the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Improving Accuracy and providing Uncertainty Estimations: Ensemble Methodologies for Ocean Forecasting
Abstract. Ensemble forecasting has emerged as an essential approach for addressing the uncertainties inherent in ocean prediction, offering a probabilistic framework that enhances accuracy of both short-term and long-range forecasts. By more effectively addressing the intrinsic chaotic nature of mesoscale and sub-mesoscale variability, ensemble methods offer critical insights into forecast errors and improve the reliability of predictions. This paper reviews the ensemble methodologies currently used in ocean forecasting, including techniques borrowed from weather prediction like virtual ensembles and Monte Carlo methods. It also explores the latest advancements in ensemble data assimilation, which have been successfully integrated into both ocean general circulation models and operational forecasting systems. These advancements enable more accurate representation of forecast uncertainties (error-of-the-day) by sampling perturbations conditioned on available observations. Despite the progress made, challenges remain in fully realizing the potential of ensemble forecasting, particularly in developing tools for analyzing results and incorporating them into decision-making processes. This paper highlights the crucial role of ensemble forecasting in improving ocean predictions and advocates for its wider adoption in operational systems.
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Status: open (until 26 Nov 2024)
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RC1: 'Comment on sp-2024-10', Anonymous Referee #1, 07 Oct 2024
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The paper provides a brief description of the motivation for producing ensemble ocean forecasts together with an overview of the different methods for generating them. A brief description of how ensembles are assessed is also provided. A table is included which lists some existing operational ensemble ocean forecasting systems. The paper is a useful introduction to the methods for ocean ensemble forecasting and the status of the field.
Main comments:
- The structure of the text in section 1.1 could be improved. The text doesn’t seem to clearly follow the different options for ensemble production as shown in Fig. 2.
- More references could be included to give the reader more information about particular advances, as suggested in the minor comments below.
- Table 1 is quite an ad hoc selection of different systems and not all up to date. Some suggestions for improving it are included in the minor comments below.
Minor comments:
- The section numbering seems quite ad hoc. Perhaps all sub (and sub-sub) sections could be made into new main sections.
- Line 31. Suggest rewording to “…to communicate forecast confidence to end users for better decision making.”
- Figure 1 caption. Not sure why SST is specified as the observation type – the rest of the Fig and caption are more general.
- Line 58. Fig. label is incorrect.
- Line 74. “Alternatively, …”. The initial condition uncertainty is an additional source of uncertainty rather than an alternative to those mentioned previously.
- Line 70. Some references for ocean model stochastic model schemes would be useful here, e.g. from Storto et al., 2021, Brankart et al., 2015.
- Line 76. It wasn’t clear to me how EDA schemes fit into these options.
- Line 79. Lateral boundary and surface forcing perturbation schemes might still be needed for some applications where the available atmospheric and global ocean ensembles may not be appropriate in a given operational setting. Perhaps some references could be included on these, e.g. Storto et al., 2023. You could also mention about the uncertainty in other inputs such as the rivers, e.g. Zedler et al., 2023.
- Line 100. You don’t mention CRPS here which is often used to assess ensemble forecasts.
- Line 110. Do you mean global systems here? Some regional operational forecasting systems have been running ensembles for a long time, e.g. TOPAZ (Bertino et al., 2008).
- Line 115. Missing comma between “horizons” and “the”.
- Line 119. Perhaps it could be stated that table 1 is a selection of systems, rather than a comprehensive list (which is difficult to provide).
- Table 1:
- The FOAM ensemble includes internal physics perturbations according to Lea et al. 2022.
- The Bluelink system now runs an operational 1/10° global ensemble using the EnKF (Brassington et al., 2023).
- The ECMWF ocean system is an ensemble system (Zuo et al., 2019) and could be included.
- A couple of surface wave systems are listed, but others also run ensemble wave forecasts, e.g. MeteoFrance, UK Met Office.
- Seasonal forecast systems are mostly ensemble-based systems but only the BoM system is listed. See https://climate.copernicus.eu/seasonal-forecasts.
- A regional system that could also be included is described by Röhrs et al., 2023.
- Line 130. SWOT is now flying so this sentence should be amended.
- Line 133. Ocean forecasting systems have been produced by DA system for a long time so I wasn’t quite sure why this sentence was included.
Possible additional references:
Bertino, L. & K A Lisæter (2008) The TOPAZ monitoring and prediction system for the Atlantic and Arctic Oceans, Journal of Operational Oceanography, 1:2, 15-18, DOI: 10.1080/1755876X.2008.11020098
Brankart, J.-M., Candille, G., Garnier, F., Calone, C., Melet, A., Bouttier, P.-A., Brasseur, P., and Verron, J.: A generic approach to explicit simulation of uncertainty in the NEMO ocean model, Geosci. Model Dev., 8, 1285–1297, https://doi.org/10.5194/gmd-8-1285-2015, 2015.
Brassington, G. B., Sakov, P., Divakaran, P., Aijaz, S., Sweeney-Van Kinderen, J., Huang, X., and Allen, S.: OceanMAPS v4. 0i: a global eddy resolving EnKF ocean forecasting system, in: OCEANS 2023-Limerick, IEEE, 1–8, https://doi.org/10.1109/OCEANSLimerick52467.2023.10244383, 2023.
Röhrs, J., Gusdal, Y., Rikardsen, E. S. U., Durán Moro, M., Brændshøi, J., Kristensen, N. M., Fritzner, S., Wang, K., Sperrevik, A. K., Idžanović, M., Lavergne, T., Debernard, J. B., and Christensen, K. H.: Barents-2.5km v2.0: an operational data-assimilative coupled ocean and sea ice ensemble prediction model for the Barents Sea and Svalbard, Geosci. Model Dev., 16, 5401–5426, https://doi.org/10.5194/gmd-16-5401-2023, 2023.
Storto A, Andriopoulos P. A new stochastic ocean physics package and its application to hybrid-covariance data assimilation. Q J R Meteorol Soc. 2021; 1691–1725. https://doi.org/10.1002/qj.3990
Storto A and Yang C (2023) Stochastic schemes for the perturbation of the atmospheric boundary conditions in ocean general circulation models. Front. Mar. Sci. 10:1155803. doi: 10.3389/fmars.2023.1155803.
Zedler S.E., Polton J.A., King R.R., Wakelin S.L., 2023. The effect of uncertain river forcing on the thermohaline properties of the north west European shelf seas. Ocean Model., 183 (2023), Article 102196
Zuo, H., Balmaseda, M. A., Tietsche, S., Mogensen, K., and Mayer, M.: The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment, Ocean Sci., 15, 779–808, https://doi.org/10.5194/os-15-779-2019, 2019.
Citation: https://doi.org/10.5194/sp-2024-10-RC1
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