4-D-Var or ensemble Kalman filter?

Kalnay, Eugenia; Li, Hong; Miyoshi, Takemasa; Yang, Shun; Ballabrera-Poy, Joaquim

doi:10.1111/j.1600-0870.2007.00261.x

Cited by 222 publications

(138 citation statements)

References 69 publications

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“…An excellent comparison of the relative merits of EnKF and 4D-Var in the context of NWP has been given by Lorenc (2003) and expanded by Kalnay et al (2005). Hamill (2004) undertakes a theoretical analysis of the two approaches.…”

Section: Comparison Between Enkf and 4d-varmentioning

confidence: 99%

“…The second method is multiplicative inflation (Anderson, 2001), where each member's deviation from the ensemble mean is multiplied by a constant. An 'online' estimation of the inflation constant is possible (Kalnay et al, 2005;Miyoshi, 2005). This increases each entry of the ensemble covariance by the square of that constant.…”

Section: Preventing Filter Divergencementioning

confidence: 99%

“…To our knowledge, this is the first comparison between variational and ensemble data assimilation based on real data. Lorenc (2003), Hamill (2004) and Kalnay et al (2005) discuss theoretically the relative merits of the two methods. They conclude that 4D-Var and EnKF have their own particular advantages and disadvantages, neither being a clear winner, although more research needs to be done at least to assess EnKF's practical merits.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble‐based chemical data assimilation. I: General approach

Constantinescu

Sandu

Chai

et al. 2007

Quart J Royal Meteoro Soc

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Data assimilation is the process of integrating observational data and model predictions to obtain an optimal representation of the state of the atmosphere. As more chemical observations in the troposphere are becoming available, chemical data assimilation is expected to play an essential role in air-quality forecasting, similar to the role it has in numerical weather prediction. Considerable progress has been made recently in the development of variational tools for chemical data assimilation. In this paper we assess the performance of the ensemble Kalman filter (EnKF) and compare it with a state-of-the-art 4D-Var approach. We analyse different aspects that affect the assimilation process, and investigate several ways to avoid filter divergence. Results with a real model and real observations show that EnKF is a promising approach for chemical data assimilation. The results also point to several issues on which further research is necessary.

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Section: Comparison Between Enkf and 4d-varmentioning

confidence: 99%

Section: Preventing Filter Divergencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ensemble‐based chemical data assimilation. I: General approach

Constantinescu

Sandu

Chai

et al. 2007

Quart J Royal Meteoro Soc

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“…A more realistic treatment might be possible by applying a more flexible data-assimilation technique (e.g. Kalnay et al, 2007). An important but unexamined area here is the applicability of the regression-based SDM to precipitation.…”

Section: Remaining Uncertaintymentioning

confidence: 99%

Statistical downscaling with Bayesian inference: Estimating global solar radiation from reanalysis and limited observed data

Iizumi¹,

Nishimori²,

Yokozawa³

et al. 2011

Intl Journal of Climatology

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Daily global solar radiation (SR) is one of essential weather inputs for crop, hydrological, and other simulation models to calculate biomass production and potential evapotranspiration. The availability of long-term observed SR data is, however, limited, especially in developing countries. This hinders climate applications in various sectors in these countries. To overcome this difficulty, we here propose a method to infer the reasonable daily SR condition for past decades from global reanalysis and limited observed SR data. The method consists of the regression-based statistical downscaling method and two empirical models for estimating the SR condition (i.e. the S-model and the DTR/RH-model). These empirical models were independent in terms of the variables explaining the SR condition. The regression models were trained on the basis of the SR conditions estimated by the S-model and the DTR/RH-model instead of the observed SR data. The Markov Chain Monte Carlo (MCMC) technique was applied to determine the parameter values of these models that guide the models to provide SR conditions that are close in value to each other at both the site and domain-mean scales. After that, we computed the SR condition over the 30 years from 1978 through 2007 at 17 sites in the Vietnam Mekong Delta area using the determined parameter values. The inferred SR condition was close in value to the corresponding observations available from the literature. This suggests that the proposed method yielded a reasonable inference of the SR condition at the sites despite the limited availability of observed SR data. The provided estimates of the daily SR condition over the past 30 years are useful for climate applications in agricultural, hydrological, and other sectors in this area.

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“…The deficiencies in the 4D-Var assumptions have also encouraged developments of alternative methods, particularly the ensemble Kalman filter. A discussion of the relative benefits of these approaches is given by Kalnay et al (2007).…”

Section: Introductionmentioning

confidence: 99%

A demonstration of 4D‐Var using a time‐distributed background term

Cullen

2010

Quart J Royal Meteoro Soc

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The standard four-dimensional variational data assimilation (4D-Var) method used at several major operational centres minimises a cost function which consists of a background term evaluated at the start of the assimilation window and a sum of observation terms evaluated throughout the assimilation window. This method can be interpreted as a smoother in which the background term is evolved in time using the forecast model, and the observations assimilated sequentially. However, in operational practice, the initial estimate of the background error is climatological and highly simplified, so that it is not clear whether this interpretation is useful. We thus interpret the background term as a regularisation term, rather than as an estimate of the true background error. We demonstrate this approach using a toy model whose solutions contain two time-scales: a slow scale which can be accurately predicted and a fast scale where the model errors are too large for deterministic prediction to be possible. This represents a common situation in operational predictions. We show that, if the regularisation term is only evaluated at the beginning of the assimilation window, as in standard 4D-Var, it is less effective at controlling rapidly growing modes. This can be resolved by applying the regularisation with equal weight throughout the window. The effect is that, in poorly observed situations, better forecasts of the slow modes can be obtained by directing information from the observations onto the slow modes rather than the unpredictable fast modes.

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4-D-Var or ensemble Kalman filter?

Cited by 222 publications

References 69 publications

Ensemble‐based chemical data assimilation. I: General approach

Ensemble‐based chemical data assimilation. I: General approach

Statistical downscaling with Bayesian inference: Estimating global solar radiation from reanalysis and limited observed data

A demonstration of 4D‐Var using a time‐distributed background term

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