Articles | Volume 16, issue 13
https://doi.org/10.5194/gmd-16-3749-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-3749-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Using the COAsT Python package to develop a standardised validation workflow for ocean physics models
David Byrne
National Oceanography Centre, Liverpool, UK
National Oceanography Centre, Liverpool, UK
Enda O'Dea
Met Office, Exeter, UK
Joanne Williams
National Oceanography Centre, Liverpool, UK
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Cited articles
Castruccio, F.:
NCAR/metric, Zenodo [data set], https://doi.org/10.5281/zenodo.4708277, 2021. a
Codiga, D. L.: Unified Tidal Analysis and Prediction Using the UTide Matlab Functions, Technical Report 2011-01, Graduate School of Oceanography, University of Rhode Island, Narragansett, RI, 59 pp., 2011. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.:
The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteorol. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Egbert, G. D. and Erofeeva, S. Y.:
Efficient Inverse Modeling of Barotropic Ocean Tides, J. Atmos. Ocean. Tech., 19, 183–204, https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2, 2002. a
Firing, E., Filipe, Barna, A., and Abernathey, R.:
TEOS-10/GSW-Python: v3.4.1, Zenodo [code], https://doi.org/10.5281/zenodo.4631364, 2021. a
Good, S. A., Martin, M. J., and Rayner, N. A.:
EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates, J. Geophys. Res.-Oceans, 118, 6704–6716, https://doi.org/10.1002/2013JC009067, 2013. a, b
Graham, J. A., O'Dea, E., Holt, J., Polton, J., Hewitt, H. T., Furner, R., Guihou, K., Brereton, A., Arnold, A., Wakelin, S., Castillo Sanchez, J. M., and Mayorga Adame, C. G.:
AMM15: a new high-resolution NEMO configuration for operational simulation of the European north-west shelf, Geosci. Model Dev., 11, 681–696, https://doi.org/10.5194/gmd-11-681-2018, 2018. a, b
Gräwe, U., Holtermann, P., Klingbeil, K., and Burchard, H.:
Advantages of vertically adaptive coordinates in numerical models of stratified shelf seas, Ocean Model., 92, 56–68, https://doi.org/10.1016/j.ocemod.2015.05.008, 2015. a
Hoyer, S. and Hamman, J.:
xarray: N-D labeled Arrays and Datasets in Python, Journal of Open Research Software, 5, 10, https://doi.org/10.5334/jors.148, 2017. a, b, c
Large, W. G. and Yeager, S. G.:
The Global Climatology of an Interannually Varying Air–Sea Flux Data Set, Clim. Dynam., 33, 341–364, https://doi.org/10.1007/s00382-008-0441-3, 2009. a
Lee, G. R., Gommers, R., Wasilewski, F., Wohlfahrt, K., and O'Leary, A.:
PyWavelets: A Python package for wavelet analysis, J. Open Source Softw., 4, 1237, https://doi.org/10.21105/joss.01237, 2019. a
MacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A. V., Fereday, D. R., Scaife, A. A., Gordon, M., Vellinga, M., Williams, A. I. L., Comer, R. E., Camp, J., Xavier, P., and Madec, G.:
Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system, Q. J. Roy. Meteorol. Soc., 141, 1072–1084, https://doi.org/10.1002/qj.2396, 2015. a
Madec, G. and Team, N. S.:
NEMO ocean engine, Zenodo, https://doi.org/10.5281/zenodo.3248739, 2016. a
Madec, G. and Team, N. S.:
NEMO ocean engine, Zenodo, https://doi.org/10.5281/zenodo.1464816, 2019. a
McKinney, W.: Data structures for statistical computing in python, in: Proceedings of the 9th Python in Science Conference, edited by: van der Walt, S. and Millman, J., 56–61, https://doi.org/10.25080/Majora-92bf1922-00a, 2010 a
Megann, A., Storkey, D., Aksenov, Y., Alderson, S., Calvert, D., Graham, T., Hyder, P., Siddorn, J., and Sinha, B.:
GO5.0: the joint NERC–Met Office NEMO global ocean model for use in coupled and forced applications, Geosci. Model Dev., 7, 1069–1092, https://doi.org/10.5194/gmd-7-1069-2014, 2014. a
Polton, J., Harle, J., Holt, J., Katavouta, A., Partridge, D., Jardine, J., Wakelin, S., Rulent, J., Wise, A., Hutchinson, K., Byrne, D., Bruciaferri, D., O'Dea, E., De Dominicis, M., Mathiot, P., Coward, A., Yool, A., Palmiéri, J., Lessin, G., Mayorga-Adame, C. G., Le Guennec, V., Arnold, A., and Rousset, C.:
Reproducible and relocatable regional ocean modelling: fundamentals and practices, Geosci. Model Dev., 16, 1481–1510, https://doi.org/10.5194/gmd-16-1481-2023, 2023a.
