High Temporal Rainfall Estimations from Himawari-8 Multiband Observations Using the Random-Forest Machine-Learning Method-Reference-Cited by-同舟云学术

High Temporal Rainfall Estimations from Himawari-8 Multiband Observations Using the Random-Forest Machine-Learning Method

Published:2019 Issue:3 Volume:97 Page:689-710
ISSN:0026-1165
Container-title:Journal of the Meteorological Society of Japan. Ser. II
language:en
Short-container-title:Journal of the Meteorological Society of Japan

Author:

HIROSE Hitoshi¹,SHIGE Shoichi¹,YAMAMOTO Munehisa K.¹,HIGUCHI Atsushi²

Affiliation:

1. Graduate School of Science, Kyoto University, Kyoto, Japan

2. Center for Environmental Remote Sensing, Chiba University, Chiba, Japan

Publisher

Meteorological Society of Japan

Subject

Atmospheric Science

Link

https://www.jstage.jst.go.jp/article/jmsj/97/3/97_2019-040/_pdf

Reference53 articles.

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2. Aminou, D. M. A., 2002: MSG's SEVIRI instrument. ESA Bull., 111, 15-17. [Available at http://www.esa.int/esapub/bulletin/bullet111/chapter4_bul111.pdf.]

3. Aonashi, K., J. Awaka, M. Hirose, T. Kozu, T. Kubota, G. Liu, S. Shige, S. Kida, S. Seto, N. Takahashi, and Y. N. Takayabu, 2009: GSMaP passive microwave precipitation retrieval algorithm: Algorithm description and validation. J. Meteor. Soc. Japan, 87A, 119-136.

4. Arkin, P. A., and P. E. Ardanuy, 1989: Estimating climatic-scale precipitation from space: A review. J. Climate, 2, 1229-1238.

5. Barrett, E. C., 1970: The estimation of monthly rainfall from satellite data. Mon. Wea. Rev., 98, 322-327.

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