[세미나] Dr. Jonathan Guerrette

January 10, 2019

Constraining aerosol emissions with 4D-Var in WRFDA


Dr. Jonathan Guerrette (NCAR)

2019년 1월 15일 (화) 11:00

과학관 553호


Abstract

Constraining aerosol emissions with 4D-Var in WRFDA

Atmospheric aerosols are known to affect health, weather, and climate, but their impacts on regional scales are uncertain due to heterogeneous source, transport, and transformation mechanisms. This work establishes incremental four-dimensional variational (4D-Var) data assimilation (DA) capabilities in a coupled meteorology-chemistry model (WRF-Chem) to constrain chemical emissions (WRFDA-Chem). WRFDA-Chem is used to constrain anthropogenic and biomass burning sources of black carbon aerosol (BC) throughout California during the 2008 Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) field campaign. We reduce the spread in total emitted BC mass between two biomass burning inventories from a factor of x10 to only x2 across three days of measurements. We also address the limited scalability of 4D-Var, which traditionally uses a iterative optimization algorithm (e.g., conjugate gradient, CG) to approximate cost function Hessian eigenmodes. A recently proposed alternative is the Randomized Incremental Optimal Technique (RIOT), which uses an ensemble of linearized model integrations to perform the Hessian eigen decomposition. The scalability and convergence properties of both algorithms are

evaluated in two on problems of varying size and degrees of nonlinearity, including the Lorenz- 96 system. This work provides a foundation for regional air quality forecasting, campaign

planning, and emissions constraint. The tools are extendible to other non-reactive or nearly non- reactive chemical species, model domains, and time periods.