dataesr/scientific-paragraphs-categorization huggingface dataset:
Observational data and evaluation protocol Table  3  summarizes the surface and satellite datasets and variables used in the evaluation. The surface meteorological datasets include observations from the Baseline Surface Radiation Network (BSRN) and European Climate Assessment & Dataset (ECA & D), reanalysis data from NCEP, and a combination of observations and interpolated data from the National Oceanic and Atmospheric Administration Climate Diagnostics Center (NOAA/CDC). The meteorological variables evaluated include downward shortwave and longwave radiation fluxes (SWDOWN and LWDOWN, respectively), outgoing longwave radiation fluxes (OLR), temperature, specific humidity, and relative humidity at 2 m (T2, Q2, and RH2, respectively), wind speed at 10 m (WS10), wind direction at 10 m (WD10), and total daily precipitation (Precip).
Simulated vertical profiles of temperature (T ), dew point (Td), as well as wind speed (WS) and wind direction (WD) are also evaluated using sounding observations from the NCAR DS353. 4 ADP. The chemical surface datasets include EMEP, the European air quality database (AirBase), and the Base de Données de la Qualité de l'Air (BDQA). Chemical variables evaluated include hourly and daily average NH 3 , SO 2 , NO 2 , daily average HNO 3 , hourly, maximum 1 h and maximum 8 h average O 3 , and hourly and daily average PM 2. 5 , PM 10 , and PM 10 composition (i. e. , sulfate (SO 2- 4 ), nitrate (NO - 3 ), ammonium (NH + 4 ), sodium (Na + ), and chloride (Cl -)). EC and organic matter (OM) are not evaluated because of a lack of observations. EMEP contains data from the Convention on Long-Range Transboundary Air Pollution and represents hourly O 3 data and daily data for other species at regional background sites mostly at farmland, rural, and lightly forested areas  (Torseth and Hov, 2003).
The AirBase database contains observations from the European Air Quality monitoring network (EuroAirnet) provided by European Union Member States, European Environment Agency member countries, and cooperating countries. It contains hourly data for all species and additional daily data for PM 2. 5 , PM 10 , and PM 10 composition at various types of sites such as rural background, rural, suburban, urban, traffic, and industrial sites. BDQA is the French database for air quality that covers France with hourly measurements at various types of sites. Because the grid resolution used in this work is not commensurate with urban, traffic and industrial sites, those sites from the AirBase and the BDQA are excluded from the model evaluation, except for urban background sites in Air-Base. Large uncertainties exist in these observational data due to artifacts in the measurements and the impacts of local geographical conditions on the measurements  (Schaap et al.
, 2004; Sartelet et al. , 2007). The satellite datasets include the Total Ozone Mapping Spectrometer/the Solar Backscatter UltraViolet (TOMS/SBUV), the Measurements Of Pollution In The Troposphere (MOPITT), the Global Ozone Monitoring Experiment (GOME), and the Moderate Resolution Imaging Spectroradiometer (MODIS). The variables evaluated column concentrations of tropospheric CO and NO 2 , tropospheric O 3 residual (TOR), and aerosol optical depth (AOD). To evaluate all observations related to MODIS, the monthly-mean AOD predictions are calculated as an average of column-integrated values during 15:00-20:00 UTC when the Terra satellite passes over Europe, following  Roy et al. (2007). The protocols for performance evaluation follow those used in  Zhang et al. (2009 Zhang et al. ( , 2012a)) , including spatial distributions, temporal variation including daily values over the whole domain and hourly values at specific sites, and domainwide statistics.
Description of selected sites The observations of precipitation and other meteorological variables such as T2, Q2, WS10, and WD10 come from different databases (i. e. , ECA&D and NCEP, respectively), and no observations of all these variables were available at the same sites. Ten sites and five co-located sites are selected for detailed temporal analysis for T2, Q2, and WS10. Eight of the ten sites are selected for evaluation of vertical profiles of T , T d , WS, and WD against sounding data. Eight sites are selected for detailed analysis for precipitation. Table  5  summarizes the major characteristics of the selected sites. Among the ten NCEP sites selected for analysis of T2, Q2, and WS10, three sites (Paris Charles de Gaulle airport, Paris Orly airport, and Melun, France) are in D03, two sites (Milan 1/Milan 2, Italy and Bilbao, Spain) are in D02 but outside D03, and five sites (Stockholm 1, Sweden; London 1/London 2, UK; Düsseldorf 1/Düsseldorf 2, Germany; Liberec, Czech Republic; and Madrid 1/Madrid 2, Spain) are in D01 but outside D02 and D03.
