dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2011b). Had-GEM2s, STOC-HadAM3 and UM-CAM input their time-varying stratospheric ozone distribution from the CMIP5 database  (Cionni et al. , 2011). Finally, LMDzORINCA uses a constant (for all simulations) climatological values of stratospheric ozone  (Li and Shine, 1995). Note that changes in stratospheric ozone do affect photolysis in all other models but HadGEM2 and UM-CAM.
, 2012) , (2) tropospheric ozone changes  (Young et al. , 2012) , (3) ozone radiative forcing and attribution  (Stevenson et al. , 2012) , (4) comparison of ozone and associated forcing with TES  (Bowman et al. , 2012) , (5) black carbon deposition  (Lee et al. , 2012) , and (6) OH and methane lifetime in the historical  (Naik et al. , 2012a)  and future  (Voulgarakis et al. , 2012)  periods. Additional contributions and simulations are also planned for future analysis, focusing on air quality issues and additional understanding of simulated trends. The structures built for ACCMIP have been designed to follow the conventions used in the climate modeling community as much as possible. This should greatly facilitate comparisons between the ACCMIP models and CMIP5 models, as well as between ACCMIP models and the many datasets that are being used for evaluation of CMIP5 models.
Acknowledgements. ACCMIP is organized under the auspices of  Atmospheric Chemistry and Climate (AC&C), a project of International Global Atmospheric Chemistry (IGAC)  and  Stratospheric Processes And their Role in Climate (SPARC)  under the  International Geosphere-Biosphere Project  ( IGBP ) and  World Climate Research Program (WCRP). The authors are grateful to the  British Atmospheric Data Centre (BADC) , which is part of the  NERC National Centre for Atmospheric Science (NCAS) , for collecting and archiving the ACCMIP data. D. S. , G. F. and Y. L. acknowledge support from the  NASA MAP  and ACMAP programs. D. P. would like to thank the  Canadian Foundation for Climate and Atmospheric Sciences  for their long-running support of CMAM development. S. G. was supported by the  US Department of Energy Office of Science Decadal and Regional Climate Prediction using Earth System Models (EaSM) program.
The  Pacific Northwest National Laboratory  (PNNL) is operated for the DOE by  Battelle Memorial Institute  under contract  DE-AC06-76RLO 1830. The work of D. B. and P. C. was funded by the  US Dept. of Energy (BER) , performed under the auspices of LLNL under Contract  DE-AC52-07NA27344 , and used the supercomputing resources of  NERSC  under contract No. DE-AC02-05CH11231. V. E. and M. R. were supported by the  DLR Earth System Model Validation Project (ESMVal)  and used the supercomputing resources of the  German Climate Computing Center (DKRZ)  and the  Leibniz Supercomputing Centre (LRZ)  for the EMAC simulations. The work of I. C. was funded by the  ENEA National Integrated Model to support the international negotiation on atmospheric pollution (Minni)  project. W. J. C. , G. A. F. and S.
The LMDz-OR-INCA simulations were done using computing resources provided by the  CCRT/GENCI  computer center of the CEA. The MIROC-CHEM calculations were performed on the NIES supercomputer system ( NEC SX-8R ), and supported by the  Environment Research and Technology Development Fund  ( S-7 ) of the  Ministry of the Environment, Japan. The CICERO-OsloCTM2 simulations were done within the projects  SLAC  ( Short Lived Atmospheric Components ) and EarthClim funded by the  Norwegian Research Council. The MOCAGE simulations were supported by  Météo-France  and  CNRS. Supercomputing time was provided by Météo-France/DSI supercomputing center. The  CESM  project (which includes CESM-CAM-Superfast,  NCAR-CAM3. 5  and  NCAR-CAM5. 1 ) is supported by the  National Science Foundation  and the  Office of Science (BER)  of the  US Department of Energy.
1 CCM 1. 875/2. 5/L30, 3. 5 hPa Prescribed distributions from NCAR-CAM3. 5 STOC-HadAM3 CGCM 5. 0/5. 0/L19, 50 hPa Prescribed globally uniform CH 4 concentrations. Different for each time slice following CMIP5 dataset UM-CAM CGCM 2. 5/3. 75/L19, 4. 6 hPa Prescribed atmospheric concentration with no spatial variation; different for each time slice.
