The Satellite Application Facility on Land Surface Analysis proposes a land evapotranspiration (ET) product, generated in near-real time. It is produced by an energy balance model forced by radiation components derived from data of the Spinning Enhanced Visible and Infrared Imager aboard Meteosat Second Generation geostationary satellites, at a spatial resolution of approximately 3 km at the equator and covering Europe, Africa, and South America. In this article, we assess the improvement opportunities from moderate spatial resolution satellites for ET monitoring at the Meteosat Second Generation satellite scale. Four variables, namely the land cover, the leaf area index (LAI), the surface albedo, and the open water fraction, derived from moderate-resolution satellites for vegetation monitoring are considered at two spatial resolutions, 1 km and 330 m, corresponding to the imagery provided by Satellite Pour l’Observation de la Terre (SPOT)-VEGETATION and future Project for On-Board Autonomy – Vegetation (...
Abstract. We present an evapotranspiration (ET) model developed in the framework of the EUMETSAT "Satellite Application Facility" (SAF) on Land Surface Analysis (LSA). The model is a simplified Soil-Vegetation-Atmosphere Transfer (SVAT) scheme that uses as input a combination of remote sensed data and atmospheric model outputs. The inputs based on remote sensing are LSA-SAF products: the Albedo (AL), the Downwelling Surface Shortwave Flux (DSSF) and the Downwelling Surface Longwave Flux (DSLF). They are available with the spatial resolution of the MSG SEVIRI instrument. ET maps covering the whole MSG field of view are produced from the model every 30 min, in near-real-time, for all weather conditions. This paper presents the adopted methodology and a set of validation results. The model quality is evaluated in two ways. First, ET results are compared with ground observations (from CarboEurope and national weather services), for different land cover types, over a full vegetation cycle in the Northern Hemisphere in 2007. This validation shows that the model is able to reproduce the observed ET temporal evolution from the diurnal to annual time scales for the temperate climate zones: the mean bias is less than 0.02 mm h−1 and the root-mean square error is between 0.06 and 0.10 mm h−1. Then, ET model outputs are compared with those from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Global Land Data Assimilation System (GLDAS). From this comparison, a high spatial correlation is noted, between 80 to 90%, around midday. Nevertheless, some discrepancies are also observed and are due to the different input variables and parameterisations used.
L’évolution actuelle de l’enneigement dans les Vosges (N-E de la France) a été simulée à une résolution de 4 km avec le modèle régional du climat MAR (version 3.13) forcé par les réanalyses ERA5. Moyennant un petit ajustement de seulement 3 paramètres (dont 1 °C d’augmentation du seuil de température neige/pluie), MAR a été optimisé et validé sur 5 et 8 hivers (DJF) par rapport à des observations quotidiennes (température, précipitation, hauteur de neige). Sur les 62 hivers (DJF) 1960-2021, MAR suggère une diminution significative statistiquement d’environ un facteur deux de la hauteur moyenne de neige, due à l’augmentation significative des températures (~+2 °C/62 ans). Bien que les précipitations aient légèrement augmenté (+10-20 %/62 ans) à cause d’un renforcement (non significatif) de la circulation d’ouest, elles tombent de plus en plus sous forme de pluie, en particulier en dessous de 1000 m. Au-dessus de 1000 m, il ne neige pas moins qu’avant mais il y a plus de fonte réduisant le manteau neigeux entre deux événements neigeux. En extrapolant les tendances actuelles, une anomalie de +2.5 °C (resp. +3.8 °C) par rapport aux hivers 1960-90 serait suffisante pour ne plus avoir de neige en moyenne en-dessous de 750 m (resp. 1000 m).
Abstract. Ice cores are influenced by local processes that alter surface mass balance (SMB) records. To evaluate if large-scale atmospheric circulation explains contrasted SMB trends at eight East Antarctic ice rises, we assimilated ice core SMB records within a high-resolution downscaled atmospheric model, while incorporating radar-derived SMB constraints to quantify local observation errors. The reconstruction captures the diverse variability from SMB records but may over-fit by introducing unrealistic wind spatial heterogeneity. While local errors are quantified, they might not cover all uncertainties. Moreover, small-scale wind circulation, unresolved in the reconstruction, could significantly affect local ice core SMB signals.
