dataesr/scientific-paragraphs-categorization huggingface dataset:
Filefjell The Filefjell data set was recorded at hourly steps over the 2013-2014 winter at a flat field at 956 m a. s. l. Filefjell is located about 200 km northwest of Oslo. Despite being 30 km inland from the end of a fjord, Filefjell is considered to have a continental snow climate, which has an average precipitation of 603 mm yr -1 and large annual temperature variation, shown in Fig. 4. Continental snow climates are characterized by thin snow covers, cold temperatures and few rain-on-snow events during the winter months  (McClung and Schaerer, 2006). Air temperatures become as cold as -28. 3 • C, and after January there is a long cold spell during which temperatures stay well below zero for about a month. The surface incident shortwave radiation is low during winter, when maximum daily values are below 300 W m -2 from 18 October 2013 to 19 February 2014, due to the high latitude.
Synthetic Figure  5  shows the 90-day synthetic data set. The peak shortwave radiation values for this data set are low and increase linearly from 100 to 200 W m -2. Temperature has a linear increase from 265 to 276 K. The temperature and radiation patterns set in the synthetic data set were chosen to induce a modest melt rate early in the simulation that is ramped up to a heavy melt rate at the end of the simulation. The synthetic data set is designed to test the new routines for a large range of water supply rates. This data set has two large snow events: the first one starts on day 3 and deposits 2. 6 m over 5 days, and the second event occurs on day 60 after the first snow event had a chance to settle down to 1. 1 m and the diurnal radiation cycle had formed a melt freeze crust at the surface.
Filefjell Simulation Simulation results for the Filefjell data set are presented in Fig. 6 , with the top (Fig. 6a  and b ) showing the amount of liquid water and snow layer density from the simulation run with the bucket routine and the bottom (Fig. 6c  and d ) with the Richards routine. The snowpack thickness reaches just over 1 m (1. 1 m for the Richards routine and 1. 2 m for the bucket routine) at its maximum in March before a fast melt out. The difference in snow thickness between the two routines is due to the wet-snow compaction that relies on the LWC. The majority of the snowpack wetting occurs during the April melt. The wetting front reaches the bottom of the snowpack at approximately the same time independent of which routine was used (Fig. 6a  and c ). The three rain and melt events that occur early in the season (23-24 October, 15 and 26 November) pass water to the soil layers for both percolation routines.
Acknowledgements. We thank the  NIFS  project (www. naturfare. no) for the financial support of this study. We would like to thank  Nander Wever  and one anonymous referee for the useful comments which improved the quality of this paper. IGE and CNRM/CEN are part of LabEX OSUG@2020. Edited by: Jeremy Fyke Reviewed by:  Nander Wever  and one anonymous referee.
Code and data availability. For directions and help with downloading, compiling and running the code you will need to register as a user at https://opensource. umr-cnrm. fr/. The SUR-FEX/CrocusV7 with the Richards routine can be downloaded via Git by following instructions at https://opensource. umr-cnrm. fr/ projects/snowtools/wiki/Install_SURFEX and downloading the branch "damboise_dev" with the "damboise_et_al. 2017" tag. Directions for running SURFEX (with Crocus) can be found here: https://opensource. umrcnrm. fr/projects/snowtools/wiki/ Basic_functioning_of_SURFEX_without_the_s2m_tool_from_ snowtools. The Wiki can be a useful resource for troubleshooting https://opensource. umrcnrm. fr/projects/snowtools/wiki/Wiki. The Filefjell and simulated data set with the namelist needed for running the simulations are provided at www.
In addition, the sensor was found to begin and increasingly fail below -55 • C rather than -65 • C. However, data from the vertical profiling system show that from 2009 to 2015, the air temperature ∼ 3 m above the snow surface was warmer than -55 • C more than 50 % of the time, and almost consistently (more than 99. 5 % of the time) warmer than -55 • C during the local summer (December-January-February). Assuming near saturation, the instrument can nominally operate for a large fraction of the time at Dome C. For our application, the frostpoint hygrometer (denoted as FP from now on) is hosted in a heated box so that the electronics and mechanics are not affected by the extreme cold temperatures in winter. By factory design, the outside air is aspirated inside the instrument The temperature and the moisture content of the heated air (3) is measured by the HMP155 (4).
