dataesr/scientific-paragraphs-categorization huggingface dataset:
The time resolution varies from 15 min. at the beginning of the period to 10 min. for the last years. Atmospheric data from a local meteorological reanalysis (SAFRAN) are also provided from 1 August 2000 to 1 August 2016. A Digital Elevation Model (DEM) of the study area (1,5 km²) at 20-cm resolution is also provided in RGF 93 Lambert 93 coordinates This dataset has been used in the past to develop and evaluate physical parameterizations and numerical models of blowing and drifting snow in alpine terrain. Col du Lac Blanc is also a target site to evaluate meteorological and climate models in alpine terrain. It belongs to the Cryobs-Clim observatory (the CRYosphere, an OBServatory of the CLIMate) which is a part of the national research infrastructure OZCAR (Critical Zone Observatories -Application and Research) (Gaillardet et al.
Recent studies have focused on fine scale processes during blowing snow events  (Naaim Bouvet et al. , 2010 , 2011 , 2013; Nishimura et al. , 2014; Schön et al. , 2015) , intercompraison of blowing snow sensors  (Cierco et al. , 2007; Trouvilliez et al. , 2015)  and the development and evaluation of blowing snow models  (Durand et al. , 2005; Vionnet et al. , 2013 Vionnet et al. , , 2014 Vionnet et al. , , 2017 Vionnet et al. , , 2018)). In this paper, we present a unique meteorological and blowing snow dataset for each winter of the period 2000-2016. Meteorological data are available from 4 automatic weather stations (AWS) surrounding the experimental site. Blowing snow data stem from two sources: (i) a database of blowing snow occurrence and (ii) blowing snow fluxes derived from Snow Particles Counters over the last 6 years.
AWS Dome lies at higher elevation on the top of the Dôme des Petites Rousses. At this station, atmospheric conditions are less influenced by the topography of the pass and closer to the synoptic conditions. Two wooden shelters are also installed at the pass (Fig. 2 ). They host the data acquisition system, the equipment storage and the living facilities. They are located on the eastern side of the pass, aside from the main wind direction, so that they have a minimum impact on measured snow and meteorological conditions at AWS Lac Blanc, Col and Muzelle. The meteorological, snow depth and blowing snow data collected at these stations are presented in the next two sections.
Meteorological and snow depth data Table  2  provides an overview of the meteorological and snow parameters measured around CLB, with the corresponding instrument type and height. Each AWS located around CLB measures wind speed and direction at a time step of 15 min or 10 min depending on the station and/or the period. Additionally, measurements of air temperature are available at three AWS. Finally, three AWS situated around the pass measure snow depth using ultrasonic sensors (at AWS Lac Blanc and Muzelle) and laser sensor (at AWS Col). These data have undergone a careful manual quality check. They are available between 1 st December and 31 st March of each winter. This period has been selected since it corresponds to the main period during which most of blowing snow events occur at CLB  (Vionnet et al.
Snow depth Snow depth is measured using ultra-sound depth sensors at AWS Lac Blanc and Muzelle. The correction of the impact of air temperature on the velocity of sound in the atmosphere is carried out using the air temperature measurement previously described. The data are further manually corrected to remove outliers in the dataset, most often occurring during snowfall. Ultra-sound depth sensors provide measurements accurate within 1 cm for a surface area of a few cm 2 on the ground. The overall accuracy of the automated snow depth record is thus on the order of 1 cm. Snow depth is measured using a laser sensor (SHM30) at AWS Col. The sensor uses an optoelectronic distance measurement principle to achieve a specified measurement uncertainty of better than 5 mm. Divergence of the SHM30 laser beam amounts to 0.
Database of blowing occurrence A database of blowing snow events at CLB was established for each winter of the period 2000-2016. It consists in an extension of the database presented in  Vionnet et al. (2013). A blowing snow event is defined as a time-period when snow on the ground was transported in the atmosphere in saltation and in turbulent suspension. Such event may occur with concurrent snowfall. Therefore, the database makes the distinction between blowing snow events with and without concurrent snowfall. The identification of a blowing snow event in the database relied on an empirical method that required the combination of several datasets (wind, precipitation and snow depth) and their detailed analysis by an expert. This method is extensively described in Vionnet et al (  2013 ) and its main characteristics are summarized here.
