PROPOSAL OF A NEW SATELLITE RAINFALL HIDREDV2 BASED ON DEEP LEARNING USING THE GEOSTATIONARY METEOROLOGICAL SATELLITE HIMAWARI
Author:
Affiliation:
1. 中央大学大学院 理工学研究科
2. 中央大学 理工学部都市環境学科
Publisher
Japan Society of Civil Engineers
Link
https://www.jstage.jst.go.jp/article/jscejj/80/16/80_23-16065/_pdf
Reference18 articles.
1. 1) Kidd, C. and Levizzani, V. : Rainfall:Modeling, Measurement and Applications. Chapter 6 : Satellite Rainfall Estimation, 2021.
2. 2) Kubota, T., K. Aonashi, T. Ushio, S. Shige, Y. N. Takayabu, M. Kachi, Y. Arai, T. Tashima, T. Masaki, N. Kawamoto, T. Mega, M. K. Yamamoto, A. Hamada, M. Yamaji, G. Liu and R. Oki : Global Satellite Mapping of Precipitation (GSMaP) products in the GPM era, Satellite precipitation measurement, Springer, 2020.
3. 3) Joyce, J. R., Janowiak, E. J., Arkin, A. P., Xie, P. : CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution. Journal of Hydrometeorology, Vol. 5, Issue 3, pp. 487-503, 2004.
4. 4) Yang, W., Fu, S., Sun, J., Zheng, F., Jie, W., Zheng, E.I. : MAComparative Evaluation of the Performances of TRMM-3B42 and Climate Prediction Centre Morphing Technique (CMORPH). Journal of the Meteorological Society of Japan, Vol. 99, Issue 6, pp. 1525-1546, Precipitation Estimates over Thailand. 2021.
5. 5) Hong, Y., Hsu, K. L., Sooshian, S., Gao, X. : Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System. JOURNAL OF APPLIED METEOROLOGY, Vol. 43, Issue12, 2004.
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