Improving the Completion of Weather Radar Missing Data with Deep Learning

Abstract

Weather radars commonly suffer from the data-missing problem that limits their data quality and applications. Traditional methods for the completion of weather radar missing data, which are based on radar physics and statistics, have shown defects in various aspects. Several deep learning (DL) models have been designed and applied to weather radar completion tasks but have been limited by low accuracy. This study proposes a dilated and self-attentional UNet (DSA-UNet) model to improve the completion of weather radar missing data. The model is trained and evaluated on a radar dataset built with random sector masking from the Yizhuang radar observations during the warm seasons from 2017 to 2019, which is further analyzed with two cases from the dataset. The performance of the DSA-UNet model is compared to two traditional statistical methods and a DL model. The evaluation methods consist of three quantitative metrics and three diagrams. The results show that the DL models can produce less biased and more accurate radar reflectivity values for data-missing areas than traditional statistical methods. Compared to the other DL model, the DSA-UNet model can not only produce a completion closer to the observation, especially for extreme values, but also improve the detection and reconstruction of local-scale radar echo patterns. Our study provides an effective solution for improving the completion of weather radar missing data, which is indispensable in radar quantitative applications.

Improving Explainability of Deep Learning for Polarimetric Radar Rainfall Estimation

“…Wang et al, 2021). DL models can analyze and learn the underlying latent features contained in radar data and transform them into high-dimensional features (Chen et al, 2020;Gong et al, 2023).…”

“…With the development of artificial intelligence, especially deep learning (DL) technology (Bi et al., 2023; Reichstein et al., 2019; Yu & Ma, 2021), various DL models have been used for classification and regression tasks, and have achieved impressive performances (He et al., 2016; Huang et al., 2017; Pan et al., 2021; C. Wang et al., 2021). DL models can analyze and learn the underlying latent features contained in radar data and transform them into high‐dimensional features (Chen et al., 2020; Gong et al., 2023).…”

An Editorial for the Special Issue “Processing and Application of Weather Radar Data”

“…Routinely, operational weather radars could suffer from many difficulties that limit their data quality and applications. Efforts are made in proposing new bin-by-bin approximation methods employing the European Centre for Medium-Range Weather Forecasts (ECMWF) re-analysis data trying to address the attenuation caused by atmospheric gases and stratiform clouds [5], training the dilated and self-attentional UNet model to improve the completion of weather radar missing data [6], developing novel optimization strategy to mitigate the effects of sidelobes in strong convection weather process [7], and developing techniques for noise cancelation and recovery of radial velocity to improve the quality of three-dimensional radar wind fields [8].…”