, 2016). The SPC-S7s could be raised manually when the snow depth increased and risked burying the sensors. Horizontal snow flux is highly dependent on height above the snow surface. Because of snowfall and blowing snow events the elevation of SPC above the snow varied substantially during the winter season preventing any direct comparison over time being made. That's why in the present database, the snow fluxes near the surface (that means the snow flux provided by the SPC closest to the snow surface) and the corresponding height of the sensor are provided. When available, snow fluxes at a higher position are used to standardize the horizontal snow flux and to estimate the mean horizontal snow flux at 1 m above the snow surface and vertically integrated over 1 m (between 0,2 and 1,2 m over the snow surface).
Therefore the SPC data were processed as follows. First a filter was applied to the raw data to supress possible electronic noise: events with a particles flux smaller than 20 particles cm -2 during 10 minutes were discarded. Then, an algorithm was designed and used to categorize each 10-min profile of SPC data set into six different groups (specified in the database):  "Undetermined": only one sensor provides data making it impossible to do the vertical interpolation.  "Power_law": the vertical distribution function can be expressed as a power law.  "Inconsistent": Data are physically inconsistent.  "No_flux": In this case no sensor of the SPC vertical profile detects more than 20 particles per cm² during ten minutes.  "Maintenance": During sensor maintenance, data acquisition is stopped.
In particular, SAFRAN provides a valuable estimation of the snowfall amount at Col du Lac Blanc that cannot be precisely measured at the site during snowfall under windy conditions due to strong gauge undercatch. Therefore, SAFRAN precipitation is considered as the reference precipitation dataset at Col du Lac Blanc. On the other hand, SAFRAN tends to underestimate wind speed at CLB due to the influence of the surrounding topography which is not included in the conceptual representation of the topography in SAFRAN. It is recommended to replace SAFRAN wind speed and direction by the observations collected at CLB when running a land surface scheme at CLB in wintertime as described inVionnet et al. (2013).
Positive values of the difference between the maximum and minimum snow depth recorded over an hour and associated large values of its standard deviation are characteristic of the presence of snow particles between the sensor and the surface of the snowpack. Snow particles in motion above the snowpack surface create indeed interference in the ultrasonic signal. The records from a webcam (installed in 2004) completed the analysis. Such an empirical method provides the start and end dates of each blowing snow event. Only events of duration longer than 4 h were recorded in the database. Overall, the database contains at an hourly time step information on the occurrence of blowing snow and the type of event (with or without snowfall).
Figure6shows an overview of snow depth, maximum wind speed and blowing snow fluxes measured at the site over the last six seasons. A strong inter-annual variability is found in terms of snow depth with a maximum snow depth reaching 4. 01 m in 2012/2013 and only 2. 84 m for winter 2013/2014. Events with a maximum wind speed above 15 m s -1 are frequently measured at the site. These windy events are generally associated with blowing snow events if erodible snow is present at the snow surface. The intensity of these events varies greatly (note the logarithmic scale on the graphs showing blowing snow fluxes). Additional and more detailed summary plots for each year of the present dataset are provided as Supplement to this article.
Acknowledgments The authors would like to thank  Fred Ousset , and  Xavier Ravanat  for their valuable help in the field. We also would like to acknowledge  SATA (Socièté d'Aménagement Touristique de l'Alpe d'Huez et des Grandes Rousses)  which provided logistical support and OSUG-DC for hosting the data. Col du  Lac Blanc  is a part of  IR OZCAR  and receive financial support from  OSUG ,  Meteo France  and Irstea. CNRM/CEN and Irstea are part of  LabEx OSUG@2020  ( ANR10 LABX56 ). Earth Syst. Sci. Data Discuss. , https://doi. org/10. 5194/essd-2018-74Open Access Earth System Science Data Discussions Manuscript under review for journal Earth Syst. Sci. Data Discussion started: 25 June 2018 c Author(s) 2018. CC BY 4. 0 License.
Conclusions The Col du Lac Blanc is a unique high-altitude experimental site in the French Alps where meteorological and blowing snow data are automatically acquired. This site is dedicated to studies of snow/atmosphere interactions and wind-induced snow transport. 16 years of quality controlled data have been combined and made freely accessible for the scientific community. They are composed of meteorological data from four automatic stations and a reanalysis product, a database of the main blowing snow events that occurred at the site over 16 years and 6 years of automatic measurements of blowing snow fluxes with Snow Particles Counters. These high-quality data have been already used to evaluate snow redistribution models for specific blowing snow events or years  (Schön et al. , 2015; Vionnet et al.
