On the dynamical downscaling and bias correction of seasonal-scale winter precipitation predictions over North India

Abstract

This study presents the results of high-resolution (30 km) climate simulations over North India using an optimized configuration of the Regional Climate Model (RegCM), driven by a global spectral model (T80 model with horizontal resolution of ∼1.4◦) for a period of 28 years (1982–2009). The main aim of this work is to analyze the capabilities of the RegCM to simulate the wintertime precipitation over North India in the recent past.The RegCM validation revealed a good improvement in reproducing the precipitation compared to results obtained from the T80 model. This improvement comes due to better representation of vertical pressure velocity, moisture transport, convective heating rate and temperature gradient at two different latitudinal zones. Moreover, orography in the high-resolution RegCM improves the precipitation simulation in the region where sharp orography gradient plays an important role in wintertime precipitation processes. Two bias correction (BC) methods namely mean bias-remove (MBR) and quantile mapping (QM) have been applied on the T80 driven RegCM model simulations. It was found that the QM method is more skillful than the MBR in simulating the wintertime precipitation over North India. A comparison of model-simulated and bias corrected precipitation with observed precipitation at 17 station locations has also been carried out. Overall, the results suggest that when the BC is applied on dynamically downscaled model, it has better skill in simulating the precipitation over North India and this model is a useful tool for further regional downscaling studies.