Premiers développements d'un modèle hybride pour le diagnostic spatial des risques d'avalanches

Bolognesi, R.

doi:10.1051/lhb/1993045

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…D'autres centres de recherche et des opérateurs privés développent des produits similaires. NXLOG, par exemple, est un modèle hybride qui combine approche statistique et connaissance experte (Bolognesi, 1993).…”

Section: Observer Analyser Et Prévoirunclassified

La prévision du risque d'avalanche en France ; bilan et perspectives

Pahaut¹

1995

Météorologie

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Depuis l'hiver 1969-1970, la prévision du risque d'avalanche en France a considérablement évolué, tant dans son organisation que dans ses moyens. En collaboration avec ses partenaires, professionnels de la montagne, Météo-France a mis en place un réseau dense d'observations nivométéorologiques couvrant les Alpes, les Pyrénées et la Corse, puis a départementalisé la prévision opérationnelle du risque d'avalanche. Plus récemment, l'échelle de risques d'avalanche est enfin devenue européenne et la mise en alerte des services de sécurité a été normalisée. Ces dernières années, les progrès réalisés dans la connaissance de la neige ont permis une modélisation numérique détaillée du manteau neigeux. Pour décrire la grande variabilité spatiale du manteau neigeux et du risque d'avalanche, ce modèle d'évolution, appelé Crocus, a été alimenté en variables météorologiques par le système d'analyse météorologique Safran, puis couplé au modèle hybride Mepra, qui analyse l'intensité et la nature du risque d'avalanche par tranche d'altitude, par exposition et par pente. Les développements en cours tendent à une meilleure prise en compte des phénomènes locaux (transport de neige par le vent, formation de plaques et accumulations de neige, meilleure connaissance de l'activité avalancheuse) dans les modèles, avec le double objectif d'améliorer la localisation du risque d'avalanche et d'assurer une meilleure gestion du risque « accidentel », c'est-à-dire du risque de déclenchement provoqué par les skieurs. Since the winter of 1969-1970, avalanche risk forecasting in France has made significant progress in its organisation and in its tools. In collaboration with its partners, mountain professionals, Météo-France has set up a dense network of snow weather stations in the Alps, the Pyrénées and Corsica. Since 1985, departmental meteorological centres have been in charge of the operational avalanche risk forecasting. More recently, avalanches have became recognised as a Europe-wide hazard and the warning System standardized. Recently, progress towards a better understanding of snow has allowed the development of Crocus, a detailed numerical model of snow cover évolution. To describe the great variability of the snow pack and the avalanche risk, a meteorological analysis model, Safran, supplies hourly meteorological data. At numerous locations of différent orientation, slope and altitude, a Crocus simulation is carried out, and an expert System, Mepra, analyses the risks of the various types of avalanche. Research is now being conducted towards a better model description of local phenomena, like wind slab and drifting which are essential to the understanding of avalanche behaviour. The double objective of thèse studies is to give a better knowing of the danger areas and better control of unstable snow pack that might be triggered by skiers.

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Section: Observer Analyser Et Prévoirunclassified

La prévision du risque d'avalanche en France ; bilan et perspectives

Pahaut¹

1995

Météorologie

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“…AMs are often used to predict daily precipitation, either in an operational forecasting context (e.g., Guilbaud, 1997;Bontron and Obled, 2005;Hamill and Whitaker, 2006;Bliefernicht, 2010;Marty et al, 2012;Horton et al, 2012;Hamill et al, 2015;Ben Daoud et al, 2016) or a climate downscaling context (e.g., Zorita and von Storch, 1999;Wetterhall, 2005;Wetterhall et al, 2007;Matulla et al, 2007;Radanovics et al, 2013;Chardon et al, 2014;Dayon et al, 2015;Raynaud et al, 2016). Other predictands are also considered, such as precipitation radar images (Panziera et al, 2011;Foresti et al, 2015), temperature (Radinovic, 1975;Woodcock, 1980;Kruizinga and Murphy, 1983;Delle Monache et al, 2013;Caillouet et al, 2016;Raynaud et al, 2016), wind (Gordon, 1987;Delle Monache et al, 2013Vanvyve et al, 2015;Alessandrini et al, 2015b;Junk et al, 2015b, a), solar radiation or power production (Alessandrini et al, 2015a;Bessa et al, 2015;Raynaud et al, 2016), snow avalanches (Obled and Good, 1980;Bolognesi, 1993), and the trajectory of tropical cyclones (Keenan and Woodcock, 1981;Sievers et al, 2000;Fraedrich et al, 2003)…”

Section: Introductionmentioning

confidence: 99%

AtmoSwing: Analog Technique Model for Statistical Weather forecastING and downscalING (v2.1.0)

Horton

2019

Geosci. Model Dev.

