Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation

By Marc Bocquet, Carlos A. L. Pires, Lin Wu. 2010

249 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/37431e43916debc8b1c11dd21bebe04cbb258dc9

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Citations in the corpus

Observation errors in all‐sky data assimilation (2011, background): “However, the radiances can exhibit substantial non-Gaussian behaviour (e.g. Errico et al., 2007; Bocquet et al., 2010) which, if left uncorrected, could degrade the quality of the analysis.”
An iterative ensemble Kalman smoother (2014, methodology): “One solution would be to abandon the Gaussian methods and opt for fully non-Gaussian ones such as particle filters (van Leeuwen, 2009; Bocquet et al., 2010).”
Gaussian anamorphosis extension of the DEnKF for combined state parameter estimation: Application to a 1D ocean ecosystem model (2012, background): “More information concerning the more general problem of Gaussian statistical modeling in data assimilation can be found in Bocquet et al. (2010).”
An exploration of the equivalent weights particle filter (2013, methodology): “Bocquet et al. (2010) also study the optimal proposal density and show how it outperforms the SIR filter on a Lorenz (1995) example for a small ensemble size.”
Ensemble Kalman filtering without the intrinsic need for inflation (2011, background): “As argued in the introduction this assumption leads to an approximation, since the ensemble members are rather samples of a (more or less) non-Gaussian distribution (Bocquet et al., 2010; Lei et al., 2010).”
Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, background): “The number of contributions to the topic has substantially grown in geophysical DA in recent years (Bocquet et al., 2010; van Leeuwen, 2009).”
A consistent interpretation of the stochastic version of the Ensemble Kalman Filter (2020, methodology): “Burgers et al. (1998) argue that the missing term is due to the fact that each ensemble member is updated with the same observation, leading them to suggest a perturbed observation scheme. Perturbing observations in an EnKF scheme was first advocated by Houtekamer and Mitchell (1998), using a Monte-”
Data assimilation: making sense of Earth Observation (2014, background): “…focused on trying to ensure that, whilst the ensemble of model runs still represents the prior knowledge of the system, they also represent samples from the high probability region of the posterior distribution (Chorin and Tu, 2009; Bocquet et al., 2010; Chorin et al., 2010; Morzfeld et al., 2012).”
A local particle filter for high-dimensional geophysical systems (2015, unknown): “Bocquet et al. (2010) further review the difficulties using observations with non-Gaussian error distributions.”
Improved variational analyses using a nonlinear humidity control variable (2013, background): “Bocquet et al. (2010) review different data assimilation techniques for dealing with non-Gaussian features including use of log-normal error distributions, c© 2012 British Crown copyright, the Met Office.”