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Dive into the research topics where Sylvain Cros is active.

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Featured researches published by Sylvain Cros.


international geoscience and remote sensing symposium | 2014

Extracting cloud motion vectors from satellite images for solar power forecasting

Sylvain Cros; Olivier Liandrat; Nicolas Sébastien; Nicolas Schmutz

The high temporal variability of solar power is a real issue to achieve a balanced production and consumption. Solar power forecasting is then necessary to better exploit this variability and to increase the penetration of photovoltaic power into the energy mix. Solar energy forecasting involves prediction of cloud property above a given point. For several hour ahead forecasts, using images from meteorological geostationary satellite is the most suitable solution. We propose a forecasting method based on a phase correlation algorithm for motion estimation between subsequent cloud maps derived from Meteosat-9 images. The method is assessed against state-of-the-art over a limited area over South of France for a 4-hour period. Cloud index maps are predicted. Our forecasting are 21 % better than persistence in relative RMSE of cloud index. If state-of-the-art shows better results (23 %), our algorithm reduces computing of 25 % and then minimize operational solar forecasting constraints.


IEEE Transactions on Geoscience and Remote Sensing | 2008

Synergy of SMOS Microwave Radiometer and Optical Sensors to Retrieve Soil Moisture at Global Scale

Sylvain Cros; André Chanzy; Marie Weiss; Thierry Pellarin; Jean-Christophe Calvet; Jean-Pierre Wigneron

Methods to retrieve surface soil moisture were assessed at the global scale for one entire year by using simulated Soil Moisture and Ocean Salinity brightness temperatures (T B) and vegetation coverage information which can be derived from optical sensors. The global T B database consists of half-degree continental pixels and accounts for within-pixel heterogeneity, based on 1-km resolution land cover maps. The retrievals were performed by using a three-parameter inversion method applied to the L-band Microwave Emission of the Biosphere model. By using a Bayesian approach, vegetation data were injected as a priori information. Two options were investigated to profit from normalized difference vegetation index products: providing an a priori knowledge either on vegetation optical depth or on the vegetation cover fraction (f cover). The latter option allows for a better description of the surface heterogeneity by considering a bare soil fraction. When an error of 1 K is applied to the T B, both synergistic schemes significantly improved the soil moisture accuracy compared with methods using microwave data only. Using the vegetation a priori information, about 80% of the pixels present soil moisture retrieval accuracy less than 0.04 m3middotm-3 in terms of root-mean-square error, whereas methods based only on the microwave data provide 63% of pixels of the studied area with this accuracy. If the error in T B is larger (2 or 3 K), the soil moisture retrieval accuracy decreases significantly for both methods. The use of optical data to give a priori value of vegetation optical option is then the best for these cases.


Remote Sensing of Clouds and the Atmosphere XXII | 2017

Cloud cover forecast from a ground-based all sky infrared thermal camera

Nicolas Schmutz; Sylvain Cros; Olivier Liandrat; Antonin Braun; Laurent Saint-Antonin; Jacques Decroix

We describe the contribution of thermal infrared ground-based cameras in the short term Global Horizontal Irradiance (GHI) forecasting. This contribution is compared to the one of visible cameras, the most widespread technology currently used for this application. Accurate forecasts at short term horizons (5 to 30 minutes) under various and changing weather conditions represent an essential data for various applications such as optical availability and solar plant control procedures. The work presented in this paper first draw up an overview of the two cameras used in the following comparative study. The segmentation methods chosen for each of the camera and the protocol are subsequently described. Finally, the results of the study are presented and discussed. Thanks to the new opportunities it offers in terms of feature extraction and its capacities to overcome visible limitations, the thermal infrared camera shows a sizeable improvement in this comparative study.


Optics in Atmospheric Propagation and Adaptive Systems XVIII | 2015

Prediction of optical communication link availability: real-time observation of cloud patterns using a ground-based thermal infrared camera

