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Long range daily ocean forecasts in the Indo-Pacific oceans with ACCESS-S1 Cover

Long range daily ocean forecasts in the Indo-Pacific oceans with ACCESS-S1

By:  and    
Open Access
|Jan 2021

References

  1. Balmaseda, M. A. , Mogensen, K. and Weaver, A. T. 2013. Evaluation of the ECMWF ocean reanalysis system ORAS4. Q. J. R. Meteorol. Soc. 139 , 11321161. doi:10.1002/qj.2063
  2. Bell, M. J. , Lefèbvre, M. , Le Traon, P.-Y. , Smith, N. and Wilmer-Becker, K. 2009. GODAE: the global ocean data assimilation experiment. Oceanography. 22 , 1421. doi:10.5670/oceanog.2009.62
  3. Blockley, E. W. , Martin, M. J. , McLaren, A. J. , Ryan, A. G. , Waters, J. and co-authors. 2014. Recent development of the Met Office operational ocean forecasting system: An overview and assessment of the new global FOAM forecasts. Geosci. Model Dev. 7 , 26132638. doi:10.5194/gmd-7-2613-2014
  4. Bowler, N. , Arribas, A. , Beare, S. , Mylne, K. E. and Shutts, G. 2009. The local ETKF and SKEB: upgrades to the MOGREPS short-range ensemble prediction system. Q. J. R. Meteorol. Soc. 135 , 767776. doi:10.1002/qj.394
  5. Brassington, G. B. , Freeman, J. , Huang, X. , Pugh, T. , Oke, P. R. and co-authors. 2012. Ocean model, analysis and prediction system (OceanMAPS): version 2. CAWCR Technical Report 52, 110 pp. A research partnership between CSIRO and the Australian Bureau of Meteorology. Australia.
  6. Donlon, C. J. , Martin, M. , Stark, J. , Roberts-Jones, J. , Fiedler, E. and co-authors. 2012. The operational sea surface temperature and sea ice analysis (OSTIA) system. Remote Sens. Environ. 116 , 140158. doi:10.1016/j.rse.2010.10.017
  7. Fortin, V. , Abaza, M. , Anctil, F. and Turcotte, R. 2014. Why should ensemble spread match the RMSE of the ensemble mean? J. Hydrometeor. 15 , 17081713.. doi:10.1175/JHM-D-14-0008.1
  8. Fujii, Y. , Remy, E. , Zuo, H. , Oke, P. R. , Halliwell, G. R. and co-authors. 2019. Observing system evaluation based on ocean data assimilation and prediction systems: on-going challenges and future vision for designing/supporting ocean observational networks. Front. Mar. Sci. 6 , 417. doi:10.3389/fmars.2019.00417
  9. Hernandez, F. , Blockley, E. , Brassington, G. B. , Davidson, F. , Divakaran, P. and co-authors. 2015. Recent progress in performance evaluations and near real-time assessment of operational ocean products. J. Oper. Oceanogr. 8 , s221s238.
  10. Hudson, D. , Alves, O. , Hendon, H. H. , Lim, E. , Liu, G. and co-authors. 2017. ACCESS-S1: The new bureau of meteorology multi-week to seasonal prediction system. J. South. Hemisphere Earth Syst. Sci. 67 , 132159. doi:10.22499/3.6703.001
  11. Large, W. G. and Yeager, S. G. 2009. The global climatology of an interannually varying air–sea flux data set. Clim. Dyn. 33 , 341364.. doi:10.1007/s00382-008-0441-3
  12. MacLachlan, C. , Arribas, A. , Peterson, K. A. , Maidens, A. , Fereday, D. and co-authors. 2014. Global seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system. Q. J. R. Meteor. Soc. 141:10721084.
  13. Madec, G. and the NEMO team 2016. NEMO ocean engine. Technical Report Note du Pole de od’Elisation No 27, ISSN No 1288-1619, Institut Pierre-Simon Laplace (IPSL), France.
  14. Martin, M. 2011. Ocean forecasting systems: product evaluation and skill. In Operational Oceanography in the 21st Century . Springer Science + Business Media B. V., pp. 611632.
  15. Martin, M. J. , Hines, A. and Bell, M. J. 2007. Data assimilation in the FOAM operational short-range ocean forecasting system: A description of the scheme and its impact. Q. J. R. Meteorol. Soc. 133 , 981995. doi:10.1002/qj.74
