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Assimilation of MODIS Dark Target and Deep Blue Observations in the Dust Aerosol Component of NMMB-MONARCH version 1.0A data assimilation capability has been built for the NMMB-MONARCH chemical weather prediction system, with a focus on mineral dust, a prominent type of aerosol. An ensemble-based Kalman filter technique (namely the local ensemble transform Kalman filter - LETKF) has been utilized to optimally combine model background and satellite retrievals. Our implementation of the ensemble is based on known uncertainties in the physical parametrizations of the dust emission scheme. Experiments showed that MODIS AOD retrievals using the Dark Target algorithm can help NMMB-MONARCH to better characterize atmospheric dust. This is particularly true for the analysis of the dust outflow in the Sahel region and over the African Atlantic coast. The assimilation of MODIS AOD retrievals based on the Deep Blue algorithm has a further positive impact in the analysis downwind from the strongest dust sources of the Sahara and in the Arabian Peninsula. An analysis-initialized forecast performs better (lower forecast error and higher correlation with observations) than a standard forecast, with the exception of underestimating dust in the long-range Atlantic transport and degradation of the temporal evolution of dust in some regions after day 1. Particularly relevant is the improved forecast over the Sahara throughout the forecast range thanks to the assimilation of Deep Blue retrievals over areas not easily covered by other observational datasets.The present study on mineral dust is a first step towards data assimilation with a complete aerosol prediction system that includes multiple aerosol species.
Document ID
20170002771
Acquisition Source
Goddard Space Flight Center
Document Type
Reprint (Version printed in journal)
Authors
Di Tomaso, Enza
(Barcelona Supercomputing Center Barcelona, Spain)
Schutgens, Nick A. J.
(Oxford Univ. Oxford, United Kingdom)
Jorba, Oriol
(Barcelona Supercomputing Center Barcelona, Spain)
Perez Garcia-Pando, Carlos
(Columbia Univ. New York, NY, United States)
Date Acquired
March 31, 2017
Publication Date
March 10, 2017
Publication Information
Publication: Geoscientific Model Development
Publisher: Copernicus Publications
Volume: 10
Issue: 3
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
GSFC-E-DAA-TN40837
Funding Number(s)
CONTRACT_GRANT: CGL-2013-46736-R
CONTRACT_GRANT: RES-AECT-2015-1-0007
CONTRACT_GRANT: EUH-2020-654169
CONTRACT_GRANT: RYC-2015-18690
CONTRACT_GRANT: SEV-2011-00067
Distribution Limits
Public
Copyright
Other
Keywords
atmospheric dust
data assimilation
dust modelling
aerosol
mineral dust

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