Offline stratospheric O 3 from 41 species CMIP5 dataset LMDzORINCA 82 gas species; no aerosols Offline stratospheric O 3 (climatol- ogy from Li and Shine, 1995) MIROC-CHEM 58 gas species; SO 4 , BC, OC, sea salt, dust; AIE in- Full stratospheric chemistry cluded MOCAGE 110 gas species; no aerosols Full stratospheric chemistry NCAR-CAM3. 5 117 gas species; BC, OC, SO 4 , NO 3 , SOA, dust, sea Full stratospheric chemistry salt NCAR-CAM5. 1 DMS, SO 2 , H 2 SO 4 gas SO 4 , BC, SOA, POA, sea salt, Prescribed distributions from and mineral dust aerosol internally mixed in 3 modes NCAR-CAM3. 5 with predicted number. AIE included. STOC-HadAM3 65 gas phase species, SO 4 and NO 3 aerosol Offline stratospheric O 3 from CMIP5 dataset UM-CAM 60 gas phase species; no aerosols Offline stratospheric ozone from CMIP5 dataset.
Start-up for a new education on headache disorders after Global Burden of Disease Study 2010 The Global Burden of Disease Study 2010 (GBD 2010), recently published in The Lancet [1], has marked new standards for all scientific societies, NGOs and patients' associations that gravitate towards headache disorders. These standards, dictated not only by the new migraine ranking but also by the appearance of tension-type headache, confirm headache in the top ten causes of global disability, in the top three ranking of global prevalence of disease and at the top of all neurological disorders as causes of years lived with disability. All this has been achieved despite the unjustified absence of medicationoveruse headache from GBD 2010. The fundamentals of these results are built on the hard-working epidemiological activity of the Neurologic Disorders Expert Group in Headache for GBD 2010, and Lifting The Burden (LTB) [2] which, on the basis of data carefully gathered (and featured in the Atlas of Headache Disorders and Resources in the World 2011  [3, 4] ), has then offered to GBD 2010 the headache-specific part of this cultural giant of modern medicine.
, 2005). Given the limitations of direct remote sensing and land surface modeling approaches for estimating ET, the objective of this paper is to demonstrate how the two approaches can be combined to create a hybrid which improves ET estimates across Africa. Examples of model hybridization include combining land surface temperature derived from remote sensing data with a soil vegetation atmosphere transfer model in  Olioso et al. (1999)  and an indirect, remote-based model in  Boni et al. (2001). Evapotranspiration estimates in   (Chen et al. , 1996). Specifically, NOAH's sophisticated transpiration component, driven by long-term average NDVI and surface climate reanalysis, is substituted with PT-JPL's simple transpiration scheme, driven by a time series of NDVI and the Enhanced Vegetation Index (EVI) and surface climate reanalysis.
Data processing and handling The PT-JPL model is run globally at a quasi-0. 05 • resolution daily time step from 2000-2008 for sub-Saharan Africa and is aggregated to a monthly time step for visualization and comparison purposes with field data. The choice of resampling to 0. 05 • resolution assumes that spatial heterogeneity is driven primarily by vegetation. The vegetation data consisted of 16-day 0. 05 • resolution NDVI and EVI derived from reflectance detected by MODIS. A piecewise weighted least squares regression filter  (Swets et al. , 1999)  is applied to the indices to further reduce atmospheric interference on the vegetation signal. Daily values were aggregated to monthly values for several reasons: (1) ground heat flux was not calculated in the PT-JPL model (ground heat flux is near zero in warm regions at a monthly time step), (2) daily station flux data was often spurious or missing, (3) the vegetation data used is extremely noisy at a daily time step, and (4) monthly timesteps are adequate to address research questions in food security monitoring and other continental scale studies.
Parameter Description Equation f C Fractional total vegetation cover e -(kLAI) B C Plant coefficient r+ r(1+C h R C )+ β Soil moisture availability 1 -W REF -W f W C Water holding capacity f C P -D -LE C S Maximum water holding capacity Optimized constant Land Information System (LIS)  (Kumar et al. , 2006) , compute surface fluxes using various LSMs. Details on GLDAS forcing data and model outputs can be found on NASA's Hydrology Data and Information Services Center webpage (http://disc. sci. gsfc. nasa. gov/hydrology). The dataset is primarily used by end-users, as the LIS currently has limited functionality for exploratory research and requires large computational resources. The version of NOAH used (2. 7. 1) is run offline by the 0. 25 • resolution forcing data. It will be referred to as GNOAH for the remainder the paper.
