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Implementing satellite NO2 data assimilation in CMAQ for identifying emissions biases and improving regional boundary conditions
Citation:
East, J., B. Henderson, S. Koplitz, S. Napelenok, F. Garcia Menendez, A. Lenzen, AND B. Pierce. Implementing satellite NO2 data assimilation in CMAQ for identifying emissions biases and improving regional boundary conditions. CMAS Conference, Cary, NC, October 26 - 30, 2020.
Impact/Purpose:
Chemical data assimilation has rarely been applied in current state-of-the art chemical transport models used for simulating air pollution. Here, we use one method to assimilate NOx satellite observations into the CMAQ model. We apply the method for simulating NOx and O3 over the continental United States. When fully developed, such a method would be tremendously useful in applying air quality models in areas where bottom-up emissions inventories are not available or possible.
Description:
We adapt a composition assimilation framework to identify potential biases in NOX emissions on the regional and hemispheric scale. The assimilation framework was developed as a part of NASA HAQAST and can be used to improve model agreement with satellite retrievals directly or to infer emissions updates. Assimilated results are useful for hemispheric-scale modeling as boundary conditions for free running simulations, while emissions inferences are valuable for emissions quality assurance and rapid emission updates at either scale. The results in this presentation will focus on inferring emissions updates, which can subsequently be used in free running simulations. We apply a 3-D variational data assimilation technique in CMAQ over the continental US (CONUS) to fuse satellite-based NO2 observations measured by OMI and TROPOMI with modeled NO2. We relate the analysis increment (the difference between the assimilated and unassimilated total NO2 vertical column) to emissions biases through a sensitivity parameter to infer spatially resolved monthly average emissions changes. On the regional scale over the US, we find that modeled NO2 columns have a small but ubiquitous low bias compared to satellite NO2, but that much of this difference occurs in the upper troposphere. This leads to an iterative inference of lightning NO production rates first and then anthropogenic emissions. After accounting for lightning NO, a priori anthropogenic emissions generally produce modeled columns that match the satellite-infused column and do not require updating. However, over some urban areas and shipping channels, we infer small emissions increases on the order of 2-3%. There are larger widespread analysis increments in the northwestern US and southern Canada, which may be attributable to uncertainties in soil NOX emissions. Results suggest possible biases on natural and anthropogenic emissions inside the CONUS domain. In addition, these results lay the framework for assimilation on the hemispheric scale. The presentation will introduce initial hemispheric results of the analysis increment and top-down constraints on uncertain emissions outside the CONUS domain that are currently underway.
