Ensemble methods in geophysical sciences
Ensemble methods in geophysical sciences
Editor(s): O. Talagrand, R. Buizza, G. Desroziers, T. Gneiting, and P. J. van Leeuwen

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23 Sep 2014
Representing model error in ensemble data assimilation
C. Cardinali, N. Žagar, G. Radnoti, and R. Buizza
Nonlin. Processes Geophys., 21, 971–985, https://doi.org/10.5194/npg-21-971-2014,https://doi.org/10.5194/npg-21-971-2014, 2014
25 Aug 2014
A non-Gaussian analysis scheme using rank histograms for ensemble data assimilation
S. Metref, E. Cosme, C. Snyder, and P. Brasseur
Nonlin. Processes Geophys., 21, 869–885, https://doi.org/10.5194/npg-21-869-2014,https://doi.org/10.5194/npg-21-869-2014, 2014
14 Jul 2014
Four-dimensional ensemble variational (4D-En-Var) data assimilation for the HIgh Resolution Limited Area Model (HIRLAM)
N. Gustafsson and J. Bojarova
Nonlin. Processes Geophys., 21, 745–762, https://doi.org/10.5194/npg-21-745-2014,https://doi.org/10.5194/npg-21-745-2014, 2014
11 Jun 2014
Assimilation of HF radar surface currents to optimize forcing in the northwestern Mediterranean Sea
J. Marmain, A. Molcard, P. Forget, A. Barth, and Y. Ourmières
Nonlin. Processes Geophys., 21, 659–675, https://doi.org/10.5194/npg-21-659-2014,https://doi.org/10.5194/npg-21-659-2014, 2014
28 May 2014
Monte Carlo fixed-lag smoothing in state-space models
A. Cuzol and E. Mémin
Nonlin. Processes Geophys., 21, 633–643, https://doi.org/10.5194/npg-21-633-2014,https://doi.org/10.5194/npg-21-633-2014, 2014
25 Apr 2014
An ETKF approach for initial state and parameter estimation in ice sheet modelling
B. Bonan, M. Nodet, C. Ritz, and V. Peyaud
Nonlin. Processes Geophys., 21, 569–582, https://doi.org/10.5194/npg-21-569-2014,https://doi.org/10.5194/npg-21-569-2014, 2014
14 Mar 2014
Controlling balance in an ensemble Kalman filter
G. A. Gottwald
Nonlin. Processes Geophys., 21, 417–426, https://doi.org/10.5194/npg-21-417-2014,https://doi.org/10.5194/npg-21-417-2014, 2014
12 Mar 2014
Provision of boundary conditions for a convection-permitting ensemble: comparison of two different approaches
C. Marsigli, A. Montani, and T. Paccagnella
Nonlin. Processes Geophys., 21, 393–403, https://doi.org/10.5194/npg-21-393-2014,https://doi.org/10.5194/npg-21-393-2014, 2014
26 Feb 2014
A hybrid variational ensemble data assimilation for the HIgh Resolution Limited Area Model (HIRLAM)
N. Gustafsson, J. Bojarova, and O. Vignes
Nonlin. Processes Geophys., 21, 303–323, https://doi.org/10.5194/npg-21-303-2014,https://doi.org/10.5194/npg-21-303-2014, 2014
05 Feb 2014
Diagnostics on the cost-function in variational assimilations for meteorological models
Y. Michel
Nonlin. Processes Geophys., 21, 187–199, https://doi.org/10.5194/npg-21-187-2014,https://doi.org/10.5194/npg-21-187-2014, 2014
08 Jan 2014
Representation of model error in a convective-scale ensemble prediction system
L. H. Baker, A. C. Rudd, S. Migliorini, and R. N. Bannister
Nonlin. Processes Geophys., 21, 19–39, https://doi.org/10.5194/npg-21-19-2014,https://doi.org/10.5194/npg-21-19-2014, 2014
28 Nov 2013
A potential implicit particle method for high-dimensional systems
B. Weir, R. N. Miller, and Y. H. Spitz
Nonlin. Processes Geophys., 20, 1047–1060, https://doi.org/10.5194/npg-20-1047-2013,https://doi.org/10.5194/npg-20-1047-2013, 2013
26 Nov 2013
The local ensemble transform Kalman filter and the running-in-place algorithm applied to a global ocean general circulation model
S. G. Penny, E. Kalnay, J. A. Carton, B. R. Hunt, K. Ide, T. Miyoshi, and G. A. Chepurin
Nonlin. Processes Geophys., 20, 1031–1046, https://doi.org/10.5194/npg-20-1031-2013,https://doi.org/10.5194/npg-20-1031-2013, 2013
22 Nov 2013
Parameter variations in prediction skill optimization at ECMWF
P. Ollinaho, P. Bechtold, M. Leutbecher, M. Laine, A. Solonen, H. Haario, and H. Järvinen
Nonlin. Processes Geophys., 20, 1001–1010, https://doi.org/10.5194/npg-20-1001-2013,https://doi.org/10.5194/npg-20-1001-2013, 2013
08 Nov 2013
Using ensemble data assimilation to forecast hydrological flumes
I. Amour, Z. Mussa, A. Bibov, and T. Kauranne
Nonlin. Processes Geophys., 20, 955–964, https://doi.org/10.5194/npg-20-955-2013,https://doi.org/10.5194/npg-20-955-2013, 2013
23 Oct 2013
Joint state and parameter estimation with an iterative ensemble Kalman smoother
M. Bocquet and P. Sakov
Nonlin. Processes Geophys., 20, 803–818, https://doi.org/10.5194/npg-20-803-2013,https://doi.org/10.5194/npg-20-803-2013, 2013
25 Sep 2013
A mechanism for catastrophic filter divergence in data assimilation for sparse observation networks
G. A. Gottwald and A. J. Majda
Nonlin. Processes Geophys., 20, 705–712, https://doi.org/10.5194/npg-20-705-2013,https://doi.org/10.5194/npg-20-705-2013, 2013
24 Sep 2013
Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction
M. Buehner, J. Morneau, and C. Charette
Nonlin. Processes Geophys., 20, 669–682, https://doi.org/10.5194/npg-20-669-2013,https://doi.org/10.5194/npg-20-669-2013, 2013
11 Sep 2013
The impact of initial spread calibration on the RELO ensemble and its application to Lagrangian dynamics
M. Wei, G. Jacobs, C. Rowley, C. N. Barron, P. Hogan, P. Spence, O. M. Smedstad, P. Martin, P. Muscarella, and E. Coelho
Nonlin. Processes Geophys., 20, 621–641, https://doi.org/10.5194/npg-20-621-2013,https://doi.org/10.5194/npg-20-621-2013, 2013
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