A Pre-Operational System Based on the Assimilation of MODIS Aerosol Optical Depth in the MOCAGE Chemical Transport Model

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2022-04, Vol.14 (8), p.1949
• Main Authors: El Amraoui, Laaziz, Plu, Matthieu, Guidard, Vincent, Cornut, Flavien, Bacles, Mickaël
• Format: Article
• Language: English
• Subjects: aerosol; aerosol optical depth; Aerosols; Air pollution; Air quality; Air safety; Assimilation; Atmosphere; Atmospheric aerosols; Atmospheric chemistry; Biomass; Biomass burning; Burning; Carbon; Chemical transport; Climate change; Data assimilation; Deserts; Dust; Earth Sciences; Meteorology; MODIS; Motivation; Ocean, Atmosphere; Optical analysis; Optical thickness; Outdoor air quality; pre-operational system; Radiation; Real time operation; Remote sensing; Root-mean-square errors; Sciences of the Universe; Spectroradiometers; Volcanic eruptions; Wildfires
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14081949

In this study we present a pre-operational forecasting assimilation system of different types of aerosols. This system has been developed within the chemistry-transport model of Météo-France, MOCAGE, and uses the assimilation of the Aerosol Optical Depth (AOD) from MODIS (Moderate Resolution Imaging Spectroradiometer) onboard both Terra and Aqua. It is based on the AOD assimilation system within the MOCAGE model. It operates on a daily basis with a global configuration of 1∘×1∘ (longitude × latitude). The motivation of such a development is the capability to predict and anticipate extreme events and their impacts on the air quality and the aviation safety in the case of a huge volcanic eruption. The validation of the pre-operational system outputs has been done in terms of AOD compared against the global AERONET observations within two complete years (January 2018–December 2019). The comparison between both datasets shows that the correlation between the MODIS assimilated outputs and AERONET over the whole period of study is 0.77, whereas the biases and the RMSE (Root Mean Square Error) are 0.006 and 0.135, respectively. The ability of the pre-operational system to predict extreme events in near real time such as the desert dust transport and the propagation of the biomass burning was tested and evaluated. We particularly presented and documented the desert dust outbreak which occurred over Greece in late March 2018 as well as the wildfire event which happened on Australia between July 2019 and February 2020. We only presented these two events, but globally the assimilation chain has shown that it is capable of predicting desert dust events and biomass burning aerosols which happen all over the globe.