, 2014 Vionnet et al. , , 2017 Vionnet et al. , , 2018) )  and to carry out detailed analysis of the physics of snow transport  (Naaim Bouvet et al. , 2010 , 2011; Nishimura et al. , 2014). We anticipate that these data will be used in the future in modelling studies of snow accumulation over multiple snow seasons and to better understand the links between the occurrence and intensity of blowing snow events and meteorological and snow conditions. We hope that further instrumental developments will allow to improve the monitoring of snow/atmosphere interactions at this site for the benefits of the snow, avalanche, mountain hydrology and weather forecasting community. Open Access Earth System Science Data Discussions Manuscript under.
Acknowledgements. The authors thank the teams involved in the development and exploitation of the MMS, MTP, FCDP and  2DS  instruments during the ATTREX campaign. Aurélien Podglajen  thanks  Martina Krämer ,  Bernard Legras  and  Claudia Stubenrauch  for their comments on this work. We sincerely thank  Peter Spichtinger  and one anonymous referee for their helpful comments and suggestions on the paper. Aurélien Podglajen ,  Riwal Plougonven  and  Albert Hertzog  acknowledge support from the  French ANR  project  StraDyVariUS (Stratospheric Dynamic and Variability ,  ANR-13-BS06-0011-01 ) and from the  French space agency (CNES)  through the  Strateole 2 project. Edited by: Peter Haynes Reviewed by:  Peter Spichtinger  and one anonymous referee.
3 ). Figure  3  summarizes the maximum proton energy recorded in these experiments (see open symbols) as a function of the peak laser intensity over three orders of magnitude, up to 1. 3 × 10 21 W μm 2 cm -2. The experimental data are fairly well reproduced by 2D PIC simulations (see filled symbols) performed in the same conditions. The solid lines plot the results of the 1D plasma expansion model employed in Figs  1  and 2 , which neglects magnetization effects. These analytical predictions reasonably agree with the experimental measurements, performed at LULI (blue symbols) and SNL (red symbols), at low laser intensities. However, both 2D PIC simulations and experimental results gradually deviate from the model's predictions when the laser intensity exceeds 10 21 W μm 2 cm -2 : compare the 1D modelpredicted solid red line vs.
It is thus a valuable resource to monitor and detect changes in coastal areas with high vulnerability, such as lagoons and coral reefs  [11]. The challenge however, is to obtain accurate estimation of the apparent (e. g. , above-water remote sensing reflectance, R rs (λ)) and inherent (e. g. , absorption and backscattering coefficients) optical properties in coastal environments with optically complex waters  [12]. To validate and develop accurate satellite observations, one needs first to build a robust in situ database. Although in situ measurements are made carefully, minimizing uncertainties is a great challenge and could propagate adversely to even be amplified in the final products  [13]. The R rs measurements can have high uncertainties (>30%) depending on the environmental conditions and the acquisition and processing methods  [14, 15].
Biogeochemical and Bio-Optical Properties In situ water turbidity was obtained by a ECO FLNTU (Wetlabs Inc. , New Caledonia, France) turbidimeter profiler that measures the light scattered at 140 degrees from a 700 nm LED light source, and converts the signal into NTU using calibration coefficients and the Formazin Standard provided by the manufacturer. Surface water turbidity was obtained by averaging the profile measurements within the first 3 m. The collection and processing of the bio-optical data are the same as described in Dupouy et al. ,  [9, [40] [41] [42]. Vertical profiles of the backscattering coefficient (b b , m -1 ) were measured using a Hydroscat-6 (HobiLabs) scatterometer calibrated 1 month before the cruise, with 10 nm bands centred at 442, 488, 510, 550 nm, and 20 nm bands at 620 and 670 nm.
Data was processed using the manufacturer calibration files and volume scattering function (β), and corrected for the path length signal loss due to absorption (sigma correction)  [54]. The seawater b b used to obtain the b bp was determined following Twardowski et al. [17]  adjusted for the mean salinity of the lagoon. The surface water b bp value was determined by averaging the first depths (1-5 m), to avoid noise caused by bubble clouds and in-water perturbations. For analysis of the light absorption coefficients and Chla, water samples were collected at a sub-surface depth of ~2 m with a rosette Niskin bottle system. Samples for determination of phytoplankton pigments and the particulate absorption coefficient were immediately filtered on board (1-2 L), using 25 mm GFF (0. 7 µm nominal pore size).
