MOSIR Project

Impacts of dense and frequent surface observations on predicting severe rainfalls

Speaker	MAEJIMA, Yasumitsu (Research associate, Research Center for RIKEN, Center for Computational Science)
Date	2019/08/26 - 2019/08/26
Seminar	CPS Seminar

Place	CPS Conference Room
Documents	Presentation (35.37MB)

Abstract	To investigate the impact of dense and frequent surface observations on severe rainfall events, we perform a series of data assimilation (DA) experiments using the Local Ensemble Transform Kalman Filter (LETKF) with the regional NWP model. This study suggests that the dense and frequent surface DA have a potential to improve the forecast accuracy for severe rainfall events.
Keywords	Data assimilation, Meso-scale meteorology