentad anal-yses match the magnitude of the monthly GPCP whiletheir high-frequency components are the same as thosein the original pentad CMAP/O. The adjustment is donefor each grid box and for each pentad time step by firstcalculating the ratio between the temporal mean of themonthly GPCP and that of the pentad CMAP/O overthe target grid box for a 3-month period centered at thetarget pentad and then multiplying the ratio by the orig-inal pentad CMAP/O.

Called the GPCP-merged analyses of pentad precip-itation, the adjusted analyses are compared to severalgauge-based datasets of precipitation. The resultsshowed that the Pentad GPCP analyses are capable ofreproducing spatial distribution patterns of total precip-itation and temporal variations of submonthly scaleswith relatively high quality. Bias, however, exist in theanalyses over some of the land areas. In addition, thequantitative accuracy is uncertain over oceanic areas dueto lack of appropriate independent observations of pre-cipitation. Preliminary analysis of the 23-yr datasetdemonstrated its potential applications in monitoringand diagnosing intraseasonal variability.

Accepted by the GPCP as one of its official products,

the Pentad GPCP dataset is being updated on a quasi-real-time basis. The current version of the Pentad GPCPmerged analyses described in this paper is experimentalin nature. Modifications and improvements are plannedfor the pentad dataset once more information about itsstrengths and shortcomings are gathered from scientistsin various fields. In particular, since gauge observationsof daily and pentad precipitation from many more sta-tions recently became available, improvements of thepentad merged analyses are expected by inclusion ofthose additional station data.

Acknowledgments. The authors would like to expresstheir thanks to J. E. Schemm, W. Shi, W.-Q. Wang, Y.Xue, S. Yang, E. Yarosh, and J.-Y. Zhou for their in-valuable discussions and comments on the work. Theyare also indebted to Y. Yarosh for her excellent workin timely updating the pentad GPCP analyses, and toG. Fullwood and S. C. Handel for providing the GTSdaily reports used in this study. Comments made by twoanonymous reviewers greatly improved the quality ofthis paper. The pentad GPCP dataset is available throughanonymous ftp from the Climate Prediction Center(CPC) online at ftp.ncep.noaa.gov/pub/precip/GPCPpPEN and from the NOAA National Climatic Data Center(NCDC) of at ftp.ncdc.noaa.gov/pub/data/gpcp.

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