dataesr/scientific-paragraphs-categorization huggingface dataset:
A limited number of geographical layers, protected by copyright issues The layers used in the previous ESPON Programme were provided by Eurogeographics, representing the European National Mapping, Cadastral and Land Registry Authorities (60 organizations from 46 countries). One important issue regarding the use of this accurate data source is the price: for an unlimited user band and a full European coverage for local territorial units, the annual license price is 51,500 euros 3. It's a major limitation for the dissemination and the reproducibility of ESPON analysis: project can disseminate the datasets, but not the geometries they used to display their results on maps.
results in practice, development of scientific Platform supported by an ESPON Database project and actions dealing with territorial indicators and monitoring as well as tools related to territorial analyses, typologies, modelling and updates of statistics. The revised version of the ESPON 2020 Cooperation Programme was adopted on the 26 May 2016 by the European Commission. The ESPON 2020 Programme aims at promoting and fostering a European territorial dimension in development and cooperation by providing evidence, knowledge transfer and policy learning to public authorities and other policy actors at all levels. Its mission remains. The main objectives were still further refined: "to support the reinforcement of the effectiveness of EU Cohesion Policy and other sectoral policies and programmes under European Structural Investment (ESI) funds as well as national and regional territorial development policies, through the production, dissemination and promotion of territorial evidence covering the entire territory of the 28 EU Members States, as well as 4 Partner States of Iceland, Liechtenstein, Norway and Switzerland".
The study period covers 2015-2016 snow season which corresponds to the first operational year of Sentinel-2A, therefore the full revisit capacity was not yet achieved. Yet, we show that the data assimilation led to a better agreement with independent observations of the snow height at an automatic weather station and the snow cover extent from MODIS. The performance of the data assimilation scheme should benefit from the continuous improvements of MERRA-2 reanalysis and the full revisit capacity of Sentinel-2.
Since 2017, the Sentinel-2 mission provides high resolution multi-spectral observations of the global land surface with a 5-day revisit time  [27]. Hence, Sentinel-2 data have the potential to further improve data assimilation schemes for snow modelling. In addition, as part of the Copernicus program of the European Commission, Sentinel-2 data are freely available and should be provided continuously for the next decade at least  [28] , which are two important assets in the perspective of an operational snow monitoring system in a developing country like Morocco. The objective of this study is to implement an assimilation scheme for Sentinel-2 data to simulate the spatial distribution of the snow water equivalent and snow melt in the High Atlas without ground data. We introduce a new assimilation scheme based on SnowModel, a spatially-distributed energy-balance snowpack model  [29] , which has been previously evaluated in the High Atlas  [13].
The data assimilation scheme is based on the particle filter, as this approach is better adapted to non-linear physically-based model like SnowModel  [30] [31] [32]. The model takes MERRA-2 data as meteorological forcing and Sentinel-2 snow cover area products as observational data. The other input data are a digital elevation model and a land cover map. The scheme is demonstrated in the case of the Rheraya catchment over the 2016-2017 snow season. The Rheraya catchment is a pilot catchment in the High Atlas near Marrakech where meteorological data are available for the evaluation of the data assimilation  [33]. We compare the output of the data assimilation scheme to the open loop simulation (i. e. , without assimilation of Sentinel-2 data), and to a reference simulation which was forced by in situ meteorological data instead of MERRA-2.
Topography and Land Cover The 90 m Shuttle Radar Topography Mission (SRTM) digital elevation model was used after resampling it to 200 m spatial resolution by using cubic interpolation. The choice of the spatial resolution is based on a previous study in the Rheraya catchment  [36] , where we analyzed the trade-off between spatial resolution and the computation time. This study suggested that a distributed snow model with a grid coarser than 250 m is not able to capture the high spatial variability in the snow cover patterns due to the importance of the incoming solar radiation in the snowpack energy budget. The land cover use data set is derived from European Space Agency Climate Change I (ESA-CCI). The majority of the area is bare soil.
