Statistical post-processing of hydrological forecasts using Bayesian model averaging

Accurate and reliable probabilistic forecasts of hydrological quantities like runoff or water level are beneficial to various areas of society. Probabilistic state-of-the-art hydrological ensemble prediction models are usually driven with meteorological ensemble forecasts. Hence, biases and dispersion errors of the meteorological forecasts cascade down to the hydrological predictions and add to the errors of the hydrological models. The systematic parts of these errors can be reduced by applying statistical post-processing. For a sound estimation of predictive uncertainty and an optimal correction of systematic errors, statistical post-processing methods should be tailored to the particular forecast variable at hand. Former studies have shown that it can make sense to treat hydrological quantities as bounded variables. In this paper, a doubly truncated Bayesian model averaging (BMA) method, which allows for flexible post-processing of (multi-model) ensemble forecasts of water level, is introduced. A case study based on water level for a gauge of river Rhine, reveals a good predictive skill of doubly truncated BMA compared both to the raw ensemble and the reference ensemble model output statistics approach.Comment: 19 pages, 6 figure

InteRessources : une plateforme de catalogage de ressources linguistiques

International audienceLe recensement, ou catalogage, des ressources linguistiques (corpus, lexiques, dictionnaires, outils) existantes en Sciences Humaines et Sociales reste compliqué à mener tant les ressources sont nombreuses et les besoins spécifiques. Il est également difficile de maintenir des descriptifs à jour qui suivent les évolutions de ces ressources. De plus, documenter ces objets en respectant les standards du domaine sous la forme de métadonnées n'est pas toujours chose aisée.Si des plateformes comme ORTOLANG ou COCOON permettent d'archiver, de stocker et de diffuser des ressources linguistiques, elles ne permettent pas pour autant une recherche exhaustive de l'existant. Un outil de recensement de ces données serait complémentaire à de telles plateformes, et pourrait être alimenté par elles.Nous présentons ici une plateforme web de recensement et de documentation de ressources linguistiques (données et outils) réalisée dans le cadre du LabEx Empirical Foundations of Linguistics : InteRessources. Elle permet de décrire une ressource via un formulaire, crée une fiche consultable en ligne avec une adresse stable et de générer automatiquement des métadonnées aux formats standards Dublin Core, OLAC, TEI Header et CMDI. Elle propose également un moteur de recherche à facettes permettant d'accéder finement aux ressources en fonction des besoins de l'utilisateur.Nous décrivons son architecture ainsi que des perspectives d'amélioration, qui pourront être menées au sein du Consortium Corpus, Langues et Interactions (CORLI)

A Framework of Evaluation for Question-Answering Systems

International audienceEvaluating complex systems is a complex task. Evaluation campaigns are organized each year to test different systems on global results, but they do not evaluate the relevance of the criteria used. Our purpose consists in modifying the intermediate results created by the components and inserting the new results into the process, without modifyingthe components. We will describe our framework of glass-box evaluation

Fauna asociada y efecto de los balanos epibiontes al crecimiento relativo e índices reproductivos de Stramonita haemastoma (Gasterópoda: Muricidae)

To better understand the impacts of biofouling on the biological processes of the basibiont, the effects of epibiotic barnacles on the relative growth and reproductive indices of Stramonita haemastoma (Linnaeus, 1767) were assessed. A total of 1035 specimens were collected monthly for one year from Bizerta Channel (northern Tunisia). Endobiotic species comprised the lithophagous bivalves Lithophaga aristata and Rocellaria dubia of different sizes, communicating with the outside through tiny perforations. Intra-shell tunnels and galleries also sheltered annelids and sipunculids. Epibiotic species comprised algae and highly diversified invertebrates represented by crustaceans, polychaetes, molluscs, echinoderms, ascidians, sponges, bryozoans and sipunculids, with barnacles being the most common group. Comparison of growth features between non-fouled and fouled S. haemastoma revealed higher growth in non-fouled specimens. Differences in reproductive condition indices were detected in few months, being mostly higher in non-fouled snails, but showed no asynchrony in the spawning period for either fouled or non-fouled gastropods hosts.Para mejorar la compresión de los impactos del biofouling en los procesos biológicos de los basibiontes, se ha evaluado los efectos de los balanos epibiontes en el crecimiento relativo y en los índices reproductivos de Stramonita haemastoma (Linnaeus, 1767). Se recogieron un total de 1032 especímenes mensualmente, durante un año, en el canal de Bizerta (norte de Túnez). Las especies endobióticas estaban compuestas por los bibalvos litófagos Lithophaga aristata y Rocellaria dubia, de diferentes tamaños, que se comunicaban con el exterior a través de pequeñas perforaciones. Los túneles y galerías del interior de la concha también albergaban anélidos y sipuncúlidos, siendo los balanos el grupo más común. La comparación del crecimiento entre los gasterópodos con y sin fouling mostró un mayor crecimiento en los S. haemastoma sin fouling. Las diferencias en los índices reproductivos se detectaron en pocos meses, siendo mayor en los caracoles no invadidos por el fouling, pero ninguno de los gasterópodos hospedadores mostró asincronía en el periodo de desove

