Japan Geoscience Union Meeting 2018

Presentation information

[EE] 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

Sun. May 20, 2018 3:30 PM - 5:00 PM 302 (3F International Conference Hall, Makuhari Messe)

convener:Hiromu Seko(Meteorological Research Institute), Chihiro Kodama(Japan Agency for Marine-Earth Science and Technology), Masayuki Takigawa(独立行政法人海洋研究開発機構, 共同), Takemasa Miyoshi(RIKEN Advanced Institute for Computational Science), Chairperson:Miyoshi Takemasa (RIKEN Advanced Institute for Computational Science), Kodama Chihiro(Japan Agency for Marine-Earth Science and Technology)

3:30 PM - 3:45 PM

[AAS01-07] Assimilating Himawari-8 infrared radiances to improve convective predictability

*Yohei Sawada1,2, Kozo Okamoto1,2, Masaru Kunii3, Takemasa Miyoshi2 (1.Meteorological Research Institute, Japan Meteorological Agency, 2.RIKEN Advanced Institute for Computational Science, 3.Forecast Department, Japan Meteorological Agency)

Keywords:Himawari-8, Data Assimilation, Sudden local severe rainfall

Improving the predictability of sudden local severe weather is a grand challenge for numerical weather prediction. Recently, the capability of geostationary satellites to observe infrared radiances has been significantly improved, and it is expected that the ‘Big Data’ from the new generation geostationary satellites contribute to improving convective predictability. In this study, we examined the potential impacts of assimilating frequent infrared observations from a new generation geostationary satellite, Himawari-8, on convective predictability. We implemented the real-data experiment in which Himawari-8 all-sky infrared radiances were assimilated into the high-resolution (2km) limited area model every 10 minutes. The frequent infrared observations from Himawari-8 improve the analysis and forecast of isolated convective cells and sudden local severe rainfall induced by weak large-scale forcing. The results imply that satellite data assimilation can contribute to forecasting severe weather events in smaller spatiotemporal scales than the previous studies.