Short‐Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks-Reference-Cited by-同舟云学术

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Short‐Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks

Published:2018-11-19 Issue:22 Volume:123 Page:
ISSN:2169-897X
Container-title:Journal of Geophysical Research: Atmospheres
language:en
Short-container-title:J. Geophys. Res. Atmos.

Author:

Akbari Asanjan Ata¹^ORCID,Yang Tiantian¹²^ORCID,Hsu Kuolin¹³,Sorooshian Soroosh¹^ORCID,Lin Junqiang⁴,Peng Qidong⁴

Affiliation:

1. Department of Civil and Environmental Engineering, Center for Hydrometeorology and Remote Sensing University of California Irvine CA USA

2. School of Civil Engineering and Environmental Science University of Oklahoma Norman OK USA

3. Center for Excellence for Ocean Engineering National Taiwan Ocean University Keelung Taiwan

4. China Institute of Water Resources and Hydropower Research Beijing China

Funder

California Energy Commission

U.S. Department of Energy

Publisher

American Geophysical Union (AGU)

Subject

Space and Planetary Science,Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Geophysics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2018JD028375

Reference73 articles.

1. Evaluation of satellite-retrieved extreme precipitation rates across the central United States

2. The estimation of global monthly mean rainfall using infrared satellite data: The GOES precipitation index (GPI)

3. Performance of 4D-Var NWP-based nowcasting of precipitation at the Met Office for summer 2012

4. PERSIANN-MSA: A Precipitation Estimation Method from Satellite-Based Multispectral Analysis

5. A North American Hourly Assimilation and Model Forecast Cycle: The Rapid Refresh

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