Dust storm Prediction

It is crucial to adopt automatic systems by using machine learning to predict or at least enable early detection of dust storms to reduce their deleterious impacts.

Background Readings
- Review of dust storm detection algorithms for multispectral satellite sensors(2021) : a review of dust storm algorithms such as empirical physical-based and machine learning-based algorithms.

- Prediction of aerosol optical depth in West Asia using deterministic models and machine learning algorithms.

Data

 * 1) Satellite images : The common used satellite sensors for monitoring dust storms is SEVIRI/MSG. MODIS is used mainly for short-term studies and in analyzing cases for few years. NOAA is used for meteorology analysis. While, multispectral satellite images are used mainly in analyzing dust storms events by scientists.
 * 2) Ground observations : this approach is commonly used to collect meteorological data about a small area. The tools used to gather the data are video surveillance, lookout towers, and ground remote sensors such as radar or Lidar.