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Numerical atmospheric dispersion models (ADMs) are used for predicting the health and environmental consequences of nuclear accidents in order to anticipate countermeasures necessary to protect the populations. However, these simulations suffer from significant uncertainties, arising in particular from input data: weather conditions and source term. Meteorological ensembles are already used operationally to characterize uncertainties in weather predictions. Combined with dispersion models, these ensembles produce different scenarios of radionuclide dispersion, called "members", representative of the variety of possible forecasts. In this study, the fine-scale operational weather ensemble AROME-EPS (Applications of Research to Operations at Mesoscale-Ensemble Prediction System) from Météo-France is coupled with the Gaussian puff model pX developed by the IRSN (French Institute for Radiation Protection and Nuclear Safety). The source term data are provided at 10 min resolution by the Orano La Hague reprocessing plant (RP) that regularly discharges 85 Kr during the spent nuclear fuel reprocessing process. In addition, a continuous measurement campaign of 85 Kr air concentration was recently conducted by the Laboratory of Radioecology in Cherbourg (LRC) of the IRSN, within 20 km of the RP in the North-Cotentin peninsula, and is used for model evaluation. This paper presents a probabilistic approach to study the meteorological uncertainties in dispersion simulations at local and medium distances (2–20 km). First, the quality of AROME-EPS forecasts is confirmed by comparison with observations from both Météo-France and the IRSN. Then, the probabilistic performance of the atmospheric dispersion simulations was evaluated by comparison to the 85 Kr measurements carried out during a period of 2 months, using two probabilistic scores: relative operating characteristic (ROC) curves and Peirce skill score (PSS). The sensitivity of dispersion results to the method used for the calculation of atmospheric stability and associated Gaussian dispersion standard deviations is also discussed. A desirable feature for a model used in emergency response is the ability to correctly predict exceedance of a given value (for instance, a dose guide level). When using an ensemble of simulations, the "decision threshold" is the number of members predicting an event above which this event should be considered probable. In the case of the 16-member dispersion ensemble used here, the optimal decision threshold was found to be 3 members, above which the ensemble better predicts the observed peaks than the deterministic simulation. These results highlight the added value of ensemble forecasts compared to a single deterministic one and their potential interest in the decision process during crisis situations. [ABSTRACT FROM AUTHOR]

A global sensitivity analysis of the convective-scale Application of Research to Operations at Mesoscale (AROME)model is performed in order to determine the most influential parameters on the forecast of different near-surface variables. For that purpose, the Morris method is applied to 21 parameters from six different physical and dynamical parametrization schemes, over different seasons. Results highlight a set of eight parameters with a noticeable influence on most variables, in particular 10m wind speed and precipitation forecasts. The sensitivity of parameter uncertainties is also examined on different spatio-temporal scales. A clear diurnal cycle of parameters influence is observed in summer, in close connectionwith the convective activity. In addition, the spatial distribution of parameters influence is mostly consistent with the underlying distribution of weather forecasts. A Sobol’ sensitivity analysis, based on surrogate models, mostly confirms Morris conclusions and highlights some interactions between parameters. [ABSTRACT FROM AUTHOR]

Agriculture is a highly weather-dependent activity, and climatic conditions impact both directly crop growth and indirectly diseases and pest developments causing yield losses. Weather forecasts are now a major component of various decision-support systems that assist farmers to optimize the positioning of crop protection treatments. However, properly accounting for weather uncertainty in these systems still remains a challenge. In this paper, three global and regional ensemble prediction systems (EPSs), covering different spatiotemporal scales, are coupled to a temperature-driven developmental model for grapevine moths in order to provide probabilistic forecasts of treatment dates. It is first shown that a parametric postprocessing of the EPSs significantly improves the prediction of treatment dates. Anticipating the need for phytosanitary treatments also requires seamless weather forecasts from the next hour to subseasonal time scales. An approach is presented to design seamless ensemble forecasts from the combination of the three EPSs used. The proposed method is able to leverage the increased performance of high-resolution EPS at short ranges, while ensuring a smooth transition toward larger-scale EPSs for longer ranges. The added value of this seamless integration on agronomic predictions is, however, difficult to assess with the current experimental setup. Additional simulations over a larger number of locations and years may be required. [ABSTRACT FROM AUTHOR]

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. 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. [ABSTRACT FROM AUTHOR]