Radiometric Measurements Radiometric data was collected using two types of in-water radiometers: (i) the free-falling Satlantic profiler and (ii) TriOS sensors, attached to a floating structure (Figure  2C ,D), for comparison and uncertainty determination. The Satlantic free-falling profiler radiometer (Hyperpro-II) (Figure  2A, B ) measures radiance between 350 and 800 nm (3. 3 nm bandwidth). With this instrument radiometric data was obtained at 40 stations with in-water profiles of downwelling irradiance (E d (λ,z)) and upwelling radiance (L u (λ,z)). Data was pre-processed using the Prosoft software with the instrument calibration files, and some profile editions to eliminate noise (using tilt and velocity thresholds). To make the profiles even smoother, filtering out noise caused by wavy facets and bubble clouds, a second order polynomial fit was also applied to each profile.
To extrapolate E d (λ,z) and L u (λ,z) to just below surface values (0 -), the diffuse light attenuation coefficients (K d (λ) and K u (λ), respectively) were determined using the intercept of the linear regression fit of the log transformed data (assuming an homogeneous profile). The depth interval used to determine K d and K u varied at each station from 5-15 m, depending on the size of the profile and avoiding noisy intervals. After the extrapolation of L u (λ) and E d (λ) to 0 -, they were extrapolated to just above surface (0 + ), i. e. , L w and E d (0 + ), using air-water transmittance factors  [61]. The R rs determined with the Satlantic free-falling profiler (R rs S) was finally obtained by the ratio of L w /E d (0 + ). The light penetration depth (Z 90 ), above which approximately 90% of the R rs signal is originated, can be approximated by 1/K d (λ)  [62].
Radiometric Measurements Radiometric data was collected using two types of in-water radiometers: (i) the free-falling Satlantic profiler and (ii) TriOS sensors, attached to a floating structure (Figure  2C ,D), for comparison and uncertainty determination. The Satlantic free-falling profiler radiometer (Hyperpro-II) (Figure  2A ,B) measures radiance between 350 and 800 nm (3. 3 nm bandwidth). With this instrument radiometric data was obtained at 40 stations with in-water profiles of downwelling irradiance (Ed(λ,z)) and upwelling radiance (Lu(λ,z)). Data was pre-processed using the Prosoft software with the instrument calibration files, and some profile editions to eliminate noise (using tilt and velocity thresholds). To make the profiles even smoother, filtering out noise caused by wavy facets and bubble clouds, a second order polynomial fit was also applied to each profile.
To extrapolate Ed(λ,z) and Lu(λ,z) to just below surface values (0 -), the diffuse light attenuation coefficients (Kd(λ) and Ku(λ), respectively) were determined using the intercept of the linear regression fit of the log transformed data (assuming an homogeneous profile). The depth interval used to determine Kd and Ku varied at each station from 5-15 m, depending on the size of the profile and avoiding noisy intervals. After the extrapolation of Lu(λ) and Ed(λ) to 0 -, they were extrapolated to just above surface (0 + ), i. e. , Lw and Ed(0 + ), using air-water transmittance factors  [61]. The Rrs determined with the Satlantic free-falling profiler (RrsS) was finally obtained by the ratio of Lw/Ed(0 + ). The light penetration depth (Z90), above which approximately 90% of the Rrs signal is originated, can be approximated by 1/Kd(λ)  [62].
Hence, Z90 was also determined for each station, within the visible spectral range (400-700 nm), to verify possible influences of bottom reflectance on the abovewater Rrs within the stations sampled at the ELNC. Radiometric data obtained with the TriOS-RAMSES sensors was measured using two hyperspectral radiometers SAM-ARC-Vis: one for the sub-surface (~2 cm) L u (λ) deployed with an adapted floating system (Figure  2C ,D), and another for the above-water downwelling irradiance (E s (λ)) installed at upper most level of the vessel (deck sensor). These measurements were made at 49 stations (9 more than with the Satlantic profiler). The TriOS instruments measure radiance and irradiance from 320 to 950 nm, with 3. 3 nm bandwidth resolution (7 nm bandwidth). To minimize platform shading effects and in-water perturbations which may bias in-water profiling systems winched beside the vessel  [15, 63] , the in-water TriOS sensor was attached to a PVC floating structure connected to a cable with buoys, and deployed to drift away and collect sub-surface L u data at least 15 m distant from the platform  [64].
