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

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

*Yohei Sawada^1,2, Kozo Okamoto^1,2, 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.