Forecasts of the July 2020 Kyushu Heavy Rain Using a 1000-Member Ensemble Kalman Filter
Le Duc, Takuya Kawabata, Kazuo Saito, Tsutao Oizumi
Abstract
Forecast performances of the July 2020 Kyushu heavy rain have been revisited with the aim of improving the forecasts for this event. While the Japan Meteorological Agency's (JMA) deterministic forecasts were relatively good, the JMA's ensemble forecasts somehow missed this event. Our approach is to introduce flow-dependence into assimilation by running a 1000-member local ensemble transform Kalman filter (LETKF1000) to extract more information from observations and to better quantify forecast uncertainties. To save computational costs, vertical localization is removed in running LETKF1000. Qualitative and quantitative verifications show that the LETKF1000 forecasts outperform the operational forecasts both in deterministic and probabilistic forecasts.