dataesr/scientific-paragraphs-categorization huggingface dataset:
The new generation of synthetic-aperture radar (SAR) satellite data provides images with metric resolution and short revisit time. The TerraSAR-X satellite, with 1. 477 m ⇥ 2. 44 m resolution and revisit time of 11 days, gives dense information both spatially and temporally on snowpack evolution. In this study, we propose a new process which uses these multi-temporal images of TerraSAR-X to constrain the Crocus model through data assimilation. Data assimilation has been widely used in meteorological studies  (Courtier et al. , 1998; Uppala et al. , 2005)  and land surface modeling  (Slater and Clark, 2006; De Lannoy et al. , 2010; Toure et al. , 2011). Data assimilation using physically based multilayer models has been initiated in recent studies, using passive microwave radiance  (Toure et al.
The adjustment made to the snowpack physical properties is based on error statistics of modeling (Crocus) and observation (SAR). This study reports, for the first time, on a new process based on the DMRT model and on the one-dimensional variational analysis (1D-Var) to assimilate TerraSAR-X data into the snow model Crocus. A global schematic of this process is presented in Fig. 1. Section 2 introduces the Crocus snowpack evolution model. Section 3 describes the DMRT electromagnetic backscattering model. The 1D-Var data assimilation method is presented in Sect. 4. Section 5 contains the study of simulations and sensitivity of snowpack at X band. Section 6 presents the first results and discussion of data assimilation method in the particular case of the Argentière glacier, where the ground beneath the snow consists of ice.
Data analysis methods, on the other hand, require a vector of guess variables relatively close to the actual values. The snowpack variables calculated by Crocus are used as guess variables in our assimilation scheme. The fundamental goal is to modify the initial guess variables, while balancing the errors of the guess variables, modeling and measurements. It should be noted that, as the problem remains underdetermined, the analysis scheme only serves as a method to improve the initial guess variables using the new observations from SAR data. The quality of improvement is based on the estimation of the initial guess vector x g and on the precision of the EBM.
Sensitivity of TerraSAR-X data For this study, TerraSAR-X descending acquisitions over the region of Chamonix Mont-Blanc, France, from 6 January 2009 to 24 March 2009 are available for continuous assimilation, with a revisit time of 11 days. Table  1  provides the main features of TerraSAR-X data sets. Figure  5  shows the location and a TerraSAR-X image of Argentière glacier captured on 6 January 2009. Meteorological forcing data provided by SAFRAN from 2400 to 3000 m altitude in steps of 100 m elevation on horizontal terrain were used to drive the detailed snowpack model Crocus throughout the entire season 2008-2009 (starting on 1 August 2008). In order to carry out the comparison between the backscattering coefficients σ sim (obtained from the EBM using Crocus snowpack profile as an input) and σ TSX (obtained from TerraSAR-X reflectivity), the images were multi-looked to (20 m ⇥ 20 m) wide pixels and a Frost filter  (Frost, 1981)  was applied using window size of 5 ⇥ 5 pixels.
Simulation of Crocus snowpack data The intrinsic parameters of a snowpack needed for EBM simulations are simulated by Crocus, which consist of a number of snow layers, their density, optical diameter, and thickness. These quantities are used as inputs for the simulation of the volume backscattering mechanism. The relation between open-loop (i. e. , without assimilation) Crocus data and the TerraSAR-X reflectivity for different altitudes over the Argentière glacier is shown in Fig. 7. The optical thickness (⌧ ) is the product of snow depth and the extinction coefficient  (Tsang et al. , 2007). In the case of multilayer snowpack, it is defined as ⌧ = n X k=1  k e d k , (22) x(t-1) Meteo. var.
Evaluation of the process and discussions Crocus snow stratigraphic profiles were computed for seven different altitudes over the Argentière glacier, from 2400 to 3000 m. The level of liquid water content per volume (LWC v ) at the times and locations of analysis is 0 %; therefore the condition of dry snow is satisfied. Figure  5  shows the approximate locations of each study area on the glacier. Figure  8  presents the implementation of the SAR data assimilation process into Crocus. The top part of the figure shows the Crocus simulation of snowpack without assimilation of SAR data. At instant t, Crocus simulates the snow stratigraphic profile from the previous state of snowpack (instant t -1) and the meteorological data hourly provided from SAFRAN. The time lag between instant t and instant t -1 is therefore one hour.
