dataesr/scientific-paragraphs-categorization huggingface dataset:
we constructed a comprehensive pCO 2 data set based on voluntary observing ship (VOS) measurements in the western English Channel (WEC). We subsequently estimated surface pCO 2 and air-sea CO 2 fluxes in northwestern European continental shelf waters using multiple linear regressions (MLRs) from remotely sensed sea surface temperature (SST), chlorophyll a concentration (Chl a), wind speed (WND), photosynthetically active radiation (PAR) and modeled mixed layer depth (MLD). We developed specific MLRs for the seasonally stratified northern WEC (nWEC) and the permanently well-mixed southern WEC (sWEC) and calculated surface pCO 2 with uncertainties of 17 and 16 µatm, respectively. We extrapolated the relationships obtained for the WEC based on the 2011-2013 data set (1) temporally over a decade and (2) spatially in the adjacent Celtic and Irish seas (CS and IS), two regions which exhibit hydrographical and biogeochemical characteristics similar to those of WEC waters.
We validated these extrapolations with pCO 2 data from the SOCAT and LDEO databases and obtained good agreement between modeled and observed data. On an annual scale, seasonally stratified systems acted as a sink of CO 2 from the atmosphere of -0. 6 ± 0. 3, -0. 9 ± 0. 3 and -0. 5 ± 0. 3 mol C m -2 yr -1 in the northern Celtic Sea, southern Celtic sea and nWEC, respectively, whereas permanently well-mixed systems acted as source of CO 2 to the atmosphere of 0. 2 ± 0. 2 and 0. 3 ± 0. 2 mol C m -2 yr -1 in the sWEC and IS, respectively. Air-sea CO 2 fluxes showed important inter-annual variability resulting in significant differences in the intensity and/or direction of annual fluxes. We scaled the mean annual fluxes over these provinces for the last decade and obtained the first annual average uptake of -1.
We used MLR in WEC to develop algorithms to predict surface pCO 2 and air-sea CO 2 fluxes from remotely sensed SST, chlorophyll a concentrations (Chl a), wind speeds (WNDs), photosynthetically active radiation (PAR) and from modeled mixed layer depth (MLD). We extrapolated the relationships obtained in the WEC based on the 2011-2013 data set (1) temporally over a decade and (2) spatially in the adjacent CS and IS, two regions where pCO 2 data are very sparse. Based on the reconstructed decadal data set, we investigated the variability of pCO 2 and air-sea CO 2 fluxes over the shelf.
, 2009). Between these two distinct structures, a frontal zone oscillates, separating well-mixed and stratified waters  (Pingree et al. , 1975). In this complex hydrographical context, high-frequency measurements from Ferry-Box data allowed us to precisely locate this thermal front and to accurately identify the real extent of each hydrographical province  (Marrec et al, 2014). Satellite SST data (Fig. 2 , Sect. 3. 2) combined with Ferry-Box measurements allowed us to further define the different hydrographical provinces of the northwestern European continental shelf. Water column characteristics similar to those in the WEC are also observed in adjacent seas, i. e. , the Irish Sea (IS) and the Celtic Sea (CS;  Pingree and Griffiths, 1978; Pingree, 1980; Holligan, 1981; Simpson et al. , 1981; Hill et al.
FerryBox data sets From January 2011 to January 2014, a FerryBox system was installed on the Voluntary Observing Ship (VOS) Armorique (Brittany Ferries). This vessel crossed the English Channel between Roscoff (France, 48 • 43 38 N, 3 • 59 03 E) and Plymouth (United Kingdom, 50 • 22 12 N, 4 • 08 31 E; Fig. 1 ) up to three times a day. The FerryBox continuously measured sea surface temperature (SST), salinity and partial pressure of CO 2 (pCO 2 , from April 2012) along the ferry track with more than 600 crossings with pCO 2 acquisition. Between January 2011 and January 2014, discrete sampling was performed on 57 return crossings between Roscoff and Plymouth with a total of 1026 sampling locations in the WEC. During each cruise, 18 water samples were taken from the FerryBox seawater circuit for the determination of dissolved inorganic carbon (DIC), total alkalinity (TA) and associated www.
