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

Abstract

This is the peer reviewed version of the following article: Tiwari, P. R., Kar, S. C., Mohanty, U. C., Dey, S., Sinha, P., Raju, P. V. S. and Shekhar, M. S., ???On the dynamical downscaling and bias correction of seasonal-scale winter precipitation predictions over North India???, quarterly Journal of the Royal Meteorological Society, Vol. 142 (699):2398-2410, June 2016, which has been published in final form at DOI: https://doi.org/10.1002/qj.2832. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. ?? 2016 Royal Meteorological SocietyThis 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

On bias correction of summer monsoon precipitation over India from CORDEX‐SA simulations

“…The approach of bias correction is based on model output statistics that aims at using information from bias containing model results. There are various potential bias correction techniques that are developed and used over the past decade in improving RCM-derived information ranging from simple linear scaling to more skilful and advanced distribution matching methods (Sharma et al, 2007;Mpelasoka and Chiew, 2009;Piani et al, 2010;Ryu et al, 2009;Chen et al, 2011;2013;Iizumi et al, 2011;Teutschbein and Seibert, 2012;Acharya et al, 2013;Ahmed et al, 2013;Gutjahr and Heinemann, 2013;Lafon et al, 2013, Fang et al, 2015Tiwari et al, 2016;Singh et al, 2017).…”

Comparison of statistical and dynamical downscaling methods for seasonal‐scale winter precipitation predictions over north India

“…The bias correction methods are explained in Tiwari et al . (). In the case of statistical downscaling, composite forecast of wintertime (DJF) precipitation have been considered to compare both the dynamical and statistical downscaling approaches.…”

“…More details of PSE computation can be found in Tiwari et al . (). It is noticed from Table that the PSE value is maximum (with 94%) for composite (i.e., model output matches the sign of with observations 94% times) of wet minus dry years for QM method followed by CCA‐based statistical downscaling method (with 88%), T80_RegCM (with 70%) and T80 model (with 58%).…”

“…Most of the precipitation during winter over the mountainous regions of NI (western Himalayas) is in the form of snow which helps in maintenance of glaciers throughout the year. Snow and ice melt (during spring and summer seasons) in the Himalayas influences the water availability downstream in the major river basins of South Asia (Tiwari et al, 2016b). Thus, advance information of winter precipitation (in terms of seasonal mean or anomaly) is useful for agriculture, water management sectors and various other sectors.…”

“…In all the studies, a clear improvement in precipitation prediction compared to parent GCM was obtained. On the other hand, studies using dynamical downscaling approaches (Dutta et al, 2009;Sinha et al, 2013b;Tiwari et al, 2016b;Sorland et al, 2018) have also emphasized that the dynamical downscaling approach has potential to reproduce finer-scale information with higher skill than the parent GCMs. A recent study conducted by Sorland et al (2018) has shown that the RCMs have systematically reduced the bias over the Alps and other regions of Europe.…”