dataesr/scientific-paragraphs-categorization huggingface dataset:
developments and discussions during the last years. This strongly consolidated the requirements for the CIMR mission. They are also grateful to the CIMR mission advisory group for numerous discussions on the CIMR mission requirements and possible exploitation. They thank the two anonymous reviewers for their careful reading of the manuscript and for their valuable suggestions. They thank the PNTS (Programme National de Télédétection Spatiale) for their support for this study. The users can access the data containing the outputs of the sea emissivity model following this link: https://www. dropbox. com/sh/ 9if3j2i9wty245b/AADm60GM8ImCvived2 XJtSG_a?dl=0. The RRDP of the sea ice CCI project is available at http://www. seaice. dk/ecv2/rrdb-v1. 1/.
Note that here we assume that the TBs are free from Radio Frequencies Interferences which is steadily increasing as the number of communication and television broadcast satellites. Detection and mitigation of Radio Frequencies Interferences is a significant challenge for microwave radiometers at these low frequencies, and dedicated solutions are planned for CIMR using both onboard and ground-based processors. In addition, we assume that at 1. 4 GHz, the extraterrestrial radiation (Galaxy, Sun, and Moon) is already accounted for, as well as the Faraday rotation in the atmosphere, using the appropriate ancillary data (Reul et al. , 2008). These effects will likely add noise to the SSS retrieval, but great care will be exercise to minimize their impact. It is planned to measure the third Stokes component at 1.
Field methods A network that presently comprises over 100 benchmarks on Etna and surroundings (Fig. 1 ) was measured once a year with Leica system 530 GPS kits, with many lines observed on multiple days, to give mean error ellipses of major and minor axes 6. 6 × 4. 5 mm after full network adjustment  (Reynolds 1934)  using Leica GeoOffice software. Requests for the data should be addressed to the lead author. The times of surveys referred to in this paper are  2001 August, 2002 September, 2003 September, 2004 October, 2005 September, 2006 September, 2007 October, 2008 September, and 2012  September. It is a continual battle to replace benchmarks as they are destroyed by eruptive activity, and those in use currently are the remnant of a total of 200 benchmarks installed since 1981, half of which have been destroyed and successively replaced over the years, including 20 during the period covered by the present paper.
Deriving the sliding vector To remove the sliding vector from the data in Fig. 2 , and reveal deformation patterns resulting from other processes, it is first necessary to determine the direction and length of the sliding vector. For any volcano experiencing both symmetric radial expansion and unidirectional basement sliding, the amount of sliding for any observed interval can be derived as follows, illustrated in Fig. 4d. Note that azimuth is measured from North through East. We define the following notation: D is the magnitude of the horizontal vector of downslope movement of the volcano (the sliding vector), M is the magnitude of the observed vector of movement at an individual station (the sliding plus radial vector), s is the azimuth of sliding in degrees, u is the azimuth of an individual station about the volcano summit in degrees, and v is the observed azimuth of displacement at an individual station in degrees.
Acknowledgments We thank field assistants  L. Allen ,  T. Balaam ,  A. Bell ,  L. Burchill ,  P. Byrne ,  G. Caravantes-Gonzalez ,  A. Carter , V. Coates,  E. Corry ,  D. Croucher ,  A. Crowther ,  A. Davies ,  K. Duncan ,  P. Fawdon ,  A. Forbes ,  A. George ,  R. Gertisser ,  K. Gladstein ,  L. Gunn ,  R. Gunn ,  M. -N. Guilbaud ,  M. Hinrichs ,  K. Hughes ,  S. van Manen ,  G. McCullum ,  A. Moore ,  H. Moretti ,  D. Mufti ,  T. Murray ,  A. Peterson ,  A. Pigott ,  R. Pyves ,  G. Sawyer ,  I. Schipper ,  J. Scott ,  D. Sourisseau ,  J. Sumner ,  D. Urquhart ,  X. Villagómez ,  R. Wall ,  J. Whalen ,  C. M. Wilkinson , and  M. Zacheis  for their good company and hard work in the field. We also thank  Douglas C. Heggie  for suggestions of improvements to the manuscript. This work was supported by the  U.