(2014). Based on the comparison between high-frequency pCO 2 data obtained with a Contros HydroC/CO 2 FT sensor and bimonthly pCO 2 data calculated from DIC/TA, we estimated high-frequency pCO 2 measurements uncertainties at the lower end ±6 µatm  (Marrec et al. , 2014) , in the same range as computed values of pCO 2 from DIC and TA. We built a composite monthly data set of in situ SST and pCO 2 data over 3 years based on both high-frequency and bimonthly (twice a month) measurements. We used bimonthly discrete pCO 2 data between January 2011 and April 2012 and high-frequency pCO 2 data from April 2012 to January 2014. The monthly pCO 2 data were then adjusted to reference month July 2012, for MLR computation, using an atmospheric growth rate of 0. 15 µatm month -1 (1. 8 µatm yr -1 ) and assuming that the surface ocean pCO 2 is growing at the same rate as the atmosphere.
Satellite and other environmental data Satellite-derived Chl a concentrations (µg L -1 ) were acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite. Daily images were provided by the Natural Environment Research Council (NERC) Earth Observation Data Acquisition and Analysis Service (NEODAAS) at a spatial resolution of 1. 1 km. Monthly mean Chl a estimates were computed from January 2003 to December 2013 from these individual images over our study area (Fig. 1 ). WEC, CS and IS waters are optically complex shelf waters  (Joint and Groom, 2000; Darecki et al. , 2003; McKee et al. , 2007). These shelf seas present both Case 1 and Case 2 optical water types  (Morel and Prieur, 1977; Morel et al. , 2006)  depending on their hydrographical properties (seasonally stratified or homogeneous), the proximity to the coast, and the period of the year.
Satellite-based SST ( • C) data were acquired from the Advanced Very High Resolution Radiometer (AVHRR) instrument. Monthly mean SST estimates were computed from January 2003 to January 2014 from individual images with a spatial resolution of 1. 1 km by the NEODAAS. A validation between monthly in situ SST and associated satellite SST showed a robust correlation (R 2 = 0. 97, N = 448, p < 0. 001 and RMSE = 0. 43 • C). We gridded 1. 1 km resolution satellite SST into 0. 05 • × 0. 05 • cells as with all other remotely sensed and modeled parameters. Photosynthetically active radiation (PAR, in E m -2 d -1 ) data were retrieved from the Ocean Biology Processing Group  (McClain, 2009; http://oceancolor. gsfc. nasa. gov). We used the Level 3 monthly merged PAR product from MODIS Aqua. PAR was used as a variable in the MLRs as an indicator of the amount of light available for phytoplankton, which presented inter-annual variation over our study period (Fig.
03 • N, 4. 37 • W; depth 75 m) from January 2006 to January 2014 (Fig. S1  in the Supplement). Measurements were undertaken every 2 weeks by the Western Channel Observatory (NERC National Capability of the Plymouth Marine Laboratory and Marine Biological Association, www. westernchannelobservatory. org. uk). Profiles were obtained by a Seabird SBE 19+ with precision for temperature and computed salinity of 0. 005 • C and 0. 002, respectively. We also compared the modeled MLD in the CS and at the E1 fixed station with Armor-3D L4 Analysis observation products provided by the Copernicus Ma-rine Environment Monitoring Service (ex-MyOcean, http: //marine. copernicus. eu). The latter are combined products from satellite observations (sea level anomalies, mean dynamic topography and sea surface temperature) and in situ (temperature and salinity profiles) data on a 1/2 • regular grid in our study area.
, 2002; Jiang et al. , 2008). Wind speeds along the ferry track and over the study area were extracted from four-times-daily wind speed data corrected at 10 m height from the ERA-interim reanalysis project (Sect. 3. 2). Atmospheric pCO 2 (pCO 2 air ) was calculated from the CO 2 molar fraction (xCO 2 ) at the Mace Head site (53 • 33 N, 9 • 00 W; southern Ireland) of the RAMCES network (Observatory Network for Greenhouse gases) and from the water vapor pressure (pH 2 O) using the  Weiss and Price (1980)  equation. Atmospheric pressure (P atm ) over the study area was obtained from the ERA-interim re-analysis project  (Dee et al. , 2011). To estimate the uncertainties of the monthly air-sea CO 2 flux values, we follow the error propagation method exposed in  Omar et al. (2007)  and used in  Lauvset et al.
