dataesr/scientific-paragraphs-categorization huggingface dataset:
This might be due to the fact that the in situ database used in P2007 encompasses a much larger variety of ice clouds, including low CWC cirrus clouds. For MT2010, largest CWC are found when CWC are calculated with the H10 parameterization for the NAMMA data. BF95 parameterization calculates relatively low CWC values for all reflectivities which is also the case for CWC calculated with the T-matrix method for reflectivities below 0 dBZ. The other parameterizations from this study and the P2007 parameterization for the global datal set are similar and also close to T-matrix calculated CWC in the range 5 to 15 dBZ. For MT2011, lowest CWC are calculated again with the BF95 parameterization. CWC calculations with H10 parameterization for convectively generated clouds and most of the parameterizations from this study are rather similar.
Furthermore, this study demonstrates how Z-CWC and Z-CWC-T relationships are impacted by different methods used to retrieve CWC from the measured PSD during MT2010 and MT2011. In general, the use of a single temperature independent m(D) relationship for all clouds is not appropriate, because it excludes the large natural variability -matrix(MT2010) -2 0 -1 -3 -5 T-matrix (MT2011) -5 -3 -5 -6 -4 MT2010(Eq. 17) -3 -1 -1 0 -7 MT2011(Eq. 18) -4 -1 -2 -1 -6 mean(MT2010)(Eq. 19) -11 -5 -7 -8 -20 mean(MT2011)(Eq. 20) -9 -4 -8 -3 -10 MT2010(Eq. 21) -1 -1 -1 0 -3 MT2011(Eq. 22) -2 0 -1 0 -5 H10(NAMMA) -13 -5 -9 -11 -26 H10(cv-gt) -9 -4 -7 -3 -9 BF95(MT2010) -16 -6 -11 -14 -36 BF95(MT2011) -10 -4 -9 -5 -14 of m(D) (as was also highlighted in  (Protat and Williams, 2011). For a series of different methods calculating CWC(α, β) (T-matrix calculation and three parameterizations presented in this study and also taken from literature such as H10 and BF95) Z-CWC and Z-CWC-T relationships were derived between CWC(α, β) and measured radar reflectivities for MT2010 and MT2011 data sets.
The fact that errors from Z-CWC-T (or Z-CWC) relations are larger for MT2011 than for MT2010 data set can be explained by the strong variability of the mean aspect ratio As of cloud particles observed during MT2011, whereas cloud particles for MT2010 show a narrower distribution of As, meaning that crystals aspect ratios are more uniform. For MT2010 the parameterization of m(D) coefficients with temperature (or altitude) seems to perform well in order to describe the m(D) coefficients with on average CWC(α T , β T )/CWC(α σ , β σ ) ≈ 1. 03 and CWC(α β,T , β σ )/CWC(α σ , β σ ) ≈ 1. 08. Likewise, values for MT2011 are CWC(α T , β T )/CWC(α σ , β σ ) ≈ 0. 94 and CWC(α β,T , β σ )/CWC(α σ , β σ ) ≈ 0. 99 respectively. Despite a good consistency for the both campaigns, the significant variability for example of α T /α σ , β T -β σ , and CWC(α T , β T )/CWC(α σ , β σ ) has to be mentioned.
The variability can be illustrated by 1st and 3rd quartiles and is significantly higher for MT2011 than MT2010. This is due to the fact that the MT2011 data set covers more variable microphysical properties and/or processes of hydrometeors (significant contribution of water vapor diffusional growth) compared to MT2010 and even temperature corrected parameterizations would need to be further refined. An explanation could be that during MT2010 over the African continent the Falcon 20 research aircraft was flying in stratiform parts of the MCS, and despite the attempt to get close to the convective cells, they were too vigourous to be entered. Therefore the microphysical properties of ice crystals in MCS systems sampled during MT2010 were relatively similar. In contrast, during MT2011 the less vigorous oceanic convection may have been sampled partly during an earlier stage of convective activity and also crystal growth regimes may have been different, leading to an increased variety of microphysical properties.