Data quality We measured the olivine standard MGK3 as an unknown, using the same rastering technique that we used for our melt inclusions and found that our method over-reads SiO 2 by 2. 8% relative, under-reads MgO by 0. 7% relative and under-reads FeO by 4. 4% relative. Individual point analyses of chromite measured on sample 03-08 also under-read FeO by 4. 7% relative and in this case over-reads MgO by 5. 4% relative. It will be noted that some of the melt compositions reported in Table  1  have low totals. These have been retained rather than artificially recasting the result to 100% because it can be seen from the composition of the mineral phases which now make up these inclusion that many are hydrous. Thus at least some of the additional material is water. However, some inclusions may have low totals in part due to surface defects created during polishing.
Melt inclusion major element compositions Here we present results for seven samples of chromitite collected from the Maqsad area of the Oman ophiolite. Six of the seven samples come from the Tuf locality, a stratiform chromitite-dunite body within the dunitic Moho Transition Zone. Sample 03-22 comes from the Maqsad dyke locality a 5m-wide dyke containing chromitite nodules ca 1 cm in diameter in a dunitic matrix. The petrology of these samples was described in detail by  Rollinson and Adetunji (2013) , although it should be noted that in the original description of this locality by  Leblanc and Ceuleneer (1991)  they also found nodules up to 3 cm across in a troctolite matrix In total we have measured 296 melt inclusions from which we have derived seven average values, one for each rock. We present a summary of our average data compositions in.
Samples with very low totals were removed from the dataset on the basis that they represent analytical error. Often low totals correlate with low SiO 2 , indicating a poor analysis. Thus individual raster analyses had totals between 84. 3-100. 1 wt % oxide, although the averages for each sample had totals in the range 92. 0-96. 5 wt % oxide (Table1). Individual analyses with high Cr 2 O 3 were removed from the data set on that basis that the analysis had accidentally included some Cr 2 O 3 from the chromite host. Indeed average Cr 2 O 3 values are high, between 2. 38-3. 64wt % indicating some post entrapment redistribution of Cr between the host mineral and inclusion(Hanski and Kamanetsky,.
Acknowledgements LM stay in the UK was sponsored by the  Brazilian Government 'Science without Borders ' programme. We are grateful to  Dr Graham Souch  for his assistance with the SEM work at Derby, to  Joey O'Neill  for his help in collecting some of the raw geochemical data used in this study and to the  University of Derby  for funding from the undergraduate summer bursary scheme. We thank  Françoise Boudier ,  Georges Ceuleneer  and a third unknown reviewer for their comments on the manuscript.
Samples with very low totals were removed from the dataset on the basis that they represent analytical error. Often low totals correlate with low SiO 2 , indicating a poor analysis. Thus individual raster analyses had totals between 84. 3-100. 1 wt % oxide, although the averages for each sample had totals in the range 92. 0-96. 5 wt % oxide (Table1). Individual analyses with high Cr 2 O 3 were removed from the data set on that basis that the analysis had accidentally included some Cr 2 O 3 from the chromite host. Indeed average Cr 2 O 3 values are high, between 2. 38-3. 64wt % indicating some post entrapment redistribution of Cr between the host mineral and inclusion(Hanski and Kamanetsky, 2013) C C E P T E D M A N U S C R I P T 05/05/2018 .
We have compared 1338 nights of Rayleigh lidar temperatures measured at L'Observatoire de Haute Provence (OHP) with co-located temperature measurements from the Microwave Limb Sounder (MLS) and the Sounding of the Atmosphere by Broadband Emission Radiometry instrument (SABER). The comparisons were conducted using data from January 2002 to January 2011 in the geographic region around the observatory (43. 93 • N, 5. 71 • E). We have found systematic dif-5 ferences between the temperatures measured from the ground based lidar and those measured from the satellites which suggest non-linear distortions in the satellite altitude retrievals. We see a winter stratopause cold bias in the satellite measurements with respect to the lidar (-6 K for SABER and -15 K for MLS), a summer mesospheric warm bias (10 K near 60 km), and a vertically structured bias for MLS (0 to 4 K).
Temperature biases between OHP lidar and SABER In the companion publication  (Wing et al. , 2018a)  we attempted to reduce the magnitude of the 255 initialization induced lidar warm bias which is often reported above 70 km. We have reduced the bias by up to 5 K near 85 km and nearly 20 K at 90 km. There still remains some residual systematic warm bias between the lidar satellite comparisons in this publication. Further work needs to be done on the problem of lidar initialization to fully address the effects of noise and a priori choice on high altitude Rayleigh lidar retrievals. However, we cannot discount the possibility that some of the 260 remaining temperature difference is due to incorrect altitudes in the satellite data product. When considering the residual temperature differences between the OHP lidars and SABER after the altitude correction based on lidar derived stratopause height we can see that much of the seasonal variability in the stratosphere and mesosphere has been reduced.
