dataesr/scientific-paragraphs-categorization huggingface dataset:
The evaluation of the datasets was conducted by assessing statistical parameters (mean bias, root mean square error (RMSE), and Pearson correlation coefficient) of gridded 1 • latitude longitude daily satellite products (level 3) versus AERONET daily averaged AOD interpolated to a reference wavelength at 0. 55 µm for all retrievals and the sun photometer measurements. All experiments started from an analysis of baseline datasets for each algorithm prior to any changes. Up to three experiments were conducted with the various precursor algorithms: use of common optical aerosol properties, additional use of a common aerosol-type climatology, and use of a common cloud mask. Across algorithms the use of a common definition of aerosol components harmonized the retrievals (in terms of their internal construction), which is documented in a clearly reduced range of global AOD mean values obtained from the participating algorithms and, in part, better similarity between regional features across the eight algorithms.
This is important as it will facilitate future merged datasets, because the AOD due to each component can be compared between the retrievals. For total AOD this harmonization is mostly important for those retrievals with the lowest information content. The common cloud mask needs further improvement: utilization of the forward view, improving the discrimination between desert dust outbreaks and clouds, post-processing applied as a secondary means to avoid cloud contamination. In a subsequent step the analysis has been extended from one month of global data to four months (one in each season) to further substantiate the results with a larger data amount in a round robin analysis (de  Leeuw et al. , 2013)  but then limited to only one "best" version for each algorithm. The choices for these "best" algorithm versions in this round robin exercise have been based on the experiments described in this paper.
This subsequent step showed further improvements of several of the algorithms (e. g. by post-processing to avoid cloud contamination, and/or by detecting and correcting bugs in some algorithms). Ultimately a full global one-year dataset of Aerosol Essential Climate Variables will be produced and will be validated and assessed by aerosol climate model users. As one potentially critical element of aerosol retrievals, the treatment of brightness and directional reflections of land surfaces could not be assessed by similar experiments since it is a core component of each algorithm that cannot be easily modified. Furthermore, it has different importance for different classes of algorithms: dual or multiple viewing instruments (AATSR, PARASOL, MISR) avoid to a first order any dependence on surface brightness by effective decoupling of the surface reflectances and the path radiance or by fully retrieving surface directional behaviour.
Nadir-only algorithms (MERIS, synergetic AATSR + SCIAMACHY), on the other hand, are dependent on an assumption or parameterization (from vegetation index and mid-infrared reflectances) of surface brightness (of dark fields or all pixels). It is thus planned to use atmospherically corrected MODIS reflectance data around selected AERONET stations in order to assess the parameterizations used in the respective precursor algorithms. Over ocean it can generally be assumed that the surface reflectance is very low in the red and infrared bands. However, for specific situations (wind speed dependent white cap fraction, coastal sediments and chlorophyll) this assumption is not valid and auxiliary datasets and parameterizations need to be used. Here, preparations have been made to harmonize auxiliary data (e.
g. ECMWF re-mapped wind field analysis) where this is feasible. However, for sediments and chlorophyll, the use of an external climatology or daily dataset would mean that the aerosol-surface separation has already been solved in a different retrieval. Furthermore, evaluation of AOD and surface treatment over ocean is difficult due to low number of ground-based (ship) observations for September 2008; for later years this situation improves with sun photometer observations from the Marine Aerosol Network  (Smirnov et al. , 2011). The results of all analysis steps are available at the Aerosol cci project website http://www. esa-aerosol-cci. org, where all datasets, experiments and documents are published and available to the scientific community; from there also links to the ftp data server at ICARE (accessible on request) and the open AeroCom and ICARE visualization and analysis tools are provided.
Acknowledgements. The results presented in this paper were achieved within the  ESA Aerosol cci project. We acknowledge also AERONET sun photometer data providers through which ground-based measurements for the evaluation activities in the project were acquired. Furthermore we acknowledge the supporting teams at AeroCom,  ICARE  and  WDC-RSAT  (hosted by DLR) which played a crucial role in the data evaluation, data storage services and documentation. The service charges for this open access publication have been covered by a  Research Centre of the Helmholtz Association. Edited by:  O. Dubovik.