Snow Cover Area We queried all available Sentinel-2 snow covered area (SCA) products between January and July 2016 in the L2B-Snow collection of the Theia Land data center  [37]. All snow products were derived from Sentinel-2A acquisitions since Sentinel-2B was only launched in 2017. The Rheraya catchment intersects tiles 29RNQ and 29RPQ, but we used only the products from tile 29RPQ since the tile 29RNQ only covers the low elevation area of the catchment where the snow is rarely observed. The downloaded snow products were clipped over the Rheraya catchment in their native projection (WGS-84 UTM 29N). To reduce the input/output operation time in the assimilation phase, we removed the products with a cloud cover higher than 70%. We obtained 13 SCA maps of the Rheraya catchment with a snow cover fraction ranging from 82% to 0% and a cloud fraction ranging from 0% to 66% (Table  1 ).
The output of LIS is a single band raster resolution having four possible values: snow, no-snow, cloud and no-data. For further details, the reader can refer to  [37]. We used the daily MODIS snow products (MOD10A1 collection 6) to validate the different simulations. These products have a spatial resolution close to 500 m and a daily revisit time. The MODIS products were post-processed to generate a gap-free time series of snow cover fraction images (SCF)  [4, 8]. First, the NDSI in each snow pixel was converted to SCF using the equation that was used in the collection 5  [40]  SCF = -0. 01 + 1. 45 × NDSI (2) Then, the data gaps due to cloud obstruction were interpolated with the algorithm of  [8] , which was specifically developed and validated in the High Atlas mountains. The products were finally clipped over the Rheraya catchment.
MERRA-2 Data The meteorological forcing were taken from NASA's second Modern-Era Retrospective analysis for Research and Applications (MERRA-2). MERRA-2 reanalysis data are widely used, including studies dealing with the assimilation of snow cover data  [24, 25, 41]. MERRA-2 data have a spatial resolution of 0. 5 • × 0. 625 • and a temporal resolution of 1 hour. MERRA-2 includes the assimilation of satellite observations that were not available to MERRA and an improved representation of key variables for simulating snowpack evolution  [10]. We extracted the precipitation, 2-m air temperature, 2-m specific humidity and 2-m wind speed in the four nearest grid cell of the Rheraya catchment. The post-processing steps to make MERRA-2 compatible with SnowModel are described in  [4].
In Situ Observations The hourly data from three automatic weather stations (AWS) were used as input to SnowModel to generate synthetic observations and as validation data (snow height at Oukaimeden). Table  2  summarizes the AWS characteristics and available data. We also used a continuous record of the snow height at Oukaimeden (3230 m. a. s. l. ) measured by an acoustic snow gauge. We also used the observed river discharge at the gauging station of Tahanaout (31. 29 • N, -7. 96 • E, 1048 m) provided by the Tensift river basin water agency (ABHT). A river stage device measures the height of the water three times per day. The gauging is performed once per month at best for logistical reasons. These measurements are known to have large errors given the difficulty to establish a robust stage-discharge relation in the Rheraya river, which has an unstable river bed.
• the synthetic data simulation (referred to as AWS) was obtained by running SnowModel with in situ meteorological observations from the AWS. The output of this simulation was considered as an independent dataset to evaluate the effect of the assimilation. The main input and output of the simulations is presented in Figure  3. The performance of the DA was qualified by computing the RMSE (Equation (  3 )) and the Pearson correlation coefficient between the output of the DA scheme and the synthetic observations of air temperature, precipitation, SWE and SCA. RMSE = 1 N N ∑ i=1 (X i -X truth i ) 2 (3) R = E[XY]E[X]E[Y] E[X 2 ] -E[X] 2 E[Y 2 ] -E[Y] 2 (4) where X i is the ith element of the vector X, and X truth i is the ith element of the synthetic observation vector X truth. E is the expectation.
