Generating atmospheric forcing perturbations for an ocean data assimilation ensemble
By: Isabelle Mirouze and Andrea Storto
References
- Alves
, O.and Robert, C. 2005. Tropical Pacific Ocean model error covariances from Monte Carlo simulations. Q. J. R. Meteorol. Soc. 131, 3643– 3658. doi: 10.1256/qj.05.113 - Blockley
, E. W., Martin, M. J., McLaren, A. J., Ryan, A. G., Waters, J. and co-authors. 2014. Recent developments of the Met Office operational ocean forecasting system: an overview and assessment of the new Global FOAM forecasts. Geosci. Model Dev. 7, 2613– 2638. doi: 10.5194/gmd-7-2613-2014 - Brankart
, J. M., Candille, G., Garnier, F., Calone, C., Melet, A. and co-authors. 2015. A generic approach to explicit simulation of uncertainty in the NEMO ocean model. Geosci. Model Dev. 8, 1285– 1297. doi: 10.5194/gmd-8-1285-2015 - Brankart
, J. M., Testut, C. E., Béal, D., Doron, M., Fontana, C. and co-authors. 2012. Towards an improved description of ocean uncertainties: effect of local anamorphic transformations on spatial correlations. Ocean Sci. 8, 121– 142. doi: 10.5194/os-8-121-2012 - Brusdal
, L., Brankart, J. M., Halberstadt, G., Evensen, G., Brasseur, P. and co-authors. 2003. A demonstration of ensemble-based assimilation methods with a layered OGCM from the perspective of operational ocean forecasting systems. J. Mar. Syst. 40–41, 253– 289. doi: 10.1016/S0924-7963(03)00021-6 - Buehner
, M. 2005. Ensemble-derived stationary and flow-dependent background-error covariances: evaluation in a quasi-operational NWP setting. Q. J. R. Meteorol. Soc. 131, 1013– 1043. doi: 10.1256/qj.04.15 - Clayton
, A. M., Lorenc, A. C.and Barker, D. M. 2013. Operational implementation of a hybrid ensemble/4D-Var global data assimilation system at the Met Office. Q. J. R. Meteorol. Soc. 139, 1445– 1461. doi: 10.1002/qj.2054 - Daget
, N., Weaver, A. T.and Balmaseda, M. A. 2009. Ensemble estimation of background-error variances in a three-dimensional variational data assimilation system for the global ocean. Q. J. R. Meteorol. Soc., 1071– 1094. doi: 10.1002/qj.412 - D’Agostino
, R. B. 1970. Transformation to normality of the null distribution of g1. Biometrika, 679– 681. - Dee
, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P. and co-authors. 2011. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553– 597. doi: 10.1002/qj.828 - Evensen
, G. 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. 99, 10143– 10162. doi: 10.1029/94JC00572 - Evensen
, G. 2003. The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn., 343– 367. doi: 10.1007/s10236-003-0036-9 - Harrison
, M. J., Rosati, A., Soden, B. J., Galanti, E.and Tziperman, E. 2002. An evaluation of air-sea flux products for ENSO simulation and prediction. Mon. Weather Rev. 130, 723– 732. doi: 10.1175/1520-0493(2002)130<;0723:AEOASF>2.0.CO;2 - Kalnay
, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D. and co-authors. 1996. The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc. 77, 437– 471. doi: 10.1175/1520-0477(1996)077<;0437:TNYRP>2.0.CO;2 - Large
, W. G.and Yeager, S. G. 2004. Diurnal to decadal global forcing for ocean and sea-ice models: the data sets and flux climatologies, NCAR/TN-460, Boulder, USA. - Lorenc
, A. 2003. The potential of the ensemble Kalman filter for NWP - a comparison with 4D-Var. Q. J. R. Meteorol. Soc. 129, 3183– 3203. doi: 10.1256/qj.02.132 - Milliff
