Enhancing multivariate post‐processed visibility predictions utilizing Copernicus Atmosphere Monitoring Service forecasts
Abstract In our contemporary era, meteorological weather forecasts increasingly incorporate ensemble predictions of visibility—a parameter of great importance in aviation, maritime navigation, and air quality assessment, with direct implications for public health. However, this weather variable fall...
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Wiley
2024-11-01
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| Series: | Meteorological Applications |
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| Online Access: | https://doi.org/10.1002/met.70015 |
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| author | Mária Lakatos Sándor Baran |
| author_facet | Mária Lakatos Sándor Baran |
| author_sort | Mária Lakatos |
| collection | DOAJ |
| description | Abstract In our contemporary era, meteorological weather forecasts increasingly incorporate ensemble predictions of visibility—a parameter of great importance in aviation, maritime navigation, and air quality assessment, with direct implications for public health. However, this weather variable falls short of the predictive accuracy achieved for other quantities issued by meteorological centers. Therefore, statistical post‐processing is recommended to enhance the reliability and accuracy of predictions. By estimating the predictive distributions of the variables with the aid of historical observations and forecasts, one can achieve statistical consistency between true observations and ensemble predictions. Visibility observations, following the recommendation of the World Meteorological Organization, are typically reported in discrete values; hence, the predictive distribution of the weather quantity takes the form of a discrete parametric law. Recent studies demonstrated that the application of classification algorithms can successfully improve the skill of such discrete forecasts; however, a frequently emerging issue is that certain spatial and/or temporal dependencies could be lost between marginals. Based on visibility ensemble forecasts of the European Centre for Medium‐Range Weather Forecasts for 30 locations in Central Europe, we investigate whether the inclusion of Copernicus Atmosphere Monitoring Service (CAMS) predictions of the same weather quantity as an additional covariate could enhance the skill of the post‐processing methods and whether it contributes to the successful integration of spatial dependence between marginals. Our study confirms that post‐processed forecasts are substantially superior to raw and climatological predictions, and the utilization of CAMS forecasts provides a further significant enhancement both in the univariate and multivariate setup. We also demonstrate that post‐processing significantly improves the predictions of low visibility events, which opens the door for aeronautical applications. |
| format | Article |
| id | doaj-art-ab43a61d6e444c2c91ea87a8043e057f |
| institution | Kabale University |
| issn | 1350-4827 1469-8080 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Meteorological Applications |
| spelling | doaj-art-ab43a61d6e444c2c91ea87a8043e057f2024-12-25T23:36:34ZengWileyMeteorological Applications1350-48271469-80802024-11-01316n/an/a10.1002/met.70015Enhancing multivariate post‐processed visibility predictions utilizing Copernicus Atmosphere Monitoring Service forecastsMária Lakatos0Sándor Baran1Faculty of Informatics University of Debrecen Debrecen HungaryFaculty of Informatics University of Debrecen Debrecen HungaryAbstract In our contemporary era, meteorological weather forecasts increasingly incorporate ensemble predictions of visibility—a parameter of great importance in aviation, maritime navigation, and air quality assessment, with direct implications for public health. However, this weather variable falls short of the predictive accuracy achieved for other quantities issued by meteorological centers. Therefore, statistical post‐processing is recommended to enhance the reliability and accuracy of predictions. By estimating the predictive distributions of the variables with the aid of historical observations and forecasts, one can achieve statistical consistency between true observations and ensemble predictions. Visibility observations, following the recommendation of the World Meteorological Organization, are typically reported in discrete values; hence, the predictive distribution of the weather quantity takes the form of a discrete parametric law. Recent studies demonstrated that the application of classification algorithms can successfully improve the skill of such discrete forecasts; however, a frequently emerging issue is that certain spatial and/or temporal dependencies could be lost between marginals. Based on visibility ensemble forecasts of the European Centre for Medium‐Range Weather Forecasts for 30 locations in Central Europe, we investigate whether the inclusion of Copernicus Atmosphere Monitoring Service (CAMS) predictions of the same weather quantity as an additional covariate could enhance the skill of the post‐processing methods and whether it contributes to the successful integration of spatial dependence between marginals. Our study confirms that post‐processed forecasts are substantially superior to raw and climatological predictions, and the utilization of CAMS forecasts provides a further significant enhancement both in the univariate and multivariate setup. We also demonstrate that post‐processing significantly improves the predictions of low visibility events, which opens the door for aeronautical applications.https://doi.org/10.1002/met.70015Copernicus Atmosphere Monitoring Service (CAMS)ensemble calibrationensemble copula couplingmultivariate post‐processingSchaake shufflevisibility |
| spellingShingle | Mária Lakatos Sándor Baran Enhancing multivariate post‐processed visibility predictions utilizing Copernicus Atmosphere Monitoring Service forecasts Meteorological Applications Copernicus Atmosphere Monitoring Service (CAMS) ensemble calibration ensemble copula coupling multivariate post‐processing Schaake shuffle visibility |
| title | Enhancing multivariate post‐processed visibility predictions utilizing Copernicus Atmosphere Monitoring Service forecasts |
| title_full | Enhancing multivariate post‐processed visibility predictions utilizing Copernicus Atmosphere Monitoring Service forecasts |
| title_fullStr | Enhancing multivariate post‐processed visibility predictions utilizing Copernicus Atmosphere Monitoring Service forecasts |
| title_full_unstemmed | Enhancing multivariate post‐processed visibility predictions utilizing Copernicus Atmosphere Monitoring Service forecasts |
| title_short | Enhancing multivariate post‐processed visibility predictions utilizing Copernicus Atmosphere Monitoring Service forecasts |
| title_sort | enhancing multivariate post processed visibility predictions utilizing copernicus atmosphere monitoring service forecasts |
| topic | Copernicus Atmosphere Monitoring Service (CAMS) ensemble calibration ensemble copula coupling multivariate post‐processing Schaake shuffle visibility |
| url | https://doi.org/10.1002/met.70015 |
| work_keys_str_mv | AT marialakatos enhancingmultivariatepostprocessedvisibilitypredictionsutilizingcopernicusatmospheremonitoringserviceforecasts AT sandorbaran enhancingmultivariatepostprocessedvisibilitypredictionsutilizingcopernicusatmospheremonitoringserviceforecasts |