We call this simulation "open loop". The bottom part of the figure shows the implementation of data assimilation into the execution of Crocus. Every 11 days, a TerraSAR-X acquisition is used to modify the snowpack stratigraphic profile of Crocus through an assimilation process. The snow profile before assimilation is called "guess", and the analyzed snow profile after assimilation is called "assimilated". Consequently, at the date of the first TerraSAR-X acquisition (6 January 2009), open-loop and guess profiles are identical. Once this first SAR acquisition is assimilated into Crocus, guess and assimilated profiles differ. This modification permits the constrainment of a physical snowpack simulation using external information acquired at different dates. Figure  9  shows the results of simulation and analysis using the TerraSAR-X time series from 6 January to 24 March 2009.
9  at 2400 m. Note that this large increase in the diameter results in a large discrepancy between open-loop and guess profiles on 13 March. It can also be noted that there is a difference of 20 cm in total snow depth between the open-and closed-loop simulations on 13 March, which shows that the modifications of optical diameter and snow density made by data assimilation also indirectly modify others physical properties of the Crocus-simulated snowpack. These results show that we have combined three models (Crocus, EBM, adjoint model) and the TerraSAR-X data to constrain spatially and temporally the snowpack evolution. The use of data assimilation on SAR data to predict certain physical properties of snowpack has been developed in  Nagler et al. (2008)  and  Takala et al. (2011). However, it is the first time that active X-band radar data have not been used directly to perform an assessment of snowpack properties but instead used to estimate physical parameters of each snow layer through a data assimilation algorithm.
Conclusions This study presents a new system using data assimilation and a multilayer snowpack backscattering model based on the radiative transfer theory to constrain the evolution of a snowpack simulated by the snow model Crocus. The proposed new backscattering model adapted to X-band and higher frequencies enables a fairly accurate calculation of EMW losses in each layer of the snowpack. Through the use of 1D-Var data assimilation based on the linear tangent and adjoint operator of the EBM, we are able to modify, in a physically consistent way, the snowpack profiles calculated using the snowpack evolution model Crocus. This process has been applied to a time series of TerraSAR-X images and Crocus simulations during the winter of 2008-2009 over the Argentière glacier. Results show that SAR data can be taken into account to efficiently modify the evolution of snowpack simulated by Crocus.
Acknowledgements. This work was funded through the European project  GlaRiskAlp  (2010GlaRiskAlp ( -2013) ) on glacial hazards in the Western Alps, as well as Météo-France. TerraSAR-X data were provided by the  German Aerospace Center (DLR). The authors would like to thank  Matthieu Lafaysse  from  CNRM-GAME/CEN  for their help with performing the Crocus runs. We thank  CNES/TOSCA  for funding through the  CESTENG  project. CNRM-GAME/CEN and GIPSA-Lab are part of  LabEx OSUG@2020  ( ANR10 LABX56 ). Edited by:  E. Larour.
www. the-cryosphere. net/8/1975/2014/ The Cryosphere, 8, 1975-1987, 2014 Altitude (m) Altitude (m) 0 2400 25002600 2700 2800 2900 3000 0 2400 25002600 2700 2800 2900 3000 Firn area Firn area Crevasses area, Crevasses area, σ o (dB) data removed σ TSX o σ o (dB) data removed σ o snow o σ snow σ o snow 0 1000 2000 3000 4000 5000 6000 0 1000 2000 3000 4000 5000 6000 Position (m) Position (m) Altitude (m) Altitude (m) 0 2400 25002600 2700 2800 2900 3000 0 2400 25002600 2700 2800 2900 3000 Firn area Firn area Crevasses area, Crevasses area, (dB) data removed (dB) data removed σ o σ o 0 1000 2000 3000 4000 5000 6000 0 1000 2000 3000 4000 5000 6000 Position (m) Position (m) Altitude (m) Altitude (m) 0 2400 25002600 2700 2800 2900 3000 0 2400 25002600 2700 2800 2900 3000 Firn area Firn area Crevasses area, Crevasses area, (dB) data removed (dB) data removed σ o σ o 0 1000 2000 3000 4000 5000 6000 0 1000 2000 3000 4000 5000 6000 Position (m) Position (m) Altitude (m) Altitude (m) 0 2400 25002600 2700 2800 2900 3000 0 2400 25002600 2700 2800 2900 3000 Firn area Firn area Crevasses area, Crevasses area, (dB) data removed (dB) data removed σ o σ o 0 1000 2000 3000 4000 5000 6000 0 1000 2000 3000 4000 5000 6000 Position (m) Position (m).
