La prévision des précipitations par recherche d'analogues : état de l'art et perspectives

La prévision des précipitations par analogie, adaptée des sorties de modèles numériques de prévision, s'est améliorée ces dix dernières années et est actuellement implémentée opérationnellement dans diverses infrastructures françaises. Le premier objectif de cette étude est de dresser l'état de l'art de cette approche. L'application de cette méthode nécessite une base de données contenant les champs des variables qui permettent de caractériser les situations météorologiques passées. Etant donné qu'il existe deux archives de génération différente (réanalyses ERA-40 et NCEP/NCAR), la sensibilité de cette méthode de prévision au choix de l'archive a été étudiée. Les résultats révèlent une faible sensibilité, même si de manière générale les performances sont légèrement supérieures avec l'archive ERA-40, notamment pour les événements pluvieux extrêmes. Enfin, des perspectives d'amélioration de la méthode susceptibles d'être exploitées à court terme sont évoquées. / Precipitation forecasting based on an adaptation of model outputs through an analog sorting technique has been improved for around ten years. The method runs operationally in several French institutions. First, this short paper presents the state of the art of this approach and the more recent developments. Second, a sensitivity analysis to the choice of the database from which the variables that characterise the past meteorological situations are extracted is performed. Two available archives are tested (ERA-40 and NCEP/NCAR re-analyses). The results show that despite the performances obtained with the ERA-40 database are slightly better, especially for heavy rainfall events, the sensitivity is weak. Finally, further ways for improvement that could be investigated are suggested

Introducing a moving time window in the analogue method for precipitation prediction to find better analogue situations at a sub-daily time step

Analogue methods (AMs) predict local weather variables (predictands), such as precipitation, by means of a statistical relationship with predictors at a synoptic scale. Predictors are extracted from reanalysis datasets that often have a six hourly time step. For precipitation forecasts, the predictand often consists of daily precipitation (06h to 30h UTC), given the length of their available archives, and the unavailability of equivalent archives at a finer time step. The optimal predictors to explain these daily precipitations have been obtained in a calibration procedure with fixed times of observation (e.g. geopotential heigths Z1000 at 12h UTC and Z500 at 24h UTC). In operational forecast, a new target situation is defined by its geopotential predictors at these fixed hours, i.e. Z1000 at 12h UTC and Z500 at 24h UTC. Usually, the search for candidate situations for this given target day is usually undertaken by comparing the state of the atmosphere at the same fixed hours of the day for both the target day and the candidate analogues. However, it can be expected that the best analogy among the past synoptic situations does not occur systematically at the same time of the day and that better candidates can be found by shifting to a different hour. With this assumption, a moving time window (MTW) was introduced to allow the search for candidates at different hours of the day (e.g. Z1000 at 00, 06, 12, 18 h UTC and Z500 at 12, 18, 24, 30 h UTC respectively). This MTW technique can only result in a better analogy in terms of the atmospheric circulation (compared to the method with fixed hours), with improved values of the analogy criterion on the entire distribution of analogue dates. A seasonal effect has also been identified, with larger improvements in winter than in summer. However, its interest in precipitation forecast can only be evaluated with an archive of the corresponding 24h-totals, i.e. not only 6-30h UTC totals, but also 0-24h, 12-12h and 18-18h totals). This was possible to assess on a set of stations from the Swiss hourly measurement network with rather long time-series. The prediction skill was found to have improved by the MTW, and even to a greater extent after recalibrating the AM parameters. Moreover, the improvement was greater for days with heavy precipitation, which are generally related to more dynamic atmospheric situations where timing is more specific. The use of the MTW in the AM can be considered for several applications in different contexts, may it be for operational forecasting or climate-related studies

The catastrophic flash-flood event of 8–9 September 2002 in the Gard region, France: a first case study for the Cévennes–Vivarais Mediterranean Hydrometeorological Observatory

The Cévennes–Vivarais Mediterranean Hydrometeorological Observatory (OHM-CV) is a research initiative aimed at improving the understanding and modeling of the Mediterranean intense rain events that frequently result in devastating flash floods in southern France. A primary objective is to bring together the skills of meteorologists and hydrologists, modelers and instrumentalists, researchers and practitioners, to cope with these rather unpredictable events. In line with previously published flash-flood monographs, the present paper aims at documenting the 8–9 September 2002 catastrophic event, which resulted in 24 casualties and an economic damage evaluated at 1.2 billion euros (i.e., about 1 billion U.S. dollars) in the Gard region, France. A description of the synoptic meteorological situation is first given and shows that no particular precursor indicated the imminence of such an extreme event. Then, radar and rain gauge analyses are used to assess the magnitude of the rain event, which was particularly remarkable for its spatial extent with rain amounts greater than 200 mm in 24 h over 5500 km2. The maximum values of 600–700 mm observed locally are among the highest daily records in the region. The preliminary results of the postevent hydrological investigation show that the hydrologic response of the upstream watersheds of the Gard and Vidourle Rivers is consistent with the marked space–time structure of the rain event. It is noteworthy that peak specific discharges were very high over most of the affected areas (5–10 m3 s−1 km−2) and reached locally extraordinary values of more than 20 m3 s−1 km−2. A preliminary analysis indicates contrasting hydrological behaviors that seem to be related to geomorphological factors, notably the influence of karst in part of the region. An overview of the ongoing meteorological and hydrological research projects devoted to this case study within the OHM-CV is finally presented