Fine-grained Linguistic Evaluation of Question Answering Systems

International audienceQuestion answering systems are complex systems using natural language processing. Some evaluation campaigns are organized to evaluate such systems in order to propose a classification of systems based on final results (number of correct answers). Nevertheless, teams need to evaluate more precisely the results obtained by their systems if they want to do a diagnostic evaluation. There are no tools or methods to do these evaluations systematically. We present REVISE, a tool for glass box evaluation based on diagnostic of question answering system results

Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting

In order to enable the transition towards renewable energy sources, probabilistic energy forecasting is of critical importance for incorporating volatile power sources such as solar energy into the electrical grid. Solar energy forecasting methods often aim to provide probabilistic predictions of solar irradiance. In particular, many hybrid approaches combine physical information from numerical weather prediction models with statistical methods. Even though the physical models can provide useful information at intra-day and day-ahead forecast horizons, ensemble weather forecasts from multiple model runs are often not calibrated and show systematic biases. We propose a post-processing model for ensemble weather predictions of solar irradiance at temporal resolutions between 30 min and 6 h. The proposed models provide probabilistic forecasts in the form of a censored logistic probability distribution for lead times up to 5 days and are evaluated in two case studies covering distinct physical models, geographical regions, temporal resolutions, and types of solar irradiance. We find that post-processing consistently and significantly improves the forecast performance of the ensemble predictions for lead times up to at least 48 h and is well able to correct the systematic lack of calibration

Machine learning for total cloud cover prediction

Accurate and reliable forecasting of total cloud cover (TCC) is vital for many areas such as astronomy, energy demand and production, or agriculture. Most meteorological centres issue ensemble forecasts of TCC; however, these forecasts are often uncalibrated and exhibit worse forecast skill than ensemble forecasts of other weather variables. Hence, some form of post-processing is strongly required to improve predictive performance. As TCC observations are usually reported on a discrete scale taking just nine different values called oktas, statistical calibration of TCC ensemble forecasts can be considered a classification problem with outputs given by the probabilities of the oktas. This is a classical area where machine learning methods are applied. We investigate the performance of post-processing using multilayer perceptron (MLP) neural networks, gradient boosting machines (GBM) and random forest (RF) methods. Based on the European Centre for Medium-Range Weather Forecasts global TCC ensemble forecasts for 2002–2014, we compare these approaches with the proportional odds logistic regression (POLR) and multiclass logistic regression (MLR) models, as well as the raw TCC ensemble forecasts. We further assess whether improvements in forecast skill can be obtained by incorporating ensemble forecasts of precipitation as additional predictor. Compared to the raw ensemble, all calibration methods result in a significant improvement in forecast skill. RF models provide the smallest increase in predictive performance, while MLP, POLR and GBM approaches perform best. The use of precipitation forecast data leads to further improvements in forecast skill, and except for very short lead times the extended MLP model shows the best overall performance

Machine learning for total cloud cover prediction

Accurate and reliable forecasting of total cloud cover (TCC) is vital for many areas such as astronomy, energy demand and production, or agriculture. Most meteorological centres issue ensemble forecasts of TCC, however, these forecasts are often uncalibrated and exhibit worse forecast skill than ensemble forecasts of other weather variables. Hence, some form of post-processing is strongly required to improve predictive performance. As TCC observations are usually reported on a discrete scale taking just nine different values called oktas, statistical calibration of TCC ensemble forecasts can be considered a classification problem with outputs given by the probabilities of the oktas. This is a classical area where machine learning methods are applied. We investigate the performance of post-processing using multilayer perceptron (MLP) neural networks, gradient boosting machines (GBM) and random forest (RF) methods. Based on the European Centre for Medium-Range Weather Forecasts global TCC ensemble forecasts for 2002-2014 we compare these approaches with the proportional odds logistic regression (POLR) and multiclass logistic regression (MLR) models, as well as the raw TCC ensemble forecasts. We further assess whether improvements in forecast skill can be obtained by incorporating ensemble forecasts of precipitation as additional predictor. Compared to the raw ensemble, all calibration methods result in a significant improvement in forecast skill. RF models provide the smallest increase in predictive performance, while MLP, POLR and GBM approaches perform best. The use of precipitation forecast data leads to further improvements in forecast skill and except for very short lead times the extended MLP model shows the best overall performance.Comment: 24 pages, 7 figure

REVISE, un outil d'évaluation précise des systèmes questions-réponses

National audienceDes campagnes d’évaluations sont organisées chaque année pour évaluer des systèmes de questions-réponses sur la validité des résultats fournis. Pour les équipes, il s’agit ensuite de réussir à mesurer la pertinence des stratégies développées ainsi que le fonctionnement des composants. À ces fins, nous décrivons un outil générique d’évaluation de type boîte transparente qui permet à un système produisant des résultats intermédiaires d’évaluer ses résultats. Nous illustrerons cette démarche en testant l’impact d’une nouvelle définition de la notion de focus