Enhancing Satellite-Based Rainfall Estimates Through Bias Correction: A Case Study in Nakhon Ratchasima, Thailand

Satellite-based rainfall datasets such as CHIRPS and GPM provide valuable information for hydrological and agricultural studies but often contain systematic biases when compared with ground measurements. The accurate estimation of rainfall is also critical for disaster management, particularly in mitigating the impacts of both floods and droughts. This study applies three bias correction techniques Variance Scaling (VS), Quantile Mapping (QM), and Linear Regression (LR), to rainfall data from 37 ground stations in Nakhon Ratchasima Province, Thailand, covering the period 2002–2022, a region that regularly faces significant drought problems. Comparative analyses show that raw satellite products generally overestimate rainfall relative to gauge observations. Among the correction methods, VS consistently delivered the best performance by reducing bias and RMSE while improving correlation across stations. QM achieved mixed results and sometimes increased error, particularly for GPM. LR, while less effective than VS in bias reduction, provided stable correlations and thus offers potential as a baseline for future rainfall forecasting models. These findings underscore the importance of bias correction for enhancing the reliability of satellite rainfall datasets in complex tropical terrains.