Adaptive Sparse Domain Selection for Weather Radar Superresolution

Accurate and high-resolution weather radar data reflecting detailed structure information of radar echo plays an important role in analysis and forecast of extreme weather. Typically, this is done using interpolation schemes, which only use several neighboring data values for computational approxima...

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Vydáno v:Scientific programming Ročník 2022; s. 1 - 12
Hlavní autoři: Yuan, Haoxuan, Zeng, Qiangyu, He, Jianxin
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Hindawi 11.01.2022
John Wiley & Sons, Inc
Témata:
Algorithms
Dictionaries
Domains
Doppler effect
Interpolation
Inverse problems
Machine learning
Meteorological radar
Precipitation
Radar data
Radar echoes
Reconstruction
Regularization
Regularization methods
Signal to noise ratio
Sparsity
Tornadoes
Velocity
Weather forecasting
China
ISSN:1058-9244, 1875-919X
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Shrnutí:Accurate and high-resolution weather radar data reflecting detailed structure information of radar echo plays an important role in analysis and forecast of extreme weather. Typically, this is done using interpolation schemes, which only use several neighboring data values for computational approximation to get the estimated, resulting the loss of intense echo information. Focus on this limitation, a superresolution reconstruction algorithm of weather radar data based on adaptive sparse domain selection (ASDS) is proposed in this article. First, the ASDS algorithm gets a compact dictionary by learning the precollected data of model weather radar echo patches. Second, the most relevant subdictionaries are adaptively select for each low-resolution echo patches during the spare coding. Third, two adaptive regularization terms are introduced to further improve the reconstruction effect of the edge and intense echo information of the radar echo. Experimental results show that the ASDS algorithm substantially outperforms interpolation methods for ×2 and ×4 reconstruction in terms of both visual quality and quantitative evaluation metrics.
Bibliografie:ObjectType-Article-1
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ISSN:1058-9244
1875-919X
DOI:10.1155/2022/9685831