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Phillip A. Arkin

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Phillip A. Arkin

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The interannual variability in the upper tropospheric tropical wind field is described through empirical orthogonal functions, teleconnections and composite analyses. The data used are 11 years of mean monthly analyzed fields of zonal and meridional components of the 200 mb wind on a 5° Mercator grid extending from 48.1° N to 48.1°S derived from the National Meteorological Center's operational tropical analysis.

A substantial portion of the interannual variability in the 200 mb circulation is shown to be related to the Southern Oscillation. The anomalous circulation in the Pacific is characterized by a pair of anticyclonic/cyclonic anomalies straddling the equator during periods of low/high Southern Oscillation Index. Zonal wind differences of 8–11 m s−1 between low- and high-Index phases of the SO were found near 25°N, 25°S and near the equator in the central Pacific.

Composites relative to El Niño events during different seasons reveal that anomalous anticyclonic circulations in the Pacific are associated with the presence of a positive sea surface temperature anomaly in the eastern and central equatorial Pacific. The anomalous circulation features outside the tropical and subtropical Pacific vary with season, with the largest anomalies in each hemisphere occurring during the winter.

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Phillip A. Arkin

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A description is given of the relationship between 6 h averages of fractional coverage of cloud above various height and temperature thresholds, derived from infrared data from the Synchronous Meteorological Satellite 1 (SMS 1), and accumulated rainfall, derived from data obtained with a group of quantitative C-band digital radars. Comparisons are made over a hexagonal area extending from 22.25–24.75°W longitude and from 7–10°N latitude (the B-scale array) for each phase of the GARP Atlantic Tropical Experiment (GATE). Scattergrams of fractional coverage above 10 km, the altitude of maximum correlation, show a linear relationship for each phase, with correlations ranging from 0.81 to 0.89. Reanalysis of Phase I, omitting a single outlier, results in a very narrow range for the regression coefficients for all three phases. An analysis of the pooled data from all three phases, omitting the single outlier, shows that 75% of the variance in 6 h rainfall accumulations over the GATE B-scale array is explained by a linear function of the fraction of the array covered by cloud higher than 10 km.

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Phillip A. Arkin

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Muthuvel Chelliah and Phillip Arkin

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The objective of this study is to examine the broad aspects of large-scale interannual and long-term variability in the monthly mean outgoing longwave radiation (OLR) data over the global tropics. These data, derived from NOAA's polar-orbiting satellites, cover a period of more than 15 years. Rotated principal component analysis (RPCA) has been performed on monthly OLR anomalies over the global tropics (30°N–30°S) on a 10° longitude by 5° latitude grid for the period from June 1974 through March 1989, excluding calendar year 1978. The leading rotated principal components to be discussed below have been tested for robustness and reproducibility.

The spatial-loading pattern and the time series for the first principal component (termed the “canonical ENSO” mode) represent the major large-scale features in the tropics during the typical phase of the major warm and cold events in the tropical Pacific during the analysis period. The characteristics of the dramatic 1982/83 warm event that were different from the canonical ENSO mode completely dominate the second RPC (termed the 1982/83 mode). The third and fourth leading RPCs appear to describe the changes in the satellite-observing system. Specifically, the third RPC is clearly associated with the different equator crossing times of the various NOAA satellites, while the fourth eigenmode may be related to the three major changes in the spectral windows of the different NOAA satellites. Of the six leading modes considered, the “nonphysical” modes (3 and 4) accounted for more than 40% of the explained variance over North Africa and northeastern South America. The physical modes (1, 2, 5, and 6) explained more than 70% of the variance in the central equatorial and eastern Pacific Ocean.

It is demonstrated that while the eigenmodes that result from unrotated principal component analysis are sensitive to small changes in analysis domain and period, those of the rotated analysis are fairly stable. However, note that the “1982/83 mode,” as the name implies, is unique to the analysis period (1974–89). The results of the sensitivity analysis do not provide strong support of the claim by other authors that the decade of the 1980s, as compared to the 1970s, experienced enhanced levels of convective activity in the tropical Pacific and Indian oceans.

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Pingping Xie and Phillip A. Arkin

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In order to further our quantitative understanding of the advantages and the shortcomings of the various sources of data used to represent climatic-scale precipitation, monthly gauge observations and satellite estimates are intercompared for global grid areas of 2.5° latitude/longitude for a period from July 1987 to June 1990. The results show that 1) at least five gauges are necessary to construct an areal-averaged monthly mean for the grids with accuracy of 10%, and 10% of the global land grids satisfy the requirement; 2) both microwave- and IR-based satellite estimates give similar spatial distributions of precipitation with good agreement with gauge observations for the warm seasons and over the tropical Pacific Ocean; and 3) the satellite estimates, especially those from the IR-based algorithm, exhibit poorer correspondence with gauge observations over land areas for the cold seasons. These results show that, for many applications, no single type of data can be used as the source for a monthly precipitation dataset with full global coverage, suggesting the need to improve the algorithms and to develop methods of combining the individual data sources, particularly in estimating extratropical precipitation.

