Assessing the Impact of Weather Conditions on Radar-Based Rainfall Estimation in the Tropics: A Case Study in Thailand

In hydrological modeling and forecasting, rainfall data is a key factor in determining the model’s accuracy. The higher the accuracy of the estimated rainfall, the more accurate the model’s predictions can be. Rain gauges can be utilized to estimate the amount of rainfall within a catchment area but their effectiveness is often limited by the sparse distribution of rain gauges and the lack of sufficient spatial information they provide for comprehensive distributed hydrological simulations. Weather radar serves as an alternative source of rainfall data, capable of providing remotely sensed rainfall estimates with high temporal and spatial resolution. However, conventional radar quantitative precipitation estimation (QPE) is subject to uncertainties, primarily arising from variations in the drop size distribution (DSD) of hydrometeors and variations in vertical profile reflectivity (VPR). Those variations are typically influenced by the local climate and weather conditions and their impacts on the performance of QPE remains a subject of research especially in tropical regions. Therefore, this study aims to investigate relationships between weather conditions and the performance of radar QPE using statistical and machine learning approaches at different time scales. In Thailand, the radar-based rainfall data is derived with a standard fixed power law relationship between radar reflectivity and rain rate, from three weather radars located in different parts of the country. The rainfall estimates from this radar rainfall product are investigated with weather conditions from ERA5 reanalysis datasets and local observations in the period of 2022-2024. The findings help us to identify the key factors influencing the accuracy of radar rainfall estimation, which can be used to improve radar rainfall estimation, for example through finding adequate predictors for the construction of a dynamic Z-R relationship in tropical conditions. Future studies could expand this analysis by integrating these impact factors into radar QPEs and implementing improved estimated rainfall products in hydrological models.