dataesr/scientific-paragraphs-categorization huggingface dataset:
Sensitivity and uncertainties The measurement sensitivity of the car-MAX-DOAS and satellite measurements is systematically different: car-MAX-DOAS observations are most sensitive for layers close to the surface and become increasingly insensitive for altitudes above about 3 km  (Frieß et al. , 2006). In contrast, the sensitivity of satellite observations decreases towards the surface. This is accounted for in the satellite NO 2 data product used in this study (the "Derivation of OMI tropospheric NO 2 (DOMINO)" product, see Sect. 2. 2) based on (relative) apriori vertical NO 2 profiles from the TM4 CTM. However, especially close to strong emission sources, the model profiles can differ strongly from the true NO 2 profiles. Here it is important to note that the uncertainty of the actual NO 2 profile usually constitutes the major uncertainty for satellite retrievals of tropospheric NO 2 VCDs.
The main limitations of the satellite observations are their coarse resolution, their large uncertainties, and their strong dependence on cloud cover and the a-priori assumptions on the NO 2 profile. The main limitations of the car-MAX-DOAS observations are their limited spatial and temporal coverage. Also the accuracy of the model data is limited by various uncertainties in emissions, chemistry and transport (e. g. Boynard et al. , 2011). The main aim of this study is to test the consistency of the three data sets during the two MEGAPOLI measurement campaigns in summer 2009 and winter 2009/2010. The most direct and usually applied way is the comparison of the original data sets. In addition to these basic comparisons, we also compare modified versions of the three data sets making synergistic use of the strengths and weaknesses of the three different data sets.
Here modifications of the model data are of particular importance, because (i) the model results depend critically on the used input data, especially the wind fields and the distribution and strength of the emission sources, and (ii) the model data play a crucial role as transfer tool to connect both remote sensing data. The following corrections to the original data sets are applied in this study. Here it should be noted that in cases when rotated CHIMERE data are used (see point a. above), not the original but the rotated CHIMERE profiles are applied to the OMI retrieval. This is an important detail, as the NO 2 profiles vary strongly depending on whether they are inside or outside the emission plume. In addition, the car-MAX-DOAS data and model results are used for satellite validation with a specific focus on the effects of clouds on the satellite retrievals (see Sects.
5 and 6). And the satellite and model data are used to investigate the representativeness of the car-MAX-DOAS data. The model data are used as a transfer tool to bridge the different scales of the MAX-DOAS and satellite observations (see  Sect. 6). Typical examples for the comparison of the three data sets during the Paris campaigns are illustrated in Figs. 3  and 4. Besides the original model data (at 3 × 3 km 2 resolution), the re-gridded model data matching the spatial resolution of the satellite observations are also shown. Car-MAX-DOAS results are displayed in the plots of the satellite data and separately in a zoomed image (also showing the results of the CHIMERE model). Figure  3  represents a day with almost ideal conditions for the comparison of the different data sets: the satellite covers the whole Paris metropolitan region and only few clouds are present (CF is below 30 % for all satellite pixels).
6. The applied rotations cause a substantial improvement of the correlation coefficients. While such an improvement has to be expected (and was the criterion to determine the rotation angles), it is interesting to note that also the slopes of the regression lines increase while the y axis intercepts decrease. However, the slopes are still systematically smaller than unity, and the y axis intercepts are larger than zero, indicating an underestimation of the maximum NO 2 VCDs and overestimation of the background NO 2 VCDs by CHIMERE. In Fig. 7  we show correlation plots between CHIMERE and car-MAX-DOAS data for 2 selected days: on 16 July 2009 (top) the correlation is largely increased for the rotated CHIMERE data; on 27 July 2009 similar correlation is found for original and rotated CHIMERE data.
On both days the slope is close to 0. 5 and is hardly affected by the rotation of the CHIMERE data. The frequency distribution of the optimum rotation angles is presented in Fig. 8. There, in addition to the results of the comparison to the car-MAX-DOAS data, the corresponding = 0. 28 between both sets of the rotation angles. The rather low correlation is probably caused by the fact that the comparisons of the model data with both observational data sets are made for different times and locations. In particular the comparisons versus OMI observations are performed for a much larger area (see Figs. 3  and 4 ). From the comparison of CHIMERE data to both observational data sets counterclockwise rotations are found more frequently than clockwise rotations, with corrections reaching up to 25 •.
To investigate the influence of the NO 2 profile on the OMI results in more detail we compared the NO 2 profiles from the TM-4 model over Paris with the corresponding profiles from the CHIMERE model (with a much higher spatial resolution of 3 × 3 km 2 ) for selected OMI observations. In Fig. 14   Thus in all further comparisons of this study, in addition to the original DOMINO v2. 0 product, we also consider the OMI results retrieved using the CHIMERE profiles (this version is referred to as modified OMI data). Here it is important to note that, the modified OMI data are partly dependent on the CHIMERE profiles. In an extreme case, for example, the application of CHIMERE profiles to a hypothetic homogenous (non-zero) OMI NO 2 field would cause a deceptive high correlation between OMI and CHIMERE data.
