Výsledky vyhledávání - "MESTRE, OLIVIER"

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Precipitaion Nowcasting using Deep Neural Network

Autor: Bakkay, Mohamed Chafik, Serrurier, Mathieu, Burda, Valentin Kivachuk, Dupuy, Florian, Cabrera-Gutierrez, Naty Citlali, Zamo, Michael, Mader, Maud-Alix, Mestre, Olivier, Oller, Guillaume, Jouhaud, Jean-Christophe, Terray, Laurent

Precipitation nowcasting is of great importance for weather forecast users, for activities ranging from outdoor activities and sports competitions to airport traffic management. In contrast to long-term precipitation forecasts which are traditionally

Externí odkaz: http://arxiv.org/abs/2203.13263

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ARPEGE Cloud Cover Forecast Post-Processing with Convolutional Neural Network

Autor: Dupuy, Florian, Mestre, Olivier, Serrurier, Mathieu, Bakkay, Mohamed Chafik, Burdá, Valentin Kivachuk, Cabrera-Gutiérrez, Naty Citlali, Jouhaud, Jean-Christophe, Mader, Maud-Alix, Oller, Guillaume, Zamo, Michaël

Cloud cover is crucial information for many applications such as planning land observation missions from space. It remains however a challenging variable to forecast, and Numerical Weather Prediction (NWP) models suffer from significant biases, hence

Externí odkaz: http://arxiv.org/abs/2006.16678

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Surrogate Models for Rainfall Nowcasting

Autor: Cabrera-Gutiérrez, Naty Citlali, Godé, Hadrien, Jouhaud, Jean-Christophe, Bakkay, Mohamed Chafik, Burdá, Valentin Kivachuk, Dupuy, Florian, Mader, Maud-Alix, Mestre, Olivier, Oller, Guillaume, Serrurier, Mathieu, Zamo, Michaël

Nowcasting (or short-term weather forecasting) is particularly important in the case of extreme events as it helps prevent human losses. Many of our activities, however, also depend on the weather. Therefore, nowcasting has shown to be useful in many

Externí odkaz: http://arxiv.org/abs/2006.14515

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Sequential Aggregation of Probabilistic Forecasts -- Applicaton to Wind Speed Ensemble Forecasts

Autor: Zamo, Michaël, Bel, Liliane, Mestre, Olivier

In the field of numerical weather prediction (NWP), the probabilistic distribution of the future state of the atmosphere is sampled with Monte-Carlo-like simulations, called ensembles. These ensembles have deficiencies (such as conditional biases) th

Externí odkaz: http://arxiv.org/abs/2005.03540

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Statistical Postprocessing for Weather Forecasts -- Review, Challenges and Avenues in a Big Data World

Statistical postprocessing techniques are nowadays key components of the forecasting suites in many National Meteorological Services (NMS), with for most of them, the objective of correcting the impact of different types of errors on the forecasts. T

Externí odkaz: http://arxiv.org/abs/2004.06582

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Forest-based methods and ensemble model output statistics for rainfall ensemble forecasting

Autor: Taillardat, Maxime, Fougères, Anne-Laure, Naveau, Philippe, Mestre, Olivier

Rainfall ensemble forecasts have to be skillful for both low precipitation and extreme events. We present statistical post-processing methods based on Quantile Regression Forests (QRF) and Gradient Forests (GF) with a parametric extension for heavy-t

Externí odkaz: http://arxiv.org/abs/1711.10937

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