Assimilating Every‐10‐minute Himawari‐8 Infrared Radiances to Improve Convective Predictability. Issue 5 (11th March 2019)

• Record Type: Journal Article
• Authors: Sawada, Yohei; Okamoto, Kozo; Kunii, Masaru; Miyoshi, Takemasa
• Is Part Of: Journal of geophysical research. Volume 124:Issue 5(2019)
• Page Start: 2546
• Page End: 2561
• Publication Date: 2019-03-11
• Subjects: geostationary satellite -- Himawari‐8 -- convective‐scale data assimilation; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2018JD029643
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: 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 could contribute to improving convective predictability. 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 moisture‐sensitive infrared radiances of band 8 (6.2 μm) and band 10 (7.3 μm) were assimilated into the high‐resolution (2 km) limited area model, Japan Meteorological Agency's Non‐Hydrostatic Model, every 10 min by the Local Ensemble Transform Kalman Filter. 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 better forecasting severe weather events in smaller spatiotemporal scales than the previous studies. Plain Language Summary: Sudden local severe rainfall and associated flash floods induce tremendous economic and human damages. To mitigate these negative impacts, the skill to predict rapidly developing precipitating systems should be improved. The Japan Meteorological Agency's new geostationary satellite, Himawari‐8, has dramatically higher spatiotemporal resolution than previous generation geostationary satellites and observes infrared radiances, which includes the information about water vapor, temperature, and clouds, every 10 min in the East Asia and Oceania region. It is expected that the "Big Data" from Himawari‐8 can improve the predictability of sudden local severe rainfall. We implemented the real‐data experiment in which the every‐10‐min Himawari‐8 infrared radiances are integrated into atmospheric computer simulation. We found that the Himawari‐8 observations improved the simulation of a sudden local severe rainfall event. Although the previous studies have focused on the application of satellite data to the relatively large‐scale phenomena such as tropical cyclones, our results imply that Himawari‐8 can contribute to better forecasting severe weather events in much smaller spatiotemporal scales. Since the new generation geostationary satellites similar to Himawari‐8 are and will be adopted by many meteorological agencies, our findings may be useful all over the world. Key Point: Assimilating frequent infrared radiance observations from Himawari‐8 can improve convective predictability