Improving air quality predictions over the United States with an analog ensemble

Air quality forecasts produced by the National Air Quality Forecasting Capability (NAQFC) help air quality forecasters across the United States in making informed decisions to protect public health from acute air pollution episodes. However, errors in air quality forecasts limit their value in the decision-making process. This study aims to enhance the accuracy of NAQFC air quality forecasts and reliably quantify their uncertainties using a statistical-dynamical method called the analog ensemble (AnEn), which has previously been found to efficiently generate probabilistic forecasts for other applications. AnEn estimates of the probability of the true state of a predictand are based on a current deterministic numerical prediction and an archive of prior analogous predictions paired with prior observations. The method avoids the complexity and real-time computational expense of model-based ensembles and is proposed here for the first time for air quality forecasting. AnEn is applied with forecasts from the Community Multiscale Air Quality (CMAQ) model. Relative to CMAQ raw forecasts, deterministic forecasts of surface ozone (O-3) and particulate matter of aerodynamic diameter smaller than 2.5 mm (PM2.5) based on AnEn's mean have lower systemic and random errors and are overall better correlated with observations; for example, when computed across all sites and lead times, AnEn's root-mean-square error is lower than CMAQ's by roughly 35% and 30% for O-3 and PM2.5, respectively, and AnEn improves the correlation by 50% for O-3 and PM2.5. Probabilistic forecasts from AnEn are statistically consistent, reliable, and sharp, and they quantify the uncertainty of the underlying prediction.

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Author Delle Monache, Luca
Alessandrini, Stefano
Djalalova, Irina
Wilczak, James
Knievel, Jason C.
Kumar, Rajesh
Publisher UCAR/NCAR - Library
Publication Date 2020-10-01T00:00:00
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Topic Category geoscientificInformation
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Metadata Date 2023-08-18T18:29:21.381843
Metadata Record Identifier edu.ucar.opensky::articles:24227
Metadata Language eng; USA
Suggested Citation Delle Monache, Luca, Alessandrini, Stefano, Djalalova, Irina, Wilczak, James, Knievel, Jason C., Kumar, Rajesh. (2020). Improving air quality predictions over the United States with an analog ensemble. UCAR/NCAR - Library. http://n2t.net/ark:/85065/d7fx7dv9. Accessed 26 July 2025.

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