dataesr/scientific-paragraphs-categorization huggingface dataset:
Results and Discussion The model system: iron containing phases. To demonstrate the capability of the technique in resolving chemical species, a sample containing several iron-bearing mineral phases was selected as a standard. The selected phases were (i) staurolite, an iron aluminium silicate containing Fe(II); (ii) magnetite, an iron oxide with Fe(II) and Fe(III), (iii) hematite, an iron oxide with only Fe(III); and (iv) hypersthene, an iron aluminium silicate containing Fe(II). The compounds, in the form of 2 to 30 μm in size grains, were mixed with an organic UV-hardening glue and placed inside a 300 µm diameter silica glass capillary with 10-20 µm wall-thickness. An optical microscopy image of the sample is shown in Fig. 2. The collected dataset consists of N E 2D cartographies of N X × N θ , called sinograms, where N E is the number of detector pixels, each corresponding to an energy slice of the spectrum; N X is the number of scanned lateral positions; and N θ is the number of scanned sample rotations around the vertical axis.
The sample was analysed slice by slice along the capillary axial length (Z direction); tomograms at different Z sample positions were collected, which encompass the third spatial dimension. The dataset was analysed similarly to μXRD and μXRF scanning Figure  1. Energy-dispersive X-ray absorption spectroscopy tomography experimental setup. An incoming X-ray beam coming from an undulator source, with an energy band-width of about 1 keV is focused horizontally by a curved crystal, the polychromator, and vertically by a vertical focusing mirror onto the sample position. After the sample, the flat horizontally diverging beam is detected by a position sensitive FReLoN detector camera  43  , with which the transmitted intensities are recorded in order to calculate the spectra; in the illustration, a spectrum from metallic iron is shown.
In detail on the bottom left, a cross-section of the sample illustrating the different energies of the incoming beam that transit the specimen at different angles and that are not necessarily intersected at the same region. tomography data sets  2, 21, 25, 44. Our acquired XAS spectra were collected pixel by pixel using N E = 1300 out of the total 2048 pixels of the FReLoN CCD detector. For each CCD pixel, which collects an energy bandwidth of 0. 2-0. 3 eV, a sinogram was built. Each sinogram consists of a N X × N θ map, as illustrated in Fig. 3a , so that one sinogram can be built for each pixel of the CCD detector over the incident X-ray energy range. Tomographic reconstructions performed for each sinogram provide the reconstructed figures (N X × N X in size) as shown in Fig. 3b. μED-XAS tomographic reconstruction has to deal with a number of issues, some of which common to XRD and XRF tomography and some which are ED-XAS specific.
epfl. ch/thevenaz/stackreg)  45. Following this procedure, good quality XAS spectra were obtained per reconstructed voxel. Figure  3c  illustrates the XANES spectra of two isolated particles of a few microns in size, where hematite and magnetite can be clearly identified. Figure  4  compares a tomographic slice by μED-XAS (Fig. 4b, c ) to those from absorption X-ray synchrotron microtomography (XCT) scans performed at the TOMCAT (Fig. 4a ) beamline where 10 times higher spatial resolution is achievable. The reconstructed slices are shown in Fig. 4b, c , corresponding to energies of 7106. 9 eV and 7125. 4 eV, respectively. More data are provided in the Supplementary Material. Different contrast patterns are observed by comparing these two images. By considering all obtained images corresponding to different energies, the XAS spectra per voxel can be obtained.
7 ka anomaly (Materials and Methods). Measurements of nssCa and other SH dust proxies in the WD core (Figs. 1  and 2 ) (SI Appendix, Fig. S1 )-complemented by new measurements (Dataset S1) in archived samples of the Byrd core (17) located 159 km from WD (Fig. 3 ) (SI Appendix, Fig. S2 )indicate that the abrupt climate change started in central West Antarctica ∼17. 7 ka, with the sharp and sustained decline in SH continental dust deposition (Fig. 1 ) nearly synchronous (SI Appendix, Continuous Ice Core Measurements) with the start of marked increases in CH 4 and CO 2 (Fig. 2 ). Starting ∼60 (±18) y before the abrupt drop in dust and extending ∼132 y after was a unique, longlived glaciochemical anomaly originally detected in limited discrete measurements of acidity, chloride, and fluoride in the Byrd core (17) (SI Appendix, Fig.
