3D-Var Hessian singular vectors and their potential use in the ECMWF Ensemble Prediction System

Barkmeijer, J.; Buizza, Roberto; Palmer, T.N.

doi:10.1256/smsqj.55817

Cited by 62 publications

(108 citation statements)

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“…Since the dry total energy norm is only a crude estimate of the initial error covariance metric (Leutbecher and Palmer, 2008), this could lead to SV structures that are inconsistent with analysis error statistics (Hamill et al, 2003). At ECMWF, Barkmeijer et al (1998Barkmeijer et al ( , 1999 calculated SVs with an initial time metric that is consistent with estimates of analysis uncertainty of a 3D-Var data-assimilation system. In their study the structure of these SVs is distinctly different from that of the total energy SVs, with the differences being much larger at initial time than at the end of the optimization interval.…”

Section: Singular Vectorsmentioning

confidence: 99%

Impact of perturbation methods in the ECMWF ensemble prediction system on tropical cyclone forecasts

Lang

Leutbecher

Jones

2012

Quart J Royal Meteoro Soc

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In the operational configuration of the ensemble prediction system of the European Centre for Medium-Range Weather Forecasts, different methods are applied to account for initial condition and model uncertainties. Singular vectors and an ensemble of data assimilations are used to generate perturbations to the initial conditions. Two stochastic tendency perturbation schemes aim to mimic model errors: the stochastic kinetic-energy backscatter scheme and the stochastically perturbed parametrization tendency scheme. In this study, the impact of the different perturbation methods on the ensemble spread during tropical cyclone (TC) events is compared and the time evolution of the spatial perturbation structure is investigated. The structure of perturbations due to different methods initially is quite different. However, our results show that they converge toward a TC displacement and an intensity-change pattern during the first two days of the forecast, on average. The perturbations generated by the stochastic tendency perturbation schemes grow rapidly and effectively excite growing modes of the flow. After 48 hours, a large part of the total energy of the singular vector perturbations in the vicinity of the TCs can be explained by the perturbations of other methods. The perturbations by the ensemble of data assimilations dominate the ensemble spread for a rather short lead time (around 24 hours). In about 40% of cases, the perturbations due to the tendency perturbation schemes or those generated from singular vectors produce a larger TC track and central pressure spread than the perturbations by the ensemble of data assimilations after two days forecast time. If all methods are applied, the average TC track spread of the ensemble matches the average error of the ensemble-mean well. In addition, the ensemble captures the anisotropy in the position uncertainty of the TCs.

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Section: Singular Vectorsmentioning

confidence: 99%

Impact of perturbation methods in the ECMWF ensemble prediction system on tropical cyclone forecasts

Lang

Leutbecher

Jones

2012

Quart J Royal Meteoro Soc

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“…Outliers tend to occur too frequently in operational ensembles, thus rendering these ensembles statistically inconsistent. The choice of the initial conditions affects the percentage of outliers (Barkmeijer et al, 1999, Bowler, 2006.…”

Section: Introductionmentioning

confidence: 99%

Predicting outliers in ensemble forecasts

Siegert

Bröcker

Kantz

2011

Quart J Royal Meteoro Soc

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An ensemble forecast is a collection of runs of a numerical dynamical model, initialized with perturbed initial conditions. In modern weather prediction for example, ensembles are used to retrieve probabilistic information about future weather conditions. In this contribution, we are concerned with ensemble forecasts of a scalar quantity (say, the temperature at a specific location). We consider the event that the verification is smaller than the smallest, or larger than the largest ensemble member. We call these events outliers. If a K-member ensemble accurately reflected the variability of the verification, outliers should occur with a base rate of 2/(K + 1). In operational forecast ensembles though, this frequency is often found to be higher. We study the predictability of outliers and find that, exploiting information available from the ensemble, forecast probabilities for outlier events can be calculated which are more skilful than the unconditional base rate. We prove this analytically for statistically consistent forecast ensembles. Further, the analytical results are compared to the predictability of outliers in an operational forecast ensemble by means of model output statistics. We find the analytical and empirical results to agree both qualitatively and quantitatively.

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“…The singular vectors were computed for the high-resolution analysis discussed in detail in Jung et al (2004). The initial conditions are further perturbed using evolved perturbations from two days earlier (Barkmeijer et al 1999). Model errors are taken into account using stochastic physics Palmer 2000).…”

Section: Methodsmentioning

confidence: 99%

Reanalysis and reforecast of three major European storms of the twentieth century using the ECMWF forecasting system. Part II: Ensemble forecasts

Jung

Klinker

Uppala

2005

Meteorological Applications

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3D-Var Hessian singular vectors and their potential use in the ECMWF Ensemble Prediction System

Cited by 62 publications

References 0 publications

Impact of perturbation methods in the ECMWF ensemble prediction system on tropical cyclone forecasts

Impact of perturbation methods in the ECMWF ensemble prediction system on tropical cyclone forecasts

Predicting outliers in ensemble forecasts

Reanalysis and reforecast of three major European storms of the twentieth century using the ECMWF forecasting system. Part II: Ensemble forecasts

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