In the sWEC, residuals as a function of the observed pCO 2 were not homogeneously distributed, especially during year 2011 (Fig. 6 ), with high negative residuals when pCO 2 was below 350 µatm and high positive residuals when pCO 2 was over 450 µatm. In the nWEC, the distribution of residuals was more homogeneous (Fig. 6c  and d ); the less pronounced inter-annual variability was responsible for the better performance of the algorithms (R 2 ) in this part of the WEC. Shadwick et al. (2010)  and  Signorini et al. (2013)  undertook similar studies on the Scotian shelf and the northeastern American continental shelf, respectively. They estimated pCO 2 as a function of SST, Chl a and k, and from SST, salinity, Chl a and a time variable (TI), respectively, using MLR. Based on 14 monthly mean values from a high-frequency data set at a moored buoy, the algorithm developed by  Shadwick et al.
(2010)  attained a R 2 of 0. 81 with an associated standard error of 13 µatm. They extrapolated this algorithm over the entire Scotian shelf region to investigate pCO 2 and air-sea CO 2 fluxes from remotely sensed data from 1999 to 2008. Signorini et al. (2013)  reported R 2 and associated RMSE ranging from 0. 42 to 0. 87 and from 22. 4 to 36. 9 µatm, respectively. They divided the northeastern American continental shelf into five distinct regions according to their phys- ical and biogeochemical attributes. Their studies were based on SOCAT surface ocean pCO 2 , and the environmental variables used to predict pCO 2 came from remotely sensed and modeled data. The performances of our MLRs are within the same range as those in these previous studies. We developed our algorithms based on a 3-year data set obtained during highly contrasting years, which contributed to the robustness of our model to predict a representative seasonal cycle of pCO 2 as seen in the nWEC.
The SOCAT and LDEO data were not representative of a homogeneous system, hindering a direct comparison. Directly comparing monthly mean pCO 2 values obtained from algorithms and the SOCAT and LDEO pCO 2 data could generate an important bias because of the timescale difference between these data sets. Monthly gridded SO-CAT and LDEO data were mainly based on measurements performed at daily scales. Computed pCO 2 values were representative of the average monthly pCO 2 variability, which tends to smooth extreme values obtained at shorter timescales and prevent any observation of short-term processes. Despite this timescale discrepancy the mean differences between observed and predicted pCO 2 were -1 ± 27 in the sCS, -3 ± 25 in the nWEC, 3 ± 26 in the nCS, -7 ± 26 in the IS and -3 ± 29 µatm in LE on an annual scale.
Considering the uncertainties relative to the MLR of 17 µatm (Sect. 4. 1), these results were very promising and allowed us to validate the extrapolation of our method over our study area. The results obtained in the sWEC, with an annual mean difference of -14 ± 26 µatm, were less promising as explained above and in Sect. 4. 1. The comparison of SOCAT and LDEO data with the model predictions provided indications on the MLR performance on a wider spatial scale. For the first time, we thus computed the seasonal and long-term dynamics of pCO 2 and associated air-sea CO 2 fluxes over a decade for this part of the northwestern European continental shelf (Sect. 4. 3) despite the relative uncertainties inherent to the method. (Pingree, 1980). The earlier onset of stratification in the CS than in the nWEC, due to less intense tidal streams  (Pingree, 1980) , is consistent with the preliminary signs of the spring bloom observed firstly in the CS (Fig.
Concluding remarks and perspectives Based on a 3-year data set of pCO 2 measurements acquired on a VOS in the WEC, we estimated surface ocean pCO 2 and air-sea CO 2 fluxes in the northwestern European continental shelf waters using MLRs from remotely sensed SST, Chl a, PAR and wind speed (in the sWEC), from modeled MLD (in the nWEC) and from a time variable TI. For the first time, seasonal and long-term dynamics of pCO 2 and air-sea CO 2 fluxes over this part of the northwestern European continental shelf were evaluated over a decade, despite the relatively high uncertainties inherent to such method. We thus provide the first estimate of air-sea CO 2 flux in the poorly documented CS, IS and WEC. As mentioned above, very few data are currently available in these coastal seas. However, the number of surface in situ pCO 2 data grows exponentially and these data are now easily available on data portals such as SOCAT and LDEO to develop and validate such algorithms.
For example, in situ data of the CO 2 system are currently acquired during seasonal cruises within the CANDYFLOSS project (NERC, collaboration National Oceanographic Center/Biological Station of Roscoff) in the CS and the IS, and with a FerryBox system operating between Roscoff (France) and Cork (Ireland). In the future, these data will improve and allow further developments of our algorithms with an adequate division of the shelf area in representative biogeochemical provinces and by developing specific algorithms in each province. The reconstructed decadal data sets highlighted the importance of multi-annual study of air-sea CO 2 fluxes in continental shelf seas. As mentioned by  Keller et al. (2014) , it can be difficult to detect relevant trends in the seawater pCO 2 signal, particularly in coastal areas with high inter-and intraannual variability.
