Received: 02 Jan 2019 – Discussion started: 15 Jan 2019
Abstract. Strongly coupled data assimilation frameworks provide a mechanism for including additional information about aerosols through the coupling between aerosol and atmospheric variables, effectively utilizing atmospheric observations to change the aerosol analysis. Here, we investigate the impact of these observations on aerosol using the Maximum Likelihood Ensemble Filter (MLEF) algorithm with Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) which includes the Godard Chemistry Aerosol Radiation and Transport (GOCART) module. We apply this methodology to a dust storm event over the Arabian Peninsula and examine in detail the error covariance and in particular the impact of atmospheric observations on improving the aerosol initial conditions. The assimilated observations include conventional atmospheric observations and Aerosol Optical Depth (AOD) retrievals. Results indicate a positive impact of using strongly coupled data assimilation and atmospheric observations on the aerosol initial conditions, quantified using Degrees of Freedom for Signal.
How to cite. Zupanski, M., Kliewer, A., Wu, T.-C., Apodaca, K., Bian, Q., Atwood, S., Wang, Y., Wang, J., and Miller, S. D.: Impact of Atmospheric and Aerosol Optical Depth Observations on Aerosol Initial Conditions in a strongly-coupled data assimilation
system, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2019-2, 2019.
The problem of under-observed aerosol observations and in particular the vertical distribution of aerosols is addressed using a strongly coupled atmosphere-aerosol data assimilation system. In the strongly coupled system the atmospheric observations, which are more numerous in general, can impact the aerosol initial conditions. In an application over a coastal zone, results indicate that atmospheric observations have a positive impact on aerosols.
The problem of under-observed aerosol observations and in particular the vertical distribution...