Laurent Descamps’s research while affiliated with Université Toulouse III - Paul Sabatier and other places

Cyclones are essential components of weather patterns in the densely populated Mediterranean region, providing necessary rainfall for both the environment and human activities. The most intense of them also lead to natural disasters because of their strong winds and heavy precipitation. Identifying sources of errors in the predictability of Mediterranean cyclones is therefore essential to better anticipate and prevent their impact. The aim of this work is to characterise the medium-range cyclone predictability in the Mediterranean. Here, it is investigated in a systematic framework using the European Centre for Medium-Range Weather Forecasts fifth-generation reanalysis (ERA5) and ensemble reforecasts in a homogeneous configuration over the 2001–2021 period. First, a reference dataset of 1960 cyclones is obtained for the period by applying a tracking algorithm to the ERA5 reanalysis. Then the predictability is systematically evaluated in the ensemble reforecasts. It is quantified using a new probabilistic score based on the error distribution of cyclone location and intensity (mean sea level pressure). The score is firstly computed for the complete dataset and then for different categories of cyclones based on their intensity, deepening rate, motion speed, and geographic area and season in which they occur. When crossing the location and intensity errors with the different categories, the conditions leading to poorer or better predictability are discriminated. The motion speed of cyclones appears to be crucial for the predictability of the location: the slower the cyclone, the better the forecast location. In particular, the location of stationary lows located in the Gulf of Genoa is remarkably well predicted. The intensity of deep and rapid-intensification cyclones, occurring mostly during winter, is for its part particularly poorly predicted. This study provides the first systematic evaluation of cyclone predictability in the Mediterranean and opens up possibilities to identify the key processes leading to forecast errors in the region.

Mediterranean cyclones are essential components of the climate in a densely populated area, providing beneficial rainfall for both the environment and human activities. The most intense of them also lead to natural disasters because of their strong winds and heavy precipitation. Identifying error sources in the predictability of Mediterranean cyclones is therefore essential to better anticipate and prevent their impact. The aim of this work is to characterise the cyclone predictability in this region. Here, it is investigated in a systematic framework using European Centre for Medium range Weather Forecasting (ECMWF) fifth generation reanalysis (ERA5) and ensemble reforecasts in a homogeneous configuration over 20 years (2001–2021). First, a reference data set of 2853 cyclones is obtained for the period by applying a tracking algorithm to the ERA5 reanalysis. Then the predictability is systematically evaluated in the ensemble reforecasts. It is quantified using a new probabilistic score based on the error distribution of cyclone location and intensity (mean sea level pressure). The score is firstly computed for the complete data set and then for different categories of cyclones based on their intensity, deepening rate, velocity and on the geographic area and the season in which they occur. When crossing the location and intensity errors with the different categories, the conditions leading to a poorer or better predictability are discriminated. The velocity of cyclones appears to be determinant in the predictability of the location, the slower the cyclone the better the forecast location. Particularly, the position of stationary lows located in the Gulf of Genoa is remarkably well predicted. The intensity of deep and rapid-intensification cyclones, occurring mostly during winter, is for its part particularly poorly predicted. This study provides the first systematic evaluation of the cyclone predictability in the Mediterranean and opens the way to identify the key processes leading to forecast errors in the region.

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 85Kr during the spent nuclear fuel reprocessing process. In addition, a continuous measurement campaign of 85Kr 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 85Kr 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.

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.

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 10 m 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 connection with 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.

The western Mediterranean region is prone to devastating flash floods induced by heavy-precipitation events (HPEs), which are responsible for considerable human and material losses. Quantitative precipitation forecasts have improved dramatically in recent years to produce realistic accumulated rainfall estimations. Nevertheless, there are still challenging issues which must be resolved to reduce uncertainties in the initial condition assimilation and the modelling of physical processes. In this study, we analyse the HPE forecasting ability of the multi-physics-based ensemble model Prévision d’Ensemble ARPEGE (PEARP) operational at Météo-France. The analysis is based on 30-year (1981–2010) ensemble hindcasts which implement the same 10 physical parameterizations, one per member, run every 4 d. Over the same period a 24 h precipitation dataset is used as the reference for the verification procedure. Furthermore, regional classification is performed in order to investigate the local variation in spatial properties and intensities of rainfall fields, with a particular focus on HPEs. As grid-point verification tends to be perturbed by the double penalty issue, we focus on rainfall spatial pattern verification thanks to the feature-based quality measure of structure, amplitude, and location (SAL) that is performed on the model forecast and reference rainfall fields. The length of the dataset allows us to subsample scores for very intense rainfall at a regional scale and still obtain a significant analysis, demonstrating that such a procedure is consistent to study model behaviour in HPE forecasting. In the case of PEARP, we show that the amplitude and structure of the rainfall patterns are basically driven by the deep-convection parametrization. Between the two main deep-convection schemes used in PEARP, we qualify that the Prognostic Condensates Microphysics and Transport (PCMT) parametrization scheme performs better than the B85 scheme. A further analysis of spatial features of the rainfall objects to which the SAL metric pertains shows the predominance of large objects in the verification measure. It is for the most extreme events that the model has the best representation of the distribution of object-integrated rain.