Helium (He), and especially 3 He, is much higher in extraterrestrial matter than in terrestrial components  (56). Approximately 3 kg of discrete meltwater samples were collected from the outer ring of the melter head for He concentration and isotope measurements above, within, and below the 17. 7 ka event. Average 3 He concentrations were 4. 4e-17 (±2. 5e-17) cm 3 •g -1 and 3. 7e-17 (±2. 5e-17) cm 3 •g -1 in the five background and four 17. 7 ka event samples, respectively. Similarly, the 3 He/ 4 He ratios were 6. 8e-5 (±1. 7e-5) in the background samples and 7. 3e-5 (±1. 9e-5) in the event samples. Interplanetary dust fluxes calculated from these data were in agreement with past measurements (57), indicating no evidence for an extraterrestrial source for the 17. 7 ka anomaly. Volcanic Tephra Measurements.
ACKNOWLEDGMENTS. We acknowledge  R. von Glasow  for help with snowpack model simulations, and  J. Stutz  and  R. Kreidberg  for helpful discussions. The  US National Science Foundation  supported this work [Grants  0538427 ,  0839093 , and  1142166  (to J. R. M. ); 1043518 (to E. J. B. );  0538657  and  1043421  (to J. P. Severinghaus); 0538553 and  0839066  (to J. C. -D. ); and  0944348 ,  0944191 ,  0440817 ,  0440819 , and  0230396  (to K. C. T. )]. We thank the  WAIS Divide Science Coordination Office  and other support organizations. P. K. and G. K. were funded by Polar Regions and Coasts in a Changing Earth System-II, with additional support from the  Helmholtz Climate Initiative.
, 2011)  (Fig. 2 ). The four representative profiles were selected from a database of 80 podzol profiles issued from 11 test areas which have been studied in detail and of which 11 have been dated; this database will be the subject of a further publication. The four profiles were used to constrain the simulations of C fluxes. We used a system dynamics modelling software package (Vensim) to simulate the formation of representative Amazonian podzol profiles by constraining both total carbon and radiocarbon with the data collected.
Podzol profiles and carbon analysis Four podzol profiles were selected from our database as representative both from the point of view of the profile characteristics and the 14 C age of the Bh organic matter (Table  1  and Fig. 3 ). The MAR9 profile was developed on the Icá sedimentary formation, and has a waterlogged A horizon, a thin eluvial (E) horizon, a sandy-clay loam Bh with young organic matter (OM) and a low C content; the DPQT profile was developed on a late Quaternary continental sediment younger than the Içá formation, and has an E horizon of intermediate thickness, a sandy Bh with young OM and a low C content; the UAU4 profile was developed on the Icá sedimentary formation, has a thick E horizon and a sandy Bh with old OM, and the C content is high; the P7C profile was developed on crystalline basement rock, and has a thick, waterlogged O horizon, an E horizon of intermediate thickness, a silt-loam Bh with old OM and a high C content.
Model running and tuning We used the Vensim ® Pro (Ventana Systems inc. ) dynamic modelling software to simulate the C dynamics. After setting the initial values for C pools, the model was run in the optimize mode, leaving the model to adjust the rate constants in order to minimize the difference between simulated and measured C pool values and ages. However, frequently the model did not converge when run in this way. We found that it was because of the great difference between the convergence times between the topsoil C pool and the slow Bh C pool. The long times required to model the genesis of the Bh horizons resulted in numerical errors when modelling the topsoil behaviour, because the values of exponential exponents exceeded the maximum values that the computer could handle (see for example Eq.
Acknowledgements. This work was funded by grants from  (1) Brazilian FAPESP (São Paulo Research Foundation. Process numbers:  2011/03250-2; 2012/51469-6 ) and  CNPq , ( 303478/2011-0 ;  306674/2014-9 ), (2)  French ARCUS (joint programme of Région PACA  and  French Ministry of Foreign Affairs)  and  (3) French ANR (Agence Nationale de la Recherche , process number  ANR-12-IS06-0002  " C-PROFOR "). Edited by: V. Brovkin Reviewed by: two anonymous referees.
