A four-dimensional variational (4DVAR) data assimilation system was developed for a regional dust model. This report presents results of the first adjoint inversion of Asian dust emissions over East Asia using NIES LIDAR observations, targeting the extreme dust phenomenon on 30 April 2005. Optimized dust emissions mitigated underestimation of dust concentrations and brought the structure of the elevated dust layer (both onset timing and extinction coefficient intensity) into better agreement with LIDAR observations. We obtained a 31% (3.2 Tg) increase of calculated dust emissions through data assimilation, especially over the Mongolian region. The assimilated results agree with the TOMS AI distribution and indicate that the 4DVAR method is very powerful for unification of observation and numerical modeling. The method provides better estimation capability.
All Science Journal Classification (ASJC) codes
- Earth and Planetary Sciences(all)