The sub-surface L u was extrapolated to surface water leaving radiance (L w ) using air-water transmittance factors  [61] , and the above-water TriOS R rs (R rs T) was obtained with the ratio of L w /E s. For the comparisons between the two approaches, and to further apply the radiometric data to the ocean colour algorithms, the hyperspectral R rs measurements were used to simulate correspondent bands of the MODIS-Aqua sensor centred at 412, 443, 490, 510 and 555 nm with 10 nm bandwidth. Both the Satlantic and TriOS R rs data were corrected for variations in solar zenith angle (SZA) to obtain equivalent values for the Sun at zenith, using f /Q factors provided by Morel et al. [65]. A closure experiment was also performed to evaluate the consistency between the radiometric measurements and bio-optical data.
Bio-Optical Algorithms Ocean colour bio-optical algorithms were tested using the in situ measured R rs (from the TriOS instrument). For Chla determinations the algorithms that were tested for the ELNC were: the empirical OC3M and OC4  [22] , and the semi-analytical GSM01  [27, 28]  and the GIOP  [32]. To estimate the absorption and backscattering coefficients we applied the GSM01, GIOP, QAA  [30]  and the LMI, in this case regionally tuned for New Caledonia coastal lagoons  [36, 37]. Surface water turbidity algorithms were also applied to the ELNC with in situ data obtained during the CALIOPE 2 campaign, testing 3 approaches: (i) the regional algorithm proposed by Ouillon et al. [38] ; (ii) the global algorithm for tropical coastal waters also proposed by Ouillon et al. [38]  and (iii) the single global algorithm for all water types proposed by Dogliotti et al.
In Situ Radiometry Of the 54 stations sampled during the CALIOPE 2 campaign, approximately 50% had cloud cover higher than 5/8 and 14 stations were sampled with high SZA (≥60°) (early in the morning and late afternoon). Strong southwest winds were predominant during the data collection (average speed, 8. 5 m•s -1 and maximum 16 m•s -1 ), indicating rough conditions for in situ radiometric measurements. The Rrs spectra characteristic of clear waters with higher values at shorter wavelengths (<490 nm), were obtained at the deepest stations across the coral reef barrier, and at the southern and northern tips of the lagoon, where oceanic waters have greater intrusions. Near the main rivers, and especially close to the mining sites (between Thio and Ponérihouen), the Rrs spectra were characteristic of water types dominated by biogenic constituents, that absorb more light in the blue wavelengths, resulting in lower Rrs (<490 nm), and mineral particles that are highly scattering and contribute to higher Rrs at longer wavelengths (>500 nm) (Figure  8 ).
In Situ Radiometry Of the 54 stations sampled during the CALIOPE 2 campaign, approximately 50% had cloud cover higher than 5/8 and 14 stations were sampled with high SZA (≥60 • ) (early in the morning and late afternoon). Strong southwest winds were predominant during the data collection (average speed, 8. 5 m•s -1 and maximum 16 m•s -1 ), indicating rough conditions for in situ radiometric measurements. The R rs spectra characteristic of clear waters with higher values at shorter wavelengths (<490 nm), were obtained at the deepest stations across the coral reef barrier, and at the southern and northern tips of the lagoon, where oceanic waters have greater intrusions. Near the main rivers, and especially close to the mining sites (between Thio and Ponérihouen), the R rs spectra were characteristic of water types dominated by biogenic constituents, that absorb more light in the blue wavelengths, resulting in lower R rs (<490 nm), and mineral particles that are highly scattering and contribute to higher R rs at longer wavelengths (>500 nm) (Figure  8 ).
Acknowledgments: The authors thank all the participants of the CALIOPE 2 cruise involved in the data collection and analysis obtained in the frame of  INSU-EC2CO  project  (TRansfErts de la Matière Organique dissoute coLOrée ). The authors also acknowledge the administrative staff of the  IRD Center of Nouméa, New Caledonia  and the captain and crew of  R/V Alis. The Government of New Caledonia and Southern and Northern Provinces of New Caledonia and Aires Coutumières et Mairies of the ELNC gave authorization to sample the eastern New Caledonia lagoon during the CALIOPE 2 cruise.