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Article

Using Objective Analysis for the Assimilation of Satellite-Derived Aerosol Products to Improve PM2.5 Predictions over Europe

by 1,*,† and 1,2,†
1
Laboratoire de Météorologie Dynamique, CNRS, Sorbonne Université, Ecole Normale Supérieure, Ecole Polytechnique, 75005 Paris, France
2
CEREA: Joint Laboratory École des Ponts ParisTech—EDF R&D, Université Paris-Est, 77455 Champs sur Marne, France
*
Author to whom correspondence should be addressed.
Current address: Department of Atmospheric Sciences, University of North Dakota, Grand Forks, ND 4149, USA.
Academic Editor: Pavel Kishcha
Atmosphere 2022, 13(5), 763; https://doi.org/10.3390/atmos13050763
Received: 8 March 2022 / Revised: 23 April 2022 / Accepted: 29 April 2022 / Published: 9 May 2022
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
We used the objective analysis method in conjunction with the successive correction method to assimilate MODerate resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) data into the Chimère model in order to improve the modeling of fine particulate matter (PM2.5) concentrations and AOD field over Europe. A data assimilation module was developed to adjust the daily initial total column aerosol concentrations based on a forecast-analysis cycling scheme. The model is then evaluated during one-month winter period to examine how such a data assimilation technique pushes the model results closer to surface observations. This comparison showed that the mean biases of both surface PM2.5 concentrations and the AOD field could be reduced from 34 to −15% and from −45 to −27%. The assimilation, however, leads to false alarms because of the difficulty in distributing AOD550 over different particle sizes. The impact of the influence radius is found to be small and depends on the density of satellite data. This work, although preliminary, is important in terms of near-real time air quality forecasting using the Chimère model and can be further developed to improve modeled PM2.5 and ozone concentrations. View Full-Text
Keywords: PM2.5; Aerosol Optical Depth; data assimilation; MODIS; satellite data; objective analysis; particulate matter forecasting; model validation PM2.5; Aerosol Optical Depth; data assimilation; MODIS; satellite data; objective analysis; particulate matter forecasting; model validation
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MDPI and ACS Style

Chrit, M.; Majdi, M. Using Objective Analysis for the Assimilation of Satellite-Derived Aerosol Products to Improve PM2.5 Predictions over Europe. Atmosphere 2022, 13, 763. https://doi.org/10.3390/atmos13050763

AMA Style

Chrit M, Majdi M. Using Objective Analysis for the Assimilation of Satellite-Derived Aerosol Products to Improve PM2.5 Predictions over Europe. Atmosphere. 2022; 13(5):763. https://doi.org/10.3390/atmos13050763

Chicago/Turabian Style

Chrit, Mounir, and Marwa Majdi. 2022. "Using Objective Analysis for the Assimilation of Satellite-Derived Aerosol Products to Improve PM2.5 Predictions over Europe" Atmosphere 13, no. 5: 763. https://doi.org/10.3390/atmos13050763

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