Both approaches have their own strengths and weaknesses. A separate problem of cloud retrievals in the Arctic is the existence of bright surfaces (ice and snow). In this study, all measurements were performed above a dark surface (ocean). In this paper, we proceed as follows: first, the cloud optical thickness and effective radius is retrieved from spectral nadir radiances (wavelengths 350-2100 nm), comparing the results of the two-wavelength (2 WL) approach to the results of the five-wavelength (5 WL) approach. Second, a field of cloud optical thickness is retrieved from the data from an imaging spectrometer (400-1000 nm). As the cloud effective radius cannot be retrieved without measurements beyond 1000 nm, the effective radius needs to be constrained externally in this retrieval. This means that we have to take an intermediate step to find out how much the choice of that constraint matters.
The imaging spectrometer AisaEAGLE (manufacturer: Specim Spectral Imaging Ltd. , Oulu, Finland) was mounted in a tail pod to measure upwelling radiances I ↑ λ,E (t, y) as a function of wavelength λ, time t and crosstrack distance y (subscript E for AisaEAGLE). The AisaEA-GLE measures radiances in the spectral range from 403 nm to 966 nm in 240 channels with a spectral resolution (full width at half maximum) of 2-3 nm. The cross-track field-of-view is 40 • wide, divided into 512 spatial pixels (1024 photo diodes with double binning). A dark measurement for the correction of electronic noise and the thermal photo-current in the detector has been performed every five minutes. An exposure time of 10 ms was used. The AisaEAGLE could be operated only during the last three flights of SoRPIC. During all SoRPIC flights, the upwelling radiance I ↑ λ,S (t, y n ) from the nadir point y n was measured by the SMART-Albedometer (subscript S), which also measured spectral downwelling and upwelling irradiances, F ↓ λ (t) and F ↑ λ (t).
Radiative transfer modelling The cloud properties are retrieved from measured radiances from nadir and imaging spectrometry aboard the Polar-5 aircraft. The measured data are checked against look-up tables (LUT) of simulated radiances. These look-up tables are produced with the radiative transfer package libRadtran  (Mayer and Kylling, 2005). The radiative transfer calculations are initialised to represent the environmental parameters during the particular SoRPIC flight. That includes the relative geometry of the Sun, the cloud layer and the aircraft; the aerosol optical thickness (AOT) as observed by the Sun photometer; and the cloud-top height which is obtained from the AMALi lidar. The surface AOT was relatively high on 17 May 2010, with a value of 0. 25 at 500 nm, while the AOT at 3 km altitude was the background value of 0.
Retrieval results The retrieval from imaging data of the AisaEAGLE results in a map of the retrieved cloud optical thickness. An example is shown in Fig. 11. This map has been drawn such that the aircraft nadir (marked with the red line) is always in the centre. The wavy edge of the map represents the rolling of the aircraft during the flight. The ES pixels that are observed by the SMART-Albedometer radiance surround the nadir point and are delineated by the blue lines. The cloud structure is clearly visible. The black-and-white colour scale makes Fig. 11    The following statistics of the cloud properties for 17 May are based on the retrieval that uses the current effective radius r (2) eff (t) from the SMART-Albedometer. The look-up tables are calculated for a range of values of solar zenith angle, relative azimuth angle, viewing zenith angle, and cloudtop height.
Acknowledgements. The authors express their deep gratitude toward the  Alfred Wegener Institute for Polar and Marine Research (AWI)  for generously providing the aircraft and flight hours as well as technical and logistical support. Kenn Borek Air Ltd. deployed a very helpful and enthusiastic aviation crew who handled our requests skilfully. We thank  Andreas Dörnbrack  and  Marc Rautenhaus  of  German Aerospace Center (DLR)  for meteorological support (Fig. 4). Funding by  DFG  ( WE1900/17-1 ), by  Centre National d'Etudes Spatiales (CNES) , and by the  Institut Polaire Francais Paul Emile Victor (IPEV)  is acknowledged. Thanks to two anonymous reviewers and the associate editor for very helpful comments. Edited by:  S. Schmidt.
