01 Dec 2021
01 Dec 2021
Status: a revised version of this preprint is currently under review for the journal ACP.

Model Output Statistics (MOS) applied to CAMS O3 forecasts: trade-offs between continuous and categorical skill scores

Hervé Petetin1, Dene Bowdalo1, Pierre-Antoine Bretonnière1, Marc Guevara1, Oriol Jorba1, Jan Mateu Armengol1, Margarida Samso Cabre1, Kim Serradell1, Albert Soret1, and Carlos Pérez Garcia-Pando1,2 Hervé Petetin et al.
  • 1Barcelona Supercomputing Center, Barcelona, Spain
  • 2ICREA, Catalan Institution for Research and Advanced Studies, Barcelona, Spain

Abstract. Air quality (AQ) forecasting systems are usually built upon physics-based numerical models that are affected by a number of uncertainty sources. In order to reduce forecast errors, first and foremost the bias, they are often coupled with Model Output Statistics (MOS) modules. MOS methods are statistical techniques used to correct raw forecasts at surface monitoring station locations, where AQ observations are available. In this study, we investigate to what extent AQ forecasts can be improved using a variety of MOS methods, including persistence (PERS), moving average (MA), quantile mapping (QM), Kalman Filter (KF), analogs (AN), and gradient boosting machine (GBM). We apply our analysis to the Copernicus Atmospheric Monitoring Service (CAMS) regional ensemble median O3 forecasts over the Iberian Peninsula during 2018–2019. A key aspect of our study is the evaluation, which is performed using a very comprehensive set of continuous and categorical metrics at various time scales (hourly to daily), along different lead times (1 to 4 days), and using different meteorological input data (forecast vs reanalyzed).

Our results show that O3 forecasts can be substantially improved using such MOS corrections and that this improvement goes much beyond the correction of the systematic bias. Although it typically affects all lead times, some MOS methods appear more adversely impacted by the lead time. When considering MOS methods relying on meteorological information and comparing the results obtained with IFS forecasts and ERA5 reanalysis, the relative deterioration brought by the use of IFS is minor, which paves the way for their use in operational MOS applications. 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 lowest errors and highest correlations. However, they are not necessarily the best in predicting the highest O3 episodes, for which simpler MOS methods can give better results. Although the complex impact of MOS methods on the distribution and variability of 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.

Hervé Petetin et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-864', Anonymous Referee #1, 26 Jan 2022
    • AC1: 'Reply on RC1', Hervé Petetin, 02 Jun 2022
  • RC2: 'Comment on acp-2021-864', Anonymous Referee #3, 03 Mar 2022
    • AC2: 'Reply on RC2', Hervé Petetin, 02 Jun 2022

Hervé Petetin et al.

Hervé Petetin et al.


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Short summary
This study investigates to which extent ozone forecasts provided by the Copernicus Atmospheric Monitoring Service (CAMS) can be improved using surface observations and state-of-the-art statistical methods. Through a case study over the Iberian Peninsula in 2018–2019, it unambiguously demonstrates the value of these methods for improving the raw CAMS O3 forecasts, while highlighting at the same time the complexity of improving the detection of highest O3 concentrations.