Abstract
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.
Highlights
Doppler weather radar with high temporal and spatial resolution e.g., China Generation Weather Radar (CINRAD) provides measurements with high temporal and spatial resolution and have been widely applied in operational research and forecasts on medium-scale and intense precipitation weather phenomena.single weather radar is susceptible to beam blocking, ground clutter, and reduced resolution at long distances due to beam broadening and averaging
We proposed a nonlocal residual network (NLRN) on the basis of convolutional neural network (CNN), which increases the depth of the network and the efficiency in exploiting the large-scale context information of the weather radar image by applying residual learning and nonlocal operation
It can be seen that NLRN exhibited considerable advantages over the conventional methods (Bicubic, iterative back projection (IBP), and nonlocally centralized sparse representation (NCSR)) and CNN-based methods (EDSR and VDSR)
Summary
Doppler weather radar with high temporal and spatial resolution e.g., China Generation Weather Radar (CINRAD) provides measurements with high temporal (approx. 6 minutes) and spatial (approx. 1 × 1 km) resolution and have been widely applied in operational research and forecasts on medium-scale and intense precipitation weather phenomena.single weather radar is susceptible to beam blocking, ground clutter, and reduced resolution at long distances due to beam broadening and averaging. The beam width increases with the detection distance which leads to a loss of information on sudden changes in radar echoes such as velocity changes in tornadoes and mesocyclones, as well as information on extreme precipitation intensity and gradients when detection target occurs at a distance from the observing radar [1, 2]. It is, worthwhile to improve the resolution of weather radar data by upgrading the observation equipment and by postprocessing the observation data such as interpolation or superresolution reconstruction. According to the means of implementation, the image super-resolution methods can be divided into three types: interpolationbased, reconstruction-based, and learning-based image super-resolution methods
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