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P. Quintana-Seguí, P. Le Moigne, Y. Durand, E. Martin, F. Habets, M. Baillon, C. Canellas, L. Franchisteguy, and S. Morel

Abstract

Système d’analyse fournissant des renseignements atmosphériques à la neige (SAFRAN) is a mesoscale atmospheric analysis system for surface variables. It produces an analysis at the hourly time step using ground data observations. One of SAFRAN’s main features is that it is based on climatically homogeneous zones and is able to take vertical variations into account. Originally intended for mountainous areas, it was later extended to cover France. This paper focuses on the validation of the extended version. The principle of the analysis is described and its quality was tested for five parameters (air temperature, humidity, wind speed, rainfall, and incoming radiation), using Météo-France’s observation network and data of some well-instrumented stations. Moreover, SAFRAN’s rainfall was compared with another analysis, known as analyse utilisant le relief pour l’hydrométéorologie (Aurelhy). Last, two different versions of SAFRAN were compared for mountain conditions. Temperature and relative humidity were well reproduced, presenting no bias. Wind speed was also well reproduced; however, its bias was −0.3 m s–1. The interpolation from the 6-h time step of the analysis to the 1-h time step was one of the sources of error. The precipitation analysis was robust and not biased; its root-mean-square error was 2.4 mm day−1. This error was mainly due to the spatial heterogeneity of the precipitation within the geographical zones of analysis (1000 km2). The analysis of incoming solar radiation presented some biases, especially in coastal areas. The results of the comparison with some well-instrumented sites were encouraging. SAFRAN is being run operationally at Météo-France on a real-time basis for various applications.

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R. Alkama, B. Decharme, H. Douville, M. Becker, A. Cazenave, J. Sheffield, A. Voldoire, S. Tyteca, and P. Le Moigne

Abstract

In earth system models, the partitioning of precipitation among the variations of continental water storage, evapotranspiration, and freshwater runoff to the ocean has a major influence on the terrestrial water and energy budgets and thereby on simulated climate on a wide range of scales. The evaluation of continental hydrology is therefore a crucial task that requires offline simulations driven by realistic atmospheric forcing to avoid the systematic biases commonly found in global atmospheric models. Generally, this evaluation is done mainly by comparison with in situ river discharge data, which does not guarantee that the spatiotemporal distribution of water storage and evapotranspiration is correctly simulated. In this context, the Interactions between Soil, Biosphere, and Atmosphere–Total Runoff Integrating Pathways (ISBA-TRIP) continental hydrological system of the Centre National de Recherches Météorologiques is evaluated by using the additional constraint of terrestrial water storage (TWS) variations derived from three independent gravity field retrievals (datasets) from the Gravity Recovery and Climate Experiment (GRACE). On the one hand, the results show that, in general, ISBA-TRIP captures the seasonal and the interannual variability in both TWS and discharges. GRACE provides an additional constraint on the simulated hydrology and consolidates the former evaluation only based on river discharge observations. On the other hand, results indicate that river storage variations represent a significant contribution to GRACE measurements. While this remark highlights the need to improve the TRIP river routing model for a more useful comparison with GRACE [Decharme et al. ( of the present study)], it also suggests that low-resolution gravimetry products do not necessarily represent a strong additional constraint for model evaluation, especially in downstream areas of large river basins where long-term discharge data are available.

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D. Carrer, S. Lafont, J.-L. Roujean, J.-C. Calvet, C. Meurey, P. Le Moigne, and I. F. Trigo

Abstract

The Land Surface Analysis Satellite Applications Facility (LSA SAF) project radiation fluxes, derived from the Meteosat Second Generation (MSG) geostationary satellite, were used in the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model (LSM), which is a component of the Surface Externalisée (SURFEX) modeling platform. The Système d’Analyze Fournissant des Renseignements Atmosphériques à la Neige (SAFRAN) atmospheric analysis provides high-resolution atmospheric variables used to drive LSMs over France. The impact of using the incoming solar and infrared radiation fluxes [downwelling surface shortwave (DSSF) and longwave (DSLF), respectively] from either SAFRAN or LSA SAF, in ISBA, was investigated over France for 2006. In situ observations from the Flux Network (FLUXNET) were used for the verification. Daily differences between SAFRAN and LSA SAF radiation fluxes averaged over the whole year 2006 were 3.75 and 2.61 W m−2 for DSSF and DSLF, respectively, representing 2.5% and 0.8% of their average values. The LSA SAF incoming solar radiation presented a better agreement with in situ measurements at six FLUXNET stations than the SAFRAN analysis. The bias and standard deviation of differences were reduced by almost 50%. The added value of the LSA SAF products was assessed with the simulated surface temperature, soil moisture, and the water and energy fluxes. The latter quantities were improved by the use of LSA SAF satellite estimates. As many areas lack a high-resolution meteorological analysis, the LSA SAF radiative products provide new and valuable information.

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L. Bouilloud, E. Martin, F. Habets, A. Boone, P. Le Moigne, J. Livet, M. Marchetti, A. Foidart, L. Franchistéguy, S. Morel, J. Noilhan, and P. Pettré

Abstract

A numerical model designed to simulate the evolution of a snow layer on a road surface was forced by meteorological forecasts so as to assess its potential for use within an operational suite for road management in winter. The suite is intended for use throughout France, even in areas where no observations of surface conditions are available. It relies on short-term meteorological forecasts and long-term simulations of surface conditions using spatialized meteorological data to provide the initial conditions. The prediction of road surface conditions (road surface temperature and presence of snow on the road) was tested at an experimental site using data from a comprehensive experimental field campaign. The results were satisfactory, with detection of the majority of snow and negative road surface temperature events. The model was then extended to all of France with an 8-km grid resolution, using forcing data from a real-time meteorological analysis system. Many events with snow on the roads were simulated for the 2004/05 winter. Results for road surface temperature were checked against road station data from several highways, and results for the presence of snow on the road were checked against measurements from the Météo-France weather station network.

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