Technical note: Accurate, reliable, and high-resolution air quality predictions by improving the Copernicus Atmosphere Monitoring Service using a novel statistical post-processing method
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| Publicado en: | Atmospheric Chemistry and Physics vol. 24, no. 3 (2024), p. 1673 |
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Copernicus GmbH
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 100 | 1 | |a Riccio, Angelo |u Department of Science and Technology, Parthenope University of Naples, Centro Direzionale, Isola C4, 80143, Naples, Italy; Department of Science and Technology, Parthenope University of Naples, Via F. Petrarca 80, 80123, Naples, Italy | |
| 245 | 1 | |a Technical note: Accurate, reliable, and high-resolution air quality predictions by improving the Copernicus Atmosphere Monitoring Service using a novel statistical post-processing method | |
| 260 | |b Copernicus GmbH |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Starting from the regional air quality forecasts produced by the Copernicus Atmosphere Monitoring Service (CAMS), we propose a novel post-processing approach to improve and downscale results on a finer scale. Our approach is based on the combination of ensemble model output statistics (EMOS) with a spatio-temporal interpolation process performed through the stochastic partial differential equation–integrated nested laplace approximation (SPDE-INLA). Our interpolation approach includes several spatial and spatio-temporal predictors, including meteorological variables. A use case is provided that scales down the CAMS forecasts on the Italian peninsula. The calibration is focused on the concentrations of several air quality pollutants (PM<inline-formula>10</inline-formula>, PM<inline-formula>2.5</inline-formula>, NO<inline-formula>2</inline-formula>, and O<inline-formula>3</inline-formula>) at a daily resolution from a set of 750 monitoring sites, distributed throughout the Italian country. Our results show the key role that conditioning variables play in improving the forecast capabilities of ensemble predictions, thus allowing for a net improvement in the calibration with respect to ordinary EMOS strategies. From a deterministic point of view, the performance of the predictive model shows a significant improvement in the performance of the raw ensemble forecast, with an almost-zero bias, significantly reduced root mean square errors, and correlations that are almost always higher than 0.9 for each pollutant; moreover, the post-processing approach is able to significantly improve the prediction of exceedances, even for very low thresholds, such as those recently recommended by the World Health Organisation. This is particularly significant if a forecasting approach is used to predict air quality conditions and plan adequate human health protection measures, even for low alert thresholds. From a probabilistic point of view, the quality of the forecast was verified in terms of reliability and credible intervals. After post-processing, the predictive probability density functions were sharp and much better calibrated than the raw ensemble forecast. Finally, we present some additional results based on a set of gridded (4 km <inline-formula>×</inline-formula> 4 km) maps covering the entire Italian country for the detection of areas where pollution peaks occur (exceedances of the current and/or proposed regulatory thresholds). | |
| 651 | 4 | |a Italy | |
| 651 | 4 | |a Europe | |
| 653 | |a Indoor air quality | ||
| 653 | |a Air quality | ||
| 653 | |a Calibration | ||
| 653 | |a Atmosphere | ||
| 653 | |a Air pollution | ||
| 653 | |a Atmospheric monitoring | ||
| 653 | |a Pollution detection | ||
| 653 | |a Prediction models | ||
| 653 | |a Geography | ||
| 653 | |a Air | ||
| 653 | |a Partial differential equations | ||
| 653 | |a Interpolation | ||
| 653 | |a Particulate matter | ||
| 653 | |a Mathematical models | ||
| 653 | |a Probability theory | ||
| 653 | |a Ground stations | ||
| 653 | |a Pollutants | ||
| 653 | |a Performance prediction | ||
| 653 | |a Maps | ||
| 653 | |a Air quality forecasting | ||
| 653 | |a Approximation | ||
| 653 | |a Stochastic processes | ||
| 653 | |a Probability density functions | ||
| 653 | |a Ensemble forecasting | ||
| 653 | |a Outdoor air quality | ||
| 653 | |a Monitoring | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Thresholds | ||
| 653 | |a Nitrogen dioxide | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Chianese, Elena |u Department of Science and Technology, Parthenope University of Naples, Centro Direzionale, Isola C4, 80143, Naples, Italy | |
| 773 | 0 | |t Atmospheric Chemistry and Physics |g vol. 24, no. 3 (2024), p. 1673 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2922384511/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/2922384511/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2922384511/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |