dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS NB, AB, CJR acknowledge support from the  ERC  through the grant ERC-Stg DRANOEL n. 714245. HE acknowledges support from the  DFG, Germany [427771150 ]. Basic research in radio astronomy at the  Naval Research Laboratory  is supported by 6. 1 Base funding. AB acknowledges support from the  VIDI research programme  with project number  639. 042. 729 , which is financed by the  Netherlands Organisation for Scientific Research (NWO). GB, RC, FG and MR acknowledge support from  INAF  mainstream project " Galaxy Clusters science with LOFAR ". AD acknowledges support by the  BMBF Verbundforschung  under the grant  05A20STA. ACE acknowledges support from  STFC  grant  ST/P00541/1. RJvW acknowledges support from the  ERC   Starting Grant ClusterWeb 804208. LOFAR, the  Low Frequency Array designed  and constructed by  ASTRON  ( Netherlands Institute for Radio Astronomy ), has facilities in several countries, that are owned by various parties (each with their own funding sources), and that are collectively operated by the  International LOFAR Telescope (ILT)  foundation under a joint scientific policy.
2017; Whetstone, 2018). For the dormant season in Indianapolis, roughly November -March, the biogenic effect on CO2 fluxes is relatively small compared to the fossil fuel contribution. Turnbull et al. (2015a)  found that wintertime CO2 enhancement in Indianapolis was nearly equivalent to the fossil-fuel CO2 enhancement using a local background. Also in Indianapolis,  Miles et al. (2017a)  used a single predominantly upwind tower site 20 km to the southwest of the city edge as background for an analysis of inter-tower differences during the dormant season 2012 -2013. Lauvaux et al. (2016)  found that inverse emission results differed by only 4 % between using a single background and using a wind direction dependent background, for their primarily dormant season analysis of data between September 2012 -April 2013.
We first consider the differences between background towers as a function of wind direction and season. Two potential background towers were available for January 2013 -December 2018, and an additional one was available for April 2017 -December 2018. We calculated 31-day median afternoon enhancements above each of the primary background towers, and composited over 5. 7 years of data to determine yearly cycles of enhancements at each tower. We compared the results from each of the background towers, and made a simple prediction to explain the difference between the towers based on their land cover types. We then compare these results to forward model CO2 enhancement using fossil fuel and biogenic fluxes.
Wind measurements The wind data used to characterize overall synoptic patterns in the city were measured at the Indianapolis International Airport, outside the southwest corner of the city. The data are part of the Integrated Surface Dataset (https://www. ncdc. noaa. gov/isd). The weather station at the airport uses the Automated Surface Observing System (http://www. nws. noaa. gov/asos/pdfs/aum-toc. pdf). The accuracy of wind speed is ±1. 0 ms -1 or 5 % (whichever is greater) and the accuracy of wind direction is 5° when the wind speed is ≥ 2. 6 ms -1. Wind directions are not reported for periods in which the wind speed is less than 1. 6 ms -1. The height of the wind instrument is about 10 m AGL. The wind data are reported at a single point in time recorded within the last 10 minutes of each hour.
We compared the CO2 measured at the INFLUX sites by calculating the 31-day running median of the enhancement in CO2 for each of the towers from Tower 01 and from Tower 09. Here we use the term enhancement although the difference can be negative, and we note that we have not isolated the anthropogenic enhancement with this calculation. The 31-day running median was chosen to focus on seasonal variability. We excluded data points for which any of the background towers were downwind of the urban area (afternoon average wind directions of 20 -65° (Tower 01 in urban plume) and 235 -280° (Tower 09 in urban plume). To determine these wind directions, we considered the angle between the background towers and the geometric edges of the urban area, as defined as the region within the expressway encircling Indianapolis (I-465) and the differences between background towers as a function of wind direction in the dormant season.
Modelled Tower CO2 We calculated forward modelled CO2 dry mole fractions at each of the INFLUX towers by convolving tower footprints, representing the relationship between mole fractions and surface fluxes with fossil fuel emissions and biogenic fluxes. The tower footprints were simulated using transport field derived from the Weather Research and Forecasting model (WRFv3. 6. 1, Skamarock and Klemp, 2008)  in Four-Dimensional Data Assimilation mode for the inner 1-km resolution 87 km x 87 km domain  (Deng et al. 2017). Data from World Meteorological Organization surface stations within the model domain were assimilated to nudge the model to the observations. The transport fields were then coupled offline to the Lagrangian Particle Dispersion Model  (Uliasz 1994; Lauvaux et al. 2012; Lauvaux et al.
