Ponente
Descripción
There are ongoing efforts to develop the operational regional inline air quality model, RRFS-CMAQ (Rapid Refresh Forecast System with CMAQ chemistry) based on the Finite Volume Cubed-Sphere Dynamic Core (FV3), under the NOAA Unified Forecast System. As part of these efforts, the chemical data assimilation (DA) capability is being developed to improve the model results. Here we present the preliminary results of using surface AIRNow in-situ measurements and a 3D-Var method in the Joint Effort for Data-Assimilation Integration (JEDI) to adjust the near-surface chemical initial conditions. Our test case for summer 2019 shows that assimilating in-situ measurements is a reliable method to reduce the bias in the aerosol and ozone initial conditions of RRFS-CMAQ. Unlike the satellite aerosol optical depth data assimilation, this DA with the in-situ measurements can be used at night or under the cloud, and is available at hourly intervals, matching well with the rapid refresh system. The duration of DA influence varied from region to region. Over polluted areas with strong emissions, the assimilation effect may fade quickly. The bias of the base model predicted PM2.5 (particle matter with diameter < 2.5 μm) has strong diurnal variations, implying that some processes (e.g. dynamics, emission, and chemical processes) in RRFS-CMAQ could have systematic diurnal biases. Cycling the assimilation system every 6 hours reduced that bias, but did not completely eliminate it. This study shows that the DA adjustment on the chemical initial condition is useful with little side effects, and other efforts are needed to reduce the models’ overall biases.
