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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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 60; pp. 1 - 14
Main Authors: Peng, Xuan, Li, Qian, Jing, Jinrui
Format: Journal Article
Language:English
Published: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
<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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Description
Summary: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 the Doppler weather radar is strongly related to precipitation rate (<inline-formula> <tex-math notation="LaTeX">R </tex-math></inline-formula>). However, conventional RQPE methods have limited capability of modeling the complex relationship between the radar echoes and the precipitation field. In this article, we propose a graph neural network (GNN)-based RQPE model named categorical node graph attention network (CNGAT) to model complex spatial-temporal features of precipitation field reflected by the radar echo field. CNGAT is derived from graph attention networks (GAT) utilizing attention mechanism to learn the importance of neighboring points to the central point, which is beneficial for learning varying local spatial patterns. Furthermore, CNGAT can handle multiple types of graph nodes by using different transform functions for different types of nodes, which makes it better at capturing diverse features of precipitation field indicated by strong and weak radar echo areas. The proposed model was trained and tested on radar network and rain gauge data distributed in East China during 2017 and 2018. The results of several experiments show that CNGAT greatly improves the estimation precision and detection rate than <inline-formula> <tex-math notation="LaTeX">Z </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">R </tex-math></inline-formula> relation models and conventional data-driven RQPE methods, and alleviates under-estimation of higher precipitation rates, which validates that CNGAT can effectively represent complex spatial-temporal features of precipitation field.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2021.3120218