Description of Collocated Data Sets Validations of SAR-derived winds mostly lie on collocations with observations from buoys (e. g. ,  [11] ), offshore meteorological masts (e. g. ,  [17] [18] [19] ), and scatterometers (e. g. ,  [3] ). To date, besides HY-2A SCAT (exceeded its three-year design lifetime since 2015) providing degraded wind products, the available scatterometers are ASCATs onboard MetOp-A/B. Unfortunately, GF-3 and MetOp are in sun-synchronous orbits with the local equator crossing time at the ascending node of 6:00 am and 9:30 am, respectively. The large local time difference could limit the comparison of ocean winds derived from GF-3 and ASCATs. Hence, in this study, the validation of GF-3 wind retrieval was performed using in situ buoys.
GF-3 SAR Data SAR data of high radiometric quality are essential for accurate ocean wind inversion. For GF-3 SAR, the external calibration campaign was carried out using active radar transponders deployed in Inner Mongolia, during the commissioning phase (from September to November 2016). The results from the in-orbit external calibration experiment reveal the NRCS radiometric accuracy of 1. 3~1. 4 dB (3σ), and Noise Equal Sigma Zero (NESZ) of -20~-22 dB  [20]  for different GF-3 SAR operating modes. In this study, a total of 37 GF-3 SAR scenes were co-located with buoys over the period of January through April 2017. These GF-3 data have been collected in five GF-3 imaging modes (see Table  1  for details) amongst the 12 modes of the sensor, including Standard Strip, Quad-Polarization Strip I, Quad-Polarization Strip II, Fine Strip I, and Narrow ScanSAR imaging mode with different polarization (dual-polarization or quad-polarization), pixel spacing, and SAR scene swath.
Buoy Data Buoy measurements are generally assumed to be of high quality up to 30 m/s  [21, 22]  and hence were used as ground truth for the validation of wind retrieval from GF-3 SAR here. Wind observations of buoys have been collected from the National Data Buoy Center (NDBC) of NOAA. Most of them are off the west coast of the U. S. , while some of them are in the region of Hawaiian Islands, southeast Pacific (Stratus buoy station), and Korean coastal sea. In this study, only the buoys located inside the GF-3 SAR scene were considered as co-located in space. The buoy winds from NDBC are measured hourly by averaging the wind speed and direction over 10 min. Therefore, the time interval between observations of SAR and buoy was limited to less the 30 min. During the period from January to April 2017, these criteria yielded 53 match-ups, whose locations are depicted in Figure  1.
Acknowledgments: The GF-3 SAR data has been provided by the  National Satellite Ocean Application Service  in the framework of National High-Resolution Project of China via website of http://dds. nsoas. org. cn/(registration required), and we would like to thank  Limin Cui  and  Wentao An  for the GF-3 SAR data ordering and delivering. The authors are also grateful for the free access to buoy wind measurements provided by  NDBC  via http: //www. ndbc. noaa. gov/. This work is supported by the  National Natural Science Foundation of China  ( 41506205 ),  National Key R&D Program of China  ( 2016YFC1401003 ,  2016YFC1401007 ),  National High-Resolution Project of China  ( Y20A14-9001-15/16 ) and Dragon-4 project ( 32249 ).
The alternative of using low cost resources Nowadays, in the context of the impetuous development of remote sensing techniques, there is a natural preference for high and very-high-resolution (VHR) remote sensing images. Unfortunately, these are expensive and more difficult to access. We also have to admit that sometimes it is not possible to capture real-time images or create time series of images. Maybe this would be possible, but the cost of raw data purchases is too high. Therefore, we would like to assess if less expensive and easier-to-acquire images, such as Landsat, despite their lower resolution, would still be sufficient and capable of detecting diffuse windthrows. Starting with 2014 Sentinel Radar images are also free, but obviously these cannot be considered because the event we studied occurred in 2010.
One should mention that in the case of the free images, very expensive software is often needed to process them. But if the purpose of a research is to rebuild the old windthrow trees, obviously, only the sources produced by the older satellites or having an archive (Landsat from 1980) can be accessed. The availability of satellite data varies and choosing the best sensor is very important in order to select the data which better suit our purpose and to get more accurate results. When detecting windthrows using optical satellite imagery it is important to know which sensor is appropriate. Schwarz et al. [44]  summarized the criteria to be considered in choosing the appropriate sensor, namely: data availability, spectral resolution, spatial resolution and temporal resolution (to increase the probability of achieving useful cloud-free data).
