Bias correction method of high-resolution satellite-based precipitation product for Peninsular Malaysia

• DOI: 10.1007/s00704-022-04007-6
• Publication Date: 2022-03-15T07:02:51Z
• Authors: Zafar Iqbal; Shamsuddin Shahid; Kamal Ahmed; Xiaojun Wang; Tarmizi Ismail; Hamza Farooq Gabriel
• Keywords: Atmospheric Science; Environmental Engineering; Hydrological Modeling; Limiting; FOS: Mechanical engineering; TA Engineering (General). Civil engineering (General); Precipitation; 310; 01 natural sciences; Environmental science; Satellite-Based Precipitation Estimation and Validation; Meteorology; Engineering; FOS: Mathematics; 0105 earth and related environmental sciences; Climatology; Geography; Statistics; FOS: Environmental engineering; Global Precipitation Measurement; Geology; FOS: Earth and related environmental sciences; Numerical Weather Prediction Models; 15. Life on land; Remote Sensing of Soil Moisture; Regression; Mechanical engineering; 6. Clean water; Earth and Planetary Sciences; Aerospace engineering; Satellite; 13. Climate action; Physical Sciences; Environmental Science; Mean squared error; Probabilistic Forecasting; Mathematics

Abstract Satellite-based precipitation (SBP) is emerging as a reliable source for high-resolution rainfall estimates over the globe. However, uncertainty in SBP is still significant, limiting their use without evaluation and often without bias correction. The bias correction of SBP remained a challenge for atmospheric scientists. In this study, the performance of six SBPs, namely, SM2RAIN-ASCAT, IMERG, GsMap, CHIRPS, PERSIANN-CDS and PERSIANN-CSS in replicating observed daily rainfall at 364 stations over Peninsular Malaysia was evaluated. The bias of the most suitable SBP was corrected using a novel machine learning (ML)-based bias-correction method. The proposed bias-correction method consists of an ML classifier to correct the bias in estimating rainfall occurrence and an ML regression model to correct the amount of rainfall during rainfall events. The performance of different widely used ML algorithms for classification and regression were evaluated to select the suitable algorithms. IMERG showed better performance, showing a higher correlation coefficient (R2) of 0.57 and Kling-Gupta Efficiency (KGE) of 0.5 compared to the other products. The performance of random forest (RF) was better than the k-nearest neighbourhood (KNN) for both classification and regression. RF classified the rainfall events with a skill score of 0.38 and estimated the rainfall amount of a rainfall event with the modified Index of Agreement (md) of 0.56. Comparison of IMERG and bias-corrected IMERG (BIMERG) revealed an average reduction in RMSE by 55% in simulating observed rainfall. The proposed bias correction method performed much better when compared with the conventional bias correction methods such as linear scaling and quantile regression. The BIMERG could reliably replicate the spatial distribution of heavy rainfall events, indicating its potential for hydro-climatic studies like flood and drought monitoring in the study area.