The weight a i represents the probability associated to the mean absorption coefficient k i for each absorption class i. The coefficients of the exponential series (a i and k i ) have been determined with reference calculations from a line-by-line (LBL) code  (Dubuisson et al. , 1996 (Dubuisson et al. , , 2005)) , using the spectroscopic database HITRAN  (Rothman et al. , 2009). The coefficients a i and k i are then calculated for a set of reference pressure P and temperature T with the LBLDOM (LBL Discrete Ordinates Method) code  (Dubuisson et al. , 1996). The k-distribution coefficients are available upon request to the authors. For a given atmospheric profile, these coefficients are calculated using interpolations. Note that the correlated k-distribution technique makes it possible to account for interaction between gaseous absorption and multiple scattering with manageable computational time.
Acknowledgements. This study was performed in the framework of work package 4 on aerosol-radiation-climate interactions of the coordinated programme ChArMEx (the  Chemistry-Aerosol Mediterranean Experiment ; http://charmex. lsce. ipsl. fr). This work is supported by the 7th  Framework Programme project Aerosols, Clouds , and  Trace Gases Research Infrastructure Network (AC-TRIS)  (grant agreement no. 262254 ); by the  Spanish Ministry of Science and Innovation  and  FEDER  funds under the projects  TEC2012-34575 ,  TEC2009-09106/TEC ,  CGL2011-13580-E/CLI  and  CGL2011-16124-E/CLI. The  Earth Sciences Division of the Barcelona Supercomputing Center  is acknowledged for the use of the Barcelona AERONET sun-photometer data. Edited by:  F. Dulac.
Statistical analyses Principal component analysis (PCA) was performed using Simca-P + software (Umetrics). Air mass categories were determined with a "hierarchical clustering analysis" (HCA) from PCA results: cloud events or groups of cloud events were lumped together in pairs based on their similarities. In our analysis, only one missing value from the six variables (pH and Na + , Cl -, NO - 3 , NH + 4 and SO 2- 4 concentrations) used for the PCA was permitted to limit artifacts. To highlight significant differences between data groups, the Kruskal-Wallis nonparametric test was carried out with Past software  (Hammer et al. , 2001). Two air mass categories were declared different when the probability for the groups to have identical data distribution was lower than 5 % (p value < 0. 05). This test was chosen due to the lack of normality of the data according to the Shapiro-Wilk normality test.
The northeastern sector was characterized by air masses with high anthropogenic influence due to eastern Europe emissions (55 % of the events were polluted). Air masses from the southwest/south sector were dominated by a pronounced marine character (64 %) and a continental influence from Spain (36 %). During the past 10 yr, in total 13 % of the air masses sampled at the pdD station were under highly marine influence, 52 % were under marine influence, 26 % were under continental influence, and 9 % were under anthropogenic influence. In the next part of this paper, we will focus on the variability of the chemical composition of cloud water as a function of air mass categories determined by our statistical analysis. Table  S2  presents the physicochemical parameters (pH, redox potential, conductivity, liquid water content) and concentrations of measured chemical compounds (average, minimum, maximum) as a function of air mass origins together with data from recent studies describing chemical composition of clouds.
Conclusions and recommendations Clouds incorporate the local pollution when droplets nucleate and then transform pollutants during their lifetime. In this context, the impact of regional and long-range transport of chemical constituents at the puy de Dôme site is apparent and due to the remote (and elevated) geographical character. In this paper, a long-term database of the aqueous-phase chemical composition of clouds sampled at the puy de Dôme station between 2001 and 2011 is presented. The abundance of inorganic and organic chemical compounds as well as a series of physicochemical parameters permitted the identification of various influences corresponding to distinct airmass histories. A classification was performed by principal component analysis that allows for defining the discriminating factors (pH and concentrations of Na + , Cl -, NO - 3 , NH + 4 and SO 2- 4 ) for four different categories: polluted, continental, marine and highly marine.
For example, the presence of inorganic compounds such as nitrate and sulfate investigated in this study are due to anthropogenic emissions, such as those from automobiles or power production. However, it is difficult to differentiate the various sources of these measured chemicals in cloud water due to the various potential processes (transfer from the gas phase, dissolution of particles, aqueous chemical reactivity). In this context, some chemical compounds that are molecular markers of emissions (such as levoglucosan, a marker of biomass burning) should be followed in the future in order to correlate cloud features with emissions. Our results also emphasize that, although this database is rather complete, the characterization of the organic matter is still incomplete, since around 10 % of the dissolved organic matter is characterized.