2020 ) in backward mode. Fossil fuel emissions from the Hestia CO2 emissions inventory product  (Gurney et al. 2017; Gurney et al. 2019) , available for each of eight economic sectors (residential, on-road mobile, off-road mobile, industrial, commercial, electricity production, airport, and railroad) were used. Hestia emissions were aggregated from the initial buildinglevel product to 1-km resolution, covering Marion County and the eight surrounding counties. For biogenic fluxes, we used the urban Vegetation Photosynthesis and Respiration Model (VPRM,  Mahadevan et al. 2008; Hardiman et al. 2017) , driven by greenness data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite product and climate data from the North America Regional Reanalysis (NARR). The fraction of impervious surface area from the National Land Cover Database  (Han et al.
2014)  within each pixel was used to adjust the carbon fluxes for the impact of urbanization on ecosystem function. Non-paved portions of the city were defined as deciduous broadleaf forest. Distributions for four land cover types (corn, soy, grassland/pasture, and forest) were derived from the United States Department of Agriculture National Agriculture Statistics Service (NASS; https://nassgeodata. gmu. edu/CropScape/,  Han et al. 2014) , and the VPRM parameters were optimized for these land cover types  (Hilton et al. 2014 ) to produce hourly carbon fluxes at 1-km resolution as the weighted average of carbon fluxes from each type. Note that Tower 14 is outside the domain of the VPRM results and is thus not included in the model-data mismatch analysis. We then compared the 31-day running median forward modelled CO2 enhancements for 2014 to observed CO2 enhancements for the same year.
The Tower 09 CO2 was predicted to be its greatest magnitude lower than Tower 01 in mid-July, only a two-week shift from the observed pattern. That is, by mid-July, the agricultural fluxes in the footprint of Tower 09 were maximally larger than the forest fluxes in the footprint of Tower 01. While the predicted CO2 difference was small in November and December, as observed, the prediction indicated an additional minimum in September (about one half the magnitude of the minimum in May) that was not observed in the tower data. According to the fluxes in Fig. 2a , the forest was expected to continue to draw down CO2 in September, whereas the agricultural fluxes indicated small magnitude respiration. The tower results, however, indicated the both forest and agricultural fluxes were roughly balanced by mid-September, averaged over the time period of the dataset.
Discussion/Conclusions We examined CO2 enhancements as a function of time throughout the year using composites of over five years of data from towers in and around the city of Indianapolis, IN. Three possible background towers were considered, Tower 01 in a forested area southwest of the city, Tower 09 in an agricultural region east of the city, and Tower 14 in an agricultural region northwest of the city. The enhancement differed significantly depending on choice of background and time of year, being 2. 8 ppm higher in June and 1. 8 ppm lower in August using Tower 01 as a background compared to Tower 09. The most striking feature in the CO2 enhancements compared to agricultural Tower 09 as a background was an apparent maximum in August, with 31-day median enhancement at the urban towers up to 4.
1 ± 0. 5 ppm during the dormant season. Developing and testing robust CO2 flux estimates for the rural ecosystems upwind of cities is therefore critical to year-round urban anthropogenic CO2 flux estimates. Further tuning of the biogenic model response, or a more advanced vegetation model in order to more fully capture the timing and productivity differences between the forested and agricultural sites considered here would likely improve the inversion results. The sensitivity of the inverse fluxes to the biogenic fluxes is of course dependent upon the fossil fuel emissions for the study area. The growing season forward model to data mismatch was larger than the dormant season mismatch, suggesting that biogenic fluxes were a larger source of mismatch than the fossil fuel fluxes. The inversion of these data, separately optimizing fossil fuel and biogenic CO2 emissions,  (Lauvaux et al.