Conclusions This paper describes two 3-D air quality model systems (WRF/Polyphemus and WRF/Chem-MADRID) to be evaluated for their capability to simulate pollutant concentrations over Europe in Part 2, the simulation design, as well as the evaluation datasets and protocols. Both models use WRF to generate meteorological fields for chemical predictions. In this Part 1, we described the application of WRF for January and July 2001 over triple-nested domains in Western and Central Europe at three horizontal grid resolutions: 0. 5 • over a large area in Western Europe; 0. 125 • over an area covering France, Germany, the Netherlands, Belgium, Switzerland, Luxembourg, Slovenia, most of Austria, and parts of the UK, Italy, the Czech Republic, Spain, Croatia, and Poland; and 0. 025 • over an area covering the greater Paris region in France.
WRF predictions were evaluated using available observational databases. The WRF simulation at a horizontal grid resolution of 0. 5 • overpredicts downward shortwave radiation and outgoing longwave radiation fluxes in both months, and downward longwave radiation fluxes in July, due to the limitations of the Goddard shortwave radiation scheme   in both months and the neglect of the effect of cumulus clouds on radiation and the contribution of convective clouds to cloud water content in July. WRF reproduces the observed spatial gradients of temperatures and specific humidity with the coldest/driest values in the northwest and the hottest/wettest in the south in both January and July. In January, although the positive bias (an MB of 0. 5 • C and an NMB of 19. 2 %) in T2 dominates, the largest cold biases (-5 to -2 • C) occur in the Alps area and the eastern por-tion of the domain, likely due to several limitations in model initialization and treatments (e.
Acknowledgements. This project is sponsored by the EPA STAR #R83337601, the NSF/USDA EaSM program AGS-1049200, and the fellowship award #704389J, the Atmospheric Environment Center (CEREA)/ École des Ponts ParisTech, France, through a visiting professorship of Y. Zhang at CEREA, the Joint Laboratory of École des Ponts ParisTech and EDF R&D, Paris, France, and COST ES1004. Thanks are due to contributions of former and current members of the air quality forecasting laboratory at NCSU, including Wei Wang, Shuai Zhu, Xu-Yan Liu, Changjie Cai, Xin Zhang, and Kai Wang, who obtained dataset information, processed data, and made plots.
5, PM 10 , Hourly (1113 for O 3 , 975 for SO 2 , http://acm. eionet. europa. eu/databases/ and PM 10 composition (SO 2-4 , NO -NH + 4 , and Cl -) 3 , 4 , 9 for NO -3 , 9 for NH + 4 , and SO 2-PM 2. 5 , 309 for PM 10 ); daily (20 for 1093 for NO 2 , 12 for NH 3 , 4 for airbase 7 for Cl -); stations in both EU and non-EU countries EMEP O 3 , SO 2 , NO 2 , HNO 3 , NH 3 , PM 10 , Hourly O 3 -H (122); daily (75 for PM 2. 5 , PM 10 , and PM 10 composition (SO 2-4 , NO -3 , NH + 4 , Na + , and Cl -) SO 2 , 59 for NO 2 , 26 for HNO 3 , 28 for NH 3 , 26 for PM 2. 5 , 37 for PM 10 , 73 for SO 2-4 , 46 for NO -3 , 48 for NH + 4 , 21 for Na + , 18 for Cl -); stations separated in nearly 30 countries http://www. emep. int/ BDQA O 3 , SO 2 , NO 2 , PM 2. 5 , PM 10 Hourly (138 for O 3 , 64 for SO 2 , 81 for NO 2 , 21 for PM 2.
5 , and 35 for PM 10 ) http://www. buldair. org/ MOPITT CO 1 • × 1 • , every 0. 4 s, compiled as daily http://www. acd. ucar. edu/mopitt/ GOME NO 2 Tropospheric column ozone (TCO) 0. 5 • × 0. 5 • , 10:30 a. m. local time https://earth. esa. int/web/guest/missions/ esa-operational-eo-missions/ers/ instruments/gomel TOMS/SBUV Tropospheric ozone residual (TOR) 1. 25 • ×1 • , 3 or 4 times per day http://asd-www. larc. nasa. gov/TOR/data. html MODIS Aerosol optical depth (AOD) 1 • ×1 • , 10:30 a. m. local time http://modis. gsfc. nasa. gov/ 1 AirBase: European air quality database, the information of number of sites based on 2003; BDQA: Base de Données de la Qualité de l'Air, a French database for air quality.