  16. Meehl, G. A. , Goddard, L. , Boer, G. , Burgman, R. , Branstator, G. and co-authors. 2014. Decadal climate prediction: An update from the trenches. Bull. Amer. Meteor. Soc. 95 , 243267. doi:10.1175/BAMS-D-12-00241.1
  17. Megann, A. , Storkey, D. , Aksenov, Y. , Alderson, S. , Calvert, D. and co-authors. 2014. GO5.0: The joint NERC–Met Office NEMO global ocean model for use in coupled and forced applications. Geosci. Model. Dev. 7 , 10691092. doi:10.5194/gmd-7-1069-2014
  18. Pujol, M.-I. , Faugère, Y. , Taburet, G. , Dupuy, S. , Pelloquin, C. and co-authors. 2016. The new multi-mission altimeter data set reprocessed over 20 years. Ocean Sci. 12 , 10671090.. doi:10.5194/os-12-1067-2016
  19. Rae, J. G. L. , Hewitt, H. T. , Keen, A. B. , Ridley, J. K. , West, A. E. and co-authors. 2015. Development of the Global Sea Ice 6.0 CICE configuration for the Met Office Global Coupled Model. Geosci. Model. Dev. 8 , 22212230.. doi:10.5194/gmd-8-2221-2015
  20. Reynolds, R. W. , Smith, T. M. , Liu, C. , Chelton, D. B. , Casey, K. S. and co-authors. 2007. Daily high-resolution blended analyses for sea surface temperature. J. Clim. 20 , 54735496.. doi:10.1175/2007JCLI1824.1
  21. Schiller, A. , Brassington, G. B. , Oke, P. , Cahill, M. , Divakaran, P. and co-authors. 2020. Bluelink ocean forecasting Australia: 15 years of operational ocean service delivery with societal, economic and environmental benefits. J. Oper. Oceanogr. 13 , 118.
  22. Storto, A. , Alvera-Azcárate, A. , Balmaseda, M. A. , Barth, A. , Chevallier, M. and co-authors. 2019. Ocean reanalyses: recent advances and unsolved challenges. Front. Mar. Sci. 6 , 418. doi:10.3389/fmars.2019.00418
  23. Usui, N. , Ishizaki, S. , Fujii, Y. , Tsujino, H. , Yasuda, T. and co-authors. 2006. Meteorological research institute multivariate ocean variational estimation (MOVE) system: some early results. Adv. Space Res. 37 , 806822. 022 doi:10.1016/j.asr.2005.09.022
  24. Walters, D. , Brooks, M. , Boutle, I. , Melvin, T. , Stratton, R. and co-authors. 2017. The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations. Geosci. Model. Dev. 10 , 14871520.. doi:10.5194/gmd-10-1487-2017
  25. Wang, G. , Hudson, D. , Yin, Y. , Alves, O. , Hendon, H. and co-authors. 2011. POAMA-2 SST skill assessment and beyond. CAWCR Research Letters, No. 6, 40–46, Bureau of Meteorology, Australia.
  26. Waters, J. , Lea, D. J. , Martin, M. J. , Mirouze, I. , Weaver, A. and co-authors. 2015. Implementing a variational data assimilation system in an operational 1/4 degree global ocean model. Q. J. R. Meteorol. Soc. 141 , 333349. doi:10.1002/qj.2388
  27. Wilkin, J. L. and Hunter, E. J. 2013. An assessment of the skill of realtime models of Mid-Atlantic Bight continental shelf circulation. J. Geophys. Res. Oceans 118 , 29192933. 1002/jgrc.20223 doi:10.1002/jgrc.20223
  28. Williams, K. D. , Harris, C. M. , Bodas-Salcedo, A. , Camp, J. , Comer, R. E. and co-authors. 2015. The Met Office Global Coupled model 2.0 (GC2) configuration. Geosci. Model. Dev. 8 , 15091524. doi:10.5194/gmd-8-1509-2015
  29. Woodham, R. H. , Alves, O. , Brassington, G. B. , Robertson, R. and Kiss, A. 2015. Evaluation of ocean forecast performance for royal Australian navy exercise areas in the Tasman Sea. J. Oper Oceanogr. 8 , 147161.
  30. Zhou, X. , Luo, J. , Alves, O. and Hendon, H. 2015. Comparison of GloSea5 and POAMA2.4 Hindcasts 1996-2009: Ocean Focus. Bureau Research Report-010.
Language: English
Page range: 1870328 - 1870328
Published on: Jan 1, 2021
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2021 Xiaobing Zhou, Oscar Alves, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.