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Search Results: 1 - 10 of 189689 matches for " G. Thirel "
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A past discharges assimilation system for ensemble streamflow forecasts over France – Part 1: Description and validation of the assimilation system
G. Thirel,E. Martin,J.-F. Mahfouf,S. Massart
Hydrology and Earth System Sciences Discussions , 2010, DOI: 10.5194/hessd-7-2413-2010
Abstract: Two Ensemble Streamflow Prediction Systems (ESPSs) have been set up at Météo-France. They are based on the French SIM distributed hydrometeorological model. A deterministic analysis run of SIM is used to initialize the two ESPSs. In order to obtain a better initial state, a past discharges assimilation system has been implemented into this analysis SIM run, using the Best Linear Unbiased Estimator (BLUE). Its role is to improve the model soil moisture by using observed streamflows in order to better simulate streamflow. The skills of the assimilation system were assessed for a 569-day period on six different configurations, including two different physics schemes of the model (the use of an exponential profile of hydraulic conductivity or not) and, for each one, three different ways of considering the model soil moisture in the BLUE state variables. Respect of the linearity hypothesis of the BLUE was verified by assessing of the impact of iterations of the BLUE. The configuration including the use of the exponential profile of hydraulic conductivity and the combination of the moisture of the two soil layers in the state variable showed a significant improvement of streamflow simulations. It led to a significantly better simulation than the reference one, and the lowest soil moisture corrections. These results were confirmed by the study of the impacts of the past discharge assimilation system on a set of 49 independent stations.
Assimilation of MODIS snow cover area data in a distributed hydrological model
G. Thirel,P. Salamon,P. Burek,M. Kalas
Hydrology and Earth System Sciences Discussions , 2011, DOI: 10.5194/hessd-8-1329-2011
Abstract: Snow is an important component of the water cycle and its estimation in hydrological models is of great importance concerning snow melting flood events simulations and forecasting. The LISFLOOD model is a spatially distributed hydrological model designed at the Joint Research Centre for large European river basins. It is used for a variety of applications including flood forecasting and assessing the effects of land use change and climate change. In order to improve the streamflow simulations of this model, especially with respect to snowmelt induced floods, the assimilation of Snow Cover Area (SCA) has been evaluated in this study. For this purpose daily 420 m-resolution MODIS satellital SCA data have been used, which were then converted in Snow Water Equivalent (SWE) using a Snow Depletion Curve. Tests were performed over the Morava basin, a tributary of the Danube, for a period of almost three years. Two data assimilation techniques, the Ensemble Kalman Filter (EnKF) and the particle filter, were compared, for assimilating the MODIS composites of SCA every seven days. Two approaches were tested, in which the SWE of the model was adjusted either using three altitudinal-based zones or seven sub-basins-based zones. These experiments showed the improvement of the SWE of the model when compared with MODIS-derived snow for both the EnKF and the particle filter. However, on average only the particle filter improved the discharge simulation, because the EnKF imposed too important water balance modifications, which deteriorated the simulation of the discharges during the snow melt periods.
A past discharge assimilation system for ensemble streamflow forecasts over France – Part 2: Impact on the ensemble streamflow forecasts
G. Thirel,E. Martin,J.-F. Mahfouf,S. Massart
Hydrology and Earth System Sciences Discussions , 2010, DOI: 10.5194/hessd-7-2455-2010
Abstract: The use of ensemble streamflow forecasts is developing in the international flood forecasting services. Such systems can provide more accurate forecasts and useful information about the uncertainty of the forecasts, thus improving the assessment of risks. Nevertheless, these systems, like all hydrological forecasts, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation system, on an ensemble streamflow prediction system over France. An assimilation system was implemented to improve the streamflow analysis of the SAFRAN-ISBA-MODCOU (SIM) hydro-meteorological suite, which initializes the ensemble streamflow forecasts at Météo-France. This assimilation system, using the Best Linear Unbiased Estimator (BLUE) and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow forecasts of Météo-France, which are based on the SIM model and use the European Centre for Medium-range Weather Forecasts (ECMWF) 10-day Ensemble Prediction System (EPS). Two different configurations of the assimilation system were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation system on the ensemble streamflow forecasts were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics) ensemble streamflow forecasts. It is shown that the assimilation system improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm Rate, etc.), especially for the first few days of the time range. The assimilation was slightly more efficient for small basins than for large ones.
A past discharges assimilation system for ensemble streamflow forecasts over France – Part 1: Description and validation of the assimilation system
G. Thirel, E. Martin, J.-F. Mahfouf, S. Massart, S. Ricci,F. Habets
Hydrology and Earth System Sciences (HESS) & Discussions (HESSD) , 2010,
Abstract: Two Ensemble Streamflow Prediction Systems (ESPSs) have been set up at Météo-France. They are based on the French SIM distributed hydrometeorological model. A deterministic analysis run of SIM is used to initialize the two ESPSs. In order to obtain a better initial state, a past discharges assimilation system has been implemented into this analysis SIM run, using the Best Linear Unbiased Estimator (BLUE). Its role is to improve the model soil moisture by using streamflow observations in order to better simulate streamflow. The skills of the assimilation system were assessed for a 569-day period on six different configurations, including two different physics schemes of the model (the use of an exponential profile of hydraulic conductivity or not) and, for each one, three different ways of considering the model soil moisture in the BLUE state variables. Respect of the linearity hypothesis of the BLUE was verified by assessing of the impact of iterations of the BLUE. The configuration including the use of the exponential profile of hydraulic conductivity and the combination of the moisture of the two soil layers in the state variable showed a significant improvement of streamflow simulations. It led to a significantly better simulation than the reference one, and the lowest soil moisture corrections. These results were confirmed by the study of the impacts of the past discharge assimilation system on a set of 49 independent stations.
