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This paper aims to explore machine learning techniques for post‐processing high‐resolution Numerical Weather Prediction (NWP) products for the early detection of convection. Data from the Arome Ensemble Prediction System and satellite observations from the Rapidly Developing Thunderstorm (RDT) product by Météo‐France are used to train a recurrent neural network model to predict areas of total convection and moderate convection. The learning task is formulated as a binary classification problem using a long short‐term memory (LSTM) network architecture. Results from the LSTM model are compared with an object‐based probabilistic approach to forecast convection using metrics such as a receiver operating characteristics (ROC) curve, the Brier score and reliability. Results indicate that the LSTM model performs similarly to the object‐based probabilistic benchmark when classifying moderate convection areas and shows improved skill when classifying areas of total convective. Finally, the LSTM model results are presented within an air traffic management context to showcase the potential use of machine learning models within an operational application. [ABSTRACT FROM AUTHOR]

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]

Numerical models of atmospheric dispersion are used for predicting the health and environmental consequences of nuclear accidents, in order to anticipate the countermeasures necessary to protect the populations. However, the simulations of these models suffer from significant uncertainties, arising in particular from input data: weather conditions and source term. To characterize weather uncertainties, it is essential to combine a well-known source term data and meteorological ensembles to generate ensemble dispersion simulations which has the potential to produce different possible scenarios of radionuclides dispersion during emergency situations. In this study, the fine-scale operational weather ensemble AROME-EPS from Météo-France is coupled to the Gaussian puff model pX developed by French Institute for Radiation Protection and Nuclear Safety (IRSN). The source term data is provided by Orano La Hague reprocessing plant (RP) that regularly discharges 85Kr during the spent nuclear fuel reprocessing process. Then, to evaluate the dispersion results, a continuous measurement campaign of 85Kr air concentration was recently conducted by the Laboratory of Radioecology in Cherbourg (LRC) of IRSN, around RP in the North-Cotentin peninsula. This paper presents a probabilistic approach to study the meteorological uncertainties in dispersion simulations at local and medium distances (2–20 km). As first step, the quality of AROME-EPS forecasts is confirmed by comparison with observations from both Météo-France and IRSN. The following step is to assess the probabilistic performance of the dispersion ensemble simulation, as well as the sensitivity of dispersion results to the method used to calculate atmospheric stability fields and their associated dispersion Gaussian standard deviations. Two probabilistic scores are used: Relative Operating Characteristic (ROC) curves and Peirce Skill Score (PSS). The results show that the stability diagnostics of Pasquill provides better dispersion simulations. In addition, the ensemble dispersion performs better than deterministic one, and the optimum decision threshold (PSS maximum) is 3 members. 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]

The weather-regime-dependent predictability of precipitation in the convection-permitting kilometric-scale AROME-EPS is examined for the entire HyMeX-SOP1 employing the convective adjustment timescale. This diagnostic quantifies variations in synoptic forcing on precipitation and is associated with different precipitation characteristics, forecast skill and predictability. During strong synoptic control, which dominates the weather on 80 % of the days in the 2-month period, the domain-integrated precipitation predictability assessed with the normalized ensemble standard deviation is above average, the wet bias is smaller and the forecast quality is generally better. In contrast, the pure spatial forecast quality of the most intense precipitation in the afternoon, as quantified with its 95th percentile, is superior during weakly forced synoptic regimes. The study also considers a prominent heavy-precipitation event that occurred during the NAWDEX field campaign in the same region, and the predictability during this event is compared with the events that occurred during HyMeX. It is shown that the unconditional evaluation of precipitation widely parallels the strongly forced weather type evaluation and obscures forecast model characteristics typical for weak control. [ABSTRACT FROM AUTHOR]

Object‐based methods are commonly used for verification and postprocessing of high‐resolution precipitation forecasts. They usually detect objects based on intensity criteria only, without considering the spatial organization of rainfall, known as texture. This article evaluates the performance of several machine‐learning methods to detect "continuous" and "intermittent" rainfall patterns in the forecasts of the French convective‐scale Arome model. A sliding‐window segmentation algorithm, which applies a classification model at each grid point, is implemented. Several classifiers and input textural features are compared. Overall, intermittent precipitation is the most difficult to detect. The random forest classifier is shown to provide the best classification results independently of the predictor used, with a surprising ability to extract a relevant signal from a synthetic descriptor such as the rainfall cumulative distribution function, as well as from the large amount of unprocessed information provided by neighbouring grid points. On the other hand, the logistic regression classifier needs a texture‐oriented predictor, such as the statistics derived from the grey‐level co‐occurrence matrix, to perform well. Global insight into model behaviour is then obtained by examining the importance of input features. Finally, we show that random forests trained on Arome deterministic forecasts can be applied successfully to discriminate between precipitation textures in different Arome configuration outputs and gridded observations. [ABSTRACT FROM AUTHOR]