There is nothing in the lidar data or retrieval algorithm which could account for this structure. 3) The persistent summertime cold bias between the lidar and SABER results from a disagreement in the thermal lapse rate above and below the stratopause which is independent of the scalar stratopause height offset. Given that lapse rate is a fundamental geophysical parameter further work 325 must be done to explore possible errors in vertical resolution and altitude definition. 4) The periods of greatest lidar-satellite temperature disagreement are located during times when the middle atmosphere is highly disturbed. In particular, the amplitude of stratospheric warming events can be underestimated and features like double stratopauses can be missed in the satellite measurements.
Abstract. New parameterizations for the formation of organic aerosols were developed. These parameterizations cover secondary organic aerosol (SOA) formation from biogenic and anthropogenic precursors, NO x dependency, oligomerization and the reactive uptake of pinonaldehyde. These parameterizations were implemented in a box model in which the condensation and/or evaporation of semi-volatile organic compounds was simulated by the Secondary Organic Aerosol Processor (SOAP) model to take the dynamic evolution of concentrations into account. The parameterizations were tested against several experiments carried out in previous studies in the EUPHORE outdoor chamber. Two datasets of experiments were used: the anthropogenic experiments (in which SOA is formed mainly from a mixture of toluene, 1,3,5-trimethylbenzene and o-xylene) and the biogenic experiments (in which SOA is formed mainly from α-pinene and limonene).
Experimental datasets EUPHORE is a 204 m 3 hemispherical Teflon outdoor chamber located in Valencia (CEAM), Spain. The chamber is surrounded by retractable steel housing, which can be opened or closed to control the time of exposure to sunlight. The housing also serves to protect the chamber from rain and strong winds. The floor of the reactor consists of aluminum panels covered with Teflon, with a cooling system designed to compensate the heating of the chamber caused by solar irradiation. Two high-powered mixing fans (with an air flow of 4000 m 3 h -1 ), located on the floor of the chamber, are used to ensure the homogeneous mixing of reactants and products. The EUPHORE facility is described in detail in  Volkamer et al. (2001). The experiments used for the comparison between the model and experiments have been published in previous studies  (Vivanco et al.
, 2011 (Vivanco et al. , , 2013)). The experiments used in this study were initially planned to increase the experimental information regarding SOA formation and to be mainly used in model performance evaluation processes. As most of the studies at that time were focused on the oxidation of individual organic gases or simple mixtures of them, those experiments constitute a contribution to the experimental database on SOA formation, by considering the oxidation of different mixtures of organic precursors. Although the experimental dataset was designed for model evaluation purposes, it was not designed to evaluate the model developed in this study. Vivanco et al. (2016)  and  Santiago et al. (2012b)  used these experiments to evaluate simple parameterizations existing in several air quality models.
OH photo-oxidation time (determined for a concen- tration of 1. 5×10 6 molecules cm -3 ) was determined to range from a few hours to more than a day. Two datasets of experiments are used: (i) the anthropogenic experiments where SOA is formed from the oxidation of toluene (TOL), 1,3,5-trimethylbenzene (TMB) and to a lesser extent o-xylene and octane and (ii) the biogenic experiments where SOA is formed from α-pinene (API) and limonene (LIM) and to a lesser extent isoprene. Experimental conditions for the anthropogenic and biogenic experiments are described, respectively, in Tables  1  and 2. For the biogenic dataset, two experiments were carried out in the presence of SO 2 to evaluate the parameterization of  Pun and Seigneur (2007). Due to its low SOA yield, SOA from isoprene oxidation should not represent a significant amount of total SOA.
Although octane is a precursor of SOA and is present in the anthropogenic experiments, SOA formation from octane oxidation was not taken into account in this study as  Vivanco et al. (2016)  showed with an experiment in the same chamber that octane leads to an insignificant amount of SOA. Due to its low yield (0. 5 % according to  Lim and Ziemann, 2005) , octane SOA should not represent more than a few percent of total SOA. PM volume concentrations were measured with a Scanning Mobility Particle Sizer Spectrometer (SMPS). Data are not corrected for wall losses as the deposition of particles is taken into account in the model.