Analytical procedure Mineral crystal preferred orientations (CPO) were determined by indexation of electron-backscattered diffraction (EBSD) patterns by using a scanning electron microscope (SEM) coupled with EBSD system at the SEM-EBSD facility at Geosciences Montpellier (France). EBSD patterns are generated by interaction of a vertical incident electron beam with a carefully polished thin section tilted at 70°in a scanning electron microscope JEOL JSM 5600. The diffraction pattern is projected onto a phosphor screen and recorded by a digital CCD camera. The image is then processed and indexed in terms of crystal orientation using the CHANNEL5 software from Oxford Instruments HKL. For each sample, we obtained crystallographic orientation maps covering almost entirely the thin section (usually 35 mm long and 20 mm wide) with sampling steps of 100, 75, or 50 μm, depending on the grain size (sampling steps are always at least 1/4 of the average grain size).
Indexation rates in the raw maps range from 50 to 80%. In general, indexation rates are lower for the pyroxenes than for olivine. Tertiary pyroxenes, due to the fine-grained size of the individual crystals composing the aggregates, are poorly indexed (b50%). Very low indexation rates were also obtained in the metasomatic veins. Post-acquisition data treatment allowed to further increase the indexation rate by: (i) Filling the non-indexed pixels, which have up to 8 identical neighbours with this orientation. (ii) Repeating this operation using respectively 7, 6, and 5 identical neighbours. (iii) Identifying the grains area, i. e. , continuous domains characterized by an internal misorientation b10°, and (iv) within each olivine crystal, searching and correcting for systematic indexation errors due to the olivine hexagonal pseudosymmetry, which results in similar diffraction patterns for orientations differing by a rotation of 60°around [100].
Acknowledgements We thank  C. Nevado  and  D. Delmas , who supplied high-quality polished thin sections for EBSD and FTIR measurements. FTIR analyses were performed at  Université Montpellier 2 (France)  with the help of  D. Maurin  at the  Laboratoire Colloïdes, Verres et Nanomateriaux. We thank  D. Mainprice  for providing software for calculating seismic properties. We acknowledge  V. Le Roux  and an anonymous referee for their constructive reviews. The research leading to these results has benefited from funding from the  Institut National des Sciences de l'Univers -Centre National de la Recherche Scientifique (CNRS-INSU)  and from the  Initial Training Network (ITN) Crystal2Plate , an  FP7-funded Marie Curie Action  under grant agreement number  PITN-GA-2008-215353.
Emissions Sulphur emissions of the Global Emission Inventory Activity (GEIA) are used. Global sulphur emissions in the GEIA database include emissions from fossil fuels combustion and industrial processes. The biomass burning sulphur emissions are taken from  Pham et al. (1996)  (Table  I ). The global annual anthropogenic SO 2 emissions from fossil fuel combustion and industrial activities are 66. 31 Tg S yr -1. A fixed 5% of sulphur from combustion sources is assumed to be emitted as sulphate mass. We are not considering volcanic source of SO 2 emissions. The anthropogenic source of H 2 S is 2. 82 Tg S yr -1  (Watts, 2000). For oceanic emissions, dimethylsulfide (DMS) concentration of  Kettle et al. (1999)  and the sea to air parameterization of  Liss and Merlivat (1986)  are used.
The NorESM meteorology fields are not exactly colocated with the corresponding fields in the other models, and this may influence the efficiency of both the direct and the indirect effects in the IPSL-CM5A and the MPI-ESM. However, these effects are expected to be rather small based on comparisons between the NorESM input fields and the low-level cloud fractions in the IPSL-CM5A and the MPI-ESM (not shown). Also, the experimental design chosen allows us to use each model to its fullest ESM capability. A similar approach was proposed for adjusting the location and concentration of stratospheric sulfur in the GeoMIP G3 experiment  [Kravitz et al. , 2011]. All input fields used in this study are based on 5 years of NorESM simulation, and incloud data are used for the CDNC fields. While the fields are influenced by large-scale dynamical patterns of the NorESM, the variability due to individual weather events will be averaged out.