The value of z 0 is the mean value reported by  Vignon et al. (2016)  for Dome C (0. 56 mm). The value of z 0q is difficult to estimate at Dome C because the very low vapor content of the atmosphere induces high uncertainties and because the scarcity of near-neutral conditions prevents an independent selection of a scheme for the stability functions. Two different approaches are used. By default, z 0q = z 0 as in  King et al. (2001) , and in a second case, z 0q is calculated with  Andreas (1987)  theoretical formula, which at Dome C yields z 0q values lower than z 0 by approximately one order of magnitude. Uncertainties on flux calculations are estimated from the variance of results obtained with the different choices of stability functions and roughness length. Figure  10  shows the monthly seasonal water flux calculated by the bulk method for 2015 using either HMP data or HMPmod reports, and also shows the cumulated difference between the two calculations.
The mean elevation of the first prognostic model level at Dome C in summer is 8. 2 m, significantly higher than the observation level. Variables are also calculated at the meteorological standard 2 m level by interpolation between the first level and the surface using gradient equations of the surface layer. Vignon et al. (2016)  show that the surface layer where gradient interpolation relationships are valid is often much shallower than 8 m in stable conditions at Dome C. The 2 m interpolated values probably encompass biases due to the interpolation formula and may have to be considered carefully. However, the elevations of the 2 m and first-level data bracket that of the observations, allowing a more detailed comparison in a region where vertical gradients can be steep. The ECMWF analyses overestimate nighttime temperature and consequently underestimate the amplitude of the diurnal cycle.
However, these sensors are subject to icing in supersaturated environments  (Rädel and Shine, 2010)  and require correction and/or adaptation. There are not many measurements of atmospheric moisture in Antarctica, and most (including by the radiosondes) are made using unadapted solid-state sensors. The atmospheric humidity of the Antarctic atmosphere where supersaturation is frequent is likely often underestimated from observations. Thus, the evaluation of meteorological and climate models from these data may be biased. Observations at Dome C using modified sensors to ensure that supersaturations can be sampled show that models that implement parameterizations of cold cloud microphysics intended to simulate cirrus clouds at high altitude qualitatively reproduce frequent supersaturations but fail with respect to the statistics of supersaturation events.
Moreover, they fail differently, both models tested here producing too much supersaturation but one model simulating much more frequent occurrences of supersaturations than the other. ECMWF and MAR supersaturation simulations are quite different for several reasons. Water vapor concentration in the first model results from data assimilation while it is fully free to respond to model equations and parameterizations in the second. Parameterization of ice crystal nucleation plays a particular role in the behavior of the supersaturation process. It is based on theoretical developments in ECMWF and in this case the number of crystals formed is rather insensitive to the aerosol physical properties. The parameterization in MAR was developed using aircraft observations in the Arctic. The results at Dome C probably show that parameterization tuning is too narrow to properly account for the near-surface conditions at Dome C, although temperature conditions probably play the most important role.
Acknowledgement. Support for field measurements was provided by the  French polar institute IPEV  through program CALVA (1013). Concordia station is jointly operated by the  IPEV  and  PNRA. INSU  provided support through programs  LEFE CLAPA  and  DEPHY2. Support by  OSUG through observatory program  GLACIOCLIM is also acknowledged. The BSRN upwelling infrared radiation data, which served to calculate the snow surface temperature, were kindly provided by  Christian Lanconelli ,  CNR ISAC. The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement no. (306045). JBM also thanks  UPMC  university for financial assistance. We thank  John King  and two anonymous reviewers for their careful evaluation and thoughtful comments and suggestions on the initial (ACPD) version of the paper.
