Constraints on Anthropogenic NOx Emissions from Geostationary Satellite Observations in a Regional Chemical Data Assimilation System: Evaluation Using Observing System Simulation Experiments

Wednesday, 16 December 2015
Poster Hall (Moscone South)
Xueling Liu1, Arthur P Mizzi2, Jeffrey L Anderson3, Inez Y Fung1 and Ronald C Cohen1, (1)University of California Berkeley, Berkeley, CA, United States, (2)National Center for Atmospheric Research, Boulder, CO, United States, (3)University Corporation for Atmospheric Research, Boulder, CO, United States
Nitrogen oxides (NOx=NO+NO2) control the tropospheric ozone (O3) budget, the abundance of the hydroxyl radical (OH), the formation of organic and inorganic nitrate aerosol, and therefore affect air quality and climate. There remain significant uncertainties in the processes responsible for NOx emissions and subsequent mixing and chemical removal. NOx has a short lifetime and its emissions show high spatiotemporal variability at urban scale. Future geostationary satellite instruments including TEMPO, GEMS and Sentinel-4 will provide hourly time resolution and high spatial resolution observations providing maps of NO2 on diurnal and local scales. Here we determine the extent to which a TEMPO like instrument can quantify urban-scale NOx emissions using a regional data assimilation (DA) system comprising of a chemical transport model, WRF-Chem, a TEMPO simulator and the DART Ensemble Adjustment Kalman Filter. We generate synthetic TEMPO observations by sampling from a nature run on an urban scale domain. We consider the effect of albedo, surface pressure, solar and viewing angles and a priori NO2 profiles on the TEMPO NO2 averaging kernel to achieve scene-dependent instrument sensitivity. We estimate NOx emissions using DART in a state augmentation approach by including NOx emissions in the state vector being analyzed. The ensemble-based statistical estimation of error correlations between concentrations and emissions are critical as they determine the impact of assimilated observations. We describe observing system simulation experiments to explore the optimal approach in the ensemble-based DA system to estimate hourly-resolved NOx emissions from TEMPO NO2 observations. Several case studies will be presented examining the role of covariance localization length and chemical perturbations on the success of the approach.