Characterization of Weekly Cumulative Rainfall Forecasts over Meteorological Subdivisions of India Using a GCM

S. A. Saseendran National Centre for Medium Range Weather Forecasting, New Delhi, India

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S. V. Singh National Centre for Medium Range Weather Forecasting, New Delhi, India

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L. S. Rathore National Centre for Medium Range Weather Forecasting, New Delhi, India

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Someshwar Das National Centre for Medium Range Weather Forecasting, New Delhi, India

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Abstract

Weekly cumulative rainfall forecasts were made for the meteorologically homogeneous areas of the Indian subcontinent, divided into meteorological subdivisions, by performing 7-day integrations of the operational Indian T80 Global Spectral Model every Wednesday during the six southwest monsoon seasons of 1994–99. Objective evaluations of the bias and accuracy of these forecasts during that 6-yr period are made through various forecast verification methods and are presented here. The skill or relative accuracy of the forecasts and some verification measures are quantified by computing the Heidke skill score (HSS), Hanssen–Kuipers discriminant (HKS), threat score (TS), hit rate (HR), probability of detection (POD), bias score, and false-alarm rate (FAR). The study revealed that the T80 model has a tendency to underpredict rainfall over most of the subdivisions falling on the windward side of the Western Ghats and sub-Himalayan areas. The model exhibited negative bias in rainfall simulations over the desert regions of Rajasthan and over the Arabian Sea and bay islands. There is a positive bias in the rainfall simulated over the subdivisions falling in the rain-shadow regions of the Western Ghats. The TS, POD, and FAR computations show that the predicted weekly rainfall over different subdivisions in the excess and scanty categories has more skill than those in the normal and deficient categories. The HR values range from 0.21 to 1 over different subdivisions. The HSS and HKS scores indicate better skill in rainfall forecast in the central belt of India where the orographic influence over rainfall distribution is comparatively less. Better correspondence between the magnitude of the predicted and observed rainfall is apparent in the all-India time series of weekly cumulative rainfall.

* Current affiliation: Great Plains Systems Research, U.S. Department of Agriculture Agricultural Research Service, Fort Collins, Colorado

Corresponding author address: S. A. Saseendran, Great Plains Systems Research, USDA Agricultural Research Service, Fort Collins, CO 80522. Email: anapalli@gpsr.colostate.edu

Abstract

Weekly cumulative rainfall forecasts were made for the meteorologically homogeneous areas of the Indian subcontinent, divided into meteorological subdivisions, by performing 7-day integrations of the operational Indian T80 Global Spectral Model every Wednesday during the six southwest monsoon seasons of 1994–99. Objective evaluations of the bias and accuracy of these forecasts during that 6-yr period are made through various forecast verification methods and are presented here. The skill or relative accuracy of the forecasts and some verification measures are quantified by computing the Heidke skill score (HSS), Hanssen–Kuipers discriminant (HKS), threat score (TS), hit rate (HR), probability of detection (POD), bias score, and false-alarm rate (FAR). The study revealed that the T80 model has a tendency to underpredict rainfall over most of the subdivisions falling on the windward side of the Western Ghats and sub-Himalayan areas. The model exhibited negative bias in rainfall simulations over the desert regions of Rajasthan and over the Arabian Sea and bay islands. There is a positive bias in the rainfall simulated over the subdivisions falling in the rain-shadow regions of the Western Ghats. The TS, POD, and FAR computations show that the predicted weekly rainfall over different subdivisions in the excess and scanty categories has more skill than those in the normal and deficient categories. The HR values range from 0.21 to 1 over different subdivisions. The HSS and HKS scores indicate better skill in rainfall forecast in the central belt of India where the orographic influence over rainfall distribution is comparatively less. Better correspondence between the magnitude of the predicted and observed rainfall is apparent in the all-India time series of weekly cumulative rainfall.

* Current affiliation: Great Plains Systems Research, U.S. Department of Agriculture Agricultural Research Service, Fort Collins, Colorado

Corresponding author address: S. A. Saseendran, Great Plains Systems Research, USDA Agricultural Research Service, Fort Collins, CO 80522. Email: anapalli@gpsr.colostate.edu

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