In order to estimate MODIS-equivalent aerosol optical thicknesses (AOTs) for dust particles during the nighttime over East Asia, an Artificial Neural Network (ANN) model approach was used to combine MODIS-measured infrared (IR) brightness temperatures (BTs) and visible (VIS) AOTs. For training the ANN model, IR BTs were used together with surface type and geometrical information as inputs to predict MODIS-derived AOTs as target data during the daytime when VIS-based AOTs are available. The training was done exclusively over dust-laid pixels during the spring (March–May) of 2006 over the East Asian domain (20°N–55°N, 90°E–145°E).
It should be noted that the obtained daytime AOTs from the ANN model are in good agreement with MODIS-derived AOTs, with a correlation coefficient of 0.77 over the analysis domain. Although a weaker correlation is found during the nighttime when derived AOTs are compared against AOTs from CALIPSO, the case study indicates that the developed ANN method appears to effectively depict both the evolutionary features and intensity of the Asian dust plume during the nighttime. Results further indicate that nighttime VIS-like AOTs can be readily used for monitoring dust movement and its intensity during the nighttime, filling the gap between consecutive daytime AOT distributions.
2012 by Meteorological Society of Japan