Data Assimilation Algorithm Based on the Sensitivity Operator for a Three-Dimensional Model of Transport and Transformation of Atmospheric Contaminants

Abstract

Three-dimensional transport and transformation models make it possible to take into account the vertical heterogeneity of atmospheric processes. However, their use requires setting a large number of parameters and significant computing resources, especially when solving inverse and data assimilation problems. A new data assimilation algorithm for a three-dimensional transport and transformation model with unknown emission sources is presented. The algorithm uses an approach based on sensitivity operators and ensembles of solutions of adjoint equations implemented in the IMDAF inverse modeling system for distributed memory computers. When tested in a realistic Baikal region scenario, the algorithm, based on the data of integrated vertical measurements simulating remote sensing data, enabled reducing the error in the concentration field by 15%. With the given vertical level of the source location, the errors in the concentration field and in the source were reduced by 93% and 85%, respectively.