PEARP, the Météo‐France short‐range ensemble prediction system

Limited‐area ensemble predictions can be sensitive to the specification of lateral boundary conditions, which are often built by subsampling larger ensembles. Using the operational PEARP and AROME‐EPS ensembles, we compare several subsampling methods, including random selection, representative members, and a new selection method. The tests show that the algorithms used for the clustering and the member selection have a significant impact on the resulting ensembles. Clustering‐based methods are shown to outperform random subsampling, mostly (but not only) because they change the ensemble spread. Cluster sizes can be highly variable, which can complicate ensemble interpretation. We present a subsampling algorithm that has little impact on performance scores, but better preserves ensemble spread and produces nearly equally likely members by limiting cluster size variability.

“…, ), and large‐scale boundary conditions provided by the global 35‐member PEARP (Prévision d'Ensemble ARPEGE) ensemble system (Descamps et al. , ).…”

Section: Methodssupporting

confidence: 94%

Clustering and selection of boundary conditions for limited‐area ensemble prediction

Bouttier

Raynaud

2018

“…A pragmatic way of accounting for model uncertainty is to construct ensembles with members that use different numerical models (Iversen et al , 2011; Weisheimer et al , 2011) or different parametrizations for physical processes. The global ensemble forecasts at Environment Canada, Météo‐France and the regional US Air‐force Weather Agency ensemble employ such a ‘multi‐physics’ approach (Charron et al , 2010; Berner et al , 2011; Descamps et al , 2015). Using different models or different physics parametrizations can increase ensemble spread, but the sampling of uncertainty is discrete and some of the spread increase is simply due to different model biases, which is not desirable, as different biases can lead to a clustering of members by model or by parametrization scheme.…”

Section: Discussionmentioning

confidence: 99%

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

Leutbecher

Lock

Ollinaho

et al. 2017

214

250

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.

“…Each AROME‐EPS member is perturbed in order to account for the main sources of uncertainty, as described below. Perturbed lateral boundary conditions (LBCs) are provided by the global EPS operational at Météo France, also known as the Prévision d'Ensemble Arpège (PEARP) system (Descamps et al , 2015), which currently runs 35 members (a control and 34 perturbed members) on a stretched grid with 10 km resolution over France, starting at 0006 and 0018 UTC. For each AROME‐EPS production, the 12 PEARP coupling members are selected from the latest PEARP production with the clustering technique described in Nuissier et al (2012). AROME‐EPS initial conditions (ICs) are built by adding to the AROME operational analysis the downscaled 3 h forecast perturbations of the selected PEARP members (Raynaud and Bouttier, 2016).…”

Section: Experimental Set‐upmentioning

confidence: 99%

The impact of horizontal resolution and ensemble size for convective‐scale probabilistic forecasts

Raynaud

Bouttier

2017

The relative benefits of ensemble size and model resolution are investigated within the AROME–France convective‐scale ensemble prediction system (EPS), which operationally runs 12 perturbed members at 2.5 km horizontal resolution. This baseline configuration is compared with two auxiliary ensemble experiments, which are run at resolutions of 2.5 and 1.3 km, with 34 and 12 members respectively. In addition, post‐processing techniques including neighbourhood approaches and time‐lagging are examined as potential alternatives to increase the sample size at a lower computational cost. Probabilistic verification of the raw EPS outputs indicates that increasing model resolution is more beneficial at very short ranges, whereas increasing the ensemble size has a larger impact at longer forecast ranges, as predictability decreases and more members are required to sample the larger uncertainty better. The neighbourhood processing confirms these conclusions and is shown to improve precipitation forecasts of all EPS configurations significantly, especially the smaller‐size ensembles. Hence, it can be considered as a viable substitute to running additional AROME members for the first 24 h of forecasts. Time‐lagging of three successive ensemble productions also appears to be a competitive approach to improve the ensemble forecast skill, particularly at very short ranges, where AROME‐EPS is known to be underdispersive. The performance of the time‐lagged 36 member ensemble is generally close to or even better than the performance of the single 34 member ensemble for the whole forecast range and for different surface weather variables. Overall, given the relative costs and skills of the different EPS configurations, we suggest that resources should primarily be spent on increasing ensemble size, by combining both additional members and post‐processing methods. In addition, the performance of the 1.3 km ensemble should be examined further for explicit high‐impact weather and with an appropriate tuning of physics and surface perturbations.