Model output statistics (MOS) applied to Copernicus Atmospheric Monitoring Service (CAMS) O₃ forecasts: trade-offs between continuous and categorical skill scores

Abstract 3 forecasts over the Iberian Peninsula during 2018–2019. A key aspect of our study is the evaluation, which is performed using a comprehensive set of continuous and categorical metrics at various timescales, along different lead times and using different meteorological input datasets. 3 forecasts can be substantially improved using such MOS corrections and that improvements go well beyond the correction of the systematic bias. Depending on the timescale and lead time, root mean square errors decreased from 20 %–40 % to 10 %–30 %, while Pearson correlation coefficients increased from 0.7–0.8 to 0.8–0.9. Although the improvement typically affects all lead times, some MOS methods appear more adversely impacted by the lead time. The MOS methods relying on meteorological data were found to provide relatively similar performance with two different meteorological inputs. Importantly, our results also clearly show the trade-offs between continuous and categorical skills and their dependencies on the MOS method. The most sophisticated MOS methods better reproduce O3 mixing ratios overall, with the lowest errors and highest correlations. However, they are not necessarily the best in predicting the peak O3 episodes, for which simpler MOS methods can achieve better results. Although the complex impact of MOS methods on the distribution of and variability in raw forecasts can only be comprehended through an extended set of complementary statistical metrics, our study shows that optimally implementing MOS in AQ forecast systems crucially requires selecting the appropriate skill score to be optimized for the forecast application of interest.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Model output statistics (MOS) applied to Copernicus Atmospheric Monitoring Service (CAMS) O₃ forecasts: trade-offs between continuous and categorical skill scores ; volume:22 ; number:17 ; year:2022 ; pages:11603-11630 ; extent:28 ; Atmospheric chemistry and physics ; 22, Heft 17 (2022), 11603-11630 (gesamt 28)

Contributors, Places and Time

• Creator: Petetin, Hervé ; Bowdalo, Dene ; Bretonnière, Pierre-Antoine ; Guevara, Marc ; Jorba, Oriol ; Mateu Armengol, Jan ; Samso Cabre, Margarida ; Serradell, Kim ; Soret, Albert ; Pérez García-Pando, Carlos

Further information

• DOI: 10.5194/acp-22-11603-2022
• URN: urn:nbn:de:101:1-2022091505433373059329
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:37 AM CEST