Articles | Volume 5-opsr
https://doi.org/10.5194/sp-5-opsr-17-2025
https://doi.org/10.5194/sp-5-opsr-17-2025
02 Jun 2025
 | OPSR | Chapter 6.3
 | 02 Jun 2025 | OPSR | Chapter 6.3

A description of model intercomparison processes and techniques for ocean forecasting

Fabrice Hernandez, Marcos Garcia Sotillo, and Angélique Melet

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Cited articles

Ahmad, H.: Machine learning applications in oceanography, Aquatic Research, 2, 161–169, https://doi.org/10.3153/AR19014, 2019. 
Aijaz, S., Brassington, G. B., Divakaran, P., Régnier, C., Drévillon, M., Maksymczuk, J., and Peterson, K. A.: Verification and intercomparison of global ocean Eulerian near-surface currents, Ocean Model., 186, 102241, https://doi.org/10.1016/j.ocemod.2023.102241, 2023. 
Alvarez Fanjul, E., Ciliberti, S., and Bahurel, P.: Implementing Operational Ocean Monitoring and Forecasting Systems, IOC-UNESCO, GOOS-275, https://doi.org/10.48670/ETOOFS, 2022. 
Balmaseda, M. A., Balan Sarojini, B., Mayer, M., Tietsche, S., Zuo, H., Vitart, F., and Stockdale, T. N.: Impact of the ocean in situ observations on the ECMWF seasonal forecasting system, Front. Mar. Sci., 11, https://doi.org/10.3389/fmars.2024.1456013, 2024a. 
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An historical review over the last 3 decades on intercomparison projects of ocean numerical reanalysis or forecast is first proposed. From this, main issues and lessons learned are discussed in order to propose an overview of best practices and key considerations to facilitate intercomparison activities in operational oceanography.
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