Abstract
Abstract. Inference of NOx emissions (NO+NO2) from satellite observations of tropospheric NO2 column requires knowledge of NOx lifetime, usually provided by chemical transport models (CTMs). However, it is known that species subject to non-linear sources or sinks, such as ozone, are susceptible to biases in coarse-resolution CTMs. Here we compute the resolution-dependent bias in predicted NO2 column, a quantity relevant to the interpretation of space-based observations. We use 1-D and 2-D models to illustrate the mechanisms responsible for these biases over a range of NO2 concentrations and model resolutions. We find that predicted biases are largest at coarsest model resolutions with negative biases predicted over large sources and positive biases predicted over small sources. As an example, we use WRF-CHEM to illustrate the resolution necessary to predict 10 AM and 1 PM NO2 column to 10 and 25% accuracy over three large sources, the Four Corners power plants in NW New Mexico, Los Angeles, and the San Joaquin Valley in California for a week-long simulation in July 2006. We find that resolution in the range of 4–12 km is sufficient to accurately model nonlinear effects in the NO2 loss rate.
Highlights
NOx (NO + NO2) is emitted to the troposphere by fossil-fuel combustion, biomass burning, soil microbial processes, and lightning
While the San Joaquin Valley appears to be an intermediate source of NOx (Fig. 9), which according to the 2-D plume model would indicate that coarse resolution prediction of NO2 should be biased high (Fig. 5h), it is important to consider the differences between the 2-D plume model, which simulates midday summertime chemistry at steady-state, with WRF-CHEM, which integrates the full diurnal cycle
We investigate the effects of NO2-OH chemical feedbacks on predicted NO2 in a 1-D plume model, a 2-D plume model, and WRF-CHEM, a fully-coupled 3-D chemical transport models (CTMs)
Summary
NOx (NO + NO2) is emitted to the troposphere by fossil-fuel combustion, biomass burning, soil microbial processes, and lightning. Satellite-based observations of tropospheric NO2 have provided unique insights into spatial and temporal patterns on regional scales of soil (e.g., Bertram et al, 2005; van der A et al, 2007; Hudman et al, 2010), biomass burning (e.g., Jaegle et al, 2005; Mebust et al, 2011), lightning (e.g., Beirle et al, 2010), and urban NOx emissions (e.g., Kim et al, 2009; Russell et al, 2010; Beirle et al, 2011) These satellite observations have provided constraints on inverse models that are used to validate emission inventories (e.g., Martin et al, 2003; Konovalov et al, 2006; Napelenok et al, 2008; Kim et al, 2009). We use the simpler models to understand the source of resolution-dependent biases and use WRFCHEM to determine the model resolution necessary to predict 10 AM and 1 PM column NO2 to 10 % and 25 % accuracy over the Four Corners and San Juan power plants, the city of Los Angeles, and the San Joaquin Valley in California for a week-long simulation in July 2006
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