Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review

Ruiz, Juan; Pulido, Manuel; Miyoshi, Takemasa

doi:10.2151/jmsj.2013-201

Cited by 111 publications

(113 citation statements)

References 75 publications

Supporting

Mentioning

100

Contrasting

Order By: Relevance

“…In particular, a non-localized EnKF is used in the parameter estimation, since localization is not useful for estimating a global parameter; 19,20 we also provide evidence to support this statement with numerical results described in Figure 6. With this EnKF separate parameter estimation method, we update an ensemble of forcing parameters F i : i ¼ 1; 2; …; k f gfor the Lorenz-96 model.…”

Section: Methodssupporting

confidence: 68%

“…This supports previous findings that localization in the parameter estimation scheme is not useful for estimating a global parameter. 19,20 Of note in both contours in Figure 6, if the localization radius in the state update is too large, the DA scheme loses any benefit from localization. Thus, the filter resolves the state and forcing poorly since there is not a sufficient number of ensembles to combat the chaotic growth of the Lorenz-96 system.…”

Section: B Results: Parameter Estimationmentioning

confidence: 99%

“…strategically targeting observations to reduce state estimation and forecast error; 3-16 2. estimating model parameters [17][18][19][20][21][22] within the targeting observation context to further reduce estimation error.…”

Section: Introductionmentioning

confidence: 99%

“…The main part of our numerical results investigates estimating parameters for the chaotic Lorenz-96 model, where we expand on previous literature by applying recently developed parameter estimation techniques [18][19][20] within the novel context of targeted observations. Model parameters are typically fixed quantities that encode physical information about a dynamical system which are often estimated within large weather and climate models.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Kalman filter data assimilation: Targeting observations and parameter estimation

Bellsky

Kostelich

Mahalov

2014

Chaos: An Interdisciplinary Journal of Nonlinear Science

View full text Add to dashboard Cite

This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation. V C 2014 AIP Publishing LLC.[http://dx.doi.org/10.1063/1.4871916]For chaotic systems like the weather, an accurate forecast requires an accurate representation of the current state. Data assimilation (DA) is a methodology that reinitializes the current state of a system by combining observational data along with an estimated state determined by a forecast model. Within data assimilation schemes, the spatial locations of the observational data can have a significant effect on the accuracy of the analysis (re-initialized) state. In this work, we use a particular strategy for locating observations and show that this targeting strategy is successful in reducing state estimation error for a conceptual chaotic model, as compared to fixed or randomly located observations. An additional facet of weather modeling that we investigate is the estimation of model parameters. Parameter estimation is a technique used within modeling that often seeks to fit parameters with historical data or to characterize subgridscale effects. We show that our utilized observation targeting strategy within a particular parameter estimation scheme is successful in accurately estimating a model parameter for this chaotic model. To motivate our study of this chaotic model, we first establish a theorem which is used to justify the use of targeted observations for a linear data assimilation scheme.

show abstract

Section: Methodssupporting

confidence: 68%

Section: B Results: Parameter Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Kalman filter data assimilation: Targeting observations and parameter estimation

Bellsky

Kostelich

Mahalov

2014

Chaos: An Interdisciplinary Journal of Nonlinear Science

View full text Add to dashboard Cite

show abstract

“…With model parameter estimation, which is desirable in offline atmospheric data assimilation, the filtering and variational methods come with two types of solution. The (ensemble) filtering approach requires the augmentation of the state variables with the parameters (Ruiz et al, 2013). 4D-Var easily lends itself to data assimilation since the parameter variables can often be accounted for in the cost function (Penenko et al, 2002;Elbern et al, 2007;Bocquet, 2012;Penenko et al, 2012).…”

Section: From State Estimation To Physical Parameter Estimationmentioning

confidence: 99%

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

et al. 2015

View full text Add to dashboard Cite

Abstract. Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models

show abstract

Review of wave‐turbulence interactions in the stable atmospheric boundary layer

Sun

Nappo²,

Mahrt

et al. 2015

Reviews of Geophysics

Self Cite

132

124

View full text Add to dashboard Cite

Flow in a stably stratified environment is characterized by anisotropic and intermittent turbulence and wavelike motions of varying amplitudes and periods. Understanding turbulence intermittency and wave-turbulence interactions in a stably stratified flow remains a challenging issue in geosciences including planetary atmospheres and oceans. The stable atmospheric boundary layer (SABL) commonly occurs when the ground surface is cooled by longwave radiation emission such as at night over land surfaces, or even daytime over snow and ice surfaces, and when warm air is advected over cold surfaces. Intermittent turbulence intensification in the SABL impacts human activities and weather variability, yet it cannot be generated in state-of-the-art numerical forecast models. This failure is mainly due to a lack of understanding of the physical mechanisms for seemingly random turbulence generation in a stably stratified flow, in which wave-turbulence interaction is a potential mechanism for turbulence intermittency. A workshop on wave-turbulence interactions in the SABL addressed the current understanding and challenges of wave-turbulence interactions and the role of wavelike motions in contributing to anisotropic and intermittent turbulence from the perspectives of theory, observations, and numerical parameterization. There have been a number of reviews on waves, and a few on turbulence in stably stratified flows, but not much on wave-turbulence interactions. This review focuses on the nocturnal SABL; however, the discussions here on intermittent turbulence and wave-turbulence interactions in stably stratified flows underscore important issues in stably stratified geophysical dynamics in general.

show abstract

Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review

Cited by 111 publications

References 75 publications

Kalman filter data assimilation: Targeting observations and parameter estimation

Kalman filter data assimilation: Targeting observations and parameter estimation

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

Review of wave‐turbulence interactions in the stable atmospheric boundary layer

Contact Info

Product

Resources

About