In order to contribute to ongoing efforts on tropical cyclone (TC) forecasting, a new, convection‐permitting, limited‐area coupled model called AROME‐Indian Ocean (AROME‐IO) was deployed in the Southwest Indian Ocean basin (SWIO) in April 2016. The skill of this numerical weather predicting system for TC prediction is evaluated against its coupling model (European Center for Medium Range Weather Forecasting‐Integrated Forecasting System [ECMWF‐IFS]) using 120‐hr reforecasts of 11 major storms that developed in this area over TC seasons 2017–2018 and 2018–2019. Results show that AROME‐IO generally provides significantly better performance than IFS for intensity (maximum wind) and structure (wind extensions, radius of maximum wind) forecasts at all lead times, with similar performance in terms of trajectories. The performance of a prototype, 12‐member ensemble prediction system (EPS), of AROME‐IO is also evaluated on the case of TC Fakir (April 2018), a storm characterized by an extremely low predictability in global deterministic and ensemble models. AROME‐IO EPS is shown to significantly improve the predictability of the system with two scenarios being produced: a most probable one (~66%), which follows the prediction of AROME‐IO, and a second one (~33%) that closely matches reality. [ABSTRACT FROM AUTHOR]

Traditional pointwise verification scores are not always appropriate for the evaluation of high‐resolution precipitation forecasts because of double‐penalty problems. An alternative approach, based on the identification of homogeneous rainfall areas called "precipitating objects", allows forecast evaluation at a larger and thus more predictable scale, and specific information about the nature of errors (e.g. location, size, intensity) can be obtained. A novel object detection method is first introduced and the object‐based verification of precipitation forecasts from the convective‐scale deterministic and ensemble models Arome and Arome‐EPS is then discussed, using several scores and diagnostics. Three types of precipitating objects characterizing total, moderate and heavy rainfall are considered. In the second part, object‐based metrics are used to compute objective weights for time‐lagged ensemble forecasts, based on their performance at early forecast ranges. The weights obtained clearly depend on the meteorological situation and on the precipitation type, reflecting for instance the lower predictability of moderate precipitation compared to total precipitation. There is also a dependence on the production time with, on average, slightly larger and more homogeneous weights associated with the most recent run. However, in some situations of moderate and heavy rainfall, a relevant signal can be extracted from older runs. It is finally shown that object‐based weights are better suited than classical quadratic weights to improve nowcasting performance. [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]

Convective-scale ensemble prediction systems (EPSs) are often initialized with downscaled initial condition perturbations (ICPs) from a global coarser EPS. Although downscaled ICPs have been shown to have a positive impact at short ranges, they cannot represent the uncertainty at small scales. Hence, there is a spin-up of around 9-12 h until the forecast perturbations develop realistic small-scale structures. On the other hand, ensemble data assimilation (EDA) is a common approach to obtain initial perturbations at all scales resolved by the numerical model. However, the high computational cost of EDA systems severely limits their size and their resolution. An alternative cheaper method to derive small-scale ICPs is considered here, based on a random sampling of themodel backgrounderror covariances. This article provides an evaluation of random and EDA-based IC perturbation methods against the baseline downscaling approach, in the framework of the pre-operational convective-scale EPS developed at Météo-France with the AROME-France model at a 2.5 km horizontal resolution. Small-scale IC perturbation methods are shown to significantly improve the short-range EPS performance for surface weather variables. For 2m temperature and 10m wind speed, random ICPs give as good results as the EDA, owing to the very short spin-up of random perturbations. Precipitation forecasts are also strongly improved during the first six forecast hours, especially when initial humidity perturbations are included. The sensitivity of the EPS performance to the EDA size, horizontal resolution and representation of model errors is discussed. It is found that a large fraction of the initial uncertainty can be properly described with an EDA of reasonable size and at a slightly coarser resolution than the EPS. [ABSTRACT FROM AUTHOR]