ACKNOWLEDGMENTS We thank  Dr. Alice Jouneau  ( INRA BDR ) and  Dr. Vinod Ranganathan  ( Johns Hopkins University ) for help in reviewing the manuscript,  Dr. Michael Young  ( Harvard Medical School ) for fruitful discussions,  Dr. Zara Melkoumian  ( Corning Life Sciences ) for useful advice regarding the use of VN-PAS, and  Dr. Tomohiro Matsuda  ( Johns Hopkins University ) for assistance with quantitative PCR experiments. This work was supported by a  Foundation   Fighting Blindness Wynn-Gund Translational Acceleration Program  grant, an  American Health Assistance Foundation Macular Degeneration Research  grant,  NIH   Core Grant   P30EY001765  to the  Wilmer Eye Institute , unrestricted funds from  Research to Prevent Blindness, Inc. , and generous gifts from Mr. and  Mrs. Robert  and  Clarice Smith , the  Raab Family Foundation , and the  Guerrieri Family Foundation.
Observational data sets 2. 1. In-situ data. To validate the coupled model, we used a long time series of hydro-biogeochemical data collected twice monthly at the Somlit station (43u14. 309N; 5u17. 309E) located in the BoM (Annex S4). High vertical resolution profiles of temperature, salinity and oxygen were obtained between 0 and 55 m using a conductivity temperature-depth-oxygen profiler (CTDO, Seabird 19+). Water samples were collected at 3 depths with hydrological Niskin bottles for the determination of inorganic and organic nutrients concentrations. Samples for nitrate, nitrite and phosphate were collected into 60 ml polyethylene nutrients flasks and were frozen at 220uC until analysis at laboratory according to Aminot and Kerouel  [64]. Samples for acid silicic were collected in 60 ml polyethylene flasks and stored at 5uC until analysis at laboratory according to  [64].
The MODIS (Moderate Resolution Imaging Spectroradiometer) and MERIS (MEdium Resolution Imaging Spectrometer) ocean color sensors have a spatial resolution of approximately 1 km, which is more course than the spatial resolution of our model (400 m). For the comparisons, we used the remotely sensed chlorophyll-a concentration processed with the algorithm OC5  [68, 69]  from the IFREMER (Institut Francais de Recherche pour l'Exploitation de la Mer) database. The OC5 method  [68]  is empirical and derived from the OC4 algorithm of NASA (or OC3M-547 for MODIS and OC4E for MERIS). This method gives results similar to OC4 in open waters but provides more realistic values over the continental shelf  [70]. The quality of the remote sensing data was evaluated in Annex S5. The remote sensing data contained observational errors, which was also the case for the in-situ data.
0360. 03 mmol. L-1) and chlorophyll-a (0. 460. 32 mg. L-1), which highlights that this oligotrophic ecosystem had a strong variability, possibly due to pulsed nutrients. This variability made the model/observations comparisons difficult, so that different statistical indicators were used. First, we computed the cost function. According to the OSPAR classification  [76] , our results were very good or good except for bottom chlorophyll-a, POC, PON and PO 4. Then, we computed the average absolute error (AAE, Equation  10 ) and the RMSD, which measure the average magnitude of the errors in a dataset, without considering their direction. AAE and RMSD were expressed in the same units as the variable. In the surface layer, the addition of the P cycle induced a decrease or stagnation in the AAE for all of the variables, except for the POC, for which the increase in the AAE was not significant.
Evaluation of spatial processes representation Ocean color observations derived from remote sensing data were used to assess the abilities of the model to capture spatial gradients and seasonality over the whole study area. 2. 1. Climatology of surface chlorophyll-a patterns. The maps of time-averaged surface chlorophyll-a concentrations permit the evaluation of the ability of the model to reproduce horizontal patterns of chlorophyll-a. The outputs from both versions of the model were compared with observed maps of surface chlorophyll-a concentrations from MODIS and MERIS during the 4 seasons of the year (Figure  6 ). The Rhone River plume area was characterized by high chlorophyll-a concentrations, in contrast with the eastern portion of the study area, where concentrations in chlorophyll-a remained very low.
