CNGAT: A Graph Neural Network Model for Radar Quantitative Precipitation Estimation

Radar quantitative precipitation estimation (RQPE) is the most common measurement for area rainfall estimation with high spatial and temporal resolution. The radar reflectivity (<inline-formula> <tex-math notation="LaTeX">Z </tex-math></inline-formula>) measured by...

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Vydáno v:	IEEE transactions on geoscience and remote sensing Ročník 60; s. 1 - 14
Hlavní autoři:	Peng, Xuan, Li, Qian, Jing, Jinrui
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Z–R relationship Atmospheric precipitations Convolution Data models Doppler radar Doppler sonar Echoes Estimation Forecasting Graph convolution graph neural network (GNN) Graph neural networks Meteorological radar Methods Neural networks Nodes Precipitation Precipitation rate Radar Radar clutter Radar echoes Radar networks radar quantitative precipitation estimation (RQPE) Rain Rain gauges Rainfall Reflectance Reflectivity Spatial discrimination learning Temporal resolution Temporal variations
ISSN:	0196-2892, 1558-0644
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