GLDAS uses NOAA/GDAS atmospheric fields, Climate Prediction Center Merged Analysis of Precipitation fields, and observation-driven shortwave and longwave radiation using the Air Force Weather Agency's AGRicultural METeorological modeling system. These data are produced at 3-hourly intervals and aggregated to a monthly time step. The latent heat simulation from GNOAH is at 0. 25 • resolution and resampled using the "nearest neighbor" approach to 0. 05 • resolution for comparison purposes with the PT-JPL model. Energy and moisture flux and meteorological data was gathered from eight sites representing major land cover types in Africa. Table  4  lists the sites, along with the station identification and name, country, geographic coordinates, the period data is obtained, ecosystem type using the International Geosphere-Biosphere Programme (IGBP) naming convention, and mean climatology.
The observed data are from three online sources: AMMA (www. amma-international. org), CarboAfrica (www. carboafrica. net) and Fluxnet (www. fluxdata. org). Five of the sites (BW-Ma1, CG-Euc, NE-Waf, NE-Wam, and ZA-Kru) include half-hourly data needed to run the PT-JPL model: surface air temperature ( • C), incoming longwave and shortwave radiation (W m -2 ), outgoing longwave and shortwave radiation (W m -2 ), relative humidity (%), precipitation (mm), and latent heat (W m -2 ). NE-Wam and NE-Waf were in close proximity and shared a rainfall gauge between them  (Ramier et al. , 2009). Latent heat flux was measured using the eddy covariance method  (Baldocchi et al. , 1988). With this technique, LE is   (Wilson et al. , 2002). The data used are not corrected for energy balance closure, because many of the energy terms were not available for most of the sites when the data was accessed, as shown in Table  5.
Half-hourly (uncorrected) data from the flux towers are used, because the PT-JPL model computes LE from daytime R N only. Average daily (corrected) data is typically filled using look-up tables  (Reichstein et al. , 2005)  and/or artificial neural networks  (Papale and Valentini, 2003) , however these data are not available, so persistent data gaps at many of the sites do exist.
Statistical analysis The analysis is done in three phases: (1) evaluation of the PT-JPL model using station data to determine the appropriateness of the LE C component, (2) sensitivity analysis of the PT-JPL model to remote sensing and surface reanalysis forcing and comparison of PT-JPL ground inputs with surface reanalysis inputs to identify sources of error propagation in model hybridization, and (3) development and performance of the hybrid model. The coefficient of determination (R 2 ) and root mean squared error (RMSE) are the primary metrics used for comparison between observations, models, and inputs. Near infrared and red reflectance are not available from the stations, so NDVI and EVI from MODIS are used instead when evaluating the PT-JPL model and conducting the sensitivity analysis.
Some of the relationships are clearly non-linear, however statistics from a linear fit are chosen for comparison purposes between inputs and across sites. It is expected that GLDAS p and q data correlates well with observed RH. Observed q and p are not used for direct comparison, because these data are not available for many of the sites. GLDAS R N , T , and q showed good agreement with observed data. Temperature from the GLDAS dataset, which is used exclusively in the LE C component, consistently shows high correlations across all the sites. Net radiation, which is the third most sensitive input, on the other hand, shows poor correlations across most of the sites. Two sites (CG-Euc and ZA-Kru) show good correlations for R N , but also large bias. Specific humidity is the second best predicted input.
Hybrid model Time series of observed LE and modeled LE using PT-JPL, GNOAH, and the combined model (PT-JPL LE C + GNOAH LE I,S ) for six of the eight stations are shown in Fig. 3. Table  9  includes goodness-of-fit statistics for six of the eight stations: ZM-Mon and NE-Wam are omitted, because the former had less than one year of data and the latter showed no noticeable difference from NE-Waf. Two of the stations (BW-Ma1 and CG-Tch) showed obvious bad (flat) LE data at the beginning and end of the time series when the sensors were just coming online or not being monitored, so statistics are computed after these points were omitted. The last month of CG-Tch was omitted as well, because the site was burned in July 2008. Grass was absent after the burn and the soil humidity was less than 2 % by volume, so H is the dominant energy flux.
