Ensemble forecasting

Leutbecher, Martin; Palmer, Tim

doi:10.1016/j.jcp.2007.02.014

Cited by 702 publications

(603 citation statements)

References 92 publications

Supporting

Mentioning

591

Contrasting

Unclassified

Order By: Relevance

“…Lewis, 2005;Leutbecher and Palmer, 2008). The ensemble forecasts consist of a number of perturbed forecasts and often a single unperturbed (control) forecast.…”

Section: Copyright C 2012 Royal Meteorological Societymentioning

confidence: 99%

“…The rationale behind using SVs as perturbations to the initial conditions is to sample the dynamically most relevant structures that will dominate the uncertainty some time in the future (Ehrendorfer and Tribbia, 1997;Leutbecher and Palmer, 2008).…”

Section: Singular Vectorsmentioning

confidence: 99%

“…The dry total energy is used as a simple and also computationally efficient approximation of the initial time-error covariance matrix in the SV computation at ECMWF (Leutbecher and Palmer, 2008). The dry total energy norm is defined as follows:…”

Section: Singular Vectorsmentioning

confidence: 99%

See 2 more Smart Citations

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

Lang

Leutbecher

Jones

2012

Quart J Royal Meteoro Soc

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Lewis, 2005;Leutbecher and Palmer, 2008). The ensemble forecasts consist of a number of perturbed forecasts and often a single unperturbed (control) forecast.…”

Section: Copyright C 2012 Royal Meteorological Societymentioning

confidence: 99%

Section: Singular Vectorsmentioning

confidence: 99%

See 1 more Smart Citation

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

Lang

Leutbecher

Jones

2012

Quart J Royal Meteoro Soc

Self Cite

View full text Add to dashboard Cite

show abstract

“…A linear combination of these SVs, scaled to have amplitudes consistent with the analysis error, is added to the best-estimate analysis to make the starting conditions for each ensemble member. The SV method is used operationally at the European Centre for Medium-Range Weather Forecasts (ECMWF) (Leutbecher and Palmer 2008) and the Japan Meteorological Agency (JMA) (Yamaguchi and Majumdar 2010).…”

Section: Initial Condition Uncertaintiesmentioning

confidence: 99%

Medium- and Extended-Range Ensemble Weather Forecasting

Richardson

2018

Weather &Amp; Climate Services for the Energy Industry

View full text Add to dashboard Cite

The chapter provides an overview of ensemble weather forecasting for the medium-and extended-range (days to weeks ahead). It reviews the methods used to account for uncertainties in the initial conditions and in the forecast models themselves. The chapter explores the challenges of making useful forecasts for the sub-seasonal timescale, beyond the typical limit for skilful day-to-day forecasts, and considers some of the sources of predictability such as the Madden-Julian oscillation (MJO) that make this possible. It then introduces some of the ensemble-based forecast products and concludes with a case study for a European heat wave that demonstrates how ensemble weather forecasts can be used to guide decision making for weather-dependent activities.

show abstract

“…the National Centers for Environmental Prediction (NCEP) and European Centre for Medium-Range Weather Forecasts (ECMWF) in developing ensemble forecasting systems, as well as a probabilistic view of meteorological forecasting. For a detailed overview of ensemble forecasting and the underlying probabilistic forecasting philosophy developed in the meteorological community, the reader is referred to Leutbecher and Palmer (2008) and references therein. From a decision-making perspective, it has been shown that pricing weather derivatives based on density forecasts would bring significant benefits (Taylor and Buizza, 2006).…”

Section: Introductionmentioning

confidence: 99%

Reliability diagrams for non‐parametric density forecasts of continuous variables: Accounting for serial correlation

Pinson

McSharry

Madsen

2010

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

Reliability is seen as a primary requirement when verifying probabilistic forecasts, since a lack of reliability would introduce a systematic bias in subsequent decisionmaking. Reliability diagrams comprise popular and practical diagnostic tools for the reliability evaluation of density forecasts of continuous variables. Such diagrams relate to the assessment of the unconditional calibration of probabilistic forecasts. A reason for their appeal is that deviations from perfect reliability can be visually assessed based on deviations from the diagonal. Deviations from the diagonal may, however, be caused by both sampling effects and serial correlation in the forecastverification pairs. We build on a recent proposal, consisting of associating reliability diagrams with consistency bars that would reflect the deviations from the diagonal that are potentially observable even if density forecasts are perfectly reliable. Our consistency bars, however, reflect potential deviations originating from the combined effects of limited counting statistics and serial correlation in the forecastverification pairs. They are generated based on an original surrogate consistency resampling method. Its ability to provide consistency bars with a significantly better coverage against the independent and identically distributed (i.i.d.) resampling alternative is shown from simulations. Finally, a practical example of the reliability assessment of non-parametric density forecasts of short-term wind-power generation is given.

show abstract

Ensemble forecasting

Cited by 702 publications

References 92 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

Medium- and Extended-Range Ensemble Weather Forecasting

Reliability diagrams for non‐parametric density forecasts of continuous variables: Accounting for serial correlation

Contact Info

Product

Resources

About