The summertime increase in enhancement was larger in forward model results than is observed, indicating that the VPRM fluxes were in general too strong, or that the modelled biosphere was too weak within the urban domain. Further analysis will assess the performance of VPRM via flux towers and tune the biogenic model to improve accuracy. Accurate modelling of ecosystems will be crucial for accurate fluxes during the growing season, for both the approach presented here, based on a simple background tower or wind-direction dependent tower, and the model-data hybrid approach for background determination  (Sargent et al. 2018 ). Another approach is to optimize the biogenic fluxes separately in the inversion  (Lauvaux et al. 2020). Enhanced intensity of drawdown due to corn during the peak growing season months of July and August northwest of Indianapolis was the likely cause of the large difference in CO2 measured at Towers 09 and 14, both in agricultural areas.
Additional measurements, including flux tower eddy covariances are planned to learn more about the differences in CO2 drawdown between these locations. Although the number of towers and timespan of the dataset is unprecedented for a study of CO2 mole fractions in and around a city, there were some limitations of this study. We used afternoon-averaged wind direction at the airport to exclude the afternoons for which the background towers were affected by the urban plume. Given the likelihood of wind direction changes throughout the day, back trajectory analysis would have been a more accurate way to exclude the urban plume, but is beyond the scope of this study. Additionally, although we estimated the effects of variable tower heights on our results and found them to be negligible (Fig. S7 ), ideally all measurements would be made at the same height.
There is, however, critical information available during other times of day. Future analyses of nighttime respiration and the use of nonafternoon greenhouse gas data in inversions are likely to prove beneficial to understanding of the carbon cycle and reducing uncertainties. This study in general highlights the importance of background choice in urban greenhouse gas studies. The magnitude of potential background differences depends on time of year and land cover types in the region. Indianapolis is a large city, but not a mega city, and determination of fluxes for larger cities with larger CO2 fluxes may be less affected by land cover-based differences in background towers. Still, careful consideration of land cover types is necessary in order to interpret CO2 tower network data throughout the growing season.
Availability of data  Miles NL, Richardson SJ, Davis KJ, Haupt BJ. In-situ   tower atmospheric measurements of carbon dioxide, methane and carbon monoxide mole fraction for the Indianapolis Flux (INFLUX) project, Indianapolis, IN, USA. Data set available on-line from The Pennsylvania State University Data Commons, 2017b; http://dx. doi. org/10. 18113/D37G6P. For further information, see http://sites. psu. edu/INFLUX.
The first case (LR for low resolution) corresponds roughly to the characteristics of good SDSS spectra. The second case (HR for high resolution) investigates what could be done with better data that could be obtained with e. g. XSHOOTER on the VLT. Given the width of the lines, higher spectral resolution is not needed. The chosen parameters for the models are presented in the first row of Table. 1, Table. 2  and Table. 3 for the spherical, wind and disk models respectively.
ACKNOWLEDGEMENTS In memoriam  Hayley Finley. We thank an anonymous referee for a thorough reading of the text and fruitful comments. We thank the  Physics and Astronomy department of Uppsala University  in which part of the work presented here has been done. Funding for SDSS-III has been provided by the  Alfred P. Sloan Foundation , the Participating Institutions, the  National Science Foundation , and the  U. S. Department of Energy Office of Science. The SDSS-III website is http://www. sdss3. org/. SDSS-III is managed by the  Astrophysical Research Consortium  for the Participating Institutions of the  SDSS-III Collaboration  including the  University of Arizona , the  Brazilian Participation Group ,  Brookhaven National Laboratory, Carnegie Mellon University, University of Florida , the  French Participation Group , the  German Participation Group, Harvard University , the  Instituto de Astrofisica de Canarias , the  Michigan State/Notre Dame/JINA Participation Group ,  Johns Hopkins University, Lawrence Berkeley National Laboratory, Max Planck Institute for Astrophysics, Max Planck Institute for Extraterrestrial Physics, New Mexico State University, New York University, Ohio State University, Pennsylvania State University, University of Portsmouth, Princeton University, the Spanish Participation Group, University of Tokyo, University of Utah, Vanderbilt University, University of Virginia, University of Washington , and  Yale University.