Discussion The study represents an initial attempt to use a suite of new flux tower data in sub-Saharan Africa to improve ET estimation using remote sensing and surface climate reanalysis data for regular near real-time continental scale monitoring. The integration of the evaporation components from GNOAH with the PT-JPL transpiration component further improves the correlations and reduces the RMSE for both humid and semi-arid sites compared to the models before hybridization. The fractional total vegetation cover of GNOAH is the single most important variable controlling transpiration. This is consistent with other findings that showed LAI was a strong control on the ratio of ET/PET at savanna sites  (Williams et al. , 2009). The use of climatology to derive this component often suffers in semi-arid climates where variability in vegetation can be high.
, 2009). These factors could lead to a drop in LE of nearly 17 W m -2 , assuming a one standard deviation drop in R N from the sensitivity analysis, which still does not account for the underestimation of LE. It is expected that the large positive bias from the reanalysis data would act to improve the correlations at the driest sites, unless offset by other factors. The largest contributor to the underestimation of LE, therefore, is most likely due to the resolution of EVI and NDVI data and strong dependence of PT-JPL LE C on EVI: a 0. 05 • resolution pixel is too coarse to adequately capture the dynamics of a sparse canopy. Even with gross underestimation of R N and EVI, the comparison of observed data with PT-JPL reveals that LE remains difficult to model in the very driest areas. Improvements to PT-JPL LE C , such as the use of an EVI ratio to formulate f C as in  Mu et al.
Acknowledgements. This work was primarily supported by the  US Agency for International Development  through a  US Geological Survey cooperative agreement  ( 04HQAG0001 ) and  National Aeronautics and Space Administration Precipitation  grant ( NNX07AG26G ). The eddy covariance data was acquired by the  Fluxnet community  via the financial support of  CarboEuropeIP , FAO-GTOS-TCO, iLEAPS,  Max Planck Institute for Biogeochemistry, National Science Foundation ,  University of Tuscia, Université Laval  and  Environment Canada and US Department of Energy  and the database development and technical support from  Berkeley Water Center ,  Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California -Berkeley, University of Virginia. The authors would also like to acknowledge the contribution of eddy covariance data through AMMA, which is based on a  French initiative  to build an international scientific group, and was funded by a large number of agencies, especially from  France , the  UK , the  US and Africa , as well as by the  European Community 's  Sixth Framework Research Programme.
M-and H-waves A bipolar cuff electrode was placed on the peroneal nerve for stimulation (0. 1 ms pulses). The cathode (positive electrode) was oriented proximally. A pair of wire electrodes was placed subcutaneously on the Tibialis anterior muscle for EMG (electromyographic) recording. Ground electrode was implanted in a nearby muscle. Exposed tissue was covered with paraffin oil to prevent drying. The recorded signal was passed to a differential amplifier (P2MP ® , 5104B) and bandpass filtered at 0. 1 Hz and 10 kHz. The analogue signal was then sent to an A/D converter and the digital waveform was stored and displayed (sampled at 20 KHz, filtered with High Pass at 150 Hz) online using data acquisition software (Biopac MP150 ® and AcqKnowledge ® software). Stimulation of the peroneal nerve produced two EMG responses.
Following the frequency series testing, the H-reflex amplitude was confirmed at 0. 3 Hz for consistency. If the amplitude at recheck was less than 90 % of the initial amplitude, data were discarded. The M-and H-waves were only recorded on the left side. The investigator was blind to the experimental groups during recording and analysis of the data. In a second step, the reflex excitability was tested using a modification of the method reported by  Skinner et al. (1996). The H-reflex response to repetitive stimulation at 10 Hz was normally reduced by the second and subsequent stimuli, probably through presynaptic inhibitory mechanisms. However, in injured animals, the normal inhibition was absent, and the H-reflex amplitude remained close to 100 % of its control value. Single square wave stimuli (0.
Straylight measurements are considered as of good quality (more details can be found in the GOMOS product handbook at http://envisat. esa. int/handbooks/gomos/toc. htm). the GOMOS data used here are obtained with the version 5 of the ESA level 1 operational processor. all the selected spectra are linearly interpolated on a common vertical grid of tangent altitudes from 0 to 60 km with 1 km step. Figure  2  shows the latitudinal distribution of the GOMOS data used in our study. A total number of about 162 000 GOMOS measurements has been processed. While During the first months of 2005 (until June), almost no measurements are available due to an instrumental breakdown. However, some measurements in the polar regions could be carried out (particularly in January). For each bin, we have built a data set: T 1 λ i , z j ,.
