A 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.

This work was funded by the SEV-2011- 00067 grant of the Severo Ochoa Program awarded by the Spanish Government, the CGL-2013-46736-R grant of the Spanish Ministry of Economy and Competitiveness, and the ACTRIS Research Infrastructure Project of the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 654169. The authors thank all the Principal Investigators and their staff for establishing and maintaining the AERONET sites, NRL/University of North Dakota for the MODIS AOD L3 product, and the MODIS and OMI mission scientists and associated NASA personnel for the production of the AOD, AAI, and AE data used in this investigation. The authors thankfully acknowledge the computer resources at MareNostrum and the technical support provided by the Barcelona Supercomputing Center (RES-AECT-2015-1-0007). They also thank Francesco Benincasa for his technical support. Carlos Pérez García-Pando acknowledges long-term support from the AXA Research Fund, as well as the support received through the Ramón y Cajal programme (grant RYC-2015-18690) of the Spanish Ministry of Economy and Competitiveness. Comments from two anonymous reviewers are gratefully acknowledged.

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