For example, the site of Anse Quitor, Rodrigues, is situated on the roof of the François Legat turtle reserve. This reserve aims to preserve giant tortoises and monitor their behaviour and biology, so it is of interest to use environmental data such as UV radiation and cloudiness. The sites of the UV-Indien network are presented in Table  1  and are indicated on the map (Fig. 1 ). The site of Saint-Denis, Reunion, is the main site of the UV-Indien network. UV measurements have been carried out since January 2009 with the Bentham DTMc300 spectroradiometer (affiliated with the Network for the Detection of Atmospheric Composition Change -NDACC) and since December 2016 with the Kipp and Zonen UVS-E-T radiometer. Cloudiness measurements have been carried out since October 2016. This site has the advantage of being co-located with several other instruments, allowing detailed studies of physical processes.
Data processing With the exception of the station on the island of Juan de Nova, raw data are transmitted daily. These data then undergo a first processing stage in which a first quality control eliminates acquisition errors or outliers. The raw data files are in different formats depending on the station and its method of data acquisition and transfer. They are therefore converted into a single format before recalibration. Depending on the most recent calibration of the instrument, the data are recalibrated according to the manufacturer's calibration procedure: Davos or Bentham. They are then reformatted in the format requested by the various distribution platforms (WOUDC or Zenodo). The frequency of updating the online databases is 3 to 6 months. For the Juan de Nova station, an annual rotation is planned in order to physically recover the data and carry out the maintenance of the instrument.
The radiometer data are filtered to define the UVI measurements made in clear skies. This filtering is done manually for 1 h intervals. The filtering process is as follows: each daily UVI and cloud fraction profile is plotted, together with an estimate of the clear-sky profile, using the Madronich analytical formula  (Madronich, 2007). To calculate the Madronich UVI estimate, we use the total ozone column from either the OMTO3d satellite product or a co-located ground-based instrument (the SAOZ for the Saint-Denis station). An observer then selects the 1 h windows considered as clear sky according to the following criteria: -Difference between observed and estimated UVI (according to the Madronich analytical formula) is higher than 10 %. -For the presence of clouds, a cloud fraction threshold of 0.
Diurnal variation in UVR and CF For each station, the variability in the UV index, as measured by the radiometers, was analysed to understand the variability in the dataset and ensure its quality. The diurnal cycle of UVI was calculated from the start of measurements at Earth Syst. Sci. Data, 13, 4275-4301, 2021  https://doi. org/10. 5194/essd-13-4275-2021  each station until June 2020 for Anse Quitor, November 2020 for Mahé, and December 2020 for Antananarivo and Saint-Denis. The results are presented in Fig. 5 : the mean UVI (blue line), the UVI maximum (continuous red line), and the first and third quartiles (blue shading) of the UVI in all-sky conditions during the day are represented for Antananarivo, Mahé, Anse Quitor, and Saint-Denis. For a given station, in clear-sky conditions, the mean UVI is represented by a green line, the maximum UVI by a dashed red line, and quartiles by shades of green.
In Mahé, the peaks of mean UVI, in all-sky and clear-sky conditions, are aligned. This is not the case for the maxima, which occur 1 to 2 h earlier in all-sky conditions than in clear-sky conditions. The position of the UVI peaks can be explained by the variabil-ity in the cloud cover, as will be shown in the next section. The monthly averages of the daily UVI maximum and the daily UVI at solar noon have also been calculated. Although the datasets do not cover a sufficient period to be climatologically significant, the annual variability and the differences between UVI and UVI daily maxima at solar noon present interesting results. Figure  6  shows these monthly averages for the four stations of Antananarivo, Saint-Denis, Mahé, and Anse Quitor. The measurement periods used to obtain these monthly averages were the same as for Fig.
5. The daily maxima of UVI are in red lines with filled circles, and the corresponding sun zenith angles are in dashed orange lines with filled circles. The UVIs at local solar noon are marked with blue lines and crosses, and the corresponding sun zenith angles are shown as dashed orange lines with crosses. For all stations and all months of the year, the maximum daily UVI (UVI DMAX ) is higher than the average UVI at solar noon (UVI SNOON ). These monthly averages are based on datasets for all types of cloud cover. Thus, the cloud cover will introduce a bias in the result. UVI DMAX includes maxima of UVI during cloud enhancements, which could occur earlier or later than the local solar noon, as discussed in the previous result (Fig. 5 ). UVI DMAX also includes maxima of UVI during clear-sky days, which will usually occur at local solar noon.
