Use of a Dense Rain-gauge Network over India for Improving Blended TRMM Products and Downscaled Weather Models

Abstract

This study makes use of a network of nearly 2100 rain gauges over India in order to statistically modify the Tropical Rainfall Measuring Mission (TRMM) algorithm 3B42. The validation and usefulness of the modified product is determined against rain gauge datasets and from the training and forecast phase of the Florida State University (FSU) multimodel superensemble. We use downscaled member model forecasts to construct superensemble forecasts. The member model forecasts are scaled down using the modified high-resolution TRMM rains during the training phase of the multimodel superensemble. We demonstrate that a mesoscale superensemble thus constructed has forecast efficiencies superior to those of all of the member models and to the ensemble mean (ENSM) and bias-corrected ensemble mean (BcorENSM). The forecast procedure includes a high-resolution downscaling of the model forecast rain and the construction of a multimodel superensemble. This procedure clearly provides a much-improved forecast of rain using metrics such as equitable threat scores (ETSs), anomaly correlations, root mean square (RMS) errors, and their bias scores. We also demonstrate that the performance of this modified TRMM algorithm provides results very clearly comparable to those obtained from the direct use of rain gauges throughout India and the TRMM 3B42 covering a domain of 50°S to 50°N.