Towards process‐level representation of model uncertainties: stochastically perturbed parametrizations in the ECMWF ensemble

Ollinaho, Pirkka; Lock, Sarah‐Jane; Leutbecher, Martin; Bechtold, Peter; Beljaars, Anton; Bozzo, Alessio; Forbes, R. M.; Haiden, Thomas; Hogan, Robin J.; Sandu, Irina

doi:10.1002/qj.2931

Cited by 107 publications

(159 citation statements)

References 35 publications

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“…At ECMWF, work has started on stochastic parameter perturbations in the framework of the Stochastically Perturbed Parametrization scheme (SPP: Ollinaho et al , 2017). SPP provides a framework in the IFS code to represent some of the key random errors of the parametrized tendencies close to their sources within the physical processes.…”

Section: Towards Process‐level Representation Of Model Uncertaintiesmentioning

confidence: 99%

Stochastic representations of model uncertainties at ECMWF: state of the art and future vision

Leutbecher

Lock

Ollinaho

et al. 2017

Quart J Royal Meteoro Soc

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Members in ensemble forecasts differ due to the representations of initial uncertainties and model uncertainties. The inclusion of stochastic schemes to represent model uncertainties has improved the probabilistic skill of the ECMWF ensemble by increasing reliability and reducing the error of the ensemble mean. Recent progress, challenges and future directions regarding stochastic representations of model uncertainties at ECMWF are described in this article. The coming years are likely to see a further increase in the use of ensemble methods in forecasts and assimilation. This will put increasing demands on the methods used to perturb the forecast model. An area that is receiving greater attention than 5–10 years ago is the physical consistency of the perturbations. Other areas where future efforts will be directed are the expansion of uncertainty representations to the dynamical core and other components of the Earth system, as well as the overall computational efficiency of representing model uncertainty.

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Section: Towards Process‐level Representation Of Model Uncertaintiesmentioning

confidence: 99%

Stochastic representations of model uncertainties at ECMWF: state of the art and future vision

Leutbecher

Lock

Ollinaho

et al. 2017

Quart J Royal Meteoro Soc

Self Cite

200

250

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“…Forecast model developments (including developments to the representation of model uncertainty; Plant and Craig 2008;Berner et al 2009;Christensen et al 2017;Ollinaho et al 2017), which improve short-range reliability for this and a variety of other flow types, should enable the ensemble to better maintain reliability out to ~10 days, as the phase-space trajectories "pass through" these different flow types. (At longer lead times, slower processes that are not assessable by the EDA reliability budget will start to become important.)…”

Section: Model or Observation Uncertainty?mentioning

confidence: 99%

Flow-Dependent Reliability: A Path to More Skillful Ensemble Forecasts

Rodwell

Richardson

Parsons

et al. 2018

Bulletin of the American Meteorological Society

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N umerical weather prediction is fundamentally a probabilistic task owing to the growth of unavoidable uncertainties in the forecast's initial conditions and in the forecast model itself (Sutton 1954;Lorenz 1963). A key question for the user is how certain they can be that a "10% probability of precipitation" really means that they will be unlucky to get wet. How would they assess the reliability of such a prediction? One approach would be for them to keep a record of the days when the forecast indicated a 10% probability of precipitation and to see if rainfall actually occurred on 10% of those days. If, in reality, it occurred on 20% of the days, this would indicate that these probabilistic forecasts were unreliable. On the other hand if, for a large set of occasions when the forecast gave a 40% chance of a hurricane making landfall in a particular region, a hurricane did make landfall 40% of the time, this would represent a "reliable" (Sanders 1958) set of forecasts. If the decision about whether to defend a vulnerable piece of infrastructure could be based purely on the forecast, then, since the forecast is reliable, a simple approach would be to defend the infrastructure if the cost to do so was less than 40% of the loss that would otherwise occur (Richardson 2000). In reality other factors will influence such a decision, but this example does highlight the importance of reliability. We may ask how we could do even better. Suppose we could partition the forecasts into two categories (based on the initial flow conditions) where the probabilities of landfall were, say, 60% and 20% (i.e., one flow situation is more likely to lead to landfall than the other), and suppose that the forecasts in both categories were reliable. It is straightforward to show (see "The benefits of flow-dependent reliability" sidebar) that, under the assumptions of this simple decision model, the expected expense in defending the infrastructure is always reduced (or matched). This result, which does not depend on the choice of numbers, emphasizes the potential utility of improving flow-dependent reliability. The key question to address here is, How do we assess and improve the flow-dependent reliability of our forecasts? Before attempting to address this question, it Improving short-range flow-dependent reliability, which may provide a practical approach to increase forecast skill out to ~10 days, is discussed and illustrated for a specific flow situation associated with convection over North America and poor skill for Europe. 1015MAY 2018 AMERICAN METEOROLOGICAL SOCIETY |

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“…iSPPT is an efficient representation of uncertainty from different parametrization schemes. For example, it uses far fewer stochastic patterns than alternative stochastically perturbed parameter approaches (Bowler et al , 2008; Ollinaho et al , 2017). This reduces the computational burden of the approach and results in a scheme that is easier to tune and maintain as the deterministic model is updated.…”

Section: Introductionmentioning

confidence: 99%

“…This reduces the computational burden of the approach and results in a scheme that is easier to tune and maintain as the deterministic model is updated. Through iSPPT, the perturbations to one parametrization scheme can be ‘turned off’, allowing for an alternative representation of uncertainty in that scheme, for example stochastic perturbation of uncertain parameters (Bowler et al , 2008; Ollinaho et al , 2017) or a stochastic scheme that targets one specific process, e.g. convection (e.g.…”

Section: Introductionmentioning

confidence: 99%

Introducing independent patterns into the Stochastically Perturbed Parametrization Tendencies (SPPT) scheme

Christensen

Lock

Moroz

et al. 2017

Quart J Royal Meteoro Soc

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The Stochastically Perturbed Parametrization Tendencies (SPPT) scheme is used at weather and climate forecasting centres worldwide to represent model uncertainty that arises from simplifications involved in the parametrization process. It uses spatio‐temporally correlated multiplicative noise to perturb the sum of the parametrized tendencies. However, SPPT does not distinguish between different parametrization schemes, which do not necessarily have the same error characteristics. A generalization to SPPT is proposed, whereby the tendency from each parametrization scheme can be perturbed using an independent stochastic pattern. This acknowledges that the forecast errors arising from different parametrizations are not perfectly correlated. Two variations of this ‘independent SPPT’ (iSPPT) approach are tested in the Integrated Forecasting System (IFS). The first perturbs all parametrized tendencies independently, while the second groups tendencies before perturbation. The iSPPT schemes lead to statistically significant improvements in forecast reliability in the Tropics in medium‐range weather forecasts. This improvement can be attributed to a large, beneficial increase in ensemble spread in regions with significant convective activity. The iSPPT schemes also lead to improved forecast skill in the extratropics for a set of cases in which the synoptic initial conditions were more likely to result in European ‘forecast busts’. Longer 13 month simulations are also considered to indicate the effect of iSPPT on the mean climate of the IFS.

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Towards process‐level representation of model uncertainties: stochastically perturbed parametrizations in the ECMWF ensemble

Cited by 107 publications

References 35 publications

Stochastic representations of model uncertainties at ECMWF: state of the art and future vision

Stochastic representations of model uncertainties at ECMWF: state of the art and future vision

Flow-Dependent Reliability: A Path to More Skillful Ensemble Forecasts

Introducing independent patterns into the Stochastically Perturbed Parametrization Tendencies (SPPT) scheme

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