Events / Media

Data Assimilation Seminar Series

Dr. Tsuyoshi Thomas Sekiyama (Dec. 11, 2017, 15:30-)

Affiliation Meteorological Research Institute, Japan Meteorological Agency
Title Data assimilation of atmospheric chemistry: past, present, and future
Abstract Atmospheric chemistry (AC) modeling aims at the reaction, production, loss, advection, diffusion, emission, and deposition processes of atmospheric constituents. These processes often involve differential equations stiffer than those in numerical weather prediction (NWP). Therefore, it requires the use of sophisticated numerical integration schemes or stiff solvers. Furthermore, the number of the forecast variables in AC models is often much larger than that of NWP models. For example, a typical stratospheric/tropospheric ozone chemistry model contains more than one hundred constituents that should be forecasted. Consequently, these stiff and complicated systems have resulted in a high computational burden and a less-accurate simulation. In addition, unfortunately, constituent observations have remained much fewer than meteorological observations for NWP for a long time. Therefore, the AC data assimilation was not well-developed or matured before the 2000s. However, since the late 1990s or early 2000s, the AC data assimilation has been gradually progressing due to the advancements in computer performance and the expansion of constituent observations especially from satellite instruments. Recently, 4D/3D-Var or EnKF schemes have been applied to obtain analyses of stratospheric ozone, photochemical oxidants, carbon dioxide/monoxide, methane, black carbon, PM2.5, mineral dust, and volcanic ash, which have been used for environment forecasts, climate change studies, or pollutant emission estimation. The emission estimation is not popular with the NWP community, but is highly appreciated by the AC community. Furthermore, in the future, the data assimilation of atmospheric constituents will definitely contribute toward NWP beyond chemical or environmental issues. For example, wind fields could be inversely estimated by the distribution of atmospheric constituents. I would like to talk about those past, present, and future of the AC data assimilation at this seminar.
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