Processing and Application of Weather Radar Data

Dear Colleagues,

In 2019, the World Meteorological Organization (WMO) pointed out the following based on statistics from 2007 to 2019: In natural disasters, 90% of losses are related to meteorology, of which heavy storms and floods account for more than 70%. Heavy precipitation plays a very important role in the early warning of meteorological, hydrological, and geological disasters. Heavy rainstorms induced by strong convection often cause serious natural disasters, such as floods, landslides, and mudslides. Accurate monitoring and early warning and forecasting of heavy rainfall induced by strong convection is the basis for improving the ability to prevent flood, landslide, and mudslide disasters.

Currently, the most powerful technique for monitoring natural hazards induced by heavy rainstorms is to use weather radars (e.g., ground based radars, profiling radars, as well as space born radars). Moreover, dual-polarization or dual-frequency radar data are used to derive water mixing ratios and number concentrations and to improve the capability of the convection-permitting numerical weather prediction (NWP) models to forecast severe storms at scales varying from a few hundred meters to kilometers. Advanced QPF products are of great assistance in short-term weather and hydrological forecasting. Associated surface in situ observations, such as from rain gauges, runoff gauges, and disdrometers, are also required for calibrating radar observational variables and products.

Although weather radars have been widely used in many fields, several valid challenges remain in radar processing and application. They can be summarized as:

• Development of radar signal processing methods, especially for polarimetric variables;
• Development of nonmeteorological echo identification methods and hydrometeor classification methods;
• Assessment of observational quality of new-developed radars, e.g., phase-array radars;
• Characterization of errors/uncertainties in remote sensing precipitation products and retrieval algorithm functions of different conditions, e.g., elevations, storm, and climatic regimes, and communicating the uncertainties for hydrogeological applications;
• Development of more accurate ground radar- and/or satellite-based quantitative precipitation estimation (QPE) algorithms;
• New sensing techniques and attenuation correction and calibration techniques;
• Application of radar data in data assimilation to improve performance of NWP models;
• Development of new analysis methods, including machine learning and data assimilation, to maximize the benefits of using extensive datasets; multi-scale remote sensing data and in situ data fusion;
• Artificial intelligence and machine (deep) learning applications;
• Application of radar data in disastrous weathers (e.g., heavy rain, hail, and tornado) analysis;
• Radar observations of hydrometeorological extremes;
• Improving the skills of quantitative precipitation forecasts (QPF);
• Improving the monitoring and forecasting of heavy rainfall for warnings triggered by hydrometeorological hazards with radar products; improving the ability of convection-induced flood forecasting and early warning capabilities in small mountain basins and urban areas with radar products;
• Improving the forecasting and early warning capabilities of geological disasters, such as landslides and mudslides, caused by convective precipitation with radar products.

Submission of manuscripts involving radar signal processing methods, applications in QPE, QPF, and severe weather observation, and data assimilation for NWP models is strongly encouraged.

Dr. Youcun Qi
Dr. Zhe Zhang
Dr. Zhanfeng Zhao
Dr. Bong-Chul Seo
Dr. Huiqi Li
Guest Editors

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