Validation and comparison of high-resolution MAIAC aerosol products over Central Asia

Abstract Aerosols are an important contributor to global atmospheric environmental changes and have critical effects on the global climate system and human health. Central Asia is one of the most important sources of dust aerosols in the world and produces a significant portion of global aerosols. Central Asia is also a scarce aerosol-data area, so it is of great significance to obtain and verify new aerosol data from this region. In this study, based on the aerosol optical depth (AOD) data from remote sensing (MYD04_L2) and ground-based observations (AERONET and Microtops II), the applicability of multiangle implementation of atmospheric correction (MAIAC) AOD in Central Asia was comprehensively analyzed, and the variation in AOD in Central Asia over the last 20 years was analyzed by the information entropy method. The results indicate that MAIAC AOD has good application prospects in Central Asia and can effectively compensate for the lack of observational data from Central Asia. MAIAC AOD exhibits excellent spatiotemporal consistency with MYD04 deep blue (DB) AOD and has a better ability than MYD04 DB AOD to describe local fine-scale features. Furthermore, MAIAC AOD demonstrates high consistency with ground-based AOD observations, showing high R (0.737) and low RMSE (0.067) values and having 65.2% of samples falling within the expected error (EE) envelope. When employing the ground-based AOD observations as a bridge, MAIAC exhibits superiority to MYD04 DB in both the richness number of valid high-quality retrievals and the retrieval accuracy of various evaluation indicators. The annual variation in AOD in Central Asia exhibits a unimodal distribution, with AOD being largest in April, followed by March and May, and comparable rangeability. Based on information entropy, interannual variation in AOD exists in most areas of Central Asia, with AOD in the Taklimakan Desert area being significantly increased and that in northern Central Asia (Kazakhstan) showing a downward trend.