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Development of an Aerosol Retrieval Method: Description and Preliminary Tests

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  • 1 Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado
  • | 2 Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado
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Abstract

A cloud-nucleating aerosol retrieval method was developed. It allows the estimation of ice-forming nuclei and cloud condensation nuclei (IFN and CCN) for regions in which boundary layer clouds prevail. The method is based on the assumption that the periodical assimilation of observations into a microscale model leads to an improved estimation of the model state vector (that contains the cloud-nucleating aerosol concentrations). The Colorado State University Cloud Resolving Model (CRM) version of the Regional Atmospheric Modeling System (RAMS@CSU) and the maximum likelihood ensemble filter algorithm (MLEF) were used as the forecast model and the assimilation algorithm, respectively. On the one hand, the microphysical modules of this CRM explicitly consider the nucleation of IFN, CCN, and giant CCN. On the other hand, the MLEF provides an important advantage because it is defined to address highly nonlinear problems, employing an iterative minimization of a cost function. This paper explores the possibility of using an assimilation technique with microscale models. These initial series of experiments focused on isolating the model response and showed that data assimilation enhanced its performance in simulating a mixed-phase Arctic boundary layer cloud. Moreover, the coupled model was successful in reproducing the presence of an observed polluted air mass above the inversion.

Corresponding author address: Gustavo Gabriel Carrió, Atmospheric Science Bldg., Office 225, Colorado State University, Fort Collins, CO 80523. Email: carrio@atmos.colostate.edu

Abstract

A cloud-nucleating aerosol retrieval method was developed. It allows the estimation of ice-forming nuclei and cloud condensation nuclei (IFN and CCN) for regions in which boundary layer clouds prevail. The method is based on the assumption that the periodical assimilation of observations into a microscale model leads to an improved estimation of the model state vector (that contains the cloud-nucleating aerosol concentrations). The Colorado State University Cloud Resolving Model (CRM) version of the Regional Atmospheric Modeling System (RAMS@CSU) and the maximum likelihood ensemble filter algorithm (MLEF) were used as the forecast model and the assimilation algorithm, respectively. On the one hand, the microphysical modules of this CRM explicitly consider the nucleation of IFN, CCN, and giant CCN. On the other hand, the MLEF provides an important advantage because it is defined to address highly nonlinear problems, employing an iterative minimization of a cost function. This paper explores the possibility of using an assimilation technique with microscale models. These initial series of experiments focused on isolating the model response and showed that data assimilation enhanced its performance in simulating a mixed-phase Arctic boundary layer cloud. Moreover, the coupled model was successful in reproducing the presence of an observed polluted air mass above the inversion.

Corresponding author address: Gustavo Gabriel Carrió, Atmospheric Science Bldg., Office 225, Colorado State University, Fort Collins, CO 80523. Email: carrio@atmos.colostate.edu

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