, R. F., Bonazzi, A., Wikle, C. K., Pinardi, N.and Berliner, L. M. 2011. Ocean ensemble forecasting. Part I: ensemble Mediterranean winds from a Bayesian hierarchical model. Q. J. R. Meteorol. Soc. 137, 858– 878. doi: 10.1002/qj.767 - Oddo
, P., Storto, A., Dobricic, S., Russo, A., Lewis, C. and co-authors. 2016. A hybrid variational-ensemble data assimilation scheme with systematic error correction for limited-area ocean models. Ocean Sci. 12, 1137– 1153. doi: 10.5194/os-12-1137-2016 - Penny
, S. G., Behringer, D. W., Carton, J. A.and Kalnay, E. 2015. A hybrid global ocean data assimilation system at NCEP. Mon. Weather Rev. 143, 4660– 4677. doi: 10.1175/MWR-D-14-00376.1 - Pinardi
, N., Bonazzi, A., Dobricic, S., Milliff, R. F., Wikle, C. K. and co-authors. 2011. Ocean ensemble forecasting. Part II: mediterranean forecast system response. Q. J. R. Meteorol. Soc. 137, 879– 893. doi: 10.1002/qj.816 - Rienecker
, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J. and co-authors. 2011. MERRA: NASA’s modern-era retrospective analysis for research and applications. J. Clim. 24, 3624– 3648. doi: 10.1175/JCLI-D-11-00015.1 - Smirnov
, N. V. 1939. On the estimation of the discrepancy between empirical curves of distribution for two independent samples. Bull. Math. Univ. Moscow. 2, 3– 11. - Storto
, A., Masina, S.and Dobricic, S. 2013. Ensemble spread-based assessment of observation impact: application to a global ocean analysis system. Q. J. R. Meteorol. Soc. 139, 1842– 1862. doi: 10.1002/qj.2071 - Storto
, A., Masina, S.and Navarra, A. 2016. Evaluation of the CMCC eddy-permitting global ocean physical reanalysis system (C-GLORS, 1982-2012) and its assimilation components. Q. J. R. Meteorol. Soc. 142, 738– 758. doi: 10.1002/qj.2673 - Storto
, A., Oddo, P., Cipollone, A., Mirouze, I.and Lemieux, D. B. 2018. Extending an oceanographic variational scheme to allow for affordable hybrid and four-dimensional data assimilation. Ocean Model, 67– 86. doi: 10.1016/j.ocemod.2018.06.005 - Storto
, A.and Randriamampianina, R. 2010. Ensemble variational assimilation for the representation of background error covariances in a high-latitude regional model. J. Geophys. Res, 1–21. doi: 10.1029/2009JD013111. - Toth
, Z.and Kalnay, E. 1997. Ensemble forecasting at NCEP and the breeding method. Mon. Weather Rev. 125, 3297– 3319. doi: 10.1175/1520-0493(1997)125<;3297:EFANAT>2.0.CO;2 - Turner
, M. R. J., Walker, J. P.and Oke, P. R. 2008. Ensemble member generation for sequential data assimilation. Remote Sens. Environ. 112, 1421– 1433. doi: 10.1016/j.rse.2007.02.042 - Whitaker
, J. S.and Hamill, T. M. 2002. Ensemble data assimilation without perturbed observations. Mon. Weather Rev. 130, 1913– 1924. doi: 10.1175/1520-0493(2002)130<;1913:EDAWPO>2.0.CO;2 - Wittenberg
, A. T. 2002. ENSO response to altered climates. PhDthesis, Princeton University. - Zhang
, S., Harrison, M. J., Wittenberg, A. T., Rosati, A., Anderson, J. L. and co-authors. 2005. Initialization of an ENSO forecast system using a parallelized ensemble filter. Mon. Weather Rev., 3176– 3201. doi: 10.1175/MWR3024.1 - Zupanski
, M., Fletcher, S. J., Navon, I. M., Uzunoglu, B., Heikes, R. P. and co-authors. 2006. Initiation of ensemble data assimilation. Tellus A, 159– 170. doi: 10.1111/j.1600-0870.2006.00173.x
DOI: https://doi.org/10.1080/16000870.2019.1624459 | Journal eISSN: 3035-9554
Language: English
Page range: 1624459 - 1624459
Published on: Jan 1, 2019
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services
Keywords:
© 2019 Isabelle Mirouze, Andrea Storto, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.