Improving Deterministic Forecasts for Heavy Snow Events Associated with Convergence Band Clouds in Western Hokkaido

抄録

This study examines the use of the Japan Meteorological Agency's mesoscale ensemble prediction system (MEPS) to improve deterministic forecasts for heavy snowfall events, focusing on the case of a December 2023 heavy snowfall event in Iwamizawa City, Hokkaido. This event involved a convergence band cloud over the northern Sea of Japan that was driven by mesoscale low-level cold air advection. To capture key thermal variability related to this phenomenon, we applied principal component analysis (PCA) to 925 hPa temperature fields from 21 MEPS members. Clustering in the principal component plane identified four representative forecast scenarios. One cluster significantly improved prediction of the location of the convergence band cloud and associated snowfall compared to the operational mesoscale model (MSM). By projecting the mesoscale analysis fields—available six hours after initialization—onto the same phase plane, the most accurate scenario could be selected up to 21 hours before peak snowfall. We validated our method by applying it to three additional heavy snowfall cases and confirmed improvement over the MSM. These results highlight that MEPS-based clustering of mesoscale cold air advection patterns provides a robust approach to enhancing precipitation forecasts and supporting earlier weather warnings in Hokkaido.