Abstract. Vegetation parameters derived from the geostationary satellite MSG/SEVIRI have been distributed at a daily frequency since 2007 over Europe, Africa and part of South America, through the LSA-SAF facility. We propose here a method to handle two new remote sensing products from LSA-SAF, leaf area index and Fractional Vegetation Cover, noted LAI and FVC respectively, for land surface models at MSG/SEVIRI scale. The developed method relies on an ordinary least-square technique and a land cover map to estimate LAI for each model plant functional types of the model spatial unit. The method is conceived to be applicable for near-real time applications at continental scale. Compared to monthly vegetation parameters from a vegetation database commonly used in numerical weather predictions (ECOCLIMAP-I), the new remote sensing products allows a better monitoring of the spatial and temporal variability of the vegetation, including inter-annual signals, and a decreased uncertainty on LAI to be input into land surface models. We assess the impact of using LSA-SAF vegetation parameters compared to ECOCLIMAP-I in the land surface model H-TESSEL at MSG/SEVIRI scale. Comparison with in-situ observations in Europe and Africa shows that the results on evapotranspiration are mostly improved, and especially in semi-arid climates. At last, the use of LSA-SAF and ECOCLIMAP-I is compared with simulations over a North-South Transect in Western Africa using LSA-SAF radiation forcing derived from remote sensing, and differences are highlighted.
We provide in this dataset maps at 5.5 km resolution of the daily and yearly accumulated snowfall over emerged land (Ice Sheet) of Dronning Maud Land (Antarctica) from 1850 to 2014. We used a statistical method to derive fine resolution maps from GCM runs (CESM2, 10 runs). In the method, we searched for analogs in a database we constructed from the association between re-analyses large-scale meteorological fields (ERA5 and ERA-Interim) and RCM daily accumulated snowfall (RACMO2.3p5.5). RACMO2.3p5.5 data are available freely on request (https://www.projects.science.uu.nl/iceclimate/models/antarctica.php). CESM2 CMIP6 runs are also freely available (https://esgf-node.llnl.gov/search/cmip6/). The complete description of the algorithm and performance is described in: Ghilain N., Vannitsem S., Dalaiden Q., Goosse H., De Cruz L.,WeiW., Reconstruction of daily snowfall accumulation at 5.5km resolution over Dronning Maud Land, Antarctica, from 1850 to 2014 using an analog-based downscaling technique, submitted to Earth System Science Data (ESSD). The MASS2ANT Snowfall dataset is composed of the annual estimations of snowfall over Dronning Maud Land, the daily time series for the total period for all the emerged grid points of the domain, the principal components time series and Empirical Orthogonal Functions (EOF) offering the possibility to analyze the synoptic weather patterns associated to snowfall over the ice sheet and the Principal Component weights (PCs) time series from the re-analysis in case one wants to extend or improve the database. Realistic weather patterns can be recomposed in associating (product of matrices) the PCs with the EOFs. Here (Daily fields - Part 3), we provide the daily snowfall time series resulting from the downscaling of the 3 last members of CESM2, using ERA-Interim (or ERA5) and RACMO2.3p5.5 for training.
Information on land surface properties finds applications in a range of areas related to weather forecasting, environmental research, hazard management and climate monitoring. Remotely sensed observations yield the only means of supplying land surface information with adequate time sampling and a wide spatial coverage. The aim of the Satellite Application Facility for Land Surface Analysis (Land-SAF) is to take full advantage of remotely sensed data to support land, land–atmosphere and biosphere applications, with emphasis on the development and implementation of algorithms that allow operational use of data from European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) sensors. This article provides an overview of the Land-SAF, with brief descriptions of algorithms and validation results. The set of parameters currently estimated and disseminated by the Land-SAF consists of three main groups: (i) the surface radiation budget, including albedo, land surface temperature, and downward short- and longwave fluxes; (ii) the surface water budget (snow cover and evapotranspiration); and (iii) vegetation and wild-fire parameters.
'/%3e%3c/g%3e%3cuse xlink:href='%23d' x='45.714'/%3e%3cuse xlink:href='%23e' transform='matrix(-1 0 0 1 479.792 0)'/%3e%3cg id='f' fill='%23fff'%3e%3cpath fill='%23039' d='M232.636 202.406v.005c0 2.212.85 4.227 2.212 5.69 1.365 1.466 3.245 2.377 5.302 2.377 2.067 0 3.944-.905 5.303-2.365 1.358-1.459 2.202-3.472 2.202-5.693v-10.768l-14.992-.013-.028 10.766'/%3e%3ccircle cx='236.074' cy='195.735' r='1.486'/%3e%3ccircle cx='244.392' cy='195.742' r='1.486'/%3e%3ccircle cx='240.225' cy='199.735' r='1.486'/%3e%3ccircle cx='236.074' cy='203.916' r='1.486'/%3e%3ccircle cx='244.383' cy='203.905' r='1.486'/%3e%3c/g%3e%3cuse xlink:href='%23f' y='-26.016'/%3e%3cuse xlink:href='%23f' x='-20.799'/%3e%3cuse xlink:href='%23f' x='20.745'/%3e%3cuse xlink:href='%23f' y='25.784'/%3e%3c/svg%3e)
