Assessing the performance of bias correction approaches for correcting monthly precipitation over India through coupled models

Abstract

The objective of the present study was to investigate the inter‐annual variation and error structure in the prediction of monthly precipitation through two global coupled models, the National Centers for Environmental Prediction Climate Forecast System version 2 (CFSv2) and the Geophysical Fluid Dynamics Laboratory model. In view of the consistent systematic bias (dry bias during summer monsoon months and wet bias during pre‐monsoon months in CFSv2) a requirement to correct the inherent error is inevitable. For this purpose, a few bias correction methods, standardization−reconstruction (Z), quantile−quantile mapping (QQ) and nonlinear transformation (NL_Zi), are explored. The methods are applied to the outputs of the dynamic models and the efficiency is examined through different statistical skill measures. A maximum error reduction is noticed for March, July, September and December. A decreasing tendency for rainfall in July is represented by the raw model and its biased counterpart. The observed probability is noticed to be overestimated (underestimated) corresponding to below normal (above normal) precipitation in the raw model. The varying relationship between monthly precipitation and the NINO3.4 index might be a reason for misleading prediction during extreme years. Among the bias correction methods, NL_Zi showed maximum improvement in terms of predicting the precipitation amount and probability distribution all through the year irrespective of the selection of the coupled model.

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).…”

“…In the context of summer monsoon (months of June, July, August and September; JJAS), which plays an important role in socio-economic sustainability of India, there are only few studies available in the literature where attempts are made to assess the skill of different bias correction methods in improving precipitation information generated by climate models (Acharya et al, 2013;Salvi et al, 2011;Singh et al, 2017;Prasanna, 2018). Moreover, these studies have been carried on GCMs which have a very coarse resolution, not sufficient to represent the heterogeneous precipitation characteristics over India.…”

Evaluation of Bias Correction Methods for Regional Climate Models: Downscaled Rainfall Analysis Over Diverse Agroclimatic Zones of India

“…Three bias correction methodologies: SCL, LOCI, and EQM, that have been used in prior studies were considered (Acharya et al, 2013;Chaudhary et al, 2019;Fang et al, 2015;Prasanna, 2018;Singh et al, 2017;Teutschbein & Seibert, 2013). The use of these methods in the rainfall variables is well explored in Fang et al (2015) and Teutschbein and Seibert (2013) at various complexity levels ranging from simple scaling to intermediate LOCI to sophisticated QM method.…”

“…Thus, it becomes imperative to correct the RCM simulated meteorological variables before they could be used for impact studies (Mall et al, 2019;Seneviratne et al, 2012). Over the past decades, various bias correction methods are in use, these range from simple scaling approaches to sophisticated distribution mapping, quantile delta mapping and regional quantile delta mapping (RQDM) (Acharya et al, 2013;Chen et al, 2011Chen et al, , 2013Fang et al, 2015;Gutjahr & Heinemann, 2013;Kim et al, 2021;Miralha et al, 2021;Piani et al, 2010;Sharma et al, 2007;A. Singh et al, 2017Teutschbein & Seibert, 2012;Tiwari, Kar, Mohanty, Dey, Kumari, et al, 2016;Voropay et al, 2021;Xavier et al, 2021).…”

The evaluation of weekly extended range river basin rainfall forecasts and a new bias correction mechanism for flood management in India

“…The results show that a bias in rainfall arising due to improper amplitude attenuations as a function of lead-time, could be corrected under many circumstances -provided climatological or observed rainfall amplitude is known for any lead day. This shows promise for correcting amplitude bias arising in operational dynamical models (Singh et al 2017;Jabbari, Bae 2020).…”