Journal of the Meteorological Society of Japan. Ser. II
Online ISSN : 2186-9057
Print ISSN : 0026-1165
ISSN-L : 0026-1165
High Temporal Rainfall Estimations from Himawari-8 Multiband Observations Using the Random-Forest Machine-Learning Method
Hitoshi HIROSEShoichi SHIGEMunehisa K. YAMAMOTOAtsushi HIGUCHI
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Article ID: 2019-040


 We introduce a novel rainfall estimation algorithm with a random-forest machine-learning method only from Infrared (IR) observations. As training data, we use nine-band brightness temperature (BT) observations obtained from IR radiometers on the third-generation geostationary meteorological satellite (GEO) Himawari-8 and precipitation radar observations from the Global Precipitation Measurement core observatory. The Himawari-8 Rainfall estimation Algorithm (HRA) enables us to estimate rain rate with high spatial and temporal resolution (i.e., 0.04° every 10 min), covering the entire Himawari-8 observation area (i.e., 85°E–155°W, 60°S–60°N) based solely on satellite observations. We conducted a case analysis of the Kanto–Tohoku heavy rainfall event to compare rainfall estimation results of HRA and the near-real-time version of the Global Satellite Mapping of Precipitation (GSMaP_NRT), which combines global rainfall estimation products with microwave and IR BT observations obtained from satellites. In this case, HRA could estimate heavy rainfall from warm-type precipitating clouds, although GSMaP_NRT could not estimate heavy rainfall when the microwave satellites were unavailable. Further, a statistical analysis showed that the warm-type heavy rain seen in the Asian monsoon region occurred frequently when the BT differences between the 6.9-μm and 7.3-μm of water vapor (WV) bands (ΔT6.9–7.3) were small. Himawari-8 is the first GEO to include the 6.9-μm band which is sensitive to middle-to-upper tropospheric WV. An analysis for the weighting functions of the WV multibands revealed that ΔT6.9–7.3 became small as WV amount in the middle-to-upper troposphere was small and there were optically thick cloud with the cloud top near the middle troposphere. Statistical analyses during boreal summer (August and September 2015 and July 2016) and boreal winter (December 2015 and January and February 2016) indicate that HRA has higher estimation accuracy for heavy rain from warm-type precipitating clouds than a conventional rain estimation method based on only one IR band.

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© The Author(s) 2019. This is an open access article published by the Meteorological Society of Japan under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
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