Land surface albedo from MSG/SEVIRI: retrieval method, validation, and application for weather forecast

The European Meteorological Satellite Organization (EUMETSAT) maintains a number of decentralized processing centers dedicated to different scientific themes. The Portuguese Meteorological Institute hosts the Satellite Application Facility on Land Surface Analysis (LSA-SAF). The primary objective of the LSA-SAF is to provide added-value products for the meteorological and environmental science communities with main applications in the fields of climate modeling, environmental management, natural hazards management, and climate change detection. Since 2005 data from Meteosat Second Generation satellite are routinely processed in near real time by the LSA-SAF operational system in Lisbon. Presently, the delivered operational products comprise land surface albedo and temperature, shortwave and long-wave downwelling radiation fluxes, vegetation parameters and snow cover. After more than ten years (1999-2010) of research, development, and progressive operational activities, a summary of the surface albedo product characteristics and performances is presented. The relevance of LSA-SAF albedo product is analyzed through a weather forecast model (ALADIN) in order to account for the inter-annual spatial and temporal variability. Results clearly show a positive impact on the 12-hour forecast of 2m temperatures

The SAFRAN-ISBA-MODCOU hydrometeorological model applied over France

An edited version of this paper was published by AGU. Copyright (2008) American Geophysical UnionThe hydrometeorological model SIM consists in a meterological analysis system (SAFRAN), a land surface model (ISBA) and a hydrogeological model (MODCOU). It generates atmospheric forcing at an hourly time step, and it computes water and surface energy budgets, the river ow at more than 900 rivergauging stations, and the level of several aquifers. SIM was extended over all of France in order to have a homogeneous nation-wide monitoring of the water resources: it can therefore be used to forecast flood risk and to monitor drought risk over the entire nation. The hydrometeorologival model was applied over a 10-year period from 1995 to 2005. In this paper the databases used by the SIM model are presented, then the 10-year simulation is assessed by using the observations of daily stream-flow, piezometric head, and snow depth. This assessment shows that SIM is able to reproduce the spatial and temporal variabilities of the water fluxes. The efficiency is above 0.55 (reasonable results) for 66 % of the simulated rivergages, and above 0.65 (rather good results) for 36 % of them. However, the SIM system produces worse results during the driest years, which is more likely due to the fact that only few aquifers are simulated explicitly. The annual evolution of the snow depth is well reproduced, with a square correlation coeficient around 0.9 over the large altitude range in the domain. The stream ow observations were used to estimate the overall error of the simulated latent heat ux, which was estimated to be less than 4 %

Typologie des sécheresses sur la France et outils de suivi de la ressource en eau utilisés à Météo-France

La France a subi des sécheresses en 2003, 2005, 2006, dont les conséquences, notamment sur les bâtiments ont mis en lumière l'importance de ce risque naturel. En raison de ses missions, Météo-France suit l'ensemble des phénomènes météorologiques, dont les sécheresses, via la quantification des déficits pluviométriques observés par un important réseau de mesure de précipitation et des systèmes d'analyse atmosphérique. Dans cet article, on présente les différents types de sécheresse et la comparaison des sécheresses récentes avec par raport avec celles connues dans le passé. De plus, Météo-France utilise des modèles de suivi du bilan en eau comme le modèle Safran-Isba-Modcou (SIM) pour mieux appréhender les conséquences des déficits pluviométriques et de la demande évaporative sur l'extension et l'intensité des sécheresses. SIM permet de calculer des bilans d'eau spatialisés et en particulier un indice d'humidité et un stock de neige sur les montagnes. Ces données sont utilisées dans le suivi hydrologique en France. Les caractéristiques et quelques résultats en terme de contenu en eau du sol de deux de ces modèles sont représentés

Vers une prévision d'ensemble des débits à l'échelle des grands bassins versants français

