Severe convective weather systems with heavy precipitation rates can lead to meteorological and hydrogeological hazards, such as tornadoes, squall lines, hail, flash floods, or landslides that can cause significant injuries, deaths, property damage, and many other social impacts. Even though these convective storm-driven natural hazards occur all over the world, their physical characteristics are quite region-dependent. Therefore, it is critical to accurately monitor and predict the precipitation intensity, frequency, and duration associated with hydrogeological and meteorological hazards.

Currently, the most powerful technique to monitor severe convective weather is to use multi-scale remotely sensed datasets, such as ground-based radars, profiling microwave radiometers, space-borne radars, and multi-channel satellite-based active and passive instruments. Meanwhile, convection-permitting numerical weather prediction (NWP) models have emerged as a promising framework to forecast severe storms at various resolutions, from a few 100m up to km scales. Associated surface in-situ observations such as weighing gauges and distrometers are also required for calibrating remote sensing products and verifying model forecasts. These platforms ultimately facilitate the identification of the physical and dynamical characteristics of severe convective weather, providing accurate precipitation estimates, predicting flash floods, and understanding and evaluating their interactions with geophysical processes such as soil erosion.

The aim of this Special Issue is to advance our understanding of severe convective precipitation systems, as although our understanding of these systems has improved in recent decades, several challenges still remain. Manuscripts involving severe weather observation platforms, in-situ instruments, as well as remote sensing platforms, numerical modelling studies that are associated with meteorological and hydrogeological operations are strongly encouraged for submission. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Development of more accurate in-situ observing platforms, ground and space-borne radars, and satellite observing systems to evaluate quantitative precipitation estimation (QPE) algorithms
• Characterization of the errors/uncertainties in the remote sensing precipitation products and retrieval algorithm function of different conditions, such as elevation or storm and climatic regimes, and the communication of these uncertainties for hydrogeological applications
• Development of physical and dynamical parameterizations based on NWP models, to better evaluate scale-dependent precipitation processes
• Improvement of the skill of quantitative precipitation forecasts (QPF)
• Improvement of QPE/QPF estimates of heavy precipitation in terms of both resolution and accuracy using downscaling or data fusion techniques
• Investigation of precipitation intensity space-time patterns using QPE/QPF estimates
• Improvement of the monitoring and forecast of heavy rainfall for warnings triggered by hydrometeorological hazards
• Development of new analyzing methods, including machine learning algorithms, to maximize the benefits of using extensive data sets
• Improvement of the ability of convection-induced flood forecast and early warning in small mountain basins
• Enhancement of urban convection-induced waterlogging forecast and early warning capabilities
• Improvement of the forecasting and early warning capabilities of geological disasters, such as landslides and mudslides, caused by convective precipitation
• Propagation of information developed for model-driven estimates of convective precipitation for meteorological and hydrogeological applications