Articles | Volume 11, issue 10
https://doi.org/10.5194/gmd-11-4011-2018
https://doi.org/10.5194/gmd-11-4011-2018
Methods for assessment of models
 | 
05 Oct 2018
Methods for assessment of models |  | 05 Oct 2018

Data assimilation cycle length and observation impact in mesoscale ocean forecasting

Paul Sandery

Related subject area

Oceanography
Barotropic tides in MPAS-Ocean (E3SM V2): impact of ice shelf cavities
Nairita Pal, Kristin N. Barton, Mark R. Petersen, Steven R. Brus, Darren Engwirda, Brian K. Arbic, Andrew F. Roberts, Joannes J. Westerink, and Damrongsak Wirasaet
Geosci. Model Dev., 16, 1297–1314, https://doi.org/10.5194/gmd-16-1297-2023,https://doi.org/10.5194/gmd-16-1297-2023, 2023
Short summary
Using the two-way nesting technique AGRIF with MARS3D V11.2 to improve hydrodynamics and estimate environmental indicators
Sébastien Petton, Valérie Garnier, Matthieu Caillaud, Laurent Debreu, and Franck Dumas
Geosci. Model Dev., 16, 1191–1211, https://doi.org/10.5194/gmd-16-1191-2023,https://doi.org/10.5194/gmd-16-1191-2023, 2023
Short summary
Multidecadal and climatological surface current simulations for the southwestern Indian Ocean at 1∕50° resolution
Noam S. Vogt-Vincent and Helen L. Johnson
Geosci. Model Dev., 16, 1163–1178, https://doi.org/10.5194/gmd-16-1163-2023,https://doi.org/10.5194/gmd-16-1163-2023, 2023
Short summary
The tidal effects in the Finite-volumE Sea ice–Ocean Model (FESOM2.1): a comparison between parameterised tidal mixing and explicit tidal forcing
Pengyang Song, Dmitry Sidorenko, Patrick Scholz, Maik Thomas, and Gerrit Lohmann
Geosci. Model Dev., 16, 383–405, https://doi.org/10.5194/gmd-16-383-2023,https://doi.org/10.5194/gmd-16-383-2023, 2023
Short summary
HIDRA2: deep-learning ensemble sea level and storm tide forecasting in the presence of seiches – the case of the northern Adriatic
Marko Rus, Anja Fettich, Matej Kristan, and Matjaž Ličer
Geosci. Model Dev., 16, 271–288, https://doi.org/10.5194/gmd-16-271-2023,https://doi.org/10.5194/gmd-16-271-2023, 2023
Short summary

Cited articles

Atlas, R., Hoffman, R. N., Ardizzone, J., Leidner, S. M., Jusem, J. C., Smith, D. K., and Gombos, D.: A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications, B. Am. Meteorol. Soc., 92, Supplement, https://doi.org/10.1175/2010BAMS2946.2, 2011. a
Bertino, L., Lisæter, K., and Scient, S.: The TOPAZ monitoring and prediction system for the Atlantic and Arctic Oceans, J. Oper. Oceanogr., 1, 15–18, 2008. a
Brassington, G. B.: Multicycle ensemble forecasting of sea surface temperature, Geophys. Res. Lett., 40, 6191–6195, 2013. a
Brassington, G. B., Pugh, T., Spillman, C., Schulz, E., Beggs, H., Schiller, A., and Oke, P. R.: BLUElink> Development of Operational Oceanography and Servicing in Australia, J. Res. Pract. Inf. Tech., 39, 151–164, 2007. a
Chassignet, E. P., Hurlburt, H. E., Metzger, E. J., Smedstad, O. M., Cummings, J. A., Halliwell, G. R., Bleck, R., Baraille, R., Wallcraft, A. J., Lozano, C., et al.: US GODAE: global ocean prediction with the HYbrid Coordinate Ocean Model (HYCOM), Tech. rep., DTIC Document, 2009. a
Download
Short summary
This article compares global mesoscale ocean forecasts with different data assimilation cycle lengths. Mean absolute increment is used to quantify differences in the overall impact of observations. Greater observation impact does not necessarily improve a forecast system. Experiments show a 1-day cycle generates improved 7-day forecasts when compared to a 3-day cycle. Cycle length is an important choice that influences system bias and predictability.