The scarcity of observations to compare with the model results was a problem during the model development phase. The Somlit station was sampled only twice monthly, and as shown by the model results, many processes occurred at shorter time scales. One risk to avoid was to tune the model to obtain good results in comparison with the in-situ data at the Somlit station. We also evaluated the spatial pattern of the processes reproduced by the model and compared them with remote sensing data, which provided spatial patterns and gave daily information (during noncloudy conditions). Nevertheless, the errors associated with the remote sensing data were high. Coastal areas are highly influenced by cross-shore and earth/sea exchange. The Marseille coastal area has two large open boundary conditions (OBC).
Acknowledgments The authors acknowledge the staff of the  Somlit national network  for the littoral observations, ( INSU-CNRS ) for providing the data time series,  IFREMER  for providing the ocean color remote sensing data computed with the OC5 algorithm and the  Compagnie Nationale du Rhone  for the data on Rhone River discharges and  Meteo France. The Rhone concentration data were provided by the national  MOOSE program  (Mediterranean Oceanic Observing System on Environment) and the  Service d'Observation of the Mediterranean Institute of Oceanography (MIO). The authors gratefully acknowledge  N. Garcia ,  V. Lagadec  and  M. Fornier  for analytical and field assistance. This study was part of the '' MERMEX WP3-C3A'' and international ''IMBER '' projects. We are grateful to  DEA-MPM (Direction des Eaux et Assainissement-Marseille Provence Metropole)  and  SERAM (Societed'Exploitation du Reseau d'Assainissement de Marseille)  for providing the Marseille urban river and WWTP discharge.
This work was supported by the PACA region (http://www. regionpaca. fr/), IFREMER  grant (www. ifremer. fr/),GIRAC (http://www. polemerpaca. com/Environnement-et-amenagement-du-littoral/Gestion-de-l-eau-en-zone-cotiere/GIRAC) and PNEC-EC2CO MASSILIA (http://www. insu. cnrs. fr/actions-sur-projets/ec2co) projects, sustained by the water agency (AERMC,www. eaurmc. fr/)and the  PERSEUS European FP7  project (http://www. perseus-fp7. eu/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Introduction Rocking curve imaging (RCI), which has developed over recent years at modern synchrotron radiation facilities  (Lu ¨bbert et al. , 2000; Mikulik et al. , 2006) , is a directly quantitative version of monochromatic beam diffraction topography. RCI involves using a two-dimensional detector (CCD camera) to record the diffracted spot. Each pixel of the camera records its own 'local' rocking curve (RC), so that several images (or 'maps') can be reconstructed by extracting data from these local RCs. Three of these maps are particularly relevant: they display the integrated intensity, the angular position of the centre of gravity and the full width at halfmaximum (FWHM) of the local rocking curves. Up to now these RCI images were exploited in the reflection case, providing a quantitative picture of the features present in a several-micrometre-thick subsurface layer.
The multi-slit device is displaced 20 times by 50 mm to scan the crystal through the entire size of the incident synchrotron X-ray beam. In practice at least 21 scans are recorded to get a superposition of the last one with the first. This modus operandi produces artefacts parallel to the slits, corresponding to the passage of the 20th slit to the first slit, which are associated with the displacement of the section setup during the scan: since the first and 20th data acquisitions are performed with an interval of several hours, the synchrotron radiation incident beam conditions can differ, and also, when analysing a compressed sample, although the sample was unloaded prior to scanning, a slight residual deformation can occur from the beginning to the end of the scan. In the RCI technique, the sample is oriented in such a way that one chosen crystallographic plane is Bragg diffracting and is stepwise rocked by an angle Á! around a reference value ! 0.
