Ensemble generation for the assimilation of dust aerosol observations

The generation of the ensemble forecast is a key step in the design of an ensemble-based data assimilation scheme as it bears a significant impact on the assimilation outcome. The ensemble of model states is used within the assimilation algorithm to derive a flow-dependent background error covariance which is used to express prior information uncertainty. The covariance matrix of the background errors is associated to both, the quality of the prior, and the relations between the elements of the control vector. This matrix drives the spread of the observational information through the control variables determining, in part, the quality of the analyses. Only a handful of studies have focused on investigating the generation of ensembles for aerosol data assimilation which might be compromising the optimal integration of model simulations and observations when it comes to the use of ensemble-based assimilation schemes. 

This work presents a series of design methodologies and approaches to create regional dust aerosol ensembles. We include in our experiments ensembles with and without perturbed meteorological boundary and initial conditions, spatially random source strength perturbations, perturbations of the size distribution at emission, and random perturbation of a linear combination of dust emission schemes. We compute analyses of dust optical depth by assimilating satellite dust optical depth retrievals and present our results qualitatively through the inspection of the prior correlation matrices structure, and quantitatively with a comparison against independent measurements of aerosol optical depth.

This work is in the framework of the next upgrade of the operational forecast for the WMO Barcelona Dust Forecast Center (http://dust.aemet.es/) as well as of a contributing model to the WMO Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS, http://sds-was.aemet.es/ ), both services hosted by the Spanish Meteorological Agency (AEMET) and the Barcelona Supercomputing Center (BSC).