However, the increasing availability of highresolution satellite imagery offers the potential to acquire data with fine spatial resolution (e. g. WorldView, Ikonos, Quickbird), but the cost of these data is a hindering factor in using them. Jackson et al. [54]  showed that high-resolution imagery, like LIDAR, offered a great potential to detect windthrows, but they required more basic operating instructions. In this study we chose to use Landsat data to detect the windthrows along the Vosges Mountains. The first reason in choosing Landsat sensor was the wide swath and the low cost. The medium resolution Landsat data is not as sharp as high-resolution images, but the amount of information it contains along with the large area coverage make this data suitable for large-or smallscale studies.
Melesse et al. [57]  presented the characteristics of 20 different satellite sensors, while  [3]  summarised the specification of sensors used for forest disturbance detection. The table below (Table  2 ) summarizes the characteristics of the most important satellite data used in windthrow detection and forest cover monitoring. The most important differences between these data are the spatial resolutions, e. g. from 30 m for Landsat-7 multispectral data to 1 m and even under 1 m for the Ikonos, WorldView, and WuickBird data. But when detecting and assessing windthrows, the choice of the appropriate sensor will depend on the availability of data, cost, scale of the fragmented landscape and area of interest, because the high resolution imagery is not available globally, like Landsat data. If the exact small scale (< 0.
Materials and Methods In this study we aimed to highlight the applicability and efficiency of Landsat data in detecting diffuse windthrows caused by the Xhyntia storm that crossed the Vosges Mountains on 28 February 2010. Windthrow detection depends on the affected area. If the affected area is larger, the detection capability increases, together with the results accuracy of the Landsat satellite images based analysis. Figure  3  illustrates the methodological workflow.
Then, the validation of results was carried out. The evaluation of results depends on the data and information used. The accuracy can be directly influenced by the quality and availability of the ground data (the resolution of orthophotos, highresolution satellite imagery). The first step in results validation is to calculate the accuracy assessment and the Kappa statistic. Another validation was made by overlapping the polygons resulting from the change map with the orthophotos from 2009 and with the RapidEye satellite image from 2010.
Pre-processing, land-water and cloud mask Landsat satellite images were used for windthrow detection. The images were purchased for the previous year and the year after the meteorological phenomenon (28 February 2010) that has led to the fall of trees in the Vosges Mountains. Thereby, a multi-temporal set of Landsat 5 TM images (with 7 spectral bands -30 m spatial resolution) was used. The images were acquired on different days 17. 06. 2009, before the Xynthia storm, and on 4. 06. 2010, after the phenomenon. The Landsat images were downloaded freely from the  [63]  website. Table  3  shows that between the two dates of image selection there were no meteorological events with extreme wind gusts, no storms or tornadoes. Therefore, the data reflected by the two images cannot be artefacts. We selected no clouds or very few clouds images that were recorded during the growing season and these were purchased for the same vegetation season (June), to reduce the impact of vegetation in different seasons.
In order to use the satellite images to detect changes occurring at the ground surface, several steps are required, namely: selecting the images that best fit the intended purpose, orthorectification, georeferencing, various corrections, calculating some indices to enhance the data information  [64]. The Landsat satellite images provided by USGS have already been radiometrically and geometrically corrected, but for the proposed operations we needed the values of the reflective satellite images. This operation was done using the formulas given by Chander & Markham  [65]. The first step was to pre-process the two sets of images, before and after the storm, by converting the digital numbers into spectral radiance using the equation: Lλ = (gain * DN) + biasλ (1) Where: Lλ -the spectral radiance at the sensor level and referring to the wavelength λ for the specific band;.
In Sect. 3 we define the radiative temperatures of opaque and thin clouds derived from combined lidar observations and reanalysis, and document them over the midlatitudes and the tropics. In Sect. 4 we use radiative transfer simulations to establish a simple expression of the OLR as a function of lidar cloud observations for opaque-cloud single columns and for thin-cloud (nonopaque) single columns, using clear-sky data from the Clouds and the Earth's Radiant Energy System (CERES) satellite instrument. We verify this relationship using CERES and CALIPSO observations, first collocated at the instantaneous 20 km scale, then monthly averaged on 2 • latitude × 2 • longitude grids. In Sect. 5 we estimate the independent contributions to the LW CRE of optically opaque clouds and optically thin clouds.