For those two experiments, SO 2 oxidation will lead to sulfuric acid formation which remains liquid over the full RH range  (Seinfeld and Pandis, 1998). Several experimental data were used to constrain the model. Temperature and relative humidity measurements inside the chamber were used as inputs for the model. SMPS measurements were used to compute the mean diameter of particles. As modeling properly nucleation and coagulation of particles would be needed to simulate the size distribution of particles adequately, particles were gathered inside a single diameter bin. The mean diameter of particles was constrained to provide the model with a realistic estimation of the diameter without modeling the nucleation in SOAP. The diameter of the particle is used to compute the kinetic rate of condensation and/or evaporation and/or diffusion and the Kelvin effect by the dynamic approach of SOAP.
However, the experiments used for the comparison present a high organic aerosol loading (between 50 and 150 µg m -3 ), much higher than the values normally observed under atmospheric conditions. More experiments at low organic aerosol loading should be carried out to provide a wider observational dataset that could be used to evaluate model developments. A good performance is also found for most of the anthropogenic experiments. However, for the experiments with the lower chemical regime ratio (Eq. 15; product of VOC concentrations and their reactivity divided by NO x concentrations), the model underestimates concentrations by 30 %, whereas for the experiments with the higher chemical regime ratio, the model overestimates concentrations by 30 %. This could indicate a more complex NO x chemistry than the one taken into account in the model.
Acknowledgements This work was supported in part by project  SIGMA  (EDF, AREVA, CEA and ENEL) and also by the  Slovak Research and Development Agency  under the contract  APVV-15-0560  (Project  ID-EFFECTS ). Part of the calculations were performed in the Computing Centre of the  Slovak Academy of Sciences  using the supercomputing infrastructure acquired in project  ITMS 26230120002  and  26210120002  (Slovak infrastructure for high-performance computing) supported by the  Research and Development Operational Programme  funded by the  ERDF. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
The spatial extent of aerosol emission and transport was obtained using Modern-Era Retrospective Analysis for Research and Applications (MERRA) atmospheric reanalysis data, Global Data Assimilation System (GDAS) archives, and NCEP/NCAR Reanalysis data. Further, visualization from the A-Train satellite constellation, including spaceborne passive sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Ozone Monitoring Instrument (OMI) and an active sensor like the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), are included. Briefly, the results are explored to highlight three exclusive but interrelated mechanisms, i. e. , aerosol chemistry, regional transport, and radiative forcing, as well as their intraseasonal variations over the middle IGP, which may well be useful in regional climate models.
Micrometeorology, ABL, and wind field The 24 h average meteorological parameters, e. g. , temperature, relative humidity (RH), and wind speed (WS), were obtained from Weather Underground (https://www. wunderground. com/) and validated with regional weather monitoring station data. The ABL heights at specific coordinates were retrieved from Global Data Assimilation System (GDAS) archives hosted at NOAA Air Resources Laboratory, which provides simulated meteorological observations at a gridded scale. The 3-hourly ABL data (0. 5 • ) were averaged on a daily basis in parallel with the period of particulate measurement. The NCEP/NCAR Reanalysis data were used to analyze the variation in 3-D wind fields at the near surface (1000 m) with a horizontal resolution of 2. 5 • × 2. 5 •. Vector wind composite mean (m s -1 ) for 925 hPa was plotted for the defined coordinate (6-38 • N, 50-105 • E) to understand the synoptic pattern of the wind field.
Aura OMI and MERRA-2 reanalysis data OMI onboard the AURA satellite has a typical daily global coverage with 13 × 24 km 2 spatial resolution at the nadir and measures solar backscatter irradiation in the UV-visible spectrum (264-504 nm;  Levelt et al. , 2006). Ultraviolet aerosol index (UVAI), tropospheric NO 2 , total columnar ozone (TCO), and single-scattering albedo (SSA) were retrieved from Aura OMI available at NASA Goddard Earth Sciences Data and Information Services Centre (GES DISC). OMI UVAI is capable of detecting aerosol absorption from satellite measured radiances without any prior assumption on aerosol composition  (Torres et al. , 2013). It is a qualitative parameter and is widely used to identify the UV absorbing aerosols (e. g. , smoke plumes, soot, and mineral dust;  Torres et al.
