Ensuring High-Quality Rainfall Datasets in Thailand: A Multi-Step Quality Control Approach and Satellite-Based Evaluation

Reliable, high-quality rainfall data are vital for soil and water management, crop forecasting, and risk assessment. These applications are essential for food security, climate resilience, biodiversity monitoring, and rural livelihoods. Rainfall monitoring in Thailand is challenging due to the limited density of official stations and the inconsistent quality of data from multiple sources, compounded by calibration issues. This study introduces a comprehensive quality control (QC) approach tailored for the Thai context, presenting a systematic pipeline that clarifies the hierarchy and sequence of operations. The method uses rainfall data from 3075 stations of the Thai Meteorological Department (TMD) and the Thaiwater network. It includes basic QC for data completeness and advanced QC using a quality (Q) index to assess station reliability, diving the stations into five groups: poor (<50), moderate (50–80), acceptable (80–85), good (85–90), and excellent (>90). The results indicate that Thaiwater consistently achieved moderate to excellent Q index values, exceeding 70% annually, with values surpassing 90% in 2023. In contrast, the TMD maintained excellent quality, with values above 90% for all years. Out of over one million daily entries, 87% were verified as correct, though the Thaiwater data for 2024 showed only 70% accuracy. The QC procedures significantly improved data reliability, reducing the root mean square error for GSMaP and IMERG by 1.7% and 1.5%, respectively, and lowering the false alarm rate by approximately 0.001–0.002 without compromising heavy rainfall detection. A systematic QC framework is essential for ensuring high-quality datasets in rainfall applications.