Evaluation of a seven-year air quality simulation using the Weather Research and Forecasting (WRF)/Community Multiscale Air Quality (CMAQ) models in the eastern United States

摘要

The performance of the Weather Research and Forecasting (WRF)/Community Multi-scale Air Quality (CMAQ) system in the eastern United States is analyzed based on results from a seven-year modeling study with a 4-km spatial resolution. For 2-m temperature, the monthly averaged mean bias (MB) and gross error (GE) values are generally within the recommended performance criteria, although temperature is over-predicted with MB values up to 2 K. Water vapor at 2-m is well-predicted but significant biases (>2 g kg~(-1)) were observed in wintertime. Predictions for wind speed are satisfactory but biased towards over-prediction with 0 < MB < 0.5 m s~(-1) and root mean square error (RMSE) around 1.5 to 2 m s~(-1). Wind direction, predicted without observation nudging, is not well-reproduced with GE values as large as 50° in summertime. Performance in other months is better with RMSE around 20-30° and MB within ± 10°. O_3 performance meets the EPA criteria of mean normalized bias (MNB) within ±0.15 and accuracy of unpaired peak (AUP) within 0.2. Normalized gross error (NGE) is mostly below 0.25, lower than the criteria of 035. Performance of PM_(10) is satisfactory with mean fractional bias (MFB) within ±0.6, but a large under-prediction in springtime was frequently observed. Performance of PM_(2.5) and its components is mostly within performance goals except for organic carbon (OC), which is universally under-predicted with MFB values as large as -0.8. The predicted frequency distribution of PM_(2.5) generally agrees with observations although the predictions are slightly biased towards more frequent high concentrations in most areas. Elemental carbon (EC), nitrate and sul-fate concentrations are also well reproduced. The other unresolved PM_(2.5) components (OTHER) are significantly overestimated by more than a factor of two. No conclusive explanations can be made regarding the possible cause of this universal overestimation, which warrants a follow-up study to better understand this problem.