Background-error length-scales are key components of the correlation model since they give an idea of the distance over which the influence of an observation extends. The length-scales can be estimated from an ensemble of perturbed forecasts, however the finite size of the ensembles in operational applications introduces a relatively large sampling noise on the estimated length-scales. We first give some insight into the statistical properties of the noise. On the one hand, the noise variance can be analytically expressed as a simple function of the length-scale and the ensemble size. On the other hand, it is shown that the spatial structure of the noise is relatively small-scale and is directly related to the heterogeneity of background error. Such spatial properties tend to support the use of local averaging techniques to remove the noise. This is explored with the application of homogeneous and heterogeneous averagings based on the integration of the diffusion equation. It is observed that both filters improve the accuracy of the length-scale estimates, with the heterogeneous filter being more efficient in the regions where the length-scales are small and rapidly varying. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

A stochastic physics scheme is tested in the AROME short range convection-permitting ensemble prediction system. It is an adaptation of ECMWF's stochastic perturbation of physics tendencies (SPPT) scheme. The probabilistic performance of the AROME ensemble is found to be significantly improved, when verified against observations over two two-week periods. The main improvement lies in the ensemble reliability and the spread/skill consistency. Probabilistic scores for several weather parameters are improved. The tendency perturbations have zero mean, but the stochastic perturbations have systematic effects on the model output, which explains much of the score improvement. Ensemble spread is an increasing function of the SPPT space and time correlations. A case study reveals that stochastic physics do not simply increase ensemble spread, they also tend to smooth out high spread areas over wider geographical areas. Although the ensemble design lacks surface perturbations, there is a significant end impact of SPPT on low-level fields through physical interactions in the atmospheric model. [ABSTRACT FROM AUTHOR]

Background-error variances estimated from a finite size ensemble of data assimilations are affected by sampling noise, which degrades the accuracy of the variance estimates. Previous work highlighted the close link between the spatial structures of background error and the associated sampling noise, and demonstrated the ability of local spatial averaging to remove this sampling noise. Existing filtering techniques commonly assume a homogeneous smoothing of the estimated variances. However, this assumption can be inadequate to represent error structures of varying scales, e.g. small-scale errors associated with localized severe weather events. To answer this problem, this article introduces and examines a heterogeneous filter based on the knowledge of the local spatial properties of the sampling noise. The filtering is realized with a diffusion process, and the diffusion coefficient is parametrized according to the local correlation length-scale of the sampling noise. This enables the diffusion coefficient to vary spatially in such a way as to encourage smoothing in regions where the background error is large scale in preference to regions where the error is small scale. A simulated 1D framework is considered to test the proposed approach. It is shown that the filtering using a spatially varying diffusion coefficient is able to preserve high-frequency variance structures, while this information tends to be smoothed with homogeneous filtering. The benefits of applying heterogeneous filtering are particularly pronounced with small ensemble sizes and in the vicinity of localized variance maxima. Copyright © 2012 Royal Meteorological Society [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]

In order to contribute to ongoing efforts on tropical cyclone (TC) forecasting, a new, convection‐permitting, limited‐area coupled model called AROME‐Indian Ocean (AROME‐IO) was deployed in the Southwest Indian Ocean basin (SWIO) in April 2016. The skill of this numerical weather predicting system for TC prediction is evaluated against its coupling model (European Center for Medium Range Weather Forecasting‐Integrated Forecasting System [ECMWF‐IFS]) using 120‐hr reforecasts of 11 major storms that developed in this area over TC seasons 2017–2018 and 2018–2019. Results show that AROME‐IO generally provides significantly better performance than IFS for intensity (maximum wind) and structure (wind extensions, radius of maximum wind) forecasts at all lead times, with similar performance in terms of trajectories. The performance of a prototype, 12‐member ensemble prediction system (EPS), of AROME‐IO is also evaluated on the case of TC Fakir (April 2018), a storm characterized by an extremely low predictability in global deterministic and ensemble models. AROME‐IO EPS is shown to significantly improve the predictability of the system with two scenarios being produced: a most probable one (~66%), which follows the prediction of AROME‐IO, and a second one (~33%) that closely matches reality. [ABSTRACT FROM AUTHOR]