Assimilation of TEMPO Data Toward Enhanced Prediction of Diurnal Variations of Surface O3 and NOx over Continental United States

Atmospheric aerosols and trace gases play a crucial role in Earth's environment, climate change, and public health. More accurate representation of the diurnal variations of these pollutants can effectively reduce daily human exposure levels. Atmospheric chemical transport models (CTMs), such as the Unified Inputs (Initial and Boundary Conditions) Weather Research and Forecasting model coupled with Chemistry (UI-WRF-Chem), can simulate and forecast aerosol and trace gases distribution, capturing these diurnal variations effectively. This capability is essential for understanding and predicting the daily maximum 8-hour averaged ozone levels, which is critical for assessing public health risks. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite instrument provides high temporal resolution of trace gases species that can potentially enhances the CTM's accuracy in representing diurnal changes of surface O3.

Here we present the development of a data assimilation (DA) technique to assimilate TEMPO NO2 and other data products into UI-WRF-Chem to improve surface O3 prediction. The Ensemble Transfer Kalman Filter (ETKF) method, designed to optimize both emission and atmospheric composition, is developed and integrated into UI-WRF-Chem. Specifically, we have adapted the WRFDA's ETKF module to perform DA for multiple trace gases . Preliminary results indicate that the ETKF method can reduce the relative error by 20-50% in model simulations of trace gas species such as NO₂ and O₃ and show good correlation with EPA ground observations of O₃ in Northeast U.S. By applying data assimilation (DA) of TEMPO NO₂ at intervals of 3-6 hours, the model's simulation of diurnal variations in ozone, NO₂, and NOₓ-related particulate matter (PM) during the daytime has been significantly improved. Assimilating TEMPO observations into UI-WRF-Chem could also update the existing anthropogenic emission inventory of trace gases and aerosols with finer temporal and spatial resolution.