1Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, U.S.A.
2Department of Atmospheric Science, Colorado State University, Fort Collins, CO, U.S.A.
3Department of Chemical and Biochemical Engineering, Interdisciplinary Graduate Program in Informatics, and Center for Global and Regional Environmental Research, The University of Iowa, Iowa City, IA, U.S.A
1Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, U.S.A.
2Department of Atmospheric Science, Colorado State University, Fort Collins, CO, U.S.A.
3Department of Chemical and Biochemical Engineering, Interdisciplinary Graduate Program in Informatics, and Center for Global and Regional Environmental Research, The University of Iowa, Iowa City, IA, U.S.A
Received: 30 Nov 2018 – Accepted for review: 15 Dec 2018 – Discussion started: 17 Dec 2018
Abstract. The Regional Atmospheric Modeling System (RAMS) has been interfaced with the Maximum Likelihood Ensemble Filter (MLEF) with the goal of improving initial conditions for aerosol weather forecasting via atmosphere-aerosol coupled data assimilation (RAMS-MLEF). In order to assimilate satellite retrieved aerosol optical depth (AOD), an AOD observation operator customized for the RAMS aerosol module is implemented. Two MLEF-RAMS experiments are carried out for a dust storm event over the Arabian Peninsula that occurred on 4 August 2016. In the first experiment, conventional atmospheric observations from the National Centre for Environmental Prediction (NCEP) Prepared Binary Universal Form for the Representation of meteorological data (PrepBUFR) dataset are assimilated (ATMONLY), while both the atmospheric observations and AOD retrievals from Moderate Resolution Imaging Spectroradiometer (MODIS) are assimilated in the second experiment (ATMAOD). In the two experiments, a list of control variables is used and it includes the three-dimensional wind components, perturbation Exner function, ice-liquid water potential temperature, total water mass mixing ratio, and the two dust modes from the aerosol module. Results indicate that the assimilation of MODIS AOD retrievals improves the representation of the dust plume over Persian Gulf, however, has no obvious impact on the dust plume interior of Saudi Arabia. Such finding is further supported by the examination of analysis increments of some control variables and the information measure in terms of degrees of freedom for signal. This is likely due to the lack of AOD retrievals interior of the Arabian Peninsula. Finally, a 12-h forecast initialized from both experiments is conducted. In general, ATMAOD forecast better represents the Persian Plume but performs poorly for the Saudi Plume, when verified against the aerosol reanalysis product from the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) dataset.
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