Flux observations collocated with lidar cloud observations The CERES radiometer, on board the Aqua satellite, measures the OLR at the location where the CALIOP lidar, on board the CALIPSO satellite, will fire 2 min and 45 s later. The instantaneous Single Scanner Footprint (SSF) of the CERES swath crossing the CALIPSO ground-track gives the OLR over atmospheric single columns sounded by the lidar. The CERES footprint has a ∼ 20 km diameter, while the CALIOP lidar samples every 333 m along-track with a 90 m diameter footprint, meaning the lidar can sample up to 60 atmospheric single columns within a single CERES footprint. To collocate the GOCCP-OPAQ instant data and the CERES SSF measurements, we use the CALIPSO, Cloud-Sat, CERES, and MODIS merged product (C3M;  Kato et al. , 2011)  which flags the instantaneous CERES SSF footprints where the CERES swath crosses the CALIPSO groundtrack.
For each of these flagged CERES SSF footprints, we matched, from geolocation information, all the GOCCP-OPAQ single columns falling into the CERES footprint. We consider that an atmospheric column with a CERES footprint base is an opaque (thin) cloud column if all matched single columns are declared as opaque (thin) cloud single column. We then use these opaque-and thin-cloud columns to validate the lidar-derived OLR. From the C3M product, we also use the estimated clearsky OLR of the instantaneous CERES SSF where the CERES swath crosses the CALIPSO ground-track. This estimated clear-sky OLR is computed from radiative transfer simulations using the synergistic information of the different instruments flying in the Afternoon Train (A-Train) satellite constellation. As C3M only covers the period when both CALIPSO and CloudSat are both fully operational (until April 2011), we also use the clear-sky OLR from 1 • × 1 • gridded data monthly mean CERES Energy Balanced and Filled (EBAF) Edition 2.
Gridded OLR Concerning gridded OLR, we used monthly mean OLR clear from CERES-EBAF in Eqs. (  4 )-(  5 ) instead of instantaneous OLR clear from C3M since this product is only available up to April 2011. Clear-sky OLR from CERES-EBAF data is derived only from measurements over clear-sky atmospheric columns which are generally drier than the clear part of a cloudy atmospheric CERES column. Because a drier atmospheric column leads to a stronger OLR (e. g. ,  Spencer and Braswell, 1997; Dessler et al. , 2008; Roca et al. , 2012) , OLR clear from CERES-EBAF should overestimate OLR clear from C3M on average. The diurnal cycle, which is taken into account in OLR clear from CERES-EBAF but not in OLR clear from C3M (since we only used nighttime observations) could also play a role in the difference.
We checked the robustness of this linear relationship against observations at two different space-and timescales. Using instantaneous collocated observations from the CALIPSO lidar and CERES broadband radiometer data at the sensor spatial scale (20 km), we found a correlation coefficient of 0. 95 between the lidar-derived T opaque and the OLR measured by the broadband radiometer CERES. Averaging the same data monthly within 2 • latitude × 2 • longitude grid boxes, our derived OLR differs by 0. 1 W m -2 from the OLR measured by CERES. To conclude, this paper proposes a simple approximate solution to the complex problem of radiative transfer in the LW domain, which could be used to explore first-order LW cloud feedbacks in both observations and climate model simulations. On the observational side, future work will analyze the interannual variability of the record collected by spaceborne lidars and broadband radiometers: CALIPSO/CERES in the A-train (10+ years), followed by EarthCARE  (Illingworth et al.
, 2014)  to be launched in 2018. On the climate model simulation side, this new framework will be included in the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP;  Bodas-Salcedo et al. , 2011)  lidar simulator  (Chepfer et al. , 2008)  and applied to climate model outputs in order to quantify the contribution of each cloud property to the simulated cloud feedbacks. Data availability. The GOCCP v3. 0 products used in this article are available online through the GOCCP website at http://climserv. ipsl. polytechnique. fr/cfmip-obs/. CERES-EBAF data were obtained from the NASA Langley Research Center CERES ordering tool at http://ceres. larc. nasa. gov/. Figure  D1a  shows the difference between lidar-derived gridded OLR (LID) total shown in Fig. 7a  and the one which would be obtained if Z | opaque was found to be 480 m higher.
Acknowledgements. We would like to thank  NASA/CNES  for access to the CALIPSO level 1 data and the  ClimServ center  for the use of the computing cluster. We also thank  CNES  for supporting the development of CALIPSO-GOCCP. The authors are grateful to the three anonymous referees of this paper for helpful comments and suggestions. Edited by: Andrew Sayer Reviewed by: three anonymous referees.