- 著者
- KAWAI Hideaki SHIGE Shoichi
- 出版者
- Meteorological Society of Japan
- 雑誌
- 気象集誌. 第2輯 (ISSN:00261165)
- 巻号頁・発行日
- pp.2020-059, (Released:2020-08-03)
- 被引用文献数
- 3 9
This review paper aims to provide readers with a broad range of meteorological backgrounds with basic information on marine low clouds and the concept of their parameterizations used in global climate models. The first part of the paper presents basic information on marine low clouds and their importance in climate simulations in a comprehensible way. It covers the global distribution and important physical processes related to the clouds, typical examples of observational and modeling studies of such clouds, and the considerable importance of changes in low cloud for climate simulations. In the latter half of the paper, the concept of cloud parameterizations that determine cloud fraction and cloud water content in global climate models, which is sometimes called cloud “macrophysics”, is introduced. In the parameterizations, the key element is how to assume or determine the inhomogeneity of water vapor and cloud water content in model grid boxes whose size is several tens to several hundreds of kilometers. Challenges related to cloud representation in such models that must be tackled in the next couple of decades are discussed.
- 著者
- HIROSE Hitoshi SHIGE Shoichi YAMAMOTO Munehisa K. HIGUCHI Atsushi
- 出版者
- Meteorological Society of Japan
- 雑誌
- 気象集誌. 第2輯 (ISSN:00261165)
- 巻号頁・発行日
- pp.2019-040, (Released:2019-03-15)
- 被引用文献数
- 23
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.