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Phillip A. Arkin and Philip E. Ardanuy

Abstract

Measurement of climatic-scale precipitation (defined here as averages over areas of >104 km2 and periods of five days or longer) is impractical for many areas of the earth without the use of space-based observations. We briefly discuss the history of satellite rainfall estimation schemes and their application to climate studies. Two approaches—direct and indirect—have dominated work until very recently, when attempts to use more integrated techniques began. Indirect schemes, primarily based on visible and infrared (IR) observations of the characteristics of clouds, have been used in the majority of such studies. Direct schemes, such as those that use microwave observations of raindrop-sized hydrometeors, have been limited by a relative lack of the required measurements. A large number of studies have used datasets not originally intended as precipitation estimates at all, such as the NOAA outgoing longwave radiation data, to produce estimates of very large scale rainfall. Current and prospective attempts to overcome some of the difficulties affecting climatic-scale precipitation estimation are described. The Global Precipitation Climatology Project will integrate data from surface obserations, geostationary IR sensors, and polar-orbiting microwave and IR sensors to produce near-global analyses of monthly rainfall. The proposed Tropical Rainfall Measuring Mission will use a single satellite with an instrument package that will make visible, IR, and microwave radiometric observations. The package will also include a precipitation radar. We discuss certain other proposed satellite missions and international programs and their contributions to the production of climatic-scale precipitation estimates. Finally, we propose the development of a global rainfall analysis system.

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Owen E. Thompson, Philip A. Arkin, and William D. Bonner

Abstract

A comprehensive summary of diurnal wind variations in the midwestern region of the United States is presented. Analyses are based on seven summers of four per day soundings at Fort Worth, Tex., Topeka, Kan., and International Falls, Minn. It is found that the diurnal oscillations are most prominent at Fort Worth, of significant amplitude at Topeka, and, although of lesser amplitude, still detectable at International Falls. An analysis is made of the forcing required to account for that part of the wind oscillation which cannot be attributed to Coriolis effects. This analysis indicates that the forcing is comparatively small at Fort Worth when the wind oscillations are largest owing to a resonance there with natural inertial oscillations. Significant forcing is present at higher latitude stations even though the manifestation of the forcing in the wind field is somewhat smaller in amplitude. The data suggest that forcing mechanisms at low and high attitudes may propagate to cause wind oscillations in the middle levels.

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Patrick W. S. King, William D. Hogg, and Philip A. Arkin

Abstract

Data from the first Algorithm Intercomparison Project(AIP/1) collected over Japan and surrounding waters in June, July, and August 1989 are used in this study to assess the importance of visible data in satellite rain estimation techniques. The purpose of the project was to compare different methods for estimating rainfall using satellite measurements. Radar and surface gauge data provided the validation set.

RAINSAT, an estimation technique using both visible (VIS) and infrared (IR) data, achieved the highest correlation with the validation data. In this paper rainfall estimates from RAINSAT (VIS+IR) am compared with two IR-only techniques to deduce the effectiveness of VIS data. Some estimates are also made using a VIS-only technique. Comparisons am made on both a spatial and diurnal basis.

Cloud climatologies for a subset of the AIP/1 data and the southern Ontario data on which RAINSAT was trained showed a marked similarity. It is found that the total volume of rain as a function of albedo is very similar for both Japanese and Ontario data.

The VIS data generally produced higher correlations with the validation data than did the IR data. This was especially the case when rain fell from warm, orogaphically induced rainfall. When rain fell from cold bright clouds. especially over the ocean, the correlations of the two types of data with the validation data were similar.

It is also shown that normalization of VIS data by the cosine of solar zenith data was inadequate to remove diurnal variations in apparent brightness.

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Simon J. Mason, Lisa Goddard, Nicholas E. Graham, Elena Yulaeva, Liqiang Sun, and Philip A. Arkin

The International Research Institute for Climate Prediction (IRI) was formed in late 1996 with the aim of fostering the improvement, production, and use of global forecasts of seasonal to interannual climate variability for the explicit benefit of society. The development of the 1997/98 El Niño provided an ideal impetus to the IRI Experimental Forecast Division (IRI EFD) to generate seasonal climate forecasts on an operational basis. In the production of these forecasts an extensive suite of forecasting tools has been developed, and these are described in this paper. An argument is made for the need for a multimodel ensemble approach and for extensive validation of each model's ability to simulate interannual climate variability accurately. The need for global sea surface temperature forecasts is demonstrated. Forecasts of precipitation and air temperature are presented in the form of “net assessments,” following the format adopted by the regional consensus forums. During the 1997/98 El Niño, the skill of the net assessments was greater than chance, except over Europe, and in most cases was an improvement over a forecast of persistence of the latest month's climate anomaly.

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John E. Janowiak, Philip A. Arkin, Pingping Xie, Mark L. Morrissey, and David R. Legates

Abstract

Very few (if any) in situ measurements of rainfall are available in the Pacific ITCZ east of the Line Islands (157°W). Hence, climatological datasets, which are assembled from various in situ sources, and satellite-derived analyses of precipitation are frequently relied upon to provide information on the distribution of rainfall in this important region. A substantial amount of disagreement exists among these information sources as demonstrated in this paper. In particular, the east–west gradient of estimated rainfall intensity in the eastern Pacific ITCZ is quite different during the Northern Hemisphere warm season among six different satellite algorithms (one infrared and five microwave) and two climatologies that are examined. Some of these data suggest that a local minimum in rainfall intensity is located near 140°W in the Pacific ITCZ during northern summer, with increasing intensity toward the east and west, while the others depict steadily decreasing rainfall intensity from west of the Americas to the date line. Conversely, all of the precipitation estimates that are examined depict a rainfall maximum in the Pacific ITCZ near 140°W during the Northern Hemisphere cool season, although the magnitudes vary substantially among them.

The authors examine estimates of seasonal mean rainfall over the eastern Pacific ITCZ (cast of the date line) from two rainfall climatologies and six satellite precipitation estimation techniques during July 1987 through June 1990. Inconsistencies among the precipitation analyses are investigated by examining several independent datasets that include atmospheric circulation data, sea surface temperature data, and ship reports of weather type.

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Elizabeth E. Ebert, Michael J. Manton, Philip A. Arkin, Richard J. Allam, Gary E. Holpin, and Arnold Gruber

Three algorithm intercomparison experiments have recently been conducted as part of the Global Precipitation Climatology Project with the goal of (a) assessing the skill of current satellite rainfall algorithms, (b) understanding the differences between them, and (c) moving toward improved algorithms. The results of these experiments are summarized and intercompared in this paper.

It was found that the skill of satellite rainfall algorithms depends on the regime being analyzed, with algorithms producing very good results in the tropical western Pacific and over Japan and its surrounding waters during summer, but relatively poor rainfall estimates over western Europe during late winter. Monthly rainfall was estimated most accurately by algorithms using geostationary infrared data, but algorithms using polar data [Advanced Very High Resolution Radiometer and Special Sensor Microwave/Imager (SSM/I)] were also able to produce good monthly rainfall estimates when data from two satellites were available. In most cases, SSM/I algorithms showed significantly greater skill than IR-based algorithms in estimating instantaneous rain rates.

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