Dust storms are known to have adverse effects on human health and significant impact on weather, air quality, hydrological cycle, and ecosystem. Atmospheric dust loading is also one of the large uncertainties in global climate modeling, due to its significant impact on the radiation budget and atmospheric stability. Observations of dust storms in humid tropical south China (e.g. Hong Kong), are challenging due to high industrial pollution from the nearby Pearl River Delta region. This study develops a method for dust storm detection by combining ground station observations (PM10 concentration, AERONET data), geostationary satellite images (MTSAT), and numerical weather and climatic forecasting products (WRF/Chem). The method is based on a hybrid neural network (NN) retrieval model for two scales: (i) a NN model for near real-time detection of dust storms at broader regional scale; (ii) a NN model for detailed dust storm mapping for Hong Kong and Taiwan. A feed-forward multilayer perceptron (MLP) NN, trained using back propagation (BP) algorithm, was developed and validated by the k-fold cross validation approach. The accuracy of the near real-time detection MLP-BP network is 96.6%, and the accuracies for the detailed MLP-BP neural network for Hong Kong and Taiwan is 74.8%. This newly automated multi-scale hybrid method can be used to give advance near real-time mapping of dust storms for environmental authorities and the public. It is also beneficial for identifying spatial locations of adverse air quality conditions, and estimates of low visibility associated with dust events for port and airport authorities.
Bibliographical noteFunding Information:
The authors wish to acknowledge the NASA Goddard Earth Science Distributed Active Archive Center for the MODIS Level 2 data, Brent Holben for the help with the Hong Kong PolyU AERONET station, PIs and site managers of AERONET stations in Asia, and Japan Meteorological Agency for the MTSAT satellite data. In addition, the authors gratefully acknowledge the Hong Kong Environmental Protection Department, Taiwan Environmental Protection Administration, Shandong province for the provision of air quality data used in this publication. Grant PolyU 1-ZVAJ from the Faculty of Construction and Environment, the Hong Kong Polytechnic University ; Grants PolyU 1-ZVBP , and PolyU 1-ZVBR from the Research Institute for Sustainable Urban Development, the Hong Kong Polytechnic University ; and Grant ECF 33/2010 from the Hong Kong Environment and Conservation Fund sponsored the research. The work for J. Kim was supported by the Eco Innovation Program of KEITI ( 2012000160002 ).
All Science Journal Classification (ASJC) codes
- Atmospheric Science