Development and Performance Evaluation of a Mountain-Focused WRF Rainfall Forecasting System: A Case Study of the July 2025 Heavy Rainfall Event in South Korea

This study evaluates a Weather Research and Forecasting (WRF) system with a 500 m horizontal grid spacing developed to provide mountain-focused rainfall guidance for forest disaster preparedness in South Korea. The system uses operational 1.5 km Local Data Assimilation and Prediction System (LDAPS) fields provided by the Korea Meteorological Administration as background and lateral boundary conditions. Quality-controlled Automatic Mountain Meteorology Observation System (AMOS) observations, excluding rainfall, are assimilated using three-dimensional variational (3D-Var) data assimilation. The resulting forecasts (DA_AMOS) are compared with LDAPS for the 16–20 July 2025 heavy rainfall event using 500 m gridded and in situ AMOS rainfall observations. Compared with LDAPS, DA_AMOS better reproduced observed accumulated and maximum rainfall magnitudes, with smaller regional station-mean accumulation errors in 13 of 15 administrative divisions with eligible AMOS stations. DA_AMOS generally showed smaller forecast errors at observed rainfall intensities ≥ 10 mm h−1 but larger errors under near-zero-rainfall conditions. During 45 observation-selected extreme-rainfall hours, DA_AMOS reduced mean absolute event-hour bias from 46.14 to 41.74 mm h−1 (9.5%) relative to LDAPS, although substantial underestimation remains. These findings support complementary guidance on rainfall magnitude, though reliable prediction of extreme-rainfall location and occurrence remains unresolved. Evaluation across additional events is required before broader generalization.