Performance of Indian Ocean Land Atmosphere (IOLA) Model in Simulating Monsoon Depression

Abstract

Monsoon depressions (MD) are among the most important synoptic-scale systems of the Indian Summer Monsoon. As these systems contribute nearly half of the monsoon-season heavy rainfall, their accurate prediction is critical for agriculture, disaster management, and early warning systems. The Indian Ocean–Land–Atmosphere (IOLA) coupled mesoscale model is a high-resolution prediction framework designed to simulate a wide range of severe weather events over the Indian region. Built on the NMM dynamical core with advanced HWRF-based nesting and ocean-coupling capabilities, IOLA employs 1–2 km moving nests that automatically follow MD and resolve their structure in detail. This study evaluates the performance of IOLA in simulating six MD using doubly nested domains (4.5 km and 1.5 km) and compares the forecasts with IMD best-track data and GPM rainfall observations. IOLA demonstrates strong skill in simulating the tracks of MD. The average direct position error is approximately 98 km at 24 hours and 212 km at 72 hours, which is substantially lower than errors reported in earlier WRF-based studies. Near-surface wind speeds are overestimated during the early forecast period (bias of ~15 knots at 6 hours), but this bias gradually decreases by 48–72 hours (to ~3 knots). Rainfall evaluation indicates that IOLA successfully captures the asymmetric rainfall structure of MD, including the characteristic southwest-sector enhancement. The equitable threat score (ETS) exceeds 0.12 for light to moderate rainfall, indicating reasonable skill; however, the ETS decreases significantly for heavy rainfall events. Overall, the results suggest that IOLA shows promising capability in predicting MD, particularly their track, structure, and rainfall distribution. Further improvements in vortex initialization, data assimilation, and physical parameterization schemes are expected to enhance the model’s performance.