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Abstract. Analog methods (AMs) use synoptic-scale predictors to search in the past for similar days to a target day in order to infer the predictand of interest, such as daily precipitation. They can rely on outputs of numerical weather prediction (NWP) models in the context of operational forecasting or outputs of climate models in the context of climate impact studies. AMs require low computing capacity and have demonstrated useful potential for application in several contexts. AtmoSwing is open-source software written in C++ that implements AMs in a flexible way so that different variants can be handled dynamically. It comprises four tools: a Forecaster for use in operational forecasting, a Viewer to display the results, a Downscaler for climate studies, and an Optimizer to establish the relationship between predictands and predictors. The Forecaster handles every required processing internally, such as NWP output downloading (when possible) and reading as well as grid interpolation, without external scripts or file conversion. The processing of a forecast requires low computing efforts and can even run on a Raspberry Pi computer. It provides valuable results, as revealed by a 3-year-long operational forecast in the Swiss Alps. The Viewer displays the forecasts in an interactive GIS environment with several levels of synthesis and detail. This allows for the provision of a quick overview of the potential critical situations in the upcoming days, as well as the possibility for the user to delve into the details of the forecasted predictand and criteria distributions. The Downscaler allows for the use of AMs in a climatic context, either for climate reconstruction or for climate change impact studies. When used for future climate studies, it is necessary to pay close attention to the selected predictors so that they contain the climate change signal. The Optimizer implements different optimization techniques, such as a semiautomatic sequential approach, Monte Carlo simulations, and a global optimization technique, using genetic algorithms. Establishing a statistical relationship between predictors and predictands is computationally intensive because it requires numerous assessments over decades. To this end, the code was highly optimized for computing efficiency, is parallelized (using multiple threads), and scales well on a Linux cluster. This procedure is only required to establish the statistical relationship, which can then be used for forecasting or downscaling at a low computing cost.

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“…Some common usages of AMs are for operational precipitation forecasting (e.g., Guilbaud 1997;Bontron and Obled 2005;Hamill and Whitaker 2006;Bliefernicht 2010;Marty et al 2012;Horton et al 2012;Hamill et al 2015;Ben Daoud et al 2016), or more recently for precipitation downscaling in a climate perspective (e.g., Radanovics et al 2013;Chardon et al 2014;Dayon et al 2015). However, AMs or equivalent methods are also employed to predict temperatures (Radinović 1975;Woodcock 1980;Kruizinga and Murphy 1983;Delle Monache et al 2013;Caillouet et al 2016), wind (Gordon 1987;Delle Monache et al 2013, 2011Vanvyve et al 2015;Alessandrini et al 2015b;Junk et al 2015a,b), solar power Bessa et al 2015), snow avalanches (Obled and Good 1980;Bolognesi 1993), insolation (Bois et al 1981), and the trajectories of tropical cyclones (Keenan and Woodcock 1981;Sievers et al 2000;Fraedrich et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Global Optimization of an Analog Method by Means of Genetic Algorithms

Horton

Jaboyedoff

Obled

2017

Monthly Weather Review

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Analog methods are based on a statistical relationship between synoptic meteorological variables (predictors) and local weather (predictand, to be predicted). This relationship is defined by several parameters, which are often calibrated by means of a semiautomatic sequential procedure. This calibration approach is fast, but has strong limitations. It proceeds through successive steps, and thus cannot handle all parameter dependencies. Furthermore, it cannot automatically optimize some parameters, such as the selection of pressure levels and temporal windows (hours of the day) at which the predictors are compared. To overcome these limitations, the global optimization technique of genetic algorithms is considered, which can jointly optimize all parameters of the method, and get closer to a global optimum, by taking into account the dependencies of the parameters. Moreover, it can objectively calibrate parameters that were previously assessed manually and can take into account new degrees of freedom. However, genetic algorithms must be tailored to the problem under consideration. Multiple combinations of algorithms were assessed, and new algorithms were developed (e.g., the chromosome of adaptive search radius, which is found to be very robust), in order to provide recommendations regarding the use of genetic algorithms for optimizing several variants of analog methods. A global optimization approach provides new perspectives for the improvement of analog methods, and for their application to new regions or new predictands.

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Premiers développements d'un modèle hybride pour le diagnostic spatial des risques d'avalanches

Cited by 3 publications

References 2 publications

La prévision du risque d'avalanche en France ; bilan et perspectives

La prévision du risque d'avalanche en France ; bilan et perspectives

AtmoSwing: Analog Technique Model for Statistical Weather forecastING and downscalING (v2.1.0)

Global Optimization of an Analog Method by Means of Genetic Algorithms

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