[43] Acknowledgments. This study was partly funded by the  European Commission 's  7th Framework Program  through the  IMPLICC  project ( FP7-ENV-2008-1-226567 ) and the  EuTRACE  project (grant  306395 ). J. E. K. and K. A. received support from the  Norwegian Research Council  through the  EarthClim  project ( 207711/E10 ) and its program for supercomputing (NOTUR) through a grant of computing time. H. S. , U. N. , and C. T. are thankful to the  MPI-ESM  development team and the DKRZ where MPI-ESM simulations have been performed and archived. The IPSL-CM5A climate simulations were performed with the HPC resources of [CCRT/TGCC/CINES/ IDRIS] under the allocation 2012-t2012012201 made by  GENCI  ( Grand Equipement National de Calcul Intensif  ),  CEA  ( Commissariat à l'Energie Atomique et aux Energies Alternatives ), and  CNRS  ( Centre National de la Recherche Scientifique ).
All parameters were estimated at different depths by the radial diffusion method  [2] , and are summarized in Table  1. The numerical modelling was performed with the coupled reactive-transport code Hytec  [8]. The argillite formation was divided into eight units to account for variations of transport properties. Each unit was divided into 1m x 1m quadrilaterals, leading to a number of computational elements equal to the column height. As the D p values were determined parallel to the bedding, they were divided by 3 to account for the known anisotropy of the argillite in the normal direction. The modelled curves for chloride are reported in Figure  3  and compared to experimental data  [2]. Simulations are shown for a large but realistic time span since marine regression started at the end of the Tithonian, some 145 Ma ago  [2].
-To provide reliable polarization parameters: degree  (DoLP)  and angle of linear polarization (AoLP). -To start examining whether the DoLP and/or AoLP variations are related to the geomagnetic activity in order to use them as proxies in space weather applications. The first goal has been reached and constitutes the core of this article. With the use of uncontaminated datasets and a more accurate data processing, the polarization parameters have been better estimated (second goal). Unfortunately the third goal could not be fulfilled. Indeed, due to harsh polar conditions, a major breakdown of the SPP occurred after a few weeks of measurements. During these weeks solar and geomagnetic activity were very low and constant, preventing us from seeing how the polarization parameters vary. In this paper, the AoLP is measured in a reference frame such that 0°corresponds to the vertical, and 90°to the horizontal.
Calibration of the SPP Photomultipliers may be anisotropic devices, detecting more intensity in one linear polarization direction than the other, hence introducing instrumental polarization. The instrumental polarization has been measured in laboratory using a calibration lamp and a reflection on a diffuse lambertian white screen. DoLP was 0. 9% with an AoLP of 16. 9°. This calibration has been performed in laboratory conditions at room temperature, which is very different from the outside temperature conditions during the measurements. In the following, it is assumed that the large differences in temperature do not modify significantly the instrumental polarization. In the future, the variation of the instrumental polarization with temperature will be calibrated in climatic chambers. To be sure that data from the SPP were not contaminated by any light pollution, the following test was performed.
These values are much larger than the instrumental polarization and ensure that most of the polarization observed is intrinsic. The range of the DoLP decreases when the intensity increases. This result is encouraging to try to use the DoLP as a proxy for geomagnetic conditions. However, before drawing any conclusion, additional observations especially during more active geomagnetically conditions are necessary. Indeed during this campaign, the geomagnetic conditions were too    homogeneous. However, it is worth noticing an interesting feature. The Ap index reaches a maximum on December 7. In Figure  11 , the DoLP maximizes at a value of ~7% on the same day at 1:30 LT. This is the maximum value obtained at night over the whole dataset. It occurred while the red line intensity remained small and almost constant for a few hours.
It is distributed around 0°(i. e. parallel to the magnetic field line) for low intensity. For high intensities, the AoLP seems to narrow around zero but here again, it is dangerous to draw further conclusions because of the small number of hours in the database (<200). The variation of the AoLP clearly indicates that the measured signal is not pure noise. Would it be so, the measured AoLP would be of any value between À90°and +90°.
