Developments in Monitoring and Multi-Model Applications of Dust Weather in SDS-WAS ASIAN REGIONAL CENTER

Sand and dust storms (SDS) are among the most impactful atmospheric hazards, affecting air quality, climate, ecosystems, and socio-economic activities across continents. East Asia is one of the world’s major dust source regions, and recent observations indicate a renewed increase in SDS frequency and intensity since the mid-2010s, with several extreme events occurring in 2021, 2023, and 2025. This contribution presents recent advances in SDS early warning and forecasting developed at the WMO Asian Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS) Regional Center, hosted by the China Meteorological Administration.

 

We highlight progress in multi-source monitoring, multi-model forecasting, and artificial intelligence (AI) applications for SDS prediction. Satellite-based minute-scale dust identification has been achieved through multi-sensor data fusion, enabling near-real-time monitoring of dust severity and three-dimensional vertical structure by integrating satellite, lidar, radar, and ground-based observations. On the forecasting side, operational multi-model ensemble systems provide regional dust concentration, optical depth, emission, and deposition products. A machine-learning-based ensemble correction approach further improves surface dust concentration forecasts by optimally combining multiple models based on their historical performance.

 

In addition, an AI-driven global coupled aerosol–meteorology forecasting system has been developed, delivering 5-day, high-resolution forecasts of dust optical depth and surface concentrations. Case studies demonstrate that this system captures long-range dust transport from both Asian and Saharan sources, including events affecting Europe, with forecast skill exceeding that of several regional numerical models.

 

As a WMO SDS-WAS Asian Regional Center, we emphasize the importance of strengthening collaboration with the WMO SDS-WAS program and other regional nodes. Enhanced data sharing, harmonized observational datasets, and coordinated multi-model and AI-based forecasting efforts are essential to improve global SDS early warning capabilities. The experience gained in Asia offers valuable insights for Europe and other downwind regions, supporting transboundary aerosol monitoring, risk assessment, and mitigation strategies at the global scale.