, 2013; Mhawish et al. , 2018). The UVAI based on the OMI near-UV aerosol retrieval algorithm (OMAERUV) was extracted from the level 2G, version 003 aerosol product containing 1 day's level 2 dataset of original pixels (13×24 km 2 ) into 0. 25 • ×0. 25 • grids. The NO 2 tropospheric column density was retrieved from the cloud-screened (cloud fraction < 30 %) level 3, version 003, daily 0. 25 • ×0. 25 • gridded OMNO2d product  (Krotkov et al. , 2017). To estimate TCO, level 3e data (OMDOAO3) at a spatial resolution of 0. 25 • × 0. 25 • were used. SSA at 550 nm was retrieved from the OMI level 2G product (OMAERUV) at 0. 25 • ×0. 25 • resolution. The carbon monoxide (CO) surface concentration (in parts per billion by volume) was retrieved from Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) atmospheric reanalysis data available at 0.
CALIPSO-CALIOP observations CALIPSO products were used to examine the vertical distribution of aerosols, altitude of aerosol layers, clouds, aerosol types, and their properties at visible (532 nm) and near-IR wavelengths (1064 nm). The v4. 10 CALIOP Level 2 altitude-orbit cross-section profiles retrieved from the CALIPSO sub-setting web application (https://www-calipso. larc. nasa. gov, last access: 9 August 2017) were used. The lidar profiles were processed for images of vertical feature masks, aerosol subtypes, and extinction coefficients (at 532 nm) at 30 m vertical resolution over the selected grid (80-86 • N and 22-28 • E). Details about data products, calibration, and uncertainty are discussed in  Rogers et al. (2011).
Air mass back trajectory The NOAA HYSPLIT model  (Draxler and Rolph, 2003)  was used to simulate particle back trajectories in a threedimensional system. HYSPLIT was run using the Global Data Assimilation System data (GDAS, 0. 5 • ×0. 5 • ) available from the archive dataset (http://ready. arl. noaa. gov/gdas1. php) to predict 120 h air mass back trajectories (00:00, 06:00, 12:00, and 18:00 IST, Indian standard time) starting from October to December 2016. Trajectories for different aerosol loading periods were then overlaid on the MODIS fire map to study the transboundary movement of emissions from biomass burning. The trajectory analysis was made using the GIS-based software TrajStat  (Wang et al. , 2009). Concentration-weighted trajectories (CWTs) were also drawn considering columnar aerosol loading to evaluate potential aerosol source fields and mechanisms of aerosol transport.
, 2016). However, in the absence of aerosol organic carbon content, contribution of biomass burning to aerosol mass was not computed. In addition to using conventional biomass burning tracers, we also evaluated the association of submicron and fine-particulate-bound levoglucosan with weekly averages of Delta-C and UVAI (Fig. 6 ). Both Delta-C and UVAI are the measures of identifying the relative dominance of absorbing aerosols. In all scenarios, a significant correlation (R 2 ) was noted between levoglucosan with Delta-C (0. 65, p < 0. 01) and UVAI (0. 66, p < 0. 01). In addition to the ground-based aerosol measurement, dynamic profiles of trace gas concentration, especially for trace gases that behave as aerosol precursors, are assessed from the Real-time Air Quality Data inventory of the Central Pollution Control Board (https://app.
Acknowledgements. This research is supported by the  Science and Engineering Research Board  (SERB),  Department of Science and Technology (DST), New Delhi  ( SR/FTP/ES-52/2014 ). Tirthankar Banerjee acknowledges the financial support from the  University Grants Commission (UGC)  under the  UGC-ISF bilateral project  ( 6-11/2018 IC ),  Ram S. Singh  acknowledges the  Indian Space Research Organization  under ARFI (code  P-32-13 ), and  Rajesh K. Mall  acknowledges financial support from the  Climate Change Programme of DST  ( DST/CCP/CoE/80/2017-G ). Nandita Singh  acknowledges financial support under the  DST Women Scientist scheme  ( SR/WOS-A/EA-1012/2015 ) and  Meytar Sorek-Hamer  acknowledges the  NASA postdoctoral fellowship , administered by  USRA. The authors duly acknowledge the guidance and cooperation provided by the dean and director of  IESD-BHU.