Acknowledgements. We would like to thank the  Regional Council of Auvergne  for PhD grants provided to  T. Charbouillot ,  M. Joly  and  M. Parazols. M. Vaïtilingom  acknowledges PhD scholarships from the  French Ministry of Research. This work on the long-term analysis of the cloud water chemical composition was supported by the  French Ministry and CNRS-INSU  through the  ORE/SO-BEAM project. Authors acknowledge additional financial support from the  Regional Council of Auvergne , from the  Observatoire de Physique du globe de Clermont-Ferrand (OPGC)  and from the  Fédération de Recherche en Environnement. The authors also thank  C. Bernard  for his technical support regarding the cloud sampling. Edited by: B. Ervens The publication of this article is financed by  CNRS-INSU.
This spatial coherence makes us feel confident about both the quality of the rainfall data and the farm sampling strategy. Thus, the lowest rainfall station was associated with low-altitudinal on-farm surveys, and so forth for midand high levels. The retrospective survey was carried out in October 2009 by using the independent interview technique, whereby each farmer was interviewed individually and not in a group setting. Thus, the responses given by an individual farmer were not influenced by those given by a different farmer. The survey was done in the local language, which was the native language of the surveyor. Seed loss recall data were collected without referring to climate or to rainfall variation in the questions. For each variety, the year of its first acquisition, years of seed loss events, and, for each event, reasons given by farmers to explain it, were recorded.
c. Data analysis We first considered seed losses during the entire 1961-2006 period, before analyzing the proportion of lost seed on a yearly basis, controlling for altitude and rainfall variables (Dur, Nrd, Ptot, Frd, and Int). To understand the effect of the different components of the rainy season on seed losses, analysis was carried out crop by crop, and on the whole farming system. Crop responses to climate variability were analyzed by using logistic and smoothing models, the proportion of lost seed being the response variable. In both models, fitted values equal the logarithm of the odds. They can be transformed to probability using model parameters (a 1 bx) and exponential function  (Agresti 2007 ): P(x) 5 exp a1bx /1 1 exp a1bx.
Acknowledgments. This study is a contribution to the project entitled '' Predictability of Climatic Information for Reducing Tropical Agriculture Vulnerability '' (PICREVAT), funded by the  French National Research Agency  (Project reference  ANR 08-VULN-01-008 ). We thank  Luc Baudouin  ( UMR AGAP, CIRAD, France ),  Christian Baron  ( UMR TETIS, CIRAD, France ),  Joseph Boyard-Micheau  ( CRS, CNRS, France ),  Geo Coppens d'Eeckenbrugge (UMR CEFE, CIRAD, France) , and the anonymous reviewers for their useful comments on the manuscript.
Meteorological data and firn temperatures An automatic weather station (AWS) located near the center of the saddle (Fig. 1 ) ran continuously between 3 July and 23 October 2012. The measurements were carried out within the surface boundary layer. Wind speed, air temperature, humidity, incident and reflected short-wave radiation and incoming and outgoing long-wave radiation were recorded as half-hourly means of measurements made every 10 s (see Table  1 ). Instantaneous values of surface position and wind direction were collected every half hour. The Vaisala hygrothermometer was artificially ventilated in the daytime to prevent measurement errors due to radiation. Five meters from the AWS, 16 thermistors (PT100) were set up in the firn at depths of 0. 11 m, 0. 24 m, 0. 34 m, 0. 44 m, 0. 65 m, 1 m, 2 m, 3 m, 5 m, 8 m, 12 m and 16 m and at heights of 0.
Modeling approach Two distinct one-dimensional and spatially distributed models are used to calculate the near-surface firn temperatures at Col du Dôme. The first is used to calculate the firn surface temperature and melting from a surface energy balance model using the meteorological data recorded by the AWS. The second is a heat flow model coupled to a water percola-tion model with temperature and melting rate at the surface as input data. This model is used successively with different input data sets obtained first from the energy balance model and then from a parameterized approach.
The time step is set to 5 min (half-hourly meteorological data are linearly interpolated for each time step) and vertical resolution is 4 cm. The heat equation is solved using the Crank-Nicholson scheme. The heat capacity of ice is set to 2050 J K -1 kg -1  (Paterson, 1994). Densities have been measured from 0 to 16 m depth into the borehole drilled to install the thermistors. The heat conductivity is calculated from density data using the relation from  Calonne et al. (2011). The initial temperature profile is taken to be similar to the profile measured at the beginning of the simulation (3 July 2012). Solar radiation penetration is accounted for assuming that short-wave radiation exponentially decreases as a function of depth  (Colbeck, 1989) : F Sw (z) = (S w ↓ -S w ↑) * exp(-z/δ), ( 11 ) where F sw is the radiative flux at depth z and δ (m) is the characteristic length of penetration (m).
