On WRF hybrid-dynamic regression approach in heavy rainfall forecasting over the Northern of Thailand

Rainfall forecasting in Northern Thailand presents considerable challenges due to the complex topography of the region and its highly variable climatic conditions, particularly during periods of heavy rainfall in August. This is compounded by limitations in ground-based observation data, such as missing data and sparse station coverage. This study has two primary objectives. First, it examines the sensitivity of rainfall simulations using the weather research and forecasting (WRF) model to different configurations of microphysics and cumulus parameterization schemes. Second, it evaluates the performance of four different approaches in forecasting models including autoregressive integrated moving average (ARIMA), artificial neural network (ANN), dynamic exponential regression and autoregressive integrated moving average (DER–ARIMA), and dynamic quadratic regression and autoregressive integrated moving average (DQR–ARIMA). The period was focused on 1–31 August from 2009 to 2023. The performance of both components is assessed using three statistical metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Symmetric Mean Absolute Percentage Error (SMAPE). The results showed that the most accurate configurations in WRF model were Kessler–New Tiedtke for the Salween (RMSE = 9.85) River Basin, WSM5–New Tiedtke for the Ping (RMSE = 8.43) and Yom (RMSE = 10.41) River Basins, and WSM6–New Tiedtke for the Upper Mekong (RMSE = 13.73), Wang (RMSE = 11.10), and Nan (RMSE = 11.08) River Basins. For forecasting models, the ANN model demonstrated the highest accuracy in the Salween (RMSE = 10.66), Upper Mekong (RMSE = 15.09), and Nan (RMSE = 16.30) River Basins, while the DER–ARIMA model performed best in the Wang (RMSE = 15.63) and Yom (RMSE = 12.08) River Basins. The DQR–ARIMA model was found to be the most suitable for the Ping (RMSE = 12.04) River Basin.