Hourly rolling correction of precipitation forecast via convolutional and long short‐term memory networks

Hourly rolling correction of precipitation forecast via convolutional and long short‐term memory networks

Abstract In order to improve precipitation forecast from GRAPES_Meso V4.0 in China, we propose a 1–6‐h rolling correction solution, based on infrared (IR) channels from geostationary meteorological satellite and surface observation data. In particular, we design a deep learning extrapolation model t...

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Bibliographic Details
Main Authors: Ruyi Yang, Jianli Mu, Shudong Wang, Lijuan Wang
Format: Article
Language:English
Published: Wiley 2022-10-01
Series:Atmospheric Science Letters
Subjects:
convolutional neural network
LSTM
precipitation forecast
rolling correction
Online Access:https://doi.org/10.1002/asl.1100

Internet

https://doi.org/10.1002/asl.1100

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