0 × 10 -1 W m -2 ), but sensitivity tests performed with various constant fluxes at 16 m have shown that assuming a zero flux at 16 m does not change the modeled temperature above 10 m depth. The initial temperature profile is taken to be similar to the profile measured at the beginning of the measurement period (3 July 2012) and the density profile is set to the one measured on 3 July 2012. This density profile is assumed to be constant over the whole simulation period. The problem is solved numerically using the Elmer/ice finite element model  (Gagliardini et al. , 2013)  based on the Elmer open-source multi-physics package (see http://www. csc. fi/elmer for details) that will make it possible to work in three dimensions and perform thermo-mechanical coupling easily in future studies. Vertical resolution varies from 10 cm near the surface to 40 cm at 16 m depth.
Acknowledgements. This study was funded by the  AQWA European program  ( 212250 ). We are grateful to  G. Picard  for providing the SEB code and we thank  Météo-France  for providing Lyon-Bron air temperature data. We thank  H. Blatter  and  R. H. Giesen  for helpful comments and suggestions, and we are grateful to  H. Harder , who revised the English of this manuscript. Edited by:  M. van den Broeke The publication of this article is financed by  CNRS-INSU.
The primary comparisons were done between filtered and unfiltered DE. A secondary comparison was done between the two filters examined. Oxidative capacity of unfiltered and filtered diesel exhaust particles was analysed using the Kruskal-Wallis One-way-analysis of variance (ANOVA) with post hoc analysis with the Games-Howell test for groups of unequal size and variances. Pro-inflammatory effects of unfiltered and filtered diesel exhaust particles were analysed using One-way analysis of variance (ANOVA) with Dunnett's Multiple Comparison Test. To compare the efficacy of filter A-D with unfiltered DEP, a two-way ANOVA with Tukey's multiple comparison test was performed. P values of < 0. 05 were considered significant. Data were analysed using SPSS (SPSS Inc. Chicago, IL, USA, version 17) and GraphPad Prism (GraphPad Software, version 5 for PC).
In vitro measurements We also examined the oxidative and inflammatory potential of both the unfiltered DE PM and the PM penetrating the filters to reach the subjects in the chamber. These data are provided in the online supplement (Additional file 5: Figure  S4  and Additional file 6: Figure  S5 ). The oxidative potential, assessed by the capacity of the sampled PM to drive the oxidation of ascorbate and glutathione from a simple synthetic respiratory tract lining fluid, was low for all PM samples and was not affected by passing the PM through either of the filters tested when examined on a per unit mass (per μg) basis (Additional file 5: Figure  S4 ). The DE particles collected after passing through Filter A, C and D significantly increased IL-8 production from A549 cells. In contrast DE particles filtered by Filter B, which included activated charcoal, did not cause any significant increase in IL-8 release vs.
In-situ observational data on the relative humidity (RH) in the upper troposphere and lowermost stratosphere (UT/LS), or tropopause region, respectively, collected aboard civil passenger aircraft in the MOZAIC (Measurements of OZone, water vapour, carbon monoxide and nitrogen oxides by in-service AIrbus airCraft) programme were reanalysed for the period 2000 to 2009. Previous analyses of probability distribution functions (PDF) of upper troposphere humidity (UTH) data from MOZAIC observations from year 2000 and later indicated a bias of UTH data towards higher RH values compared to data of the period 1994 to 1999. As a result, PDF of UTH data show a substantial fraction of observations above 100 % relative humidity with respect to liquid water (RH liquid ), which is not possible from thermodynamical principles.
However, the data base is restricted to the major global flight routes and to the cruising altitude band of 9-13 km, i. e. the data refer to a large extent to the upper troposphere and lowermost stratosphere (UT/LS). In addition, vertical profiles of atmospheric composition (O 3 , CO) collected during ascents after take-off and descent into airports are of increasing importance for satellite validation (e. g. ,  Cooper et al. , 2011; Zbinden et al. , 2013)  and regional air quality studies including the impact of trans-boundary long-range transport of air pollutants  (Cooper et al. , 2010; Solazzo et al. , 2013). Atmospheric relative humidity (RH) is measured in the framework of MOZAIC by means of a compact airborne humidity sensing device using capacitive sensors (MOZAIC Capacitive Hygrometer MCH).