The extreme forecast index (EFI) and shift of tails (SOT) are commonly used to compare an ensemble forecast to a reference model climatology, in order to measure the severity of the current weather forecast. In this study, the feasibility and the relevance of EFI and SOT computations are examined within the convection-permitting Application of Research to Operations at Mesoscale (AROME-France) ensemble prediction system (EPS). First, different climate configurations are proposed and discussed, in order to overcome the small size of the ensemble and the short climate sampling length. Subjective and objective evaluations of EFI and SOT for wind gusts and precipitation forecasts are then presented. It is shown that these indices can provide relevant early warnings and, based on a trade-off between hits and false alarms, optimal EFI thresholds can be determined for decision-making. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The AROME-EPS convection-permitting ensemble prediction system has been evaluated over the HyMeX-SOP1 period. Objective verification scores are computed using dense observing networks prepared for the HyMeX experiment. In probabilistic terms, the AROME-EPS ensemble performs better than the AROME-France deterministic prediction system, and a state-of-the-art ensemble at a lower resolution. The strengths and weaknesses of AROME-EPS are discussed. Here, impact experiments are used to study perturbation schemes for the initial conditions and the model surface. Both have a significant effect on the ensemble performance. The interactions between the perturbations of lateral boundaries, initial conditions and surface perturbations are studied. The consistency between initial and lateral perturbations is found to be unimportant from a meteorological point of view. Ensemble data assimilation is not as effective as a simpler surface perturbation scheme, and it is noted that both approaches could be usefully combined. [ABSTRACT FROM AUTHOR]

Since agriculture is highly exposed to weather-related risks such as drought constraints, Decision Support Tools (DSTs) are now frequently used in irrigation management. The current use of DSTs mainly relies on deterministic weather forecasts that do not account for the associated weather uncertainties. Few irrigation DST users take uncertainty into consideration by utilizing an ensemble of historical weather observations (EHO) as input to their models. In order to account for the atmospheric state uncertainties, weather centers are progressively shifting to ensemble predictions. This is done by running several times the numerical weather prediction model with slightly perturbed initial conditions and model formulations. The final forecast hence takes the form of an ensemble instead of a deterministic single value. However, ensemble forecasts could present bias and dispersion errors and therefore need post-processing. In this work, we introduce a novel approach that uses ensemble weather forecasts as input to a vine irrigation DST, named "Water baLance for Intercropped Systems" (WaLIS), that computes a crop water stress index as output. Our results show that this approach has significantly better probabilistic performance than the use of EHO as input to the WaLIS model, especially between forecast days 5 and 7 (∼ 35% of gain in performance). We also investigate the effect of statistical post-processing on the probabilistic skill of the water stress index with two different approaches. In the first case, we directly post-process the output of the WaLIS model. In the second case, only weather forecasts are post-processed, before being used as inputs of the WaLIS model. Our results show that locally in 4 out of 10 tested sites in southern France, the raw ensemble forecasts could be significantly improved by the post-processing, and that generally there is no significant performance difference between the two competing post-processing approaches. • Weather-induced uncertainty on a crop water stress model predictions is studied. • WaLIS model is used to simulate water stress in 10 southern France vine sites. • Weather ensemble forecasts are better than historical data in predicting water stress. • The most significant performance gain (∼ 35%) is between 5 and 7 days of forecast. • Post-processing predictions with Ensemble Model Output Statistics can improve the performance. [ABSTRACT FROM AUTHOR]

Ensemble prediction systems at the convective scale are often under-dispersive. In order to alleviate this problem, a time-lagged ensemble can be created from ensemble forecasts initialized at different production times. While an equal-weight combination of lagged forecasts generally provides competitive results, this article introduces and discusses the efficiency of an objective weighting. The proposed approach is based on nonlinear Bayesian filtering, and the weights are determined online for each member according to the observation likelihood. The method is illustrated with short-range ensemble forecasts provided by the cloud-resolving AROME-France model. A time-lagged ensemble is then constructed from the current ensemble forecasts combined with older ensemble forecasts started 6 and 12 h earlier. It is first shown that the weighting scheme provides reasonable results, in particular it is able to detect differences in forecast quality due to different production times. The question whether these unequal flow-dependent weights can be successfully applied to the members of the time-lagged ensemble is then examined. Results indicate that the weighting does not lead to a noticeable gain in forecast quality. Possible reasons for this limited impact are discussed. [ABSTRACT FROM AUTHOR]