Dust storm Prediction
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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.
- 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(2018).
Conferences, Journals, and Professional OrganizationsEdit
Libraries and ToolsEdit
- 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.
- 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.
Relevant Groups and OrganizationsEdit
- Li, Jing; Wong, Man Sing; Lee, Kwon Ho; Nichol, Janet; Chan, P.W. (2021-03). "Review of dust storm detection algorithms for multispectral satellite sensors". Atmospheric Research. 250: 105398. doi:10.1016/j.atmosres.2020.105398. ISSN 0169-8095. Check date values in:
- "Prediction of aerosol optical depth in West Asia using deterministic models and machine learning algorithms". Aeolian Research. 35: 69–84. 2018-12-01. doi:10.1016/j.aeolia.2018.10.002. ISSN 1875-9637.
- Cuevas Agulló, Emilio (2013). "Establishing a WMO sand and dust storm warning advisory and assessment system regional node for West Asia: current capabilities and needs: technical report". WMO, UNEP.
- Muhammad Akhlaq; Sheltami, Tarek R.; Mouftah, Hussein T. (2012-05-29). "A review of techniques and technologies for sand and dust storm detection". Reviews in Environmental Science and Bio/Technology. 11 (3): 305–322. doi:10.1007/s11157-012-9282-y. ISSN 1569-1705.