τ is the temperature lapse rate ( • C•km -1 ), which depends on the month of the year. As a first step, MicroMet adjusts the station data to a common elevation Z = 0 using Equation (5). Then, the temperature is interpolated using a Gaussian distance-dependent weighting function to the model grid  [46]. Finally, Equation (  5 ) is used again to adjust the gridded temperatures values back to the actual elevation taken from the DEM. The same method is used for the relative humidity and precipitation. The relative humidity at the station is first converted to dew point temperature. The same function as for the air temperature is used to account for elevation (Equation (5), but with different lapse rate values. In the case of the precipitation, a different function of elevation is used: P x = P stn × [1 + χ(Z x -Z stn )] [1 -χ(Z x -Z stn )] (6) where P stn is the precipitation at the station, P x (mm) the precipitation in the target grid cell, Z x the elevation of the grid cell.
χ is the precipitation correction factor (km -1 ), which also depends on the month of the year. The shortwave and longwave radiations are derived from the interpolated temperature and humidity on each grid cell accounting for the effect of the slope and orientation of the terrain  [43]. The performance of MicroMet in a high-mountain semi-arid area was evaluated by Gascoin et al. [47]. The results of this study indicate that the uncertainties in the meteorological inputs due to MicroMet spatial interpolation are typically lower than the expected uncertainties in MERRA-2 data. As noted above, MicroMet was originally designed to spatially distribute meteorological variables from weather station measurements. However it can also be used to downscale large scale meteorological data like MERRA-2 data.
In this case, the MERRA-2 data are simply considered as the meteorological measurements of a virtual weather station that is located in the center of the MERRA-2 grid cell. The elevation of the virtual station is calculated from the surface geopotential height of the MERRA-2 cell. This approach was successfully applied in the High-Atlas  [4]  and in the Andes  [12]. Preliminary sensitivity tests revealed that the parameterization of the precipitation solid fraction caused significant biases in the simulated snow cover area after the snowfalls. Excessive model biases should be corrected as much as possible to make the assimilation scheme efficient. By default, MicroMet uses a temperature threshold of 2 • C to distinguish between solid and liquid precipitation  [43]. We found that theses biases could be reduced by enhancing the default parameterization in SnowModel to account for the influence of the relative humidity on the precipitation phase using the empirical relation of Froidurot et al.
Ensemble of Meteorological Forcings At each assimilation step, the MERRA-2 air temperature and precipitation are perturbed to generate an ensemble of meteorological forcing spanning the next assimilation window. It is important to emphasize that the perturbation is done before the downscaling by MicroMet, i. e. , from the coarse scale MERRA-2 data, which are considered as virtual station measurements (Section 3. 2). For the air temperature, the perturbation is done by adding a white noise with zero mean and standard deviation of 2 • C  [18, 31]. The precipitation data were perturbed by a multiplicative correction factor randomly sampled between 0. 75 and 1. 5  [56].
Implementation The DA workflow uses code in Matlab, Fortran and shell scripts. The scheme was implemented in the CNES high performing computing cluster (HPCC). The main script uses the Portable Batch System (PBS Pro) job scheduler to execute the different steps. As a first step, the main HPCC PBS script creates N ens perturbed meteorological forcing files (ASCII) from the initial meteorological file. Thereafter, N ens simulations are performed with SnowModel in parallel (Figure  7 ). This allows to get N ens of SWE simulations (GDAT binary files). For each particle the SWE is converted to SCA, then its weight is computed based on the observed snow cover area (GeoTiff). Finally, all particles enter the particle filtering process. These steps are repeated until the end of the observations (Figure  7 ).
Discussion The results show that the data assimilation scheme has reduced the bias and increased the correlation with two independent observation datasets of snow height and snow cover area. However, these observations provide only a partial evaluation of the simulations: (i) the snow height was measured only at the Oukaimeden weather station, therefore it is not necessarily representative of the snow height evolution over the entire catchment, even at similar elevations; (ii) a better agreement with the MODIS snow cover area does not necessarily mean that other snowpack properties like the SWE were better simulated. However, the combination of these observations suggest that the assimilation improves the model outputs. The comparison with the synthetic data (AWS simulation) showed that MERRA-2 provided a good estimate of the meteorological forcing in agreement with a previous study in the neighbouring catchment of the Ourika catchment  [4].