The green flakes were separated from the white flakes and the evolution of the fraction of green flakes with time (residence time distribution curve) was determined by weighing. The green flakes were then analysed for their residual concentrations of the applied surrogates. The Panel noted that decontamination efficiencies, calculated only on the basis of residual surrogates in contaminated (green coloured) flakes could be overestimated. In fact, cross-contamination by transfer of contaminants from green to white flakes may occur. 9 erefore to take into account of the cross-contamination phenomenon and in the absence of specific data provided by the applicants, some assumptions and considerations were made, as follows: -The mass fraction of green to white flakes at each residence time, was derived from the data provided.
This ''flush-down effect'' results from a combination of convection and gravitational flux, since the diving of dense surface waters breaks the stratification of the water  Chiavérini, 2002; Fig. 2 ). Indeed, high vertical export fluxes were observed within the present dataset in spring, as a result of biological productivity (e. g. 2000). (3) In summer and fall, the heating of the sea surface results in a stratified water column with a shallow ML which is rapidly exhausted of nutrients. During such oligotrophic conditions (June to November), the concentrations of biogenic matter in the photic layer are minimal. Therefore, owing to insignificant transfer of POC, atmospherically-transported individual particles presumably do not sink (or sink with negligible sinking velocity), according to Stokesian settling calculations  (Stokes, 1901).
e. POC is a carrier of mineral ballast or mineral material is a POC carrier), the amount of matter will be reduced and its export will be less efficient because aggregation processes will be less important. Among the examples cited above, the low vertical export fluxes observed in the winters 1990 and 2002 are likely a consequence of effective scavenging during fall. Atmospheric inputs during the oligotrophic stratified period may also indirectly cause vertical export flux of matter accumulated in surface waters. Nutrient-rich atmospheric inputs have long been known to fertilize oligotrophic surface waters and to trigger enhanced primary production  (Bergametti et al. , 1992; Guerzoni et al. , 1999; Migon and Sandroni, 1999; Bartoli et al. , 2005). The present dataset shows several examples of vertical export fluxes observed during the oligotrophic period in the absence of concomitant changes of the MLD.
Acknowledgements This work was supported by the  LEFE-CYBER Program COMET (COnstructing MEditerranean Time-series ). We thank  Joséphine Ras  for biomass data. We also wish to thank the crew of the RV Téthys II for their valuable assistance at sea, the team of the DY-FAMED time-series station ( Observatoire Océanologique de Villefranche-sur-Mer, CNRS-INSU ). The  International Atomic Energy Agency  is grateful to the  Government of the Principality of Monaco  for the support provided to its  Environment Laboratories. We wish to thank in particular the handling editor  Michael Landry  for his help in getting this final version of our manuscript together. We also thank  Alan Hally  for language corrections.
Episode selection and observational data set The modeling study was conducted from 2 to 18 July 2011. During this period, meteorological and air quality measurements were conducted at the Faculty of Sciences of Saint Joseph University campus (USJ site) in the region of  Mansourieh (33. 86  • N, 35. 56 • E) distant by 6 km from the center of Beirut. The topography of Lebanon including that of the region surrounding the measurement site, is characterized by a coastal strip in the western part of the country and the Lebanon mountains and an inland plateau in the eastern part between the two mountain chains. The coastal region has a Mediterranean climate with land-sea breeze circulation. The eastern part presents continental characteristics. Meteorological measurements included wind speed (anemometer), wind direction (weather vane), surface temperature (thermometer), relative humidity (hygrometer) and atmospheric pressure (barometer).
, 2012 ) was adopted following  Couvidat et al. (2012)  to estimate SVOC emissions. The sensitivity of OC concentrations to this ratio has been tested by  Couvidat et al. (2012). A value of five has been shown to give satisfactory results in previous modeling studies  (Couvidat et al. , 2012 (Couvidat et al. , , 2013) )  and, therefore, sensitivity to this model parameter was not tested here. The USGS land cover was used. Initial and boundary conditions for the outer domain (D2) were extracted from the output of the Model for Ozone And Related chemical Tracers version 4 (MOZART-4; http://www. acd. ucar. edu/wrf-chem/ mozart. shtml), which is an off-line global tropospheric CTM  (Emmons et al. , 2010). It is driven by NCEP/NCAR reanalysis meteorology and uses emissions based on a database of surface emissions of ozone precursors (POET), Regional Emission Inventory in Asia (REAS) and Global Fire Emissions Database (GEFD2).