Acknowledgements. We are very thankful to the whole  Hornsund  staff for helping with so much enthusiasm to install SPP in their marvelous base for the next winter. We thank the  Hornsund Polish Polar Station  for using GLACIO-TOPOCLIM service. The present research project has been supported by the  French Polar Institute  (IPEV No. 1026 ) and by the  European  project  COST ES0803 ''Developing space weather products and services in Europe' '. It is also part of the  European  project  COST MP1104  ''Polarization as a tool to study the Solar System and beyond'' and the European  FP7 Project ESPAS , Grant Agreement No. 283676. We thank  S. Maret  and  S. Bardeau  for helping us in computer fixing. This project has been funded by the European  FP7 ESPAS action  (Near-Earth space data infrastructure for e-Science).
In the course of the ICOS (Integrated Carbon Observation System) Demonstration Experiment a feasibility study on the usefulness of a travelling comparison instrument (TCI) was conducted in order to evaluate continuous atmospheric CO 2 and CH 4 measurements at two European stations. The aim of the TCI is to independently measure ambient air in parallel to the standard station instrumentation, thus providing a comprehensive comparison that includes the sample intake system, the instrument itself as well as its calibration and data evaluation. Observed differences between the TCI and a gas chromatographic system, which acted as a reference for the TCI, were -0. 02 ± 0. 08 µmol mol -1 for CO 2 and -0. 3 ± 2. 3 nmol mol -1 for CH 4. Over a period of two weeks each, the continuous CO 2 and CH 4 measurements at two ICOS field stations, Cabauw (CBW), the Netherlands and Houdelaincourt (Observatoire Pérenne de l'Environnement, OPE), France, were compared to co-located TCI measurements.
There are currently two calibration strategies implemented in the atmospheric measurement community: (1) The centralised working standard (WS) calibration approach Published by Copernicus Publications on behalf of the European Geosciences Union. that is maintained e. g. in the AGAGE (Advanced Global Atmospheric Gases Experiment) project, and (2) the individual WS calibration approach generally performed by WMO-GAW (Global Atmosphere Watch) stations. Within the AGAGE network, WSs are provided to all field stations by a central laboratory, the Scripps Institution of Oceanography. Each working standard is calibrated prior to and after usage against the Scripps primary calibration scale  (Prinn et al. , 2000). This centralised WS calibration approach avoids multiple calibration chains, e. g. at each field station, and ensures good data compatibility within the network.
Measurements like these can validate the complete chain from sample collection, analysis and data evaluation, but this type of comparison is not suitable for comparing continuous measurement sites. The atmospheric observational network in the new European ICOS infrastructure (http://www. icos-infrastructure. eu/) will consist of field stations equipped with continuous analysers. This network aims for the highest possible quality and compatibility of measurements. As such, a feasibility study on the use of a travelling comparison instrument (TCI) within the network was initiated. This TCI will be set up at a monitoring station and run in parallel to the existing monitoring system, sampling the same air for a sufficiently long comparison period. It is vital to this quality control (QC) concept that the TCI be a completely independent instrument that measures precisely enough to determine concentration offsets on the order of the WMO ILC targets (i.
Sample intake system test (SIS test) The agreement of the WSs and thus the calibration reference when analysed with one instrument (the TCI) is essential but not in itself sufficient to guarantee compatibility between ambient air datasets from different stations. The ambient air intake system comprises additional hardware, e. g. sample intake tubing, pumps, drying systems etc. , which are potential sources for artefacts. At most stations, the sample intake system is not used for routine cylinder analysis and is also not tested regularly by other measures. To overcome this limitation, the entire sampling path of the ambient air was inspected by the sample intake system test where air from a high pressure cylinder is "sampled" via the ambient air inlet. Performing this test simultaneously for the routine atmospheric monitoring intake and the TCI intake allows for direct comparison of the two independent sample intake systems.
Unfortunately, data evaluation was performed only after the campaign so the test could not be repeated. The most likely explanation for the failure is a leakage in the connection of the CRDS sampling line to the buffer volume. The simultaneously recorded TCI results agree much better to the direct tank measurements, indicating that the experimental set-up worked as expected for the TCI. The CO 2 measurement via the intake system and the direct cylinder measurement differ by 0. 09 ± 0. 03 µmol mol -1. This difference is too large and of opposite sign to be explained by the cylinder drainage effect. At the applied drainage rate of 7 slpm, the CO 2 mole fraction should be depleted by 0. 035 µmol mol -1 and not enriched. At the same time the CH 4 mol fraction is 0. 5 nmol mol -1 higher when measured through the TCI intake system.