At each crystal position, the Bragg diffracted beam RC is registered on a FReLoN-type two-dimensional CCD detector, equipped with a scintillator and visible light optics. The pixel size of the CCD camera is 10 mm. The final data are composed of a stack of RCI images, where each image corresponds to the diffracted beam intensity I(y, z, !) for the sections provided by the multislit device for a crystal orientation ! and at each camera pixel (y, z). When taking into account the detector spatial resolution, the width of the slit and the geometrical projection effects, our spatial resolution corresponds to a voxel of 50 Â 50 Â 50 mm. By analysing all the local rocking curves (Gaussian shape in our study approximates each local RC), it is possible to draw, as indicated before, maps of the integrated intensity, of the angular position of the centre of gravity of the RC and of the FWHM.
Data acquisition The experimental procedure described was applied to the study of the microstructure evolution of one grain of an ice tricrystal under planar compression (Grain 1 in Fig. 1 ). The time for data acquisition of all the experimental RCI data for a given plane is in the range 4-18 h. This time depends on the beam intensity for the detector exposure time, the angular step size used for the rocking acquisition and the quality of the analysed crystal. Data acquisition was performed during three main steps: firstly, with the sample in its initial state and then after two 40 min compression sessions (under 1. 1 MPa) along the longitudinal axis of the sample. For each of these steps the 3D-RCI procedure, which aims at measuring the complete threedimensional curvature tensor and therefore having access to three independent angles for each voxel, consisted of measuring two reflections (the basal 0002 and prismatic 1100) plus performing a series of pinhole topographs to measure the plane misorientation around the direction of the incident beam  (Kluender et al.
html). A more-detailed comparative analysis of this genome with those of other N. meningitidis strains will provide further insight into the specific properties related to this strain that are linked to the recent outbreak among men who have sex with men in Europe. Nucleotide sequence accession numbers. This whole-genome shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession no. AVOU00000000. The version described in this paper is version AVOU01000000.
A third gene that codes for a putative ornithine decarboxylase is also present and is associated with a predicted amino acid transporter; this likely represents the lysine decarboxylase pathway genes (unpublished results). Nucleotide sequence accession numbers. This Whole Genome Shotgun project has been deposited at DDBJ/EMBL/ GenBank under the accession no. ANAG00000000. The version described in this article is the first version, ANAG01000000.
Science operations and data processing The SWUSV Mission Operation Center (MOC), operation facility and full data archive, is foreseen at the level of a large European Institute (LATMOS most probably). The volume of data (2-6 Gbit/day) is reasonable for current technology and trends. It will not pose serious problems for ground stations or archives in view of the present availability of large band equipment driven down by the rapid development of communications and Earth sciences satellites. Note that the operations by themselves are eased by the permanent pointing of the satellite and unique target. Some changes in scheduling (change of filters, calibration sequence, etc. ) could be envisaged that would find place in the weekly organized science observing plan. Data are expected to be downloaded every orbit (90 min) in order to have flares/events warnings at a 4 h rate for Space Weather issues.
SWUSV camera and data handling system are conceived so as to offer a tremendous capability and flexibility for in-flight operations and adaptability to the mission (e. g. , increased downlink capacity, extended mission, etc. ). The operational modes of SWUSV will make full use of its large internal storage capacity (>32 Gbit), of its processing power for image compression and image selection, and of the MYRIADE platform telemetry allowance (a daily rate of 6 Gbit), to maximize the science output. For instance, lossless compression (fac-tor % 3) will allow downloading 700 equivalent full frame CCD images each day while the nominal 2 per 10 min lead to only 400 a day. Extra images (300 or so) at higher cadence can then be taken when necessary and in particular at possible flaring/CME events following the precursor indicators provided by the mission.
After evaluation of the data, a new science operation planning could be elaborated for the following hours. Command uplink requirement on the MOC is limited normally to a single daily upload that will likely take place on working days and during working hours. The SWUSV observations would however benefit from extended uplink possibilities if precursor indicators are effective and useful to warn of catastrophic events. Science data from SWUSV will be stored and distributed as uncompressed, uncalibrated Flexible Image Transport System (FITS) files, in which a binary data array in units of digital counts is preceded by an ASCII header. This product is referred to as Level 0. 5 data. One FITS file will be generated for each image in the spacecraft telemetry stream. The FITS file headers will include keywords to indicate instrument orbit and attitude information, all instrument settings associated with the image, information on all onboard and ground processing steps, image statistics, and any other ancillary information necessary to interpret the image data.