Model simulation configurations The results obtained from WRF were evaluated with meteorological data collected at the USJ site. Reliable meteorological data at other locations were not available within the D1 and D2 domains, thereby preventing a more complete model performance evaluation. Different simulations from M1 to M5 (Table  1 ) were performed to select the meteorological simulation that has the lowest biases and errors when compared to observations. For physical parameterizations, the YSU PBL scheme (simulation M2) and the MYNN scheme (simulation M3) were tested with the use of UCM. In addition, two simulations (M1 and M4) with the YSU and MYNN schemes, respectively, were performed without the use of UCM. To test numerical options, a simulation (M5) was performed using segmented simulations with two-day restarts to assess whether the model tends to diverge significantly after two days of simulation.
Acknowledgements. Funding for this study was provided by  École des Ponts ParisTech , the  Lebanese National Council for Scientific Research and the Saint Joseph University (Faculty of Sciences  and the  Council for Research). The authors acknowledge  Thierry Leonardis ,  Servanne Chevaillier  and  Sylvain Triquet  for providing observational data for CO, VOC, NO x , O 3 and major components of fine particulate matter ( PM 2. 5  ). Edited by:  Y. Balkanski.
Neutrophil oxidative burst assay for determining the antioxidant potential of bromide Blood was withdrawn from healthy volunteers into blood collection tubes containing sodium citrate (Sarstedt). Neutrophils were isolated using density gradient centrifugation and resuspended in Hanks Balanced Salt solution at a density of 1,000,000 cells ml -1. Neutrophils were mixed 1:1 with 20 μM 2',7'-dichlorofluorescein diacetate and incubated (37 °C, 5% CO 2 ) for 30 minutes prior to seeding into a 96-well plate. Test compounds were added to triplicate wells at a range of concentrations (0. 01-100 mM). Oxidative burst was stimulated by addition of 0. 1 nM phorbol myristate acetate  (Repine et al. , 1974). The plate was incubated at 37 °C with fluorescence readings taken every 10 minutes. V max was calculated for each test group over four data points.
It is possible to interpret this result as due to the presence of free Br -which co-precipitates with KCl upon cooling of the sample for the XAS measurement, but the concept that Br -is also associated with solid KCl under physiological conditions in Laminaria cannot be excluded. It should be noted that the highest concentrations of K + and Cl -ions in the cortex and medulla of dry weight Laminaria (120,000 and 109,000 ppm, respectively  (Verhaeghe et al. , 2008a) , which turn out to be equimolar amounts upon correction for the atomic weight) imply that 22. 9 % of the solid material was KCl. The new data are in accordance with the report of bromine localization in Laminaria  (Verhaeghe et al. , 2008a) , which suggests that bromine is less diffusible than iodine in stipe tissues and is proportionally more abundant in strong association with the cell walls.
Acknowledgements The authors are grateful to  Dieter Hanelt  and the staff at  AWI-Biologische Anstalt Helgoland  for providing L. digitata for the XAS experiments in this study. Funding from the  UK Natural Environment Research Council (NERC)  through the  SOLAS and Oceans 2025  ( WP4. 5 )programmes to LJC and to FCK, the  Deutsche Forschungsgemeinschaft (Germany)  to PMHK, the  French National Program 'Toxicologie Nucléaire Environnementale ' to PP, EV, and CL, the  National Science Foundation  and the  National Oceanic & Atmospheric Administration  to GWL, and the  MASTS pooling initiative (Marine Alliance for Science and Technology for Scotland , funded by the  Scottish Funding Council  and contributing institutions; grant reference  HR09011 ) is gratefully acknowledged. FCK would also like to extend special thanks to  Alison Butler  ( University of California, Santa Barbara ) for her support and useful discussions.
(  1  following: ∂ρ k ∂t chem = dρ k dt = P k (ρ) -L k (ρ) , (2) where ρ = {ρ 1 , ρ 2 ,. , ρ N } are the number density of the N reagent species considered, P k and L k correspond to the net production and lost of the species k. The equation uses in its solution the initial condition ρ(t 0 ) = {ρ 1 (t 0 ), ρ 2 (t 0 ),. , ρ N (t 0 )}. The lost and production terms include gaseous photochemistry, kinetic and aqueous chemistry. Figure  1  illustrates the main sub-grid scale processes involved in the trace gas/aerosol distribution in the CCATT-BRAMS system, which will be described below. The CCATT-BRAMS system was developed using advanced numerical tools in order to get a flexible multipurpose model that can be run for both operational forecasts and research simulations. Moreover, the model system is a fully comprehensive code package designed to allow the user for flexibility in the choice of the chemical species, the chemical mechanism, the emission types and databases.