Influence of horizontal rotations of the model data on the comparison with OMI As for the car-MAX-DOAS data, we investigated the effects of rotations of the CHIMERE results around the centre of Paris on the comparison with the OMI data. Two examples are shown in Figs. 15 and 16. On 28 July 2009 (Fig. 15 ) the emission plume extended in north-east direction. Good spa-  tial agreement between the model simulations (re-sampled to OMI ground pixel extent) and satellite observations is found after the model data is rotated by -20 •. On that day the simulated tropospheric NO 2 VCDs are systematically underestimated compared to OMI data, especially far away from the city centre (of course, in principle, also the OMI data might have a systematic offset). The differences between model simulations and satellite observations become even larger for the modified OMI product because for observations of the most polluted areas the replacement of the TM-4 profile by the CHIMERE profile has the strongest effect.
S4 ). Here it should again be noted that for the modified OMI data (see Sect. 5. 1) not the original but the rotated CHIMERE profiles were applied to the OMI retrieval. This is an important detail, as the NO 2 profiles vary strongly depending on whether they are inside or outside the emission plume. In Fig. 17  the results of spatial correlation analyses between CHIMERE and OMI for all days with coincident data are shown. Like for the comparison with the car-MAX-DOAS measurements, for most days the rotation of the CHIMERE data leads to an improvement of the correlation coefficients (as has to be expected). However, the improvement of the correlation coefficient is smaller than for the comparison with the car-MAX-DOAS measurements. Also, for the slopes and y axis intercepts, only small changes are found.
Both findings indicate that the determination of the rotation is less well constrained by the OMI observations compared to the car-MAX-DOAS measurements. This can be explained both by the much coarser resolution of the OMI data and the frequent gaps due to clouds. Since the spatial resolution of the CHIMERE data is much finer than the OMI resolution, the shape of the OMI ground pixels has no significant effect on the determination of the rotation angles, as CHIMERE data is re-sampled to the OMI pixel extent. Figure  18  presents the daily averages and maxima for both data sets for effective CF < 30%. Like for the comparison between car-MAX-DOAS and CHIMERE, the day to day variability is well represented in both data sets. The average ratios of daily maximum and average values are summarised in Table  3.
For the ratios of the averages the modification of the OMI data using the CHIMERE profiles has a small effect. The ratio between CHIMERE and OMI data is about 0. 76 in summer and 1. 00 in winter. Concerning the maxima, the effect of the modification of the OMI data using the CHIMERE profiles is stronger: the ratios between CHIMERE and OMI data decrease from 0. 83 to 0. 68 in summer and from 0. 85 to 0. 80 in winter. Figure  19  presents the correlation results between CHIMERE and OMI for all individual data pairs. In addition to the original OMI (v2. 0) and CHIMERE data, results for the rotated CHIMERE data and the modified OMI data are also shown. Again an orthogonal linear regression is performed, where the uncertainties of the CHIMERE data are  described by a constant (2 ×10 15 molec cm -2 ) and a linear term (20 %).
For OMI the individual errors are taken from the DOMINO data product  (Boersma et al. , 2011). After each modification step the correlations between both data sets improve (r 2 increases from 0. 31 to 0. 71 in summer and from 0. 24 to 0. 50 in winter). In contrast, the slopes of the regression lines hardly changes (in winter they even  decrease). For the comparison between the rotated CHIMERE and the modified OMI data, the slopes are 0. 52 and 0. 96, respectively (but for winter the slope has to interpreted with care, because of the rather poor correlation).
Quantitative interpretation of the comparison results We quantify the agreement of the tropospheric NO 2 background VCDs based on the ratios of the daily average values (Fig. 18a ). For winter, the ratios both for the original and the modified OMI data are close to unity (see Table  3 ), indicating good agreement between both data sets. In contrast, for summer the ratios are much lower (0. 74 and 0. 76 for the original and modified OMI data, respectively), indicating that CHIMERE systematically underestimates the OMI measurements. The agreement of the tropospheric NO 2 VCDs in the Paris plume is quantified by the ratios of the daily maxima (Fig. 18b ) and slopes of the regression lines of the individual data pairs (Fig. 19 ). For the original OMI data, the ratios and slopes are between 0. 52 and 0.
