JpGU-AGU Joint Meeting 2020

Presentation information

[E] Oral

A (Atmospheric and Hydrospheric Sciences ) » A-AS Atmospheric Sciences, Meteorology & Atmospheric Environment

[A-AS01] High performance computing for next generation weather, climate, and environmental sciences

convener:Hiromu Seko(Meteorological Research Institute), Takemasa Miyoshi(RIKEN), Chihiro Kodama(Japan Agency for Marine-Earth Science and Technology), Masayuki Takigawa(Japan Agency for Marine-Earth Science and Technology)

[AAS01-05] Impacts of vessel GNSS data on the heavy rainfall forecasts obtained by JMA's mesoscale data assimilation system (NAPEX)

*Hiromu Seko1, Yoshinori Shoji1, Daisuke Hotta1, Ko Koizumi2, Yasutaka Ikuta2 (1.Meteorological Research Institute, 2.Japan Meteorological Agency)

Keywords:Data assimilation , GNSS

It is expected that the assimilation of low-level water vapor data on the upstream side of the heavy rainfall will improve the rainfall forecast of heavy rainfall, because the water vapor is the source of rainfall. In this study, the precipitable water vapor obtained by 8 Vessels in the East China Sea were assimilated by the JMA's mesoscale data assimilation system (NAPEX), and their impacts on the rainfall forecasts of the Kyushu were investigated by the comparing the rainfall forecasts.
By the data assimilation of GNSS data obtained by Vessels (Vessel GNSS data), the water vapor around the vessels were modified, and the modified regions moved to northeast by the low-level inflow (southwesterly flow) . Though the assimilation period was only 2 days, there were a few events of which rainfall distributions were becoming closer to the observed distributions by the assimilation of Vessel GNSS data. Furthermore, it was shown that the water vapor data that were observed on the hour and at 30 min and 15 min before made the improvements of rainfall forecasts larger. These results indicate that more frequent accurate GNSS data are expected to improve the rainfall forecast more effectively.

This work was partly supported by JST AIP Grant Number JPMJCR19U2, Japan, “Advancement of Meteorological and Global Environmental Predictions Utilizing Observational ‘Big Data’ of the MEXT, Social and Scientific Priority Issues (Theme 4) to be Tackled by Using Post ‘K’ Computer” (hp180194, hp190156).