ASAR Data The along-track wave spectra retrieved by a quasi-linear algorithm  [12]  from ASAR wave mode during 2003-2011 from the Globwave project are employed in this study. The wave spectra of these products are first smoothed and then partitioned by the procedure found in  [18]  with only the two most energetic swell components remaining (hereafter, the most energetic swell component will be denoted as the 1st swell, and so forth). The integral wave parameters including significant wave height (SWH), peak period, and peak direction are estimated for each partition. All the SAR records (hereafter, a SAR record means the along-track record with one or two swell partitions) with bad or suspicious quality flags are discarded from the dataset, and the along-track data are gridded into 2. 5 • × 2.
Results The global distributions of crossing swell probabilities from ASAR and from WW3 data are shown correspondingly in the left and middle columns of Figure  1. The results from ASAR are almost identical with those of  [6] , as the criteria are similar, while those from WW3 seem not to be reported elsewhere. Both the distributions from ASAR and WW3 show that crossing swells are likely to occur at low latitudes, and all ocean basins have some regions with high crossing swell probabilities which are referred to as "crossing swell pools" (CSPs) by  [6]. However, the spatial distributions of crossing swell probability derived from WW3 are, in fact, very different from those from ASAR data in any basin or season. Remote Sens. 2017, 9, 107 3 of 7 Both the distributions from ASAR and WW3 show that crossing swells are likely to occur at low latitudes, and all ocean basins have some regions with high crossing swell probabilities which are referred to as "crossing swell pools" (CSPs) by  [6].
However, the spatial distributions of crossing swell probability derived from WW3 are, in fact, very different from those from ASAR data in any basin or season. The distributions of crossing swell probabilities from ASAR have been described in  [6]. In the Pacific Ocean, although both the CSPs in WW3 and in ASAR data are located in the equatorial area, their shapes are different. The Pacific CSP in WW3 data is a zonal region across the basin connecting the north of New Zealand to Central America. This CSP in WW3 data is stable all over the seasons, with slight moving southward in DJF and northward in JJA, which means that crossing swells are more likely to occur in tropical regions of the summer hemisphere. In the Atlantic Ocean, the North and South Atlantic CSPs defined in ASAR data are not well-defined in WW3 data, while a small CSP in WW3 data is found almost in between the two CSPs in ASAR in all seasons, which means the distributions are opposite for WW3 and ASAR in the tropic Atlantic to some degree.
In The distributions of crossing swell probabilities from ASAR have been described in  [6]. In the Pacific Ocean, although both the CSPs in WW3 and in ASAR data are located in the equatorial area, their shapes are different. The Pacific CSP in WW3 data is a zonal region across the basin connecting the north of New Zealand to Central America. This CSP in WW3 data is stable all over the seasons, with slight moving southward in DJF and northward in JJA, which means that crossing swells are more likely to occur in tropical regions of the summer hemisphere. In the Atlantic Ocean, the North and South Atlantic CSPs defined in ASAR data are not well-defined in WW3 data, while a small CSP in WW3 data is found almost in between the two CSPs in ASAR in all seasons, which means the distributions are opposite for WW3 and ASAR in the tropic Atlantic to some degree.
Therefore, the spatial-temporal sampling of ASAR is not the reason for this difference. Meanwhile, the result of virtual observation also shows that the spatial sampling of SAR is sufficient for wave climate provided that the data is of high quality. Many studies of wave climate have been conducted using numerical models (e. g. ,  [4, 16] ). Although numerical models produce unrealistic outputs under some certain conditions, the results of most model studies are consistent regarding climatology and seasonal variation. The comparison of wave climatology in Hawaii, where crossing swells are common, between model outputs and buoy measurements also shows good agreement implying that numerical models can reproduce crossing swells  [22]. Meanwhile, few wave climate studies used ASAR data of quasi-linear inversion until now.