, 2001; Spichtinger et al. , 2002; Kunz et al. , 2008)  and ice-supersaturation regions  (Gierens et al. , 2000; Gierens and Spichtinger, 2000; Spichtinger et al. , 2002 Spichtinger et al. , , 2003) )  in the upper troposphere. The distribution of upper troposphere humidity was investigated in tropical  (Bortz et al. , 2006; Kley et al. , 2007; Luo et al. , 2007 Luo et al. , , 2008;; Sahu et al. , 2009 Sahu et al. , , 2011) )  and polar  (Nedoluha et al. , 2002)  regions. MOZAIC RH data were also used for the validation of satellite instruments (e. g. ,  Ekstroem et al. , 2008) , global chemistry transport models (e. g. ,  Law et al. , 2000)  and ECMWF models (e. g. ,  Oikonomou and O'Neill, 2006). The reanalysis period for atmospheric RH data presented here focuses on the first 15 years of MOZAIC observations.
As is reported by  Lamquin et al. (2012) , the probability distribution functions (PDF) of RH ice as calculated from the MCH data show a significant shift in RH ice towards higher values for data since 2000 while data are in agreement with theoretical expectations and experimental findings for the period 1994 to 1999 (e. g. ,  Gierens et al. , 1999; Spichtinger et al. , 2004). The reason for this bias towards higher humidity values is identified as an artefact in the pre-and post-flight calibration regularly conducted in the environmental simulation chamber at Jülich  (Helten et al. , 1998; Smit et al. , 2000)  from year 2000 onward. Here we report the procedures followed to reanalyse the calibrations and to reprocess the MOZAIC RH data. An in-depth evaluation of the RH data before and after the reprocessing of calibrations and flight data since year 2000 is presented and compared to MOZAIC RH data for the previous period 1994-1999.
MOZAIC dataset 1994 to 2009 In the first 15 years of MOZAIC between the start of the programme in August 1994 and the end of the reanalysis period in December 2009, in total 32 678 flights were conducted. Table  1  summarises the airlines contributing to the MOZAIC programme and the fraction of flights conducted by the respective aircraft. The global distribution of flights in the period 1994-2009 is shown in Fig. 1. The vast majority of 93 % of flights is confined to the Northern Hemisphere and there between Europe and North America. Major gaps of the MOZAIC data set exist for the Pacific region (no flights) and for flights to the Southern Hemisphere across the Equator (7 % of all flights). In addition to the global distribution of flights shown in Fig. 1 , the worldwide distribution of airports visited by MOZAIC aircraft is presented in Fig.
Artefact in the calibration procedure As pointed out in the previous section, the sudden jump of MCH data towards higher RH values is caused by an artefact introduced in the sensor calibration since fall 1999 after (1) the calibration procedure was expanded by two additional temperature levels at 0 and +20 • C, and (2) the data acquisition software was switched from Pascalto LabView-programming language. In the new data acquisition software the air flow temperature (T AFL ) was no longer used but instead, by mistake, the wall temperature (T ACH ) of the flow duct of the LAH reference instrument. Since calibration was and is conducted at a variety of temperatures, adjustment of the wall temperature T ACH of the LAH to the changed air temperature (lower panel of Fig. 7 ) requires time. Because a standard calibration run always starts at the lowest air temperature level of -40 • C and then increases in steps of 10-20 • C towards higher temperature levels, T ACH values are systematically 1-3 • C, or even more, lower than the air flow temperature T AFL or the three sensor temperatures T S1 , T S2 and T S3 (upper panel of Fig.
Smit et al. (2008)  have shown that the sensor offset drifts are the most dominating parameter in determining the uncertainty of the measurements, while the sensitivity (slope) is more stable in time. The observed consensus of data underpins the consistency of the RH data set which has emerged from the MOZAIC programme. The long-term stability of sensor calibrations was investigated by checking calibration parameters of the same sensor over the entire analysed decade from 2000 to 2009. Results are shown in Fig. 10  with different colours referring to different sensor units; they agree well with previous findings reported by  Helten et al. (1998). Although a significant scatter of calibration factors is observed among different sensor units, the behaviour of each single sensor unit is robust.