Seasonal distribution and variation in UVR and CF Early results from the cameras provide a brief description of the mean diurnal cloud cycles over each station. Figure  7a  shows the mean diurnal cycle of the cloud fraction (CF) over Antananarivo for the entire year (black curve), along with the distribution of the first and third quartiles (blue shaded areas). Seasonal means of CF are also presented for December, January, and February (DJF, in green), March, April, and May (MAM, in red), September, October, and November (SON, in orange), and June, July, and August (JJA, in purple). The density of the corresponding dataset for each month of the year is represented in Fig. 7b. Figure  7c  represents the difference of UVI maxima between clear-sky UVI (CS-UVI) and all-sky UVI for the same period.
The monthly averages of daily UVI maxima and UVI measured at solar noon at the four sites of Antananarivo, Saint-Denis, Mahé, and Anse Quitor are consistent with the variation in the zenith solar angle at these differhttps://doi. org/10. 5194/essd-13-4275-2021 Earth Syst. Sci. Data, 13, 4275-4301, 2021 ent sites. For the Antananarivo and Saint-Denis sites, coherent behaviour is observed between the all-sky camera measurements and the radiometer measurements over the whole dataset or over the four seasons represented here. In order to compare ground UVI measurements with the UVI product from satellites or forecast modelling, we have gathered together the multiple datasets presented in Table  2. The Bentham spectrometer (called UVI-BENTHAM hereafter) and Kipp and Zonen radiometer (called UVI-RADIO hereafter) constitute the ground-based-instrument part of the UV network.
For CAMS, the closest grid point available for each station was taken: 14. 8, 9. 6, 7. 2, and 4. 7 km from Antananarivo, Anse Quitor, Mahé, and Saint-Denis, respectively. For TEMIS, the closest grid point available for each station was used: 7. 8, 13. 0, 12. 1, and 11. 8 km from Antananarivo, Anse Quitor, Mahé, and Saint-Denis, respectively. Here, it was chosen to compare the satellite product or model forecast with results from the four stations of the UV-Indien network that have the longest time periods covered (Antananarivo, Anse Quitor, Mahé, and Saint-Denis). Figure  2  shows the period covered by each dataset at these four stations. Bentham has been collecting data since 2009, but, in this study, only the period between 2016 and the present was chosen because the radiometers were installed only in 2016 or later (in 2017) depending on the stations.
The Pearson correlation coefficient and absolute and relative differences were calculated for each ground-based instrument installed at each station. Then, the means and medians of the absolute and relative differences between ground-based measurements and the satellite or model estimates were computed (called Mean-AD, Median-AD, Mean-RD, and Median-RD hereafter). The correlation between the UVI-RADIO at Saint-Denis and the other measurements is shown in Fig. 3. All datasets are well correlated with the UVI-BENTHAM under clear-sky conditions; Pearson correlation coefficients are greater than 0. 9 except for OMUVBG (0. 55) at Saint-Denis. Correlation results for the radiometers at Saint-Denis, Antananarivo, Mahé, and Anse Quitor stations are available in the appendix (Figs. A1, A3, A5, and A7, respectively).
Looking at the median of the absolute differences (Median-AD) under all-sky conditions, it can be seen that all datasets, with the exception of OMUVBG, overestimate UVI relative to the UVI-RADIO measurements; median overestimation ranges between 0. 02 (TROPOMI) and 1. 12 (TEMIS). In clear-sky conditions, the median absolute differences decrease, but a UVI overestimation can still be observed for CAMS (+0. 21) and TEMIS (+0. 20). The smallest median difference is obtained for GOME (-0. 01). UVI-RADIO is expected to be aligned with UVI-BENTHAM since it has been recalibrated with the UVI-BENTHAM measurements, so it will not be discussed here. The mean of the absolute differences under all-sky conditions ranges from 0. 38 ± 2. 47 (OMUVBG) to +2. 39 ± 3. 24 (TEMIS). Under clear-sky conditions, this absolute difference is smaller and ranges between -0.