Although  [6]  provides reasonable explanations for the results from ASAR in Figure  1 , these qualitative explanations are also applicable to our WW3 results. Therefore, we tend to believe there might be some problem with the way ASAR data are used. Examples of WW3 not in line with ASAR are shown in Figure  2  using the WW3-ASAR collocated dataset. In Figure  2a ,b, each swell system inversed by ASAR can find a corresponding swell system in WW3. However, their orders regarding the wave height are not consistent in ASAR and WW3. In Figure  2a , the 1st swell system in ASAR has good agreement with that from WW3, but the 2nd swell system in ASAR corresponds to the 3rd swell system from WW3 instead of the 2nd one, and the 2nd swell system is missed by ASAR. Similar conditions happen in Figure  2b , where the 1st swell system is missed by ASAR.
On the other hand, due to other complex effects of SAR processing, such as non-linear imaging, not all swell components can be captured by the sensor even if they have more energy. Another noteworthy point about these problems is that the validation of ASAR in  [25]  is conducted using a "dynamic" scheme by which the refocusing and propagation of swells have already served as a quality control of ASAR data so that all the questionable partitions are filtered before comparison. However, these questionable partitions will still remain in the along-track ASAR data. Moreover, in the "dynamic" validation, the comparison between SAR-measured wave partitions and buoy-measured wave partitions is made without considering the energy order of swell components. Because of these potential problems, it would be helpful if a flag can be included in the Level 2 SAR quasi-linear product to indicate whether the first several partitions of the SAR are in accordance with the real sea state.
Although it is hard to set this flag, as no reference for the sea state is available, this flag is important for many applications, especially with respect to the fact that new SAR missions such as Sential-1 might also deliver the same data products. A feasible way of doing it is to compare the SAR result with the model output, but this will cause other problems in the case where the model does not perform well. In the case of Globwave data, all these four kinds of errors can be summarized as follows: the 1st and the 2nd swells retrieved by SAR is not correspondingly in line with the 1st and the 2nd swells in the model, and these errors are very common in ASAR data. To investigate the extent to which the SAR records would be impacted by such problems, the spectral distances between the 1st swell in the Regarding the reasons of these errors, on the one hand, the performance of SAR is influenced by the angle between swell direction and antenna direction.
Sometimes swells with a short wavelength can still be cut off in the azimuth direction, while other times the wind-sea with a similar wavelength can be retrieved in the range direction. It is noted that there is not a clear distinction between wind-seas and swells, especially for those at the transition state, which makes this issue more complex. On the other hand, due to other complex effects of SAR processing, such as non-linear imaging, not all swell components can be captured by the sensor even if they have more energy. Another noteworthy point about these problems is that the validation of ASAR in  [25]  is conducted using a "dynamic" scheme by which the refocusing and propagation of swells have already served as a quality control of ASAR data so that all the questionable partitions are filtered before comparison.
However, these questionable partitions will still remain in the along-track ASAR data. Moreover, in the "dynamic" validation, the comparison between SAR-measured wave partitions and buoy-measured wave partitions is made without considering the energy order of swell components. Because of these potential problems, it would be helpful if a flag can be included in the Level 2 SAR quasi-linear product to indicate whether the first several partitions of the SAR are in accordance with the real sea state. Although it is hard to set this flag, as no reference for the sea state is available, this flag is important for many applications, especially with respect to the fact that new SAR missions such as Sential-1 might also deliver the same data products. A feasible way of doing it is to compare the SAR result with the model output, but this will cause other problems in the case where the model does not perform well.
In the case of Globwave data, all these four kinds of errors can be summarized as follows: the 1st and the 2nd swells retrieved by SAR is not correspondingly in line with the 1st and the 2nd swells in the model, and these errors are very common in ASAR data. To investigate the extent to which the SAR records would be impacted by such problems, the spectral distances between the 1st swell in the SAR data and the 1st swell in the WW3 data are calculated using the definition in  [21] : D = 1 f m ( f m cosθ m -f s cosθ s ) 2 + ( f m sinθ m -f s sinθ s ) 2 1 2 where f is the peak frequency, θ is the peak direction, and the subscripts m and s indicate being from the model and from the SAR, respectively. The same calculation is conducted for the 2nd swells. If both the spectral distances of the 1st and the 2nd swells are less than 0.
