Remote Sensing-Driven Advances from Mesoscale to Convective-Scale Predictions: Process Modelling, Observations, Data Assimilation and Machine Learning

Dear Colleagues,

Mesoscale convective systems (MCSs), known as organized groupings of thunderstorms, frequently occur across the global tropics, subtropics, and mid-latitudes that span thousands of square kilometres. MCSs are responsible for much hazardous weather, including extreme rainfall, flooding, and, sometimes, tornadoes and hail, all of which have significant impacts, posing severe threats to economic and social development. While a large number of previous efforts have increased understanding of MCSs over the past several decades, state-of-the-art weather numerical models still struggle to obtain reliable forecasts, partly due to limited observations.

The availability of more remote sensing datasets could provide the potential for deep insights into underlying MCSs. Therefore, this Special Issue aims to deepen our understanding of MCSs, from the aspects of modelling, observations, assimilation, and machine learning, drawing on satellite and radar data in particular.

We welcome the submission of original research, review articles, and MCS case studies utilizing remote sensing observations, AI technologies, and other advanced methodologies. By integrating multidisciplinary research findings, this Special Issue aims to deepen understanding of the mechanisms driving the evolution of MCSs, while also developing new forecasting skills.

Topics of interest for this Special Issue include, but are not limited to, the following:

• Routine observations (from remote sensing platforms, such as radars and satellites) and targeted field campaigns;
• Analysis related to the formation and evolution of MCSs;
• Assimilating or integrating new datasets into numerical models;
• Development of sophisticated methods for modelling output;
• Utilization of AI (Artificial Intelligence) technology for MCS forecasting.

Dr. Bin Chen
Dr. Stephan Havemann
Guest Editors

Manuscript Submission Information

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