Acknowledgements. We thank the  Brittany Ferries (B. A. I)  for providing access to their vessels and especially the captains and crew of the Armorique ferry for their hospitality and their assistance. The authors thank the  NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS)  for supplying data for this study, especially  B. Taylor. We acknowledge the work of the  Western Channel Observatory  funded under the National Capability of the  UK Natural Environmental Research Council  for the E1 data. Additional thanks go to  G. Charria  from  IFREMER  for providing modeled MLD. We thank  SNAPOCO2  for help with the CO 2 parameter analysis. We thank  M. Ramonet  for providing the atmospheric CO 2 data from the  RAMCES network (Observatory Network  for Greenhouse gases). We thank  I.
Probert  for his help with this paper. The  Surface Ocean CO 2 Atlas (SOCAT)  is an international effort, supported by the  International Ocean Carbon Coordination Project (IOCCP) , the  Surface Ocean Lower Atmosphere Study (SOLAS) , and the  Integrated Marine Biogeochemistry and Ecosystem Research program  (IMBER), to deliver a uniformly quality-controlled surface ocean CO 2 database. The many researchers and funding agencies responsible for the collection of data and quality control are generously thanked for their contributions to SOCAT and to the Global Surface pCO 2 Database (LDEO). We are very grateful to the four anonymous reviewers for improving the overall quality of our manuscript. A special thanks for the EAG team, which provided us new insights for the manuscript. This work was funded by the European Project  INTERREG IV/MARINEXUS , by the  Conseil Général du Finistère  ( CG29 ), by the  Region Bretagne  (program  ARED , project  CHANNEL ) and by  INSU (program LEFE/CYBER , project  CHANNEL ).
In this paper, in a first part we will present kinematic caracteristics of the flow whereas in a second part of this work, the data will provide some concentration profiles of solid particles. The results presented concerning the velocity and concentration field are related to streamwise vertical planes at the center of the wind tunnel at successive longitudinal positions. For velocity field we will report different regimes for smooth and rough plate. Only one regime will be presented for solid particles. We present in a first part the kinematic study and in the second part results on the concentrations of solid particles.
Conclusions Idealized laboratory experiments related to the flow field kinematics around a Gaussian hill embedded in a turbulent boundary layer were carried out with PIV measurements and digital image treatment. The hill was formed by a two dimensional Gaussian shape of 1cm height and spanned the wind tunnel. Mean solid particle injection was done upstream the hill to obtain 1) some information on the way the hill modifies the concentration levels of solid particles; 2) some data basis for numerical code validation. Interesting but classical information was obtained on the way recirculation zone is modified by the regimes and the roughness. Original information was obtained for a given regime concerning the effect of hill on the solid particle concentration. Numerical simulation is on the way to reproduce such case and other regime in order to validate deeply the LES code that we used for more systematic parameter studies as in  14, 15  that was one of the final aims of this work.
, 2013]  data of the Helium Oxygen Proton Electron (HOPE) detector for electron fluxes below 50 keV  [Funsten et al. , 2013] , and the Magnetic Electron Ion Spectrometer (MagEIS) 30 keV to 4 MeV measurements  [Blake et al. , 2013]. c 2015 American Geophysical Union. All Rights Reserved. The waveform data was transmitted at 16,384 samples/s (35,000 from EMFISIS) for 5 s continuous intervals in the burst mode (triggered automatically by large amplitude signals), as well as continuously every 6 s under the form of full spectral matrices for 65 frequencies (logarithmically spaced from 3 Hz to 16 kHz for EMFISIS). We present results based on an analysis of waveform and spectral data captured by Van Allen Probe B on November 1, 2012, consisting of about 80 burst-mode intervals collected over 15:30 16:30 UT and about 30 intervals during 21:00-22:00 UT.
Acknowledgments. The work by O. A. , F. M. , and V. K. was performed under JHU/APL Contract No. 922613  ( RBSP-EFW ). A. V. A. is grateful to  Dmitry Zimin Dynasty Foundation  for support. We also thank all the people associated with the electric field, magnetic field, and particle teams on the  Van Allen Probes  and the project team at the  Johns Hopkins Applied Physics Laboratory  that manages this program. All data used in this paper can be found at RBSP/EFW database (http://www. space. umn. edu/missions/rbspefw-home-university-of-minnesota/).
