Improvement in rainfall estimates using bias correction with the IAP-DCP Global Model

This study uses a bias correction method to modify the estimates of the Institute of Atmospheric Physics Dynamical Seasonal Prediction (IAP-DCP) model over the Indo-China Peninsula (ICP). Bias correction is a way of improving the results from post processing model simulation to nearly observation data and increase accuracy. This study used the result from the global IAP-DCP model of Humphries et al. (2018) and improved it by using bias correction. Observation data from the Global Precipitation Climatology Project Version 2.3 (GPCP) were compared with the results of Humphries et al. (2018) and bias correction. Experiments with two bias correction weighting factors w of 5% (0.05) and 8% (0.08) were used. The results are shown in terms of spatial patterns and statistical analysis. In the statistical analysis, during the southern monsoon (JJA), bias correction at w = 0.05 decreased RMSE member mean from 3.598 to 3.022 (decrease = 0.576) and MAE member mean from 2.804 to 2.441 (decrease = 0.363). Bias correction of w = 0.05 decreased RMSE member mean from 3.598 to 2.927 (decrease = 0.671) and decreased MAE member mean from 2.804 to 2.364 (decrease = 0.440). In the spatial pattern case, bias correction improved on the results of Humphries et al. (2018). Furthermore, the results using bias correction of w = 0.05 were better than those with w = 0.05. However, from the mean spatial pattern and statistical analyses, that is demonstrated that bias correction can help to decrease the error and increase the accuracy of model results. (C) 2019 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.