Numerical study of a direct variational data assimilation algorithm in Almaty city conditions

Traffic is the primary source of pollution in the city of Almaty. Due to the changing dynamics of traffic flows and a variety of technical conditions of the road vehicles, an accurate accounting of this emission source is a difficult task in the present time. Data assimilation algorithms can be applied to estimate the air quality in this case. The effectiveness of the direct variational data assimilation algorithm with quasi-independent data assimilation at individual steps of the splitting scheme was studied in a realistic scenario of assessing the air quality for the city of Almaty using the synthetic measurement data from the city monitoring network. The data assimilation is carried out by reconstructing the uncertainty (control) function. The cost functional with a stabilizer, including the spatial derivative of the uncertainty function, is minimized. The use of this stabilizer allowed us to obtain the smooth recovered uncertainty functions. This positively affected the quality of pollutant concentration field reconstruction in the scenario with routine pollutants.