Dust Detection Over East Asia From Multispectral and Multi-Temporal Himawari-8/AHI Thermal Infrared Observations

Abstract

The frequent outbreak of dust storms every spring is one of the extreme weathers in East Asia. Geostationary meteorological satellite thermal infrared (TIR) imagery provides observations for high-frequency, all-day, large-scale monitoring of dust sources and transport. This study proposes an integrated method to detect dust over East Asia by using the multispectral and multi-temporal Himawari-8/Advanced Himawari Imager (AHI) TIR observations, based on a Feedforward Neural Network (FNN) model and the Robust Principal Component Analysis based Anomalous Dust Detection (ADD) method. The FNN model is trained to map the multispectral Brightness Temperatures (BTs) to the three category probabilities (i.e., cloud, dust, and clear sky), and the ADD method calculates the dust score by normalizing multi-temporal BT difference anomalous variation. The Integrated Dust Index (IDI) reflects the quantified dust confidence of the AHI pixel, which is defined as the sum of the dust probability and the dust score. The IDI was evaluated using 2 months of the Sentinel-5P TROPOMI Ultraviolet Aerosol Index and achieved a total dust detection accuracy above 90%. A comparison with Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation nighttime observations shows that the IDI can also detect dust at night. The times series IDI of the massive 14–16 March 2021 dust storm show its capabilities to trace dust sources and monitor dust transport.