Two alternative implementations of the ensemble‐transform (ET) analysis‐perturbation scheme: The ET with extended cycling intervals, and the ET without cycling

McLay, Justin; Reynolds, C. A.

doi:10.1002/qj.437

Cited by 9 publications

(28 citation statements)

References 31 publications

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“…Another distinction relates to the fact that if the local ET is applied in conjunction with a diagonal P a (as is likely the case in practice) then there is an implicit limit on how small the domain partitions can be. This can be understood as follows: If the size n j of domainpartition j decreases, then K / n j and by property 4 of McLay et al (2008) (P e a ) j ! P j a .…”

Section: Miscellaneous Discussion For the Cycled Banded And Block Etmentioning

confidence: 98%

“…One of the main issues is that the resulting analysis perturbations can be regionally inconsistent with the estimates of analysis error covariance from the data assimilation system (e.g., McLay et al 2008). The point emphasized here is that a means to mitigate the regional inconsistency exists within the framework of the local ETKF.…”

Section: Introductionmentioning

confidence: 98%

“…In particular, one notes that the ensemble size is necessarily larger relative to the state dimension of a given subdomain than it is relative to the state dimension of the entire NWP model domain. It is a property of the ET that, as the ensemble size approaches the state dimension or the rank of the estimated analysis error covariance matrix P a from the data assimilation system (whichever is smaller), the ET analysis perturbation covariance matrix P a e will converge to P a [see property 4 of McLay et al (2008)]. It follows that the solution of the ET on multiple subdomains rather than on a single, global domain should, in theory, reduce regional inconsistency between the ET analysis perturbation covariance P a e and the estimated analysis error covariance P a .…”

Section: Introductionmentioning

confidence: 99%

“…Ensemble transform methods include the ensemble transform (ET) analysis perturbation scheme (Wei et al 2008;McLay et al 2008) and the ensemble transform Kalman filter (ETKF) data assimilation scheme (e.g., Bishop et al 2001;Wang and Bishop 2003;Ott et al 2004;Hunt et al 2007;Szunyogh et al 2008;Bowler 2006;Bowler et al 2009). These methods obtain analysis perturbations for the current analysis time as linear combinations of forecast perturbations initialized at the previous analysis time.…”

Section: Introductionmentioning

confidence: 99%

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A Local Formulation of the Ensemble Transform (ET) Analysis Perturbation Scheme

McLay

Bishop

Reynolds

2010

Weather and Forecasting

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Following ideas from the local ensemble transform Kalman filter, a local formulation of the ensemble transform (ET) analysis perturbation scheme is developed by partitioning the numerical weather prediction model domain into latitude bands or latitude-longitude blocks. In comparison with analysis perturbations from the original ''global'' ET formulation, analysis perturbations from the ''banded'' or ''block'' ET formulations are much more consistent with estimates of analysis error variance. Banded or block ET forecast ensembles also perform better under a variety of verification metrics than do global ET forecast ensembles. Substantial performance gains are observed for both the midlatitudes and the tropics. A local ET is scheduled to be made operational at the Fleet Numerical Meteorology and Oceanography Center.

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Section: Miscellaneous Discussion For the Cycled Banded And Block Etmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Local Formulation of the Ensemble Transform (ET) Analysis Perturbation Scheme

McLay

Bishop

Reynolds

2010

Weather and Forecasting

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“…The method produces an estimate of the analysis error variance at any location based on the observations and background within the observation prism in which the location is contained. It has been implemented successfully at FNMOC and NRL, and it generates the analysis error variance estimate from the NAVDAS for both global and regional ensemble forecast systems at FNMOC (McLay et al, 2007Reynolds et al, 2008;McLay and Reynolds, 2009;Bishop et al, 2009). Similar to the NCEP global EFS, the initial perturbations at FN-MOC are generated by using the ET (Ensemble Transform) method.…”

Section: Wei Et Al: Estimation and Calibration Of Observation Impmentioning

confidence: 99%

Estimation and calibration of observation impact signals using the Lanczos method in NOAA/NCEP data assimilation system

Wei

Pondeca

Tóth³

et al. 2012

Nonlin. Processes Geophys.

