Conditions for successful data assimilation

Chorin, Alexandre J.; Morzfeld, Matthias

doi:10.1002/2013jd019838

Cited by 37 publications

(62 citation statements)

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“…The present paper complements the work in Chorin and Morzfeld [23], where it was shown that, in physically reasonable vector-valued data assimilation problems, the various one-time components of the noise must be correlated; in particular, they must be spatially correlated if the several components describe physical variables estimated at different spatial points. In the present paper we show that the noise components must be correlated in time as well.…”

Section: Discussionsupporting

confidence: 59%

Estimating the uncertainty in underresolved nonlinear dynamics

Chorin¹,

Hald

2013

Mathematics and Mechanics of Solids

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Section: Discussionsupporting

confidence: 59%

Estimating the uncertainty in underresolved nonlinear dynamics

Chorin¹,

Hald

2013

Mathematics and Mechanics of Solids

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“…On the other hand, gradient-15 based methods are more prone to getting stuck in local minima and, furthermore, they require the complex calculation of the gradient of the cost function with respect to all parameters. With idealised models, Chorin and Morzfeld (2013) have shown that the assimilation can be optimal with particle filters or variational methods, under the condition that the dimension of the problem is not excessive. The choice of the minimisation method was shown to have little impact on the overall optimisation efficiency for relatively simple ecosystem models (Trudinger et al, 2007;Fox et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Land surface model parameter optimisation using in-situ flux data: comparison of gradient-based versus random search algorithms

Bastrikov

MacBean

Bacour

et al. 2018

Preprint

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Abstract. Land surface models (LSMs), used within earth system models, rely on numerous processes for describing carbon, water and energy budgets, often associated with highly uncertain parameters. Data assimilation (DA) is a useful approach for optimising the most critical parameters in order to improve model accuracy and refine future climate predictions. In this study, we compare two different DA methods for optimising the parameters of seven plant functional types (PFTs) of the 15 ORCHIDEE land surface model using daily averaged eddy-covariance observations of net ecosystem exchange and latent heat flux at 78 sites across the globe. We perform a technical investigation of two classes of minimisation methods -local gradient-based (the L-BFGS-B algorithm) and global random search (the genetic algorithm) -by evaluating their relative performance in terms of the model-data fit and the difference in retrieved parameter values. We examine the performance of each method for two cases: when optimising parameters at each site independently (-single-site‖ approach) and when 20 simultaneously optimising the model at all sites for a given PFT using a common set of parameters (-multi-site‖ approach).We find that for the single site case the random search algorithm results in lower values of the cost function (i.e. lower model -data root mean square differences) than the gradient-based method; the difference between the two methods is smaller for the multi-site optimisation due to a smoothing of the cost function shape with a greater number of observations. The spread of the cost function, when performing the same tests with 16 random first guess parameters, is much larger with the gradient 25 based method, due to the higher likelihood of being trap in local minima. When using pseudo-observations tests the genetic algorithm results in a closer approximation of the true posterior parameter value in the L-BFGS-B algorithm. We demonstrate the advantages and challenges of different DA techniques and provide some advice on using it for the LSM parameters optimisation.Geosci. Model Dev. Discuss., https://doi

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“…We note here that Chorin and Morzfeld (2013) have investigated a different, but related, effective dimension of a Gaussian data assimilation problem. In particular, they define a ''feasibility criterion'' to be the Frobenius norm of the steady-state posterior covariance matrix (which can be exactly calculated in the linear, Gaussian regime.)…”

Section: Introductionmentioning

confidence: 99%

Exploring Practical Estimates of the Ensemble Size Necessary for Particle Filters

Slivinski

Snyder

2016

Monthly Weather Review

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Particle filtering methods for data assimilation may suffer from the ''curse of dimensionality,'' where the required ensemble size grows rapidly as the dimension increases. It would, therefore, be useful to know a priori whether a particle filter is feasible to implement in a given system. Previous work provides an asymptotic relation between the necessary ensemble size and an exponential function of t 2 , a statistic that depends on observationspace quantities and that is related to the system dimension when the number of observations is large; for linear, Gaussian systems, the statistic t 2 can be computed from eigenvalues of an appropriately normalized covariance matrix. Tests with a low-dimensional system show that these asymptotic results remain useful when the system is nonlinear, with either the standard or optimal proposal implementation of the particle filter. This study explores approximations to the covariance matrices that facilitate computation in high-dimensional systems, as well as different methods to estimate the accumulated system noise covariance for the optimal proposal. Since t 2 may be approximated using an ensemble from a simpler data assimilation scheme, such as the ensemble Kalman filter, the asymptotic relations thus allow an estimate of the ensemble size required for a particle filter before its implementation. Finally, the improved performance of particle filters with the optimal proposal, relative to those using the standard proposal, in the same low-dimensional system is demonstrated.

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Conditions for successful data assimilation

Cited by 37 publications

References 50 publications

Estimating the uncertainty in underresolved nonlinear dynamics

Estimating the uncertainty in underresolved nonlinear dynamics

Land surface model parameter optimisation using in-situ flux data: comparison of gradient-based versus random search algorithms

Exploring Practical Estimates of the Ensemble Size Necessary for Particle Filters

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