The western Mediterranean region is prone to devastating flash-flood induced by heavy precipitation events (HPEs), which are responsible for considerable human and material damage. Quantitative precipitation forecasts have improved dramatically in recent years to produce realistic accumulated rainfall estimations. Nevertheless, challenging issues remain in reducing uncertainties in the initial conditions assimilation and the modeling of physical processes. In this study, the spatial errors resulting from a 30-year (1981–2010) ensemble hindcast which implement the same physical parametrizations as in the operational Météo-France short-range ensemble prediction system, Prévision d'Ensemble ARPEGE (PEARP), are analysed. The hindcast consists of a 10-member ensemble reforecast, run every 4-days, covering the period from September to December. 24-hour precipitation fields are classified in order to investigate the local variation of spatial properties and intensities of rainfall fields, with particular focus on the HPEs. The feature-based quality measure SAL is then performed on the model forecast and reference rainfall fields, which shows that both the amplitude and structure components are basically driven by the deep convection parametrization. Between the two main deep convection schemes used in PEARP, we qualify that the PCMT parametrization scheme performs better than the B85 scheme. A further analysis of spatial features of the rainfall objects to which the SAL metric pertains shows the predominance of large objects in the verification measure. It is for the most extreme events that the model has the best representation of the distribution of object integrated rain.

A 32‐year global ensemble reforecast dataset has recently been developed at Météo‐France that is approximatively consistent with the operational global ensemble forecast system PEARP. Unlike ECMWF or NCEP, Météo‐France does not possess a reanalysis of its own operational forecast system. Therefore, the initial atmospheric state and boundary conditions of the reforecasts are from the ECMWF ERA‐Interim reanalysis. This article presents a study of the sensitivity of the reforecasts to the method of land‐surface initialization. To this end, two sets of short‐range hindcasts using different land‐surface initialization approaches are compared. The first set is initialized from interpolated ERA‐Interim land‐surface fields based on a transfer function. The second set is initialized from offline simulations of the Météo‐France land‐surface model (SURFEX) driven by the 3‐hourly near‐surface atmospheric fields of the ERA‐Interim reanalysis. Each set is run from 1800 UTC initial conditions and up to +108 h. Because better results overall are found using offline SURFEX simulations, this latter approach was chosen to perform an ensemble reforecast dataset. Then, this ensemble reforecast database will be used to build a climatology of the operational ensemble prediction system of Météo‐France, which will, in turn, help to better estimate systematic forecast errors and, more importantly, improve the forecasting of rare extreme weather events.

This study presents a method that improves extreme windstorm early warning in regards to past events that hit France during the last 30 years. From a 21-member ensemble forecast, the extreme forecast index (EFI) and the shift of tails (SOT) are used to produce calibrated forecasts for a selection of 59 windstorm cases. The EFI and SOT forecasts are evaluated for windstorms of different levels of severity and for various forecast index thresholds using the Heidke skill score (HSS), hit rate (HR), and false alarm rate (FA). The HR and FA show that a "zero misses" level always goes conjointly with a high level of false alarms. The HSS shows maxima that are associated with EFI (or SOT) thresholds that could be used as a rationale for decision-makers to issue warnings. For most extreme events, it is found that a higher level of HR can be achieved using the SOT rather than the EFI. Overall, most of the windstorms are well anticipated 3-4 days ahead. To facilitate the use of EFI or SOT forecasts, it is suggested that extra information in the form of conditional probabilities be added, hence linking the EFI (or SOT) values to a risk of occurrence of a severe event. Finally, this anticipation of extreme events is illustrated by maps of EFI and SOT for four historical windstorms.