Dear Colleagues,

In 2019, the World Meteorological Organization (WMO) highlighted that, according to statistics from 2007 to 2019, 90% of the losses caused by natural disasters 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. The heavy rainstorms induced by strong convection often cause serious natural disasters such as floods, landslides, and mudslides. Accurate monitoring, early warning, and forecasting of heavy rainfall induced by strong convection are central to improving the ability to prevent these disasters.

Recently, remote sensing techniques such as radar and satellite have become powerful tools for monitoring natural hazards such as flash floods and waterlogging induced by heavy rainstorms. Advanced remote sensing-based products such as QPE and QPF are extremely helpful for short-term weather and hydrological forecasting. Also, dual-polarization or dual-frequency radar data and satellite data are used to assess water mixing ratios and winds, and to improve the capability of convection-permitting numerical weather prediction (NWP) models to forecast severe storms at scales varying from a few hundred meters to kilometers. Associated surface in situ observation equipment, such as rain gauges, runoff gauges, and distrometers, is also required for calibrating the observational variables and products of radars and satellites.

Although such remote sensing equipment has been widely used in weather and hydrological monitoring and forecasting, several valid challenges remain:

• Developing radar and satellite signal processing methods;
• Assessing observational quality for newly developed radars and satellites;
• Characterizing errors/uncertainties in remote sensing precipitation products and retrieval algorithm functions of different conditions, e.g., elevations, storms, and climatic regimes, and communicating the uncertainties for hydrogeological applications;
• Developing more accurate ground radar- and/or satellite-based quantitative precipitation estimation (QPE) algorithms;
• New sensing, attenuation correction, and calibration techniques;
• The application of radar and satellite data in data assimilation to improve the performance of NWP models;
• Developing new analysis methods, including machine learning and data assimilation, to maximize the benefits of using extensive datasets, multiscale remote sensing data, and in-situ data fusion;
• Artificial intelligence and machine (deep) learning applications;
• The application and analysis of radar and satellite data in disastrous weather conditions (e.g. heavy rain, flash floods, and waterlogging);
• Radar and satellite observations of hydrometeorological extremes;
• Improving quantitative precipitation forecasting (QPF) skills;
• Improving the monitoring and forecasting of heavy rainfall for hydrometeorological hazards warnings triggered by remote sensing products;
• Improving the ability of convection-induced flood forecasting and early warning in small mountain basins and urban areas with remote sensing products;
• Improving flood simulation and forecasting capabilities for hydrological modelling using remote sensing products combining other types of precipitation data;
• Improving the forecasting of and early warning capabilities for geological disasters, such as landslides and mudslides caused by convective precipitation, with radar and satellite products.

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

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• weather radars
• satellites
• flash floods and waterlogging
• quantitative precipitation estimation (QPE)
• remote sensing data assimilation
• extreme weather and hydrological events
• artificial intelligence and machine (deep) learning
• application of remote sensing equipment in geological disaster, disastrous weather analysis, and observation of hydrometeorological extremes
• weather and hydrological forecast

• Processing and Application of Weather Radar Data in Remote Sensing (16 articles)