International audienceUn système de prévisions d'ensemble hydrologiques à 10 jours sur la France a été construit à partir de la chaîne SAFRAN-ISBA-MODCOU et des sorties du modèle de prévision du CEPMMT. Dans une première étape, les données météorologiques sont désagrégées sur la grille de calcul du modèle de surface. Ensuite, un ensemble de prévisions hydrologiques est calculé. Le comportement du modèle a été testé sur une année par comparaison avec une simulation de référence, montrant de bons résultats globaux. Des travaux de recherches sont poursuivis en vue d'améliorer le système, en parallèle de tests utilisateurs. Ces travaux ont montré que l'utilisation d'une source de prévisions météorologiques d'ensemble adaptée à la courte échéance améliore nettement les scores hydrologiques à ces échéances. Enfin, un système d'assimilation des débits passés est en cours de développement afin de permettre de fournir un état initial plus réaliste au système de prévision

Analysis of Near-Surface Atmospheric Variables: Validation of the SAFRAN Analysis over France

International audienceSystème 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 1h 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

Safran-Isba-Modcou (SIM) : Un outil pour le suivi hydrométéorologique opérationnel et les études

Titre traduit en anglais : A hydrometeorological tool for operational monitoring and research: the Safran-Isba-Modcou application (SIM) Résumé traduit en anglais : The Safran-Isba-Modcou model combines an analysis system of the atmospheric forcing, a land surface scheme, and a hydrogeological model. Advanced research has been done around this suite during the last fifteen years. Since 2003, it has been transferred to Météo-France real-time operational environment and has been progressively supplied with an extended climatology. Its operational use now covers water resources monitoring, flood forecasting, and drought assessment. It is also particularly useful for studying the impact of climate change on the water cycle.International audienceL'application Safran-Isba-Modcou, qui combine un système d'analyse de forçage atmosphérique, un schéma de surface et un module hydrogéologique, a fait l'objet de nombreux travaux de recherche au cours des quinze dernières années. Depuis 2003, ce modèle a été porté dans l'environnement opérationnel de Météo-France et s'est enrichi progressivement d'une climatologie étendue. Son utilisation opérationnelle couvre aujourd'hui les domaines du suivi de la ressource en eau, de la prévision des crues et de l'identification des sécheresses. Il se révèle également être un outil particulièrement adapté pour les études d'impact du changement climatique sur le cycle de l'eau

Road Surface Condition Forecasting in France

International audienceA 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 networ

Coordinating an operational data distribution network for CMIP6 data

The distribution of data contributed to the Coupled Model Intercomparison Project Phase 6 (CMIP6) is via the Earth System Grid Federation (ESGF). The ESGF is a network of internationally distributed sites that together work as a federated data archive. Data records from climate modelling institutes are published to the ESGF and then shared around the world. It is anticipated that CMIP6 will produce approximately 20 PB of data to be published and distributed via the ESGF. In addition to this large volume of data a number of value-added CMIP6 services are required to interact with the ESGF; for example the citation and errata services both interact with the ESGF but are not a core part of its infrastructure. With a number of interacting services and a large volume of data anticipated for CMIP6, the CMIP Data Node Operations Team (CDNOT) was formed. The CDNOT coordinated and implemented a series of CMIP6 preparation data challenges to test all the interacting components in the ESGF CMIP6 software ecosystem. This ensured that when CMIP6 data were released they could be reliably distributed. No. DE-ACO2-05CH11231 and authors at Lawrence Livermore National Laboratory (LLNL) under contract DE-AC52-07NA27344 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).Funding Agencies|US Department of EnergyUnited States Department of Energy (DOE) [DE-AC02-05CH11231, DE-AC52-07NA27344]; European UnionEuropean Commission [824084]; French National Research Agency project CONVERGENCEFrench National Research Agency (ANR) [ANR-13-MONU-0008-02]; National Collaborative Research Infrastructure Strategy (NCRIS)-funded National Computational Infrastructure (NCI) Australia; Australian Research Data Commons (ARDC)</p