a
Polton, J. A., Byrne, D., and O'Dea, E.: JMMP-Group/NEMO_validation: v1.0.1 (v1.0.1), Zenodo [code], https://doi.org/10.5281/zenodo.7949115, 2023b. a
Polton, J. A., Byrne, D., and O'Dea, E.: Analysis datasets for NEMO_validation workflow Byrne et al 2023 GMD. “Using the COAsT Python package to develop a standardised validation workflow for ocean physics models”, Zenodo [data set], https://doi.org/10.5281/zenodo.8108965, 2023c. a
Polton, J. A., Byrne, D., Wise, A., Holt, J., Katavouta, A., Rulent, J., Gardner, T., Cazaly, M., Hearn, M., Jennings, R., Luong, Q., Loch, S., Gorman, L., and de Mora, L.: British-Oceanographic-Data-Centre/COAsT: v3.2.1 (v3.2.1), Zenodo [code], https://doi.org/10.5281/zenodo.7799863, 2023d. a
Prandle, D. and Wolf, J.:
The interaction of surge and tide in the North Sea and River Thames, Geophys. J. Int., 55, 203–216, https://doi.org/10.1111/j.1365-246X.1978.tb04758.x, 1978. a
Roberts, C.:
cdr30/RapidMoc: RapidMoc v1.0.1, Zenodo, https://doi.org/10.5281/zenodo.1036387, 2017. a
Rocklin, M.: Dask: Parallel computation with blocked algorithms and task scheduling, in: Proceedings of the 14th Python in Science Conference (SciPy 2015), Austin, Texas, 6–12 July 2015, edited by: Huff K. and Bergstra, J., 126–132, https://doi.org/10.25080/Majora-7b98e3ed-013, 2015. a, b, c
Siddorn, J. A. and Furner, R.:
An analytical stretching function that combines the best attributes of geopotential and terrain-following vertical coordinates, Ocean Model., 66, 1–13, 2013. a
Vogel, M.:
Sea-level Science: Understanding Tides, Surges, Tsunamis and Mean Sea-level Changes, by David Pugh and Philip Woodworth, Contemp. Phys., 56, 394–394, https://doi.org/10.1080/00107514.2015.1005682, 2015. a, b, c
Woodworth, P. L., Hunter, J. R., Marcos, M., Caldwell, P., Menéndez, M., and Haigh, I.:
Towards a global higher-frequency sea level dataset, Geosci. Data J., 3, 50–59, https://doi.org/10.1002/gdj3.42, 2016. a
Short summary
Validation is a crucial step during the development of models for ocean simulation. The purpose of validation is to assess how accurate a model is. It is most commonly done by comparing output from a model to actual observations. In this paper, we introduce and demonstrate usage of the COAsT Python package to standardise the validation process for physical ocean models. We also discuss our five guiding principles for standardised validation.
Validation is a crucial step during the development of models for ocean simulation. The purpose...