Using genetic algorithms to achieve an automatic and global optimization of analogue methods for statistical downscaling of precipitation

Analogue methods (AMs) rely on the hypothesis that similar situations, in terms of atmospheric circulation, are likely to result in similar local or regional weather conditions. These methods consist of sampling a certain number of past situations, based on different synoptic-scale meteorological variables (predictors), in order to construct a probabilistic prediction for a local weather variable of interest (predictand). They are often used for daily precipitation prediction, either in the context of real-time forecasting, reconstruction of past weather conditions, or future climate impact studies. The relationship between predictors and predictands is defined by several parameters (predictor variable, spatial and temporal windows used for the comparison, analogy criteria, and number of analogues), which are often calibrated by means of a semi-automatic sequential procedure that has strong limitations. AMs may include several subsampling levels (e.g. first sorting a set of analogs in terms of circulation, then restricting to those with similar moisture status). The parameter space of the AMs can be very complex, with substantial co-dependencies between the parameters. Thus, global optimization techniques are likely to be necessary for calibrating most AM variants, as they can optimize all parameters of all analogy levels simultaneously. Genetic algorithms (GAs) were found to be successful in finding optimal values of AM parameters. They allow taking into account parameters inter-dependencies, and selecting objectively some parameters that were manually selected beforehand (such as the pressure levels and the temporal windows of the predictor variables), and thus obviate the need of assessing a high number of combinations. The performance scores of the optimized methods increased compared to reference methods, and this even to a greater extent for days with high precipitation totals. The resulting parameters were found to be relevant and spatially coherent. Moreover, they were obtained automatically and objectively, which reduces efforts invested in exploration attempts when adapting the method to a new region or for a new predictand. In addition, the approach allowed for new degrees of freedom, such as a weighting between the pressure levels, and non overlapping spatial windows. Genetic algorithms were then used further in order to automatically select predictor variables and analogy criteria. This resulted in interesting outputs, providing new predictor-criterion combinations. However, some limitations of the approach were encountered, and the need of the expert input is likely to remain necessary. Nevertheless, letting GAs exploring a dataset for the best predictor for a predictand of interest is certainly a useful tool, particularly when applied for a new predictand or a new region under different climatic characteristics

Automatic and global optimization of the Analogue Method for statistical downscaling of precipitation - Which parameters can be determined by Genetic Algorithms?

The Analogue Method (AM) aims at forecasting a local meteorological variable of interest (the predictand), often the daily precipitation total, on the basis of a statistical relationship with synoptic predictor variables. A certain number of similar situations are sampled in order to establish the empirical conditional distribution which is considered as the prediction for a given date. The method is used in operational medium-range forecasting in several hydropower companies or flood forecasting services, as well as in climate impact studies. The statistical relationship is usually established by means of a semi-automatic sequential procedure that has strong limitations: it is made of successive steps and thus cannot handle parameters dependencies, and it cannot automatically optimize certain parameters, such as the selection of the pressure levels and the temporal windows on which the predictors are compared. A global optimization technique based on Genetic Algorithms was introduced in order to surpass these limitations and to provide a fully automatic and objective determination of the AM parameters. The parameters that were previously assessed manually, such as the selection of the pressure levels and the temporal windows, on which the predictors are compared, are now automatically determined. The next question is: Are Genetic Algorithms able to select the meteorological variable, in a reanalysis dataset, that is the best predictor for the considered predictand, along with the analogy criteria itself? Even though we may not find better predictors for precipitation prediction that the ones often used in Europe, due to numerous other studies which consisted in systematic assessments, the ability of an automatic selection offers new perspectives in order to adapt the AM for new predictands or new regions under different meteorological influences

AtmoSwing, an analog technique model for statistical downscaling and forecasting

Analog methods (AMs) allow predicting local meteorological variables of interest (predictand), such as the daily precipitation, based on synoptic variables (predictors). They rely on the hypothesis that similar atmospheric conditions are likely to result in similar local effects. The statistical relationship is first defined (e.g. which predictors, and how many subsampling steps) and calibrated (e.g. which spatial domain, and how many analogues) before being applied to the target period, may it be for operational forecasting or for climate impact studies. A benefit of AMs is that they are lightweight and can provide valuable results for a negligible cost. AtmoSwing is an open source software that implements different AM variants in a very flexible way, so that they can be easily configured by means of XML files. It is written in C++, is object-oriented and multi-platform. AtmoSwing provides four tools: the Optimizer to establish the relationship between the predictand and predictors, the Downscaler to apply the method for climate impact studies, the Forecaster to perform operational forecasts, and the Viewer to display the results. The Optimizer provides a semi-automatic sequential approach, as well as Monte-Carlo analyses, and a global optimization technique by means of Genetic Algorithms. It calibrates the statistical relationship that can be later applied in a forecasting or climatic context. The Downscaler takes as input the outputs of climate models, either GCMs or RCMs in order to provide a downscaled time series of the predictand of interest at a local scale. The Forecaster automatically downloads and reads operational NWP outputs to provide operational forecasting of the predictand of interest. The processing of a forecast is extremely lightweight in terms of computing resources; it can indeed run on almost any computer. The Viewer displays the forecasts in an interactive GIS environment. It contains several layers of syntheses and details in order to provide a quick overview of the potential critical situations in the coming days, as well as the possibility for the user to go into the details of the forecasted predictand distribution

Resource-oriented therapeutic models in psychiatry:conceptual review

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