A past discharge assimilation system for ensemble streamflow forecasts over France – Part 2: Impact on the ensemble streamflow forecasts
G. Thirel, E. Martin, J.-F. Mahfouf, S. Massart, S. Ricci, F. Regimbeau,F. Habets
Hydrology and Earth System Sciences (HESS) & Discussions (HESSD) , 2010,
Abstract: The use of ensemble streamflow forecasts is developing in the international flood forecasting services. Ensemble streamflow forecast systems can provide more accurate forecasts and useful information about the uncertainty of the forecasts, thus improving the assessment of risks. Nevertheless, these systems, like all hydrological forecasts, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation system, on an ensemble streamflow prediction system over France. An assimilation system was implemented to improve the streamflow analysis of the SAFRAN-ISBA-MODCOU (SIM) hydro-meteorological suite, which initializes the ensemble streamflow forecasts at Météo-France. This assimilation system, using the Best Linear Unbiased Estimator (BLUE) and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow forecasts of Météo-France, which are based on the SIM model and use the European Centre for Medium-range Weather Forecasts (ECMWF) 10-day Ensemble Prediction System (EPS). Two different configurations of the assimilation system were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation system on the ensemble streamflow forecasts were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics) ensemble streamflow forecasts. It is shown that the assimilation system improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm Rate, etc.), especially for the first few days of the time range. The assimilation was slightly more efficient for small basins than for large ones.
Assimilation of MODIS Snow Cover Area Data in a Distributed Hydrological Model Using the Particle Filter
Guillaume Thirel,Peter Salamon,Peter Burek,Milan Kalas
Remote Sensing , 2013, DOI: 10.3390/rs5115825
Abstract: Snow is an important component of the water cycle, and its estimation in hydrological models is of great significance concerning the simulation and forecasting of flood events due to snow-melt. The assimilation of Snow Cover Area (SCA) in physical distributed hydrological models is a possible source of improvement of snowmelt-related floods. In this study, the assimilation in the LISFLOOD model of the MODIS sensor SCA has been evaluated, in order to improve the streamflow simulations of the model. This work is realized with the final scope of improving the European Flood Awareness System (EFAS) pan-European flood forecasts in the future. For this purpose daily 500 m resolution MODIS satellite SCA data have been used. Tests were performed in the Morava basin, a tributary of the Danube, for three years. The particle filter method has been chosen for assimilating the MODIS SCA data with different frequencies. Synthetic experiments were first performed to validate the assimilation schemes, before assimilating MODIS SCA data. Results of the synthetic experiments could improve modelled SCA and discharges in all cases. The assimilation of MODIS SCA data with the particle filter shows a net improvement of SCA. The Nash of resulting discharge is consequently increased in many cases.
A Characterization of the Members of a Subfamily of Power Series Distributions  [PDF]
G. Nanjundan
Applied Mathematics (AM) , 2011, DOI: 10.4236/am.2011.26099
Abstract: This paper discusses a characterization of the members of a subfamily of power series distributions when their probability generating functions satisfy the functional equation where a, b and c are constants and is the derivative of f.
Double Negative Left-Handed Metamaterials for Miniaturization of Rectangular Microstrip Antenna  [PDF]
G. Singh
Journal of Electromagnetic Analysis and Applications (JEMAA) , 2010, DOI: 10.4236/jemaa.2010.26044
Abstract: In this paper, I have explored a significant concept for the miniaturization of microstrip patch antenna configuration by using the double negative (DNG) left-handed Metamaterials, which have dielectric permittivity and magnetic permeability both negative, simultaneously. It is achieved through the concept of phase-compensation by thin slab consist of the double positive (DPS) material, which have dielectric permittivity and magnetic permeability both positive, simultaneously and DNG metamaterials as a substrate of the microstrip patch antenna. By combining the DNG metamaterial slab with the slab made of DPS materials form a cavity resonator whose dispersion relation is independent of the sum of thickness of the slabs filling this cavity but it depends on the ratio of their thicknesses. This cavity constitutes by DPS and DNG material is used as substrate of the microstrip antennas and the DNG material slab is behave as phase compensator.
Dynamic and Configurational Approach to the Glass Transition by Nanoscale Cooperativity  [PDF]
G. Romeo
Open Journal of Biophysics (OJBIPHY) , 2012, DOI: 10.4236/ojbiphy.2012.23012
Abstract: Here we examine the findings obtained for disaccharide/water mixtures near glass transition that involves cooperative relaxation features on kinetic by viscosity and on thermodynamic behaviour by neutron scattering. Then to address cooperative phenomena that mitigate the Debye-Waller behaviour we invoke Adam-Gibbs’ idea of a cooperative rearranging region. Neutron results suggest that the excess mean square displacement behaves as free volume and is closely connected to an elementary step of the structural relaxation. Then viscosity data evidence a breakdown of the Einstein-Debye relation, decoupling attributed to the intermolecular cooperativity.
Confidence Level Estimator of Cosmological Parameters  [PDF]
G. Sironi
Journal of Modern Physics (JMP) , 2012, DOI: 10.4236/jmp.2012.329157
Abstract: Cosmological Models frequently suggest the existence of physical, quantities, e.g. dark energy, we cannot yet observe and measure directly. Their values are obtained indirectly setting them equal to values and accuracy of the associated model parameters which best fit model and observation. Apparently results are so accurate that some researchers speak of precision cosmology. The accuracy attributed to these indirect values of the physical quantities however does not include the uncertainty of the model used to get them. We suggest a Confidence Level Estimator to be attached to these indirect measurements and apply it to current cosmological models.
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