All the cases of bimodal distributions observed in our study are due to measurements done inside and outside the polar vortex. In this case, both modes are studied separately and, after visual inspection, we have selected the one that represents the high latitude band, in other words the one inside the polar vortex with greater transmittances (the other modes will be analyzed in the frame of some case studies). Then, an outlier detection is performed for each dataset using a jackknife method. Finally, for each tangent altitude and each wavelength, the weighted median transmittance is calculated. A weighted median calculation starts by sorting the transmittance values in increasing order, and rearranging the associated weights in the of the absolute values of the difference between each transmittance values and the weighted median.
Monthly climatology of OClO In Fig. 8 , we show the time series of OClO SCDs in three latitude bands: the equatorial band (10 • S-10 • N, middle panel), the north polar band (60 • N-90 • N, top panel) and the south polar band (60 • S-90 • S, bottom panel). In the equatorial region, the missing data in 2005 are due to a GOMOS failure. The discontinuity observed in the two polar time series are due to the spatial coverage of GOMOS at these altitudes (cf. Fig. 2 ). Regarding the northern latitude band, we can clearly observe an annual increase of OClO followed by a decrease in the lower stratosphere (below about 25 km). The increase begins in December and reaches a maximum of about 5 × 10 15 cm -2 to 3 × 10 16 cm -2. The corresponding OClO concentrations (obtained after the spatial inversion described in Sect.
We present a simulation analysis of weak gravitational lensing flexion and shear measurement using shapelet decomposition, and identify differences between flexion and shear measurement noise in deep survey data. Taking models of galaxies from the Hubble Space Telescope Ultra Deep Field (HUDF) and applying a correction for the HUDF point spread function, we generate lensed simulations of deep, optical imaging data from Hubble's Advanced Camera for Surveys, with realistic galaxy morphologies. We find that flexion and shear estimates differ in our measurement pipeline: whereas intrinsic galaxy shape is typically the dominant contribution to noise in shear estimates, pixel noise due to finite photon counts and detector read noise is a major contributor to uncertainty in flexion estimates, across a broad range of galaxy signal-to-noise.
2005) , the Cosmic Evolution Survey (COSMOS; see, e. g. Scoville et al. 2007; Leauthaud et al. 2007 ) and the Space Telescope A901/902 Galaxy Evolution Survey (STAGES; see, e. g. Gray et al. 2009). The imaging data from these surveys share important characteristics as regards lensing measurement, having a small but non-Gaussian PSF and significant correlation in the noise between pixels due to the dithering and drizzling strategies employed (see e. g. Leauthaud et al. 2007 ). In addition, there exist a wealth of galaxy cluster imaging data in the ACS archive that are also of interest for shear and flexion lensing analysis. Mindful of these factors, we construct realistic simulations of ACS lensing data using real sky noise taken from blank areas in the GEMS survey data, and with a PSF that matches the radial profile of the GEMS PSF.
Image deconvolution using shapelets Within the shapelet framework, there are two possible methods with which to correct galaxy images for the effects of the optical system. Both approaches begin with the construction of a shapelet model of the PSF g(θ) at the location of each galaxy: this model should be as accurate as possible and will include any variation of the PSF across the image plane of the instrument (e. g. Hoekstra 2004 ; The deconvolution method used in this work is that proposed by  Massey & Refregier (2005) , which is implemented within the SHAPELETS software package made available by these authors. 1 In this approach, the deconvolved shapelet coefficients f n 1 ,n 2 are estimated by 'forward' convolving the shapelet basis functions with the PSF model in advance, creating a new basis set which we label D n 1 ,n 2 (θ; β) = g(θ ) * B n 1 ,n 2 (θ ; β), ( 18 ) with an equivalent expression for the case of the polar shapelet basis functions P n,m (θ ; β).
2005 Heymans et al. , 2008)) , we choose the V 606 filter image for the construction of the shapelet starter set, as representative of typical lensing source galaxies. A detailed description of the modelling of galaxies in the HUDF can be found in Appendix A. Given the input parameters, postage stamps, and model of the HUDF PSF described in this appendix, the SHAPELETS software was then used to find a best-fitting model of each HUDF galaxy. A total of 3867 of the 4128 input HUDF galaxies (94 per cent) were successfully modelled. On examination of these 6 per cent of catastrophic modelling failures, it was found that they were caused either by: (i) Galaxy images lying too close to the edge of the HUDF or other objects in the field (being thus automatically rejected and flagged in the output shapelet catalogue); or (ii) A population at faint magnitudes which appear to suffer from either star-galaxy confusion or general modelling degeneracy.