Deep-learning post-processing of short-term station precipitation based on NWP forecasts-Reference-Cited by-同舟云学术

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Deep-learning post-processing of short-term station precipitation based on NWP forecasts

Published:2023-11 Issue: Volume:295 Page:107032
ISSN:0169-8095
Container-title:Atmospheric Research
language:en
Short-container-title:Atmospheric Research

Author:

Liu Qi,Lou Xiao,Yan Zhongwei,Qi Yajie,Jin Yuchao,Yu Shuang,Yang Xiaoliang,Zhao Deming,Xia Jiangjiang

Funder

Excellent Young Scientists Fund

Key Project of Research and Development Plan of Hunan Province

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Atmospheric Science

Reference62 articles.

1. Evaluation of TIGGE Ensemble forecasts of precipitation in Distinct climate Regions in Iran [J];Aminyavari;Adv. Atmos. Sci.,2018

2. An overview of the National Weather Service’s centralized statistical quantitative precipitation forecast [J];Antolik;J. Hydrol.,2000

3. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation [J];Bach;PLoS One.,2015

4. Configuration and Intercomparison of Deep Learning Neural Models for Statistical Downscaling [J];Baño-Medina;Geosci. Model Dev.,2020

5. Indicator patterns of forced change learned by an artificial neural network [J];Barnes;J. Adv. Modeli. Earth Syst.,2020

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