Statistical Modeling with a Hidden Markov Tree and High-resolution Interpolation for Spaceborne Radar Reflectivity in the Wavelet Domain

Zeng

2021

Journal of Mathematics

Accurate and high-resolution weather radar images reflecting detailed structure information of radar echo are vital for analysis and forecast of extreme weather. Typically, this is performed by using interpolation schemes, which only use several neighboring data values for computational approximation to get the estimated value regardless of the large-scale context feature of weather radar images. Inspired by the striking performance of the convolutional neural network (CNN) applied in feature extraction and nonlocal self-similarity of weather radar images, we proposed a nonlocal residual network (NLRN) on the basis of CNN. The proposed network mainly consists of several nonlocal residual blocks (NLRB), which combine short skip connection (SSC) and nonlocal operation to train the deep network and capture large-scale context information. In addition, long skip connection (LSC) added in the network avoids learning low-frequency information, making the network focus on high-level features. Extensive experiments of ×2 and ×4 super-resolution reconstruction demonstrate that NLRN achieves superior performance in terms of both quantitative evaluation metrics and visual quality, especially for the reconstruction of the edge and detailed information of the weather radar echo.

Section: Introductionmentioning

confidence: 99%

Weather Radar Image Superresolution Using a Nonlocal Residual Network

Zeng

2021

Journal of Mathematics

“…In the research article [9] proposed a spline interpolation method to generate satellite precipitation estimates with more refined resolution, which slightly outperformed the methods based on linear regression and artificial neural network. In the research article [10] proposed an interpolation method to improve the resolution of radar reflectivity data, which effectively uses hidden Markov tree (HMT) model as a priori information to well capture the multiscale statistical characteristics of radar reflectivity data in small-scale strong precipitation condition.…”

Section: Literature Reviewmentioning

confidence: 99%

“…can be obtained by Equation(10).For each radar echo patch, an AR model is adaptively selected from  …”

mentioning

confidence: 99%

Adaptive Sparse Domain Selection for Weather Radar Super-Resolution using Decision Support System

Ihsan

2022

Preprint

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 super-resolution 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 pre-collected data of model weather radar echo patches. Second, the most relevant sub-dictionaries are adaptively select for each low-resolution echo patches during the spare coding using a complex decision support system. 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.

“…e research article [9] proposed a spline interpolation method to generate satellite precipitation estimates with more refined resolution, which slightly outperformed the methods based on linear regression and artificial neural network. e research article [10] proposed an interpolation method to improve the resolution of radar reflectivity data, which effectively uses the hidden Markov tree (HMT) model as a priori information to well capture the multiscale statistical characteristics of radar reflectivity data in small-scale strong precipitation condition.…”

Section: Literature Reviewmentioning

confidence: 99%

“…. , a K 􏼈 􏼉 can be obtained by equation (10). For each radar echo patch, an AR model is adaptively selected from a 1 , a 2 , .…”

Section: Adaptive Autoregressive Regularizationmentioning

confidence: 99%

Adaptive Sparse Domain Selection for Weather Radar Superresolution

Zeng

2022

Scientific Programming

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.