Search

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Mechanisms leading a synoptic surface cyclone to cross an upper-level zonal jet and its subsequent deepening are investigated using a two-layer model on a [beta] plane. The baroclinic interaction of a low-level circular cyclonic perturbation with an upper-level one is first studied in vertical and horizontal cyclonic or anticyclonic uniform shears. A first nonlinear effect acting on the shape and energetics of the perturbations is analyzed. If the background shear is anticyclonic, the perturbations are stretched horizontally; they lose energy barotropically but gain it baroclinically by a well-maintained westward tilt with height. Conversely, if the shear is cyclonic, perturbations remain quite isotropic, but they do not keep a favorable vertical tilt with time and the baroclinic interaction is thus only transient. The latitudinal motion of the perturbations also results from a nonlinear effect. It is found to depend strongly on the background potential vorticity (PV) gradient. This effect is a baroclinic equivalent of the so-called nonlinear barotropic '[beta] drift' and combines the nonlinear advection and vertical stretching terms. These results are confirmed when the anomalies are initially located south of a confined westerly jet. The poleward shift of the lower cyclonic anomaly occurs faster when the vertically averaged PV gradient is strongly positive, which happens when the jet has a large barotropic component. The lower anomaly crosses the jet from the warm to the cold side and deepens afterward. After a detailed description of this regeneration process with the help of an energy budget, it is shown that linear dynamics are not able to reproduce such behavior.

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]

Midlatitude cyclogenesis as interpreted in the framework of either baroclinic development or potential vorticity thinking heavily relies on the concept of synoptic-scale anomaly. Given the existence of potential vorticity inversion and attribution, what is at stake to provide a mathematical definition for this concept is a complete finite-amplitude alternative to the linear-based theory of cyclogenesis. The existence of a reasonably objective way to represent anomalies in both real and idealized flows would not only help understanding cyclogenesis, it would also have many other applications for both theory and in practical forecasts. Inspired by the recent theory of wavelet representation of coherent structures in two-dimensional fluid mechanics, a wavelet representation of three-dimensional potential vorticity anomalies is built. This algorithm relies on the selection of the appropriate two-dimensional wavelet coefficients from the stationary wavelet transform in order to guarantee the critical translation-invariance property. The sensitivity of the algorithm to the position, size, and shape of the structures is assessed. The wavelet extraction is then applied to the upper-level precursor of a real-case storm of December 1999 and is compared to a basic monopolar extraction. Using potential vorticity inversion and forecasts with a primitive-equation model, it is found that both anomalies have similar implications on the development of the surface cyclone. However, the coherence in time of the extracted wavelet structure in the forecast and analysis sequence is more satisfactory than the extracted monopole: this suggests that the underlying mathematical description of an anomaly proposed here does, indeed, point toward the direction of an actual physical reality of the concept.

High quality volcanic ash forecasts are crucial to minimize the economic impact of volcanic hazards on air traffic. Decision-making is usually based on numerical dispersion modeling with only one model realization. Given the inherent uncertainty of such approach, a multi-model multi-source term ensemble has been designed and evaluated for the Eyjafjallajökull eruption in May 2010. Its use for air traffic management is discussed. Two multi-model ensembles were built: the first is based on the output of four dispersion models and their own implementation of ash ejection. All a priori model source terms were constrained by observational evidence of the volcanic ash cloud top as a function of time. The second ensemble is based on the same four dispersion models, which were run with three additional source terms: (i) a source term obtained with background modeling constrained with satellite data (a posteriori source term), (ii) its lower bound estimate, and (iii) its upper bound estimate. The a priori ensemble gives valuable information about the probability of ash dispersion during the early phase of the eruption, when observational evidence is limited. However, its evaluation with observational data reveals lower quality compared to the second ensemble. While the second ensemble ash column load and ash horizontal location compare well to satellite observations, 3D ash concentrations are negatively biased. This might be caused by the vertical distribution of ash, which is too much diluted in all model runs, probably due to defaults in the a posteriori source term and vertical transport and/or diffusion processes in all models. Relevant products for the air traffic management are horizontal maps of ash concentration quantiles (median, 75 %, 99 %) at a fine-resolved flight level grid. These maps can be used for route optimization in the areas where ash does not pose a direct and urgent threat to aviation. Cost-optimized consideration of such hazards will result in much less impact on flight cancellations, reroutings, and traffic flow congestions. [ABSTRACT FROM AUTHOR]

This contribution adresses the characterization of the model-error covariance matrix from the new theoretical perspective provided by the parametric Kalman filter method which approximates the covariance dynamics from the parametric evolution of a covariance model. The classical approach to obtain the modified equation of a dynamics is revisited to formulate a parametric diagnosis of the model-error covariance matrix. As an illustration, the particular case of the advection equation is considered as a simple test bed. After the theoretical derivation of both the forecast-error and the predictability-error covariance matrices, a numerical simulation is proposed which demonstrates the skill of the parametric methodology in reproducing the model-error covariance matrix information. [ABSTRACT FROM AUTHOR]

Volcanic eruptions comprise one of the most important airborne hazards for aviation. Although significant events are rare, they have a very high impact. The current state of tools and abilities to mitigate aviation hazards associated with an assumed volcanic cloud was tested within an international demonstration exercise. Experts in the field assembled at the Schwarzenberg barracks in Salzburg, Austria, in order to simulate the sequence of procedures for the volcanic case scenario of an artificial eruption of Etna volcano in Italy. The scope of the exercise ranged from the detection of the assumed event to the issuance of early warnings. Volcanic emission concentration charts were generated applying modern ensemble techniques. The exercise products provided an important basis for decision making for aviation traffic management during a volcanic eruption crisis. By integrating the available wealth of data, observations and modelling results directly into a widely used flight planning software, it was demonstrated that route optimization measures could be implemented effectively. With timely and rather precise warnings available, the new tools and processes tested during the exercise demonstrated vividly that a vast majority of flights could be conducted despite a volcanic plume widely dispersed within a high-traffic airspace over Europe. The resulting number of flight cancellations was minimal. [ABSTRACT FROM AUTHOR]