A multiple length scale correlation operator for ocean data assimilation
References
, , , , , co-authors. Recent developments of the Met Office operational ocean forecasting system: an overview and assessment of the new Global FOAM forecasts. Geosci. Model. Dev. 2014; 7: 2613– 2638. . Dual formulation of four-dimensional variational assimilation. Q. J. Roy. Meteorol. Soc. 1997; 123: 2449– 2462. , , . Ensemble estimation of background-error variances in a three-dimensional variational data assimilation system for the global ocean. Q. J. Roy. Meteorol. Soc. 2009; 135: 1071– 1094. . Atmospheric Data Analysis. 1991; Cambridge University Press, Cambridge. , . A reformulation of the background error covariance in the ECMWF global data assimilation system. Tellus A. 1999; 51: 195– 221. , . A global oceanic data assimilation system. J. Phys. Oceanogr. 1989; 19: 1333– 1347. , . An oceanographic three-dimensional variational data assimilation scheme. Ocean Model. 2008; 22: 89– 105. , , . Topex/Poseidon tides estimated using a global inverse model. J. Geophys. Res. 1994; 99: 24821– 24852. , . Construction of correlation functions in two and three dimensions. Q. J. Roy. Meteorol. Soc. 1999; 125: 723– 757. . Compactly supported correlation functions. J. Multivariate. Anal. 2002; 83: 493– 508. , , . EN4: quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates. J. Geophys. Res. Oceans. 2013; 118: 6704– 6716. , . Table of Integrals, Series, and Products. 8th ed. 2014; Academic Press, San Diego. , . An observation-space formulation of variational assimilation using a restricted preconditioned conjugate gradient algorithm. Q. J. Roy. Meteorol. Soc. 2009; 135: 1573– 1585. , , , . On potentially negative space time covariances obtained as sum of products of marginal ones. Ann. Inst. Stat. Math. 2008; 60: 865– 882. , , , , , co-authors. B-preconditioned minimization algorithms for variational data assimilation with the dual formulation. Q. J. Roy. Meteorol. Soc. 2014; 140: 539– 556. . Spartan Gibbs random field models for geostatistical applications. SIAM J. Sci. Comput. 2003; 24: 2125– 2162. , . CICE: The Los Alamos Sea Ice Model. 2010. Documentation and software users manual, Version 4.1 (LA-CC-06012), T-3 Fluid Dynamics Group, Los Alamos National Laboratory, Los Alamos. , , . An optimal definition for ocean mixed layer depth. J. Geophys. Res. 2000; 105: 16803– 16821. , , , . Impacts of distinct observations during the 2009 Prince William Sound field experiment: a data assimilation study. Cont. Shelf Res. 2013; 63: 209– 222. , , , . A multiscale variational data assimilation scheme: formulation and illustration. Mon. Weather. Rev. 2015; 143: 3804– 3822. . Optimal nonlinear objective analysis. Q. J. Roy. Meteorol. Soc. 1988; 114: 205– 240. . Iterative analysis using covariance functions and filters. Q. J. Roy. Meteorol. Soc. 1992; 118: 569– 591. . Linear combinations of space-time covariance functions and variograms. IEEE. Trans. Signal Process. 2005; 53: 857– 864. . NEMO Ocean Engine. 2008. Note du Pole de modélisation No 27 ISSN No 1288-1619, Institut Pierre-Simon Laplace, Paris. , , . Data assimilation in the FOAM operational short-range ocean forecasting system: a description of the scheme and its impact. Q. J. Roy. Meteorol. Soc. 2007; 133: 981– 995. , . Representation of correlation functions in variational assimilation using an implicit diffusion operator. Q. J. Roy. Meteorol. Soc. 2010; 136: 1421– 1443. , , . An examination of a multi-scale three-dimensional variational data assimilation scheme in the Kuroshio Extension using the naval coastal ocean model. Cont. Shelf. Res. 2014; 73: 41– 48. , . The National Meteorological Center's spectral statistical interpolation analysis system. Mon. Weather. Rev. 1992; 120: 1747– 1763. , , , . Numerical aspects of the application of recursive filters to variational statistical analysis. part I: spatially homogeneous and isotropic Gaussian covariances. Mon. Weather. Rev. 2003a; 131: 1524– 1535. , , , . Numerical aspects of the application of recursive filters to variational statistical analysis. part II: spatially inhomogeneous and anisotropic general. Covariances. Mon. Weather. Rev. 2003b; 131: 1536– 1548. . Interpolation of Spatial Data. Some Theory for Kriging. 1999; Springer, NewYork. , , , , , co-authors. Forecasting the ocean state using NEMO: the new FOAM system. J. Oper. Oceanogr. 2010; 3: 3– 15. , , , , , co-authors. Status and future of global and regional ocean prediction systems. J. Oper. Oceanogr. 2015; 8: 201– 220.. , . Statistical Analysis in Climate Research. 1999; Cambridge University Press, Cambridge. , , , , , co-authors. Implementing a variational data assimilation system in an operational 1/4 degree global ocean model. Q. J. Roy. Meteorol. Soc. 2015; 141: 333– 349. , . Correlation modelling on the sphere using a generalized diffusion equation. Q. J. Roy. Meteorol. Soc. 2001; 127: 1815– 1846. , , , , . A multivariate balance operator for variational ocean data assimilation. Q. J. Roy. Meteorol. Soc. 2005; 131: 3605– 3625. , . On the diffusion equation and its application to isotropic and anisotropic correlation modelling in variational assimilation. Q. J. Roy. Meteorol. Soc. 2013; 139: 242– 260. , , . A space-time multiscale analysis system: a sequential variational analysis approach. Mon. Weather. Rev. 2011; 139: 1224– 1240.
DOI: https://doi.org/10.3402/tellusa.v68.29744 | Journal eISSN: 3035-9554
Language: English
Page range: 29744 - 29744
Submitted on: Sep 13, 2015
Accepted on: Feb 2, 2016
Published on: Dec 1, 2016
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services
Keywords:
© 2016 Isabelle Mirouze, Edward W. Blockley, Daniel J. Lea, Matthew J. Martin, Michael J. Bell, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.