Ensemble data assimilation and forecast experiments for the September 2015 heavy rainfall event in Kanto And Tohoku regions with atmospheric motion vectors from Himawari-8

Abstract

Himawari-8, a next-generation geostationary meteorological satellite that has been in operation since July 2015, incorporates significant improvements in resolution, scan frequency, and number of bands, bringing new capabilities to weather forecasting. By taking advantage of the availability of high-frequency data with high spatial resolution, an ensemble Kalman filter implemented with a mesoscale regional model assimilated rapid-scan atmospheric motion vectors (RS-AMVs) from Himawari-8. Data assimilation and ensemble forecast experiments were conducted for a heavy rainfall event that occurred in September 2015 in the Kanto and Tohoku regions of Japan. The results showed that the inclusion of RS-AMVs improved precipitation scores, especially for weak and moderate rainfall. In addition, the subsequent model forecast simulated successfully the band of heavy rainfall. Ensemble-based probabilistic forecasts showed that when RSAMVs were assimilated, the results captured the occurrence of torrential rainfall with a relatively high probability. The ensemble- based correlation analysis indicated that the strong rainfall was related to advection of moisture at low to mid levels and moisture flux convergence at lower levels. Simulations with a higher resolution model initialized by nested data assimilation showed that the assimilation of frequent RS-AMVs improved the forecast results.