Assessment of an atmospheric transport model for annual inverse estimates of California greenhouse gas emissions. Issue 3 (2nd February 2017) - Bagley, Justin E.; Jeong, Seongeun; Cui, Xinguang; Newman, Sally; Zhang, Jingsong; Priest, Chad; Campos‐Pineda, Mixtli; Andrews, Arlyn E.; Bianco, Laura; Lloyd, Matthew; Lareau, Neil; Clements, Craig; et al.
Abstract: Atmospheric inverse estimates of gas emissions depend on transport model predictions, hence driving a need to assess uncertainties in the transport model. In this study we assess the uncertainty in WRF‐STILT (Weather Research and Forecasting and Stochastic Time‐Inverted Lagrangian Transport) model predictions using a combination of meteorological and carbon monoxide (CO) measurements. WRF configurations were selected to minimize meteorological biases using meteorological measurements of winds and boundary layer depths from surface stations and radar wind profiler sites across California. We compare model predictions with CO measurements from four tower sites in California from June 2013 through May 2014 to assess the seasonal biases and random errors in predicted CO mixing ratios. In general, the seasonal mean biases in boundary layer wind speed (< 0.5 m/s), direction (< 15°), and boundary layer height (< 200 m) were small. However, random errors were large ( 1.5–3.0 m/s for wind speed, 40–60° for wind direction, and 300–500 m for boundary layer height). Regression analysis of predicted and measured CO yielded near‐unity slopes (i.e., within 1.0 ± 0.20) for the majority of sites and seasons, though a subset of sites and seasons exhibit larger ( 30%) uncertainty, particularly when weak winds combined with complex terrain in the South Central Valley of California. Looking across sites and seasons, these results suggest that WRF‐STILT simulations are sufficient to estimate emissions of CO to up to 15% on annual time scales across California. Key Points: Meteorological observations used to select site‐specific WRF model schemes CO measurements compared with WRF‐STILT model predictions Model biases and random errors estimated for measurement sites