Therefore, the quasi-linear inversion can still provide large amounts of swell measurements of good quality. These data can be very useful in applications such as storm locating and swell tracking. Wave models especially do not perform well in many conditions, such as waves with strong currents or waves passing through small islands or atolls, while SAR can bring invaluable data to investigate and understand these phenomena. Despite its limitation on studying wave climate directly, SAR is a very powerful and irreplaceable tool for studying ocean waves, especially swells  [9].
Conclusions The numerical wave models provide an opportunity to investigate the propagation and distributions of global crossing swells. We find that the global patterns of crossing swells from WW3 are not in line with those from ASAR data of quasi-linear inversion. By a detailed comparison, the ASAR data are found to be questionable due to four types of common errors: (1) missing sampling of energetic swell components; (2) 180 • ambiguity; (3) instability in retrieving wind-sea information; and (4) bad-quality inversion. Therefore, the first two swells inversed by SAR are often not in accordance with the first two swells in the ocean. This problem will not have a large impact on case studies of swells but will limit the application of SAR quasi-linear-inversed products on wave climate. Decades of SAR wave mode data are available and more will come in the future, so these problems should be considered when using SAR in wave climate studies.
Acknowledgments: The Globwave ASAR data are accessed from  Globwave Web  portal (globwave. ifremer. fr)and the WW3 data are from  IFREMER  ftp (ftp. ifremer. fr). This work is supported by the  National Natural Science Foundation of China  ( 41331172  and  U1406405 , and  61361136001 ). The authors would like to thank  Xiaoming Li  for the helpful discussions and the anonymous reviewers for their helpful suggestions.
Acknowledgments The research reported in this paper was carried out within the collaborative  SINBAD  project, funded through the  UK's Engineering and Physical Sciences Research Council  ( EPSRC grants   EP/J00507X/1  and  EP/ J005541/1 ), and the  Dutch Stichting Technische Wetenschappen (STW  grant  12058 ). Data presented in this paper are available on request to the first author (d. a. vandera@abdn. ac. uk). We are grateful to two anonymous reviewers for their constructive comments that helped to improve the manuscript. Journal of Geophysical Research: Oceans.
Statistical and geostatistical analyses Considering either the whole dataset or subsets of the 112 sampling locations according to vine varieties or fields, Chi 2 tests were carried out between soil characteristics, past landuse, missing vine frequency and vine characteristics, in order to examine the possible footprint of past landuse, soil, and viticultural practices on missing vine frequency. However, as Chi 2 tests assume the independency of samples, and are no longer valid when observations are spatially structured  (Schabenberger and Gotway, 2005) , the structures of spatial dependence were also explored through experimental variograms and cross-variograms were built with couples of quantitative variables, in order to evidence their spatial correlations. Calculations were carried out with the statistical software R and packages gstat2.
Soil type appeared to relate to past landuse, as some fields were formerly occupied by wood, or by orchard. The long term frequency of missing vines exhibited no spatial structure when considering the whole dataset (Fig. 12 ) but was slightly structured and characterized with a 200 m-range for the Grenache variety only (Fig. 13 ). It had a spatial negative correlation with the planting age, which is logical considering that the oldest vines were still alive. All other quantitative variables were spatially structured (Figs. 12 and 13), particularly SUM and SOC content, sar, and topsoil C/N ratio. The cross variograms between SUM and the soil parameters showed a spatial correlation with SUM only for SOC content (negative), and the calcium carbonate content (slightly positive). The cross variograms between the frequency of missing vines and soil parameters show spatial correlations: negative for iron, clay and SOC content, and positive for calcium carbonate content.