Physics-Informed Multitemporal Ensemble Learning for Near Real-Time Precipitation Estimates From Himawari-8/-9-Reference-Cited by-同舟云学术

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Physics-Informed Multitemporal Ensemble Learning for Near Real-Time Precipitation Estimates From Himawari-8/-9

Published:2024 Issue: Volume:21 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Wang Yifan¹,Wang Yuan²^ORCID,Xiao Yi³^ORCID,Wan Bincheng²,Yang Yuanjian²,Yuan Qiangqiang³^ORCID,Ma Yi¹,Wang Guofang¹

Affiliation:

1. Electric Power Science Research Institute, Yunnan Power Grid Company Ltd., Kunming, China

2. School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China

3. School of Geodesy and Geomatics, Wuhan University, Wuhan, China

Funder

China Postdoctoral Science Foundation

Yunnan Fundamental Research Projects

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8859/10365397/10416260.pdf?arnumber=10416260

Reference21 articles.

1. Evaluation of the TRMM 3B42 and GPM IMERG products for extreme precipitation analysis over China

2. A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons

3. Global and Regional Increase of Precipitation Extremes under Global Warming

4. Complex networks reveal global pattern of extreme-rainfall teleconnections

5. A Binned Triple Collocation for Estimating Regime-Dependent Uncertainties of Precipitation

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