83 and between 0. 85 and 1. 16 for summer and winter, respectively. Overall, these results indicate a systematic underestimation of the OMI data by CHIMERE (the slope of 1. 16 is probably not very meaningful because of the rather low correlation coefficient). If the modified OMI data are considered, the ratios and slopes become even smaller: they are between 0. 52 and 0. 68 and 0. 80 and 0. 90 in summer and winter, respectively. This finding reflects the fact that the use of the CHIMERE profiles causes an increase of the OMI results.
Comparison between OMI and car-MAX-DOAS In this section we first directly compare car-MAX-DOAS to OMI observations. Then we use the CHIMERE model as a transfer tool to correct for the differences in the spatial coverage. For the comparison between car-MAX-DOAS and OMI data we averaged all MAX-DOAS measurements within each OMI ground pixel with effective CF below 0. 3, for days where large circles around Paris are sampled. Like for the comparison between CHIMERE and car-MAX-DOAS data  (Sect. 4), the choice for large circles was made because for these observations the rotation of the CHIMERE data can be more accurately determined. The rotation angle was determined from the comparison to the car-MAX-DOAS data (see Sect. 4). The (rotated) CHIMERE profiles were used for the modification of the OMI data.
In In Table  4  the average ratios of the daily maximum and average values are shown. In summer, similar ratios for the averages and maxima (about 0. 90) are derived for the original and smoothed car-MAX-DOAS data. If, however, the car-MAX-DOAS data are scaled to full OMI pixels (based on CHIMERE spatial patterns), the ratios between OMI and car-MAX-DOAS data becomes close to unity. The correction for  the effect of spatial gradients is performed by multiplying the car-MAX-DOAS data by the average ratio CHIMERE (OMI)/CHIMERE (DOAS), which is ∼ 0. 90. For winter, again similar ratios for the averages and maxima are found for the original and smoothed car-MAX-DOAS data, but now they are much lower (around 0. 60). After correction for the effect of spatial gradients within the satellite ground pixels, the ratios increase from 0.
72 to 0. 82, but still are systematically below unity, indicating that the OMI results underestimate the car-MAX-DOAS data. The results of the correlation analyses for individual measurements are shown in Fig. 21. Again an orthogonal linear regression is performed, where the uncertainties of the car-MAX-DOAS data are described by the standard deviation of the individual observations divided by the number of observations inside the OMI pixels. For OMI the individual errors are taken from the DOMINO data product  (Boersma et al. , 2011). The different sub-plots show results for different versions of the car-MAX-DOAS data: original car-MAX-DOAS data (Fig. 21a ), smoothed car-MAX-DOAS data (Fig. 21b ) and scaled car-MAX-DOAS (Fig. 21c ). Here the car-MAX-DOAS data are again scaled to the full OMI pixels, but now the correction is applied for individual car-MAX-DOAS measurements using the respective ratios CHIMERE (OMI)/CHIMERE (DOAS).
The correction of the car-MAX-DOAS data leads to a much better agreement between both data sets: the correlation coefficients r 2 increase from 0. 29 to 0. 49 in summer, and from 0. 64 to 0. 73 in winter. The slopes increase from 0. 49 to 0. 85 in summer, and from 0. 56 to 0. 77 in winter. It should, however, be noted that the slopes of the regression lines are still smaller than unity, indicating that OMI underestimates the tropospheric NO 2 VCD in the emission plume of Paris.
Comparison with the results of the bilateral comparisons OMI versus CHIMERE and car-MAX-DOAS versus CHIMERE The data selection in this section is quite different compared to the selections for the bilateral comparisons presented in Sect. 4 and 5. Especially for the comparison between OMI and CHIMERE much larger areas are covered in Sect. 5. Thus it is interesting to see how the different data selections affect the comparison results. In Table  5  the respective ratios of daily averages and maxima as well as the results of the regression analyses are compared for the different data selections. For the comparison between CHIMERE and car-MAX-DOAS slightly higher ratios are found if only coincident CHIMERE and car-MAX-DOAS data are compared (the additional constraint of coincident OMI observations mainly excludes cloudy situations from the comparison, because only OMI observations with effective CF below 0.
3 are considered). This finding probably indicates that car-MAX-DOAS observations under mostly overcast conditions slightly underestimate the true tropospheric NO 2 VCD, because a small fraction of the tropospheric NO 2 VCDs might be inside the cloud layer. For the comparison between CHIMERE and OMI similar ratios are found for winter, but in summer CHIMERE underestimates the OMI observations more than for the comparison presented in Sect. 5. This discrepancy is probably related to the fact that the observations selected in Sect. 5 cover a much larger area around Paris and indicates that the underestimation of the OMI data by CHIMERE increases with increasing distance from the emission source. One possible explanation for this finding is that the underestimation of the NO x lifetime in the CHIMERE model (potentially due to too high OH levels).
