Articles | Volume 16, issue 4
https://doi.org/10.5194/os-16-831-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-16-831-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Jul 2020
Research article |
| 17 Jul 2020
| 17 Jul 2020
An approach to the verification of high-resolution ocean models using spatial methods
Ric Crocker
CORRESPONDING AUTHOR
Verification, Impacts and Post-Processing, Weather Science, Met
Office, Exeter, EX1 3PB, UK
Jan Maksymczuk
Ocean Forecasting Research & Development, Weather Science, Met
Office, Exeter, EX1 3PB, UK
Marion Mittermaier
Verification, Impacts and Post-Processing, Weather Science, Met
Office, Exeter, EX1 3PB, UK
Marina Tonani
Ocean Forecasting Research & Development, Weather Science, Met
Office, Exeter, EX1 3PB, UK
Christine Pequignet
Ocean Forecasting Research & Development, Weather Science, Met
Office, Exeter, EX1 3PB, UK
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18 citations as recorded by crossref.
- High-resolution stochastic downscaling method for ocean forecasting models and its application to the Red Sea dynamics G. Shapiro et al. https://doi.org/10.5194/os-17-891-2021
- Forecasting the eddying ocean with a deep neural network Y. Cui et al. https://doi.org/10.1038/s41467-025-57389-2
- A description of model intercomparison processes and techniques for ocean forecasting F. Hernandez et al. https://doi.org/10.5194/sp-5-opsr-17-2025
- Satellite‐Based Sea Surface Salinity Designed for Ocean and Climate Studies J. Boutin et al. https://doi.org/10.1029/2021JC017676
- Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop A. Dasgupta et al. https://doi.org/10.1111/jfr3.12880
- Deep learning weather models for subregional ocean forecasting: A case study on the Canary current upwelling system G. Cuervo-Londoño et al. https://doi.org/10.1016/j.ocemod.2026.102782
- Locally Modified Winds Regulate Circulation in a Semi‐Enclosed Shelf Sea A. Akpınar et al. https://doi.org/10.1029/2021JC018248
- Modelled dispersal pathways of non-indigenous species in the Danish Wadden Sea V. Schourup-Kristensen et al. https://doi.org/10.1016/j.marenvres.2023.106111
- GULF18, a high-resolution NEMO-based tidal ocean model of the Arabian/Persian Gulf D. Bruciaferri et al. https://doi.org/10.5194/gmd-15-8705-2022
- How Efficient Is Model-to-Model Data Assimilation at Mitigating Atmospheric Forcing Errors in a Regional Ocean Model? G. Shapiro & M. Salim https://doi.org/10.3390/jmse11050935
- The Met Office operational wave forecasting system: the evolution of the regional and global models N. Valiente et al. https://doi.org/10.5194/gmd-16-2515-2023
- A Lagrangian uncertainty quantification approach to validate ocean model datasets G. García-Sánchez et al. https://doi.org/10.1016/j.physd.2025.134690
- Ensemble forecasting greatly expands the prediction horizon for ocean mesoscale variability P. Thoppil et al. https://doi.org/10.1038/s43247-021-00151-5
- Assessing storm surge model performance: what error indicators can measure the model's skill? R. Campos-Caba et al. https://doi.org/10.5194/os-20-1513-2024
- Using feature-based verification methods to explore the spatial and temporal characteristics of the 2019 chlorophyll-a bloom season in a model of the European Northwest Shelf M. Mittermaier et al. https://doi.org/10.5194/os-17-1527-2021
- Multi-model analysis of the Adriatic dense-water dynamics P. Pranić et al. https://doi.org/10.5194/os-19-649-2023
- Beyond Inflation: Backscatter Parameterizations to Address the Variability Deficit in Global Ocean Data Assimilation K. Boden et al. https://doi.org/10.3390/jmse14141273
- Nesting and data assimilation considerations in regional operational ocean forecasting H. Ngodock et al. https://doi.org/10.1080/1755876X.2022.2147696
Saved (final revised paper)
Latest update: 24 Oct 2026
Short summary
We assessed the potential benefit of a new verification metric, developed by the atmospheric community, to assess high-resolution ocean models against coarser-resolution configurations. Typical verification metrics often do not show any benefit when high-resolution models are compared to lower-resolution configurations. The new metric showed improvements in higher-resolution models away from the grid scale. The technique can be applied to both deterministic and ensemble forecasts.
We assessed the potential benefit of a new verification metric, developed by the atmospheric...