The e-Science environment developed in the framework of the EU-funded DRIHM project was used to demonstrate its ability to provide relevant, meaningful hydrometeorological forecasts. This was illustrated for the tragic case of 4 November 2011, when Genoa, Italy, was flooded as the result of heavy, convective precipitation that inundated the Bisagno catchment. The Meteorological Model Bridge (MMB), an innovative software component developed within the DRIHM project for the interoperability of meteorological and hydrological models, is a key component of the DRIHM e-Science environment. The MMB allowed three different rainfall-discharge models (DRiFt, RIBS and HBV) to be driven by four mesoscale limited-area atmospheric models (WRF-NMM, WRF-ARW, Meso-NH and AROME) and a downscaling algorithm (Rain-FARM) in a seamless fashion.
, 2009 , http: //pcraster. geo. uu. nl/pcraster-4-0-0). The advantage of using OpenStreams is that it enables direct communication with OpenDA (2014) (www. openda. org. ), an open source data assimilation toolbox. OpenDA provides a number of algorithms for model calibration and assimilation which renders it suitable for connection to any kind of environmental model (e. g. Ridler et al. , 2014). The WFLOW-HBV model (one of the hydrologic models available in OpenStreams) requires (gridded) time series of precipitation, temperature and potential evaporation as input data. Besides dynamic data, static input data such as a DEM, land cover map, soil map and model parameters per soil and/or land use type are required. For each of the WFLOW-HBV grid cells, the water balance and resulting runoff is computed.
The model consists of three routines: a snow routine, a soil routine and a runoff response routine. The specific runoff is routed by a kinematic wave approach. For more details on the HBV-96 model see  Rakovec et al. (2014)  or www. openstreams. nl. For the Genoa flash flood, a WFLOW-HBV model was set-up with an hourly time step. The river network was derived from the OpenStreams preprocessing functions using spatial data extracted from SRTM 3 arc-second resolution DEMs and from the GLC2000 project. The final grid size of the WFLOW-HBV model is 0. 001 • latitude/longitude. Precipitation data was available from 24 precipitation stations and one hydrological station, Passarella Firpo. Temperature data were available from only four stations. The measured data was interpolated to grids using Thiessen polygons.
Hydrometeorological modelling chains None of the meteorological and hydrological models considered here provide standard interfaces to pass information from one to another. This seriously hampers the devel-opment of any multi-model hydrometeorological ensemble with a substantial number of different models. To deal with this problem, a component named Meteorological Model Bridge (MMB), which ingests data structured around model grids or meshes into other models, has been designed within the DRIHM project. The MMB allows the creation of a link between meteorological model forecast outputs and other models, thus enabling chains of hydrometeorological workflows to be generated. Starting from meteorologically gridded outputs produced by meteorological models such as those mentioned in Sect. 3. 1, the MMB provides usable data files in a standardised format with the twofold aim of supporting intra-model interoperability during workflow execution and organising model data in an easy and more manageable way.
The MMB converts the output of each meteorological model from its original projection to a regular latitudelongitude grid. The MMB output files contain the variables necessary to correctly drive the hydrological models, including: total accumulated surface precipitation, 2 m air temperature, 2 m specific humidity and both northward and eastward 10 m wind components. The standardised format chosen to be implemented in the DRIHM project is the netCDF-CF format. The Open Geospatial Consortium (OGC) netCDF encoding supports electronic encoding of geospatial data, that is, digital geospatial information representing space and time-varying phenomena. In December 2012, the CF-netCDF Data Model Extension Standard obtained a new OGC standard. This standard specifies the CF-netCDF data model extension introducing the extra semantics required to capture and formalise the metadata describing multi-dimensional gridded and multi-point data.
Acknowledgements. The authors would like to acknowledge the support of the  DRIHM (Distributed Research Infrastructure for Hydro-Meteorology ,  2011-2015 ) project, funded within the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under grant agreement no. RI-283568. The Italian authors are also grateful for the support provided by the  Italian Civil Protection Department  and the  Regione Liguria. The authors also acknowledge  Regione Liguria  and  Regione Piemonte  for providing data from the regional meteorological observation networks, as well as the  Italian Civil Protection Department  and  Jelena Marković  for providing the  Italian Radar Network data.