Preprints
https://doi.org/10.5194/acp-2017-1214
https://doi.org/10.5194/acp-2017-1214
 
02 May 2018
02 May 2018
Status: this preprint has been withdrawn by the authors.

Air Quality Predictions with an Analog Ensemble

Luca Delle Monache1, Stefano Alessandrini1, Irina Djalalova1, James Wilczak2, and Jason C. Knievel1 Luca Delle Monache et al.
  • 1National Center for Atmospheric Research, P.O. Box 3000, Boulder, Colorado, USA, 80307-3000
  • 2National Oceanic and Atmospheric Administration, 325 Broadway, Boulder, Colorado, USA, 80305-3337

Abstract. The authors demonstrate how the analog ensemble (AnEn) can efficiently generate deterministic and probabilistic forecasts of air quality. AnEn estimates the probability of future observations of a predictand based on a current deterministic numerical weather prediction and an archive of prior analog predictions paired with prior observations. The method avoids the complexity and real-time computational expense of dynamical (i.e., model-based) ensembles. The authors apply AnEn to observations from the Environmental Protection Agency's (EPA's) AIRNow network and to forecasts from the Community Multiscale Air Quality (CMAQ). Compared to raw forecasts from CMAQ, deterministic forecasts of O3 and PM2.5 based on AnEn's mean have lower errors, both systemic and random, and are better correlated with observations. Probabilistic forecasts from AnEn are statistically consistent, reliable, and sharp, and they quantify the uncertainty of the underlying prediction.

This preprint has been withdrawn.

Luca Delle Monache et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Luca Delle Monache et al.

Luca Delle Monache et al.

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Short summary
The authors demonstrate how the analog ensemble (AnEn) can efficiently generate deterministic and probabilistic forecasts of air quality. The method avoids the complexity and real-time computational expense of dynamical (i.e., model-based) ensembles. AnEn deterministic predictions have lower errors and are better correlated with observations. Probabilistic forecasts from AnEn are statistically consistent, reliable, and sharp, and they quantify the uncertainty of the underlying prediction.
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