This image was captured only 3 days after the precipitation event of the 14 February, the largest precipitation event of the study period (Figure  12 ). As a result this observation was important in the general performance of the assimilation scheme. The approach presented in this study is based on the downscaling of large scale MERRA-2 data with MicroMet. Each MERRA-2 grid cell is considered as a virtual station. In the proposed data assimilation scheme, the meteorological forcing is perturbed at each virtual station before downscaling to the resolution of the snowpack model. This enables to restrain the number of particles (and thus the computation cost), in comparison with a data assimilation scheme which perturbs the forcing at the model grid cell level. Another advantage of this approach is that it guarantees a spatially smooth distribution of the posterior SWE, because MicroMet distributes the meteorological forcing using an optimal Gaussian distance-dependent weighting function  [43, 46].
Funding: This research was funded by  Centre National d'Etudes Spatiales (CNES)  and the  Région Occitanie. Acknowledgments: M. W. B. acknowledges the  CNES and the Région Occitanie  for funding his Ph. D. grant. S. G. acknowledges support by the  CNES  through the  Tosca programme. We thank the  Agence du Bassin Hydraulique du Tensift and Laboratoire Mixte International (LMI TREMA)  for providing the discharge and meteorological data. The MERRA-2 data used in this study have been provided by the  Global Modeling and Assimilation Office (GMAO)  at  NASA Goddard Space Flight Center. Sentinel-2 is a mission of the  European Commission Copernicus program  operated by the  ESA. We thank the  Theia Land data center  for providing the Sentinel-2 snow products.
Introduction The Caviar software was initially developed at Queen Mary University of London (QMUL) in 2003-4 as the Cassini spacecraft was approaching Saturn. Its development fulfilled an immediate need for a software package capable of performing astrometry of the Saturnian satellites using images recorded via the Cassini Imaging Science Subsystem (ISS). The software has since been used to generate approximately 5900 measurements of the positions of the Saturnian moons from Cassini ISS images  (Murray et al. 2005; Cooper et al. 2008 Cooper et al. , 2014 Cooper et al. , 2015;; Zhang et al. 2017) , contributing a significant part of the astrometric dataset recently used to provide observational constraints on Saturn's internal structure  (Lainey et al. 2017). Cassini ISS astrometry of Saturn's satellites has also been performed by  Spitale et al.
(2006)  and  Tajeddine et al. (2013 Tajeddine et al. ( , 2015) )  using different software, generating several thousands of observations. The Caviar software is available for free download from: ftp://ftp. imcce. fr/pub/softwares/caviar. Full Tables  1  and 5  are only available at the CDS via anonymous ftp to cdsarc. u-strasbg. fr (130. 79. 128. 5)  or via http://cdsarc. u-strasbg. fr/viz-bin/qcat?J/A+A/610/A2 Following an ongoing collaboration between the Cassini group at QMUL and the Institut de Mécanique Céleste et de Calcul des Éphémérides (IMCCE) of the Paris Observatory, Caviar has been extensively redesigned, incorporating a new graphical user interface (GUI) and a number of new options. This version of the software is now available to the community for general use, and in this paper we provide a broad outline of the features and capabilities of the new software, illustrated with examples.
This paper is not intended to be a complete guide to using Caviar. For detailed parameter descriptions and instructions for use, we refer the reader to the user guide bundled with the software. Caviar is written using the IDL language (Harris Geospatial Solutions). The computational capabilities of Caviar also rely heavily on the SPICE software toolkit  (Acton 1996)  as well as internal IDL routines and a number of other external IDL libraries (see Sect. 3). In addition to describing Caviar, in this paper we provide a total of 256 new astrometric measurements of the positions of five of the main moons of Saturn, reduced using the new version of the software. Observed-minus-computed residuals are provided relative to the latest ephemerides from JPL and IMCCE. Throughout this paper, we use the Cassini ISS convention of referring to the image pixel coordinate along the x-axis as "sample" and the y-coordinate as "line".