Clément Bertin; Sylvain Cros; Laurent Saint-Antonin; Nicolas Schmutz

The growing demand for high-speed broadband communications with low orbital or geostationary satellites is a major challenge. Using an optical link at 1.55 μm is an advantageous solution which potentially can increase the satellite throughput by a factor 10. Nevertheless, cloud cover is an obstacle for this optical frequency. Such communication requires an innovative management system to optimize the optical link availability between a satellite and several Optical Ground Stations (OGS). The Saint-Exupery Technological Research Institute (France) leads the project ALBS (French acronym for BroadBand Satellite Access). This initiative involving small and medium enterprises, industrial groups and research institutions specialized in aeronautics and space industries, is currently developing various solutions to increase the telecommunication satellite bandwidth. This paper presents the development of a preliminary prediction system preventing the cloud blockage of an optical link between a satellite and a given OGS. An infrared thermal camera continuously observes (night and day) the sky vault. Cloud patterns are observed and classified several times a minute. The impact of the detected clouds on the optical beam (obstruction or not) is determined by the retrieval of the cloud optical depth at the wavelength of communication. This retrieval is based on realistic cloud-modelling on libRadtran. Then, using subsequent images, cloud speed and trajectory are estimated. Cloud blockage over an OGS can then be forecast up to 30 minutes ahead. With this information, the preparation of the new link between the satellite and another OGS under a clear sky can be prepared before the link breaks due to cloud blockage.


international geoscience and remote sensing symposium | 2007

Aggregation and disaggregation of synthetic l-band soil moisture data over South-western France in preparation of SMOS

Christoph Rüdiger; Jean-Christophe Calvet; Béatrice Berthelot; Aurore Brut; André Chanzy; Sylvain Cros; Jean-Pierre Wigneron; Michael Berger

For the preparation of the soil moisture and ocean salinity (SMOS) mission, due for launch in 2008, a synthetic study for the aggregation and disaggregation of L-band brightness temperature fields is currently being undertaken for a 5-year period (2000-2005) over south-western France. The observed soil moisture is derived from offline simulations obtained from the Meteo-France land surface model ISBA-A-gs. The radiative transfer model L-MEB is used to estimate the corresponding brightness temperature at 8 km resolution, with a large number of possible incidence angles for each time step. Aggregation of the distributed information is then undertaken by simulating overpasses of SMOS over the region. Finally, the disaggregation method is an extension of the approach presented by Merlin et al. (2005).


international geoscience and remote sensing symposium | 2006

Using Optical Satellite based Data to Improve Soil Moisture Retrieval from SMOS Mission

Sylvain Cros; André Chanzy; Thierry Pellarin; Jean-Christophe Calvet; Jean-Pierre Wigneron

SMOS mission will provide micro-wave soil emission data at L-band (1.4 GHz) at global scale with a half-degree spatial resolution. Inversion of model emission permits to retrieve surface soil moisture from SMOS data. These retrievals suffer from a lack of accuracy mainly because of the heterogeneity of land coverage encountered inside a given pixel. The addition of land surface parameters such as vegetation fractions coverage and surface temperature permits to overcome this problem. A feasibility study is presently undertaken with synthetic SMOS data and simulated optical satellite information. The results present significant improvements of soil moisture retrieval accuracy. A future operational scheme including these features will be of relevance for a global surface soil moisture mapping.


Meteorologische Zeitschrift | 2018

A Review of the Use of Geostationary Satellite Observations in Regional-Scale Models for Short-term Cloud Forecasting

Frederik Kurzrock; Sylvain Cros; Fabrice Chane Ming; Jason A. Otkin; Axel Hutt; Laurent Linguet; Gilles Lajoie; Roland Potthast


EUMETSAT Meteorological Satellite Conference | 2017

Development of an operational tool for early detection of fire with new Indian Ocean Data Coverage Meteosat-8

Christophe Révillion; Sébastien Peillet; Sylvain Cros; Nicolas Sébastien; Télesphore Brou


International Conference on Earth Observations and Societal Impacts ICEO&SI 2016 | 2016

Enhanced solar power forecasting in the tropics using satellite data assimilation

Frederik Kurzrock; Sylvain Cros; Fabrice Chane-Ming; Roland Potthast; Laurent Linguet; Gilles Lajoie


European geosciences union general assembly | 2016

The use of satellite data assimilation methods in regional NWP for solar irradiance forecasting

Frederik Kurzrock; Sylvain Cros; Nicolas Sébastien; Fabrice Chane-Ming; Laurent Linguet; Roland Potthast; Gilles Lajoie

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André Chanzy

Institut national de la recherche agronomique

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Jean-Pierre Wigneron

Institut national de la recherche agronomique

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Thierry Pellarin

Centre national de la recherche scientifique

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Marie Weiss

Institut national de la recherche agronomique

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Jason A. Otkin

Cooperative Institute for Meteorological Satellite Studies

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Axel Hutt

University of Reading

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