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Abstract. Despite the tremendous progress that has been made in data assimilation (DA) methodology, observing systems that reduce observation errors, and model improvements that reduce background errors, the analyses produced by the best available DA systems are still different from the truth. Analysis error and error covariance are important since they describe the accuracy of the analyses, and are directly related to the future forecast errors, i.e., the forecast quality. In addition, analysis error covariance is critically important in building an efficient ensemble forecast system (EFS).Estimating analysis error covariance in an ensemble-based Kalman filter DA is straightforward, but it is challenging in variational DA systems, which have been in operation at most NWP (Numerical Weather Prediction) centers. In this study, we use the Lanczos method in the NCEP (the National Centers for Environmental Prediction) Gridpoint Statistical Interpolation (GSI) DA system to look into other important aspects and properties of this method that were not exploited before. We apply this method to estimate the observation impact signals (OIS), which are directly related to the analysis error variances. It is found that the smallest eigenvalue of the transformed Hessian matrix converges to one as the number of minimization iterations increases. When more observations are assimilated, the convergence becomes slower and more eigenvectors are needed to retrieve the observation impacts. It is also found that the OIS over data-rich regions can be represented by the eigenvectors with dominant eigenvalues.Since only a limited number of eigenvectors can be computed due to computational expense, the OIS is severely underestimated, and the analysis error variance is consequently overestimated. It is found that the mean OIS values for temperature and wind components at typical model levels are increased by about 1.5 times when the number of eigenvectors is doubled. We have proposed four different calibration schemes to compensate for the missing trailing eigenvectors. Results show that the method with calibration for a small number of eigenvectors cannot pick up the observation impacts over the regions with fewer observations as well as a benchmark with a large number of eigenvectors, but proper calibrations do enhance and improve the impact signals over regions with more data.When compared with the observation locations, the method generally captures the OIS over regions with more observation data, including satellite data over the southern oceans. Over the tropics, some observation impacts may be missed due to the smaller background errors specified in the GSI, which is not related to the method. It is found that a large number of eigenvectors are needed to retrieve impact signals that resemble the banded structures from satellite observations, particularly over the tropics. Another benefit from the Lanczos method is that the dominant eigenvectors can be used in preconditioning the conjugate gradient algorithm in the GSI to speed up ...

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The US Navy's RELO ensemble prediction system and its performance in the Gulf of Mexico

Wei

Rowley

Martin

et al. 2013

Quart J Royal Meteoro Soc

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The US Navy's relocatable (RELO) ensemble prediction system is fully described and is examined in the Gulf of Mexico for 2010. After briefly describing the ensemble transfer (ET) method for the initial perturbation generation, we introduce a new time‐deformation technique to generate the surface forcing perturbations from the atmospheric model fields. The extended forecast time (EFT) is introduced to quantify the advantages of the ensemble mean forecasts over a single deterministic forecast. The ensemble spread and its growth are investigated together with their relations with the ensemble forecast accuracy, reliability and skill. Similar to many other operational ensemble forecast systems at numerical weather prediction (NWP) centres, the initial analysis error is underestimated by the technique used in the data assimilation (DA) system. Growth of the ocean ensemble spread is also found to lag the growth of the ensemble mean error, a tendency attributed to insufficiently accounting for model‐related uncertainties. As an initial step, we randomly perturb the two most important parameters in the ocean model mixing parametrizations, namely the Smagorinsky horizontal and Mellor–Yamada vertical mixing schemes. We examine three different parameter perturbation schemes based on both uniform and Gaussian distributions. It is found that all three schemes improve the ensemble spread to a certain extent, particularly the scheme with Gaussian distribution of perturbations imposed on both the horizontal and vertical mixing parameters. The findings in this article indicate that the RELO ensemble forecast demonstrates superior accuracy and skill relative to a single deterministic forecast for all the variables and over all the domains considered here. The ensemble spread provides a valuable estimate of forecast uncertainty. However, the RELO uncertainty forecast capability could be further improved by accounting for more model‐related uncertainties, for example, by the development of an error parametrization that imposes stochastic forcing at each model grid point.

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Two alternative implementations of the ensemble‐transform (ET) analysis‐perturbation scheme: The ET with extended cycling intervals, and the ET without cycling

Cited by 9 publications

References 31 publications

A Local Formulation of the Ensemble Transform (ET) Analysis Perturbation Scheme

A Local Formulation of the Ensemble Transform (ET) Analysis Perturbation Scheme

Estimation and calibration of observation impact signals using the Lanczos method in NOAA/NCEP data assimilation system

The US Navy's RELO ensemble prediction system and its performance in the Gulf of Mexico

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