Obtaining Caviar The Caviar software is available for free download under the terms of a Creative Commons Attribution-NonCommercial-ShareAlike 4. 0 International License 1. The software only, without SPICE kernels is available at the IMCCE website 2. At the time of writing, the Cassini mission is still in progress and mission-specific SPICE kernels typically cover relatively short time segments, requiring a large number of kernels to span the entire mission to date. For convenience, a complete set of SPICE kernels covering the Cassini tour from 2000 to 2016 has also been assembled to avoid the need to download them individually. It is anticipated that, following the end of the Cassini mission in late 2017, reconstructed and concatenated kernels will be made available, reducing the number of kernels required to cover the entire mission.
Requirements for running Caviar Caviar has so far been tested on Mac OS X and Linux. The following requirements are necessary in order to run Caviar: -A full license for IDL. -An active internet connection to allow access to star catalogues via the Vizier web server. -The IDL version of the SPICE toolkit 4. -SPICE kernels: Caviar accesses spacecraft instrument information, ephemerides, and other information relating to planetary bodies from SPICE kernels. At the time of writing, several hundred different kernels are needed to cover the Cassini tour. To simplify installation, a complete set of the kernels has been assembled for use with Caviar (see Sect. 2). Alternatively, kernels may also be obtained from the NAIF website 5. -The CDSCLIENT package, used to access reference star information via the Vizier web server 6.
2017) and Tycho2  (Høg et al. 2000)  catalogues have been selected in preference to Gaia-DR1  (Lindegren et al. 2016 ) because of the lack of proper motion information in the first release of the Gaia catalogue. However, the code can easily be modified by the user to access any desired catalogue by making the appropriate changes to the parameters in the Vizquery command inside the Caviar code. This is recommended as soon as the next release of the Gaia catalogue becomes available in 2018. The pointing correction involves firstly the detection of starlike objects in the image, using the DAOPHOT Find algorithm  (Stetson 1987). The positions of these imaged objects are then matched to nearby reference stars from the star catalogues. It may be necessary to perform an approximate alignment of the reference star field with the imaged stars, before matching, if the offset is more than a few pixels.
The exposure length might still be an issue, however, and in fact no stars of any magnitude were actually detectable in this image, due to the short (70 ms) exposure duration, and consequently the pointing correction could not be performed (this image is not included in the reduced dataset). For images with no catalogue star coverage, or images with star coverage but no detectable stars, perhaps due to the choice of exposure length, it may still be possible to correct the camera pointing direction using other reference features in the image. Caviar allows circular rings of arbitrary radius, centred on the planet, to be projected onto the image. A published model of Saturn's F ring  (Cooper et al. 2013)  can also be superimposed. The ring template can then be manually moved using the mouse buttons until it aligns with the imaged rings, allowing an approximate pointing correction to be performed without the use of reference stars.
New observations To illustrate the use of Caviar, we include a total of 256 new astrometric positions of five of the main moons of Saturn, Mimas (44), Tethys (58), Dione (55), Rhea (33), Iapetus (63), and Hyperion (3), for the period 2013 August 30 to 2016 June 04. While there is a high volume of data covering this period, we selected and reduced only the highest resolution images where the moons appeared large in size. As a consequence, these new data will provide the tightest constraints on the orbits compared to other ISS data of the same period. The raw images were obtained from the PDS Imaging Atlas 7  by searching the database for the chosen satellites, selecting only images taken using clear filters (CL1 and CL2). We excluded Enceladus from the list of chosen targets because of existing published Cassini astrometry for the period 2004 to 2011  (Tajeddine et al.
Summary and future work The Caviar software in its original form has a proven track record in generating thousands of high-precision astrometric observations of the Saturnian moons using Cassini ISS images. The software is now available for general release in the form of the new and updated version described and demonstrated in this paper, incorporating a complete restructuring of the code and a new graphical user interface. The structure of the code lends itself relatively easily to further modification within the framework of the IDL language and.