A schematic view of the model system, including its pre-processing tools is given in Fig. 2. The packed system comprises three preprocessors tools for emissions databases (PREP CHEM SRC,  Freitas et al. , 2011) , chemical mechanisms, and boundary and initial conditions and the CCATT-BRAMS code itself. The first step for any simulation is the choice of the gaseous species and of the gaseous kinetic and photochemistry reactions to account for. To make straightforward the use of different chemistry mechanisms in CCATT-BRAMS, it was chosen to use a modified version of the pre-processing tool SPACK (Simplified Pre-processor for Atmospheric Chemical Kinetics) developed by  Djouad et al. (2002)  following  Damian-Iordache and Sandu (1995). The modified-SPACK (called hereafter M-SPACK) basically allow the passage of a list of species and chemical reactions from symbolic notation (text file) to a mathematical one (ODEs), automatically preprocesses chemical species aggregation and creates Fortran 90 routines files directly compatible to be compiled within the main CCATT-BRAMS code.
The M-SPACK output code also feeds the emissions preprocessor tool (PREP-CHEM-SRC) in order to ensure consistency between the emissions database to be used in CCATT-BRAMS and the list of species treated in chemical mechanism. The PREP-CHEM-SRC provides the emissions from different sources (see Sect. 2. 2 for details) interpolated onto the simulation grid chosen, which are then used as input for CCATT-BRAMS. It basically links the species that are in the emission databases to those in the chemical mechanism. When the species are the same, this is straightforward. But commonly in the mechanisms, there are species names representing a group of species. In this case, the PREP-CHEM-SRC converts the species from the database into the model species. The M-SPACK output code also feeds the BC-PREP code that generates the initial and boundary fields for the chemical species to be used in the CCATT-BRAMS simulation.
Emissions The PREP-CHEM-SRC tool provides emission fields interpolated onto the transport model grid. The preprocessor is coded using Fortran90 and C and is driven by a namelist allowing the user to choose the type of emissions, the databases and a chemical species specification file from the M-SPACK. The considered emissions are from the most recent databases of urban/industrial, biogenic, biomass burning, volcanic, biofuel use and burning from agricultural waste sources. The current version includes anthropogenic emission inventories provided by "REanalysis of the Figures.
Back Close Full    Olivier et al. , 1996 Olivier et al. , , 1999)). This very recent version includes most of the direct greenhouse gases, 4 ozone precursor species and 3 acidifying gases, primary aerosol particles and stratospheric ozone depleting species. Specifically for the South American continent, it also includes urban/industrial based on local inventories  (Alonso et al. , 2010). For biomass burning, it includes emissions provided by the Global Fire Emissions Database (GFEDv2) based on  Giglio et al. (2006)  and  van der Werf et al. (2006)  or emissions can be also estimated directly from satellite remote sensing fire detections using the Brazilian Biomass Burning Emission Model (3BEM,  Longo et al. , 2010)  included in the tool. In both cases, fire emissions are available for 107 different species.
The biomass burning emission estimate is split into two contributions: smoldering, which releases material in the first model layer above the surface, and flaming, which makes use of an in-line 1-D plume rise model to determine the injection vertical layer  (Freitas et al. , 2007 (Freitas et al. , , 2010)). Biogenic emissions are also considered via GEIA/ACCENT Activity on Emission Databases, (http://www. aero. jussieu. fr/projet/ ACCENT/description. php) for 12 species and derived by the Model of Emissions of Gases and Aerosols from Nature (MEGAN,  Guenther et al. , 2006)  for 15 species. Other emissions include: volcanic ashes  (Mastin et al. , 2009) , volcanic degassing  (Diehl, 2009; Diehl et al. , 2011) , biofuel use and agricultural waste burning inventories developed by  Yevich and Logan (2003).