Applying a four-dimensional local ensemble transform Kalman filter (4D-LETKF) to the JMA nonhydrostatic model (NHM)

Abstract

A four-dimensional local ensemble transform Kalman filter (4D-LETKF) is applied to the Japan Meteorological Agency (JMA)'s nonhydrostatic model (NHM) with explicit cloud microphysics to enable mesoscale ensemble prediction and data assimilation. Convective-scale data assimilation experiments in a perfect model scenario with 5-km grid spacing are performed, which indicates that the 4D-LETKF system works appropriately. Observations are taken every 10 minutes and every 2 × 2 × 2 grid points for horizontal winds, temperature, relative humidity, surface pressure, and precipitation rate. Although fixed lateral boundary conditions cause error reduction even without data assimilation, the advantages of 4D-LETKF are clear. When precipitation-rate observations are assimilated, some convective systems are better captured, although the impact is not always positive. Overall, 4D-LETKF shows encouraging results; it would be a tool adopted in future researches in convective-scale data assimilation and ensemble prediction.