Summary and outlook In this study we compared extensive data sets of tropospheric NO 2 VCDs obtained from car-MAX-DOAS observations in the Paris metropolitan area with coincident satellite measurements from OMI and results from the regional model CHIMERE. The car-MAX-DOAS measurements were carried out on 25 days in summer 2009 and 29 days in winter 2010 within the European research project MEGAPOLI  (Mahura and Baklanov, 2011). On most of these days car-MAX-DOAS measurements were made along large circles (diameter ∼ 35 km) around Paris. The duration of individual car-MAX-DOAS measurements was about 1 min corresponding to about 1 km. The car-MAX-DOAS measurements were primarily made to determine the entire NO x emissions from Paris (see e. g. Shaiganfar et al. , 2011). The derived NO x emissions will be published in a separate paper  (Shaiganfar et al.
Here, rather large systematic differences and low correlations are found. In particular the enhanced NO 2 VCDs inside the emission plume from Paris measured by car-MAX-DOAS are largely underestimated by the satellite observations and model results. In a second attempt, we compare the three data sets after they were modified by making synergistic use of the specific advantages of the different data sets: -CHIMERE model results were rotated around the centre of Paris until best spatial agreement with car-MAX-DOAS or satellite measurements was obtained. The observation of a "tilt" between the spatial patterns of CHIMERE and MAXDOAS are probably related to a mismatch in wind direction in the MM5 meteorological model. A spatial misplacement of emissions in the Paris region, in particular as a function of the distance from Paris centre, is also a possible error source, as has been shown by comparing NO x emission fields in and around the Paris agglomeration from different emission inventories  (Petetin et al.
, 2014). -Car-MAX-DOAS measurements were spatially smoothed until best match with the model data was achieved. The resulting smoothing Kernels of about 6-8 km suggest an effective resolution of CHIMERE of this order. -OMI data were corrected by using the vertical NO 2 concentration profiles from the regional CHIMERE model. -The effect of spatial gradients within the satellite ground pixels on the comparison between car-MAX-DOAS and OMI observations was accounted for and partially corrected using the CHIMERE model data. The last two points underline the need for a regional model in order to compare MAX-DOAS with satellite measurements in a meaningful way. Using these modified data sets, the correlation of individual data pairs largely improved. Also, much better quantitative agreement between the data sets was found.
However, still the satellite observations and the CHIMERE model results systematically underestimate the car-MAX-DOAS observations inside the emission plume from Paris, although the underestimation is much less compared to the original data sets. For the tropospheric NO 2 VCDs outside the emission plume, a much better agreement is found. From these results we conclude that close to strong emission sources, the applied improvements of the observational and simulation data sets are essential for a meaningful quantitative comparison of the tropospheric NO 2 VCDs; for example, in the measurements in the Paris metropolitan area, about 10-40 % of the observed differences can be attributed to the effects of spatial gradients (see also  Chen et al. , 2009; Ma et al. , 2013; Lin et al. , 2014). By not properly considering these effects, wrong conclusions on the accuracy of the considered data sets of the same order may be drawn.
Information on the spatial gradients within the satellite ground pixels may be obtained e. g. by simultaneously using multiple car-MAX-DOAS measurements, or by combining (car-) MAX-DOAS measurements with results from a regional model. Similar changes of the absolute values may occur if inappropriate vertical profiles are used in the satellite data analysis. One additional interesting finding of our study is that in summer the underestimation of the OMI observations by the CHIMERE model increases with increasing distance from the emission source. This finding could indicate a too low atmospheric NO x lifetime in the model simulations. We suggest that future studies should use more sophisticated methods for the extraction of the tropospheric NO 2 VCD from car-MAX-DOAS measurements than the geometric approximation.
AcKNOWLEDGMENts This research was supported by grants from the  Fondation ARC ,  Fondation de France , the  ANR , the  INCA, la Ligue Nationale Contre le Cancer (JP, équipe Labellisée LNCC) ,  METOXIA  ( FP7-EU  programs), MRT and  Canceropôle PACA. The laboratory is funded by the  Centre A. Lacassagne, CNRS  and  INSERM. We thank  Frédéric Bost  for technical assistance and the use of its Ysi 2300 STAT Plus analyzer (YSI Life We thank  Dr M. C. Brahimi-Horn  for editorial assistance.
Acknowledgements We are extremely grateful to  Randolf Pohl  for discussion and private communications. We would like to thank  Herbert Dreiner  for useful communications. C. B. is supported by a  Royal Society University Research Fellowship. P. B. acknowledges partial support from the  European Union   FP7 ITN INVISIBLES (Marie Curie Actions ,  PITN-GA-2011-289442 ) and from the  Agence Nationale de la Recherche  under contract  ANR 2010 BLANC 0413 01.
Acknowledgments: We thank  W. Atkinson ,  E. Dalla Torre ,  S. Fratini ,  M. Hirata ,  M. Hücker ,