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Pingping Xie and Phillip A. Arkin

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An algorithm is developed to construct global gridded fields of monthly precipitation by merging estimates from five sources of information with different characteristics, including gauge-based monthly analyses from the Global Precipitation Climatology Centre, three types of satellite estimates [the infrared-based GOES Precipitation Index, the microwave (MW) scattering-based Grody, and the MW emission-based Chang estimates], and predictions produced by the operational forecast model of the European Centre for Medium-Range Weather Forecasts. A two-step strategy is used to: 1) reduce the random error found in the individual sources and 2) reduce the bias of the combined analysis. First, the three satellite-based estimates and the model predictions are combined linearly based on a maximum likelihood estimate, in which the weighting coefficients are inversely proportional to the squares of the individual random errors determined by comparison with gauge observations and subjective assumptions. This combined analysis is then blended with an analysis based on gauge observations using a method that presumes that the bias of the gauge-based field is small where sufficient gauges are available and that the gradient of the precipitation field is best represented by the combination of satellite estimates and model predictions elsewhere. The algorithm is applied to produce monthly precipitation analyses for an 18-month period from July 1987 to December 1988. Results showed substantial improvements of the merged analysis relative to the individual sources in describing the global precipitation field. The large-scale spatial patterns, both in the Tropics and the extratropics, are well represented with reasonable amplitudes. Both the random error and the bias have been reduced compared to the individual data sources, and the merged analysis appears to be of reasonable quality everywhere. However, the actual quality of the merged analysis depends strongly on our uncertain and incomplete knowledge of the error structures of the individual data sources.

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Robert Joyce and Phillip A. Arkin

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Nine years (1986–94) of tropical and subtropical precipitation estimates based on the GOES precipitation index (GPI) are examined. The GPI, based on the results of studies relating fractional coverage of cold cloud to convective rainfall, uses IR observations gathered by geostationary and polar-orbiting satellites. Longitudinal discontinuities in mean GPI coincident with the boundaries of satellite coverage led to a comparison of GPI derived from each geostationary satellite in overlap regions. This study revealed both intersatellite calibration differences and satellite zenith angle dependence. Its goals are to remove these sources of systematic error within the GPI, investigate the climatology of the corrected GPI, and compare against other estimated rainfall datasets. To correct calibration differences, Global Precipitation Climatology Project geostationary satellite IR data are standardized to one satellite by temperature adjustments deduced by the International Satellite Cloud Climatology Project. The resulting GPI values are corrected for zenith angle dependence based on a comparison between GOES-7 and Meteosat-3 that found a systematic increase in GPI of 9% for every 10° of zenith angle beyond 25°. The corrections remove noticeable discontinuities in time-averaged GPI and are largest (>2 mm day−1) over the eastern Indian Ocean, the equatorial Pacific near the date line, and South America. The spatial correlation between corrected GPI and rainfall derived from rain gauges is greater than 0.8 in tropical regions with adequate gauge density. Empirical orthogonal functions of monthly anomalies of corrected GPI show the expected El Niño–Southern Oscillation spatial pattern.

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Phillip A. Arkin and Pingping Xie

The Global Precipitation Climatology Project (GPCP) was established by the World Climate Research Programme to produce global analyses of area- and time-averaged precipitation for use in climate research. To achieve the required spatial coverage, the GPCP uses simple rainfall estimates derived from IR and microwave satellite observations. In this paper, we describe the GPCP and its first Algorithm Intercomparison Project (AIP/1), which compared a variety of rainfall estimates derived from Geostationary Meteorological Satellite visible and IR observations and Special Sensor Microwave/Imager microwave observations with rainfall derived from a combination of radar and raingage data over the Japanese islands and the adjacent ocean regions during the June and mid-July through mid-August periods of 1989. To investigate potential improvements in the use of satellite IR data for the estimation of large-scale rainfall for the GPCP, the relationship between rainfall and the fractional coverage of cold clouds in the AIP/1 dataset is examined. Linear regressions between fractional coverage and rainfall are analyzed for a number of latitude-longitude areas and for a range of averaging times. The results show distinct differences in the character of the relationship for different portions of the area. In general, to the south and east of the mountainous axis of Japan, rainfall and fractional coverage are highly correlated for thresholds colder than 245 K, and correlations can be increased by averaging in space and in time up to the dominant period of the precipitation events. To the north and west of the axis, the correlations between rainfall and fractional coverage, while generally smaller for all scales, are highest for thresholds warmer than 245 K. The proportional coefficients relating rainfall to fractional coverage at cold thresholds, however, differ greatly between the two periods and both differ significantly from those found for the GARP (Global Atmospheric Research Program) AtlanticTropical Experiment. These results suggest that the simple IR-based estimation technique currently used in the GPCP can be used to estimate rainfall for global tropical and subtropical areas, provided that a method for adjusting the proportional coefficient for varying areas and seasons can be determined.

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Pingping Xie and Phillip A. Arkin

Gridded fields (analyses) of global monthly precipitation have been constructed on a 2.5° latitude–longitude grid for the 17-yr period from 1979 to 1995 by merging several kinds of information sources with different characteristics, including gauge observations, estimates inferred from a variety of satellite observations, and the NCEP–NCAR reanalysis. This new dataset, which the authors have named the CPC Merged Analysis of Precipitation (CMAP), contains precipitation distributions with full global coverage and improved quality compared to the individual data sources. Examinations showed no discontinuity during the 17-yr period, despite the different data sources used for the different subperiods. Comparisons of the CMAP with the merged analysis of Huffman et al. revealed remarkable agreements over the global land areas and over tropical and subtropical oceanic areas, with differences observed over extratropical oceanic areas. The 17-yr CMAP dataset is used to investigate the annual and interannual variability in large-scale precipitation. The mean distribution and the annual cycle in the 17-yr dataset exhibit reasonable agreement with existing long-term means except over the eastern tropical Pacific. The interannual variability associated with the El Niño-Southern Oscillation phenomenon resembles that found in previous studies, but with substantial additional details, particularly over the oceans. With complete global coverage, extended period and improved quality, the 17-yr dataset of the CMAP provides very useful information for climate analysis, numerical model validation, hydrological research, and many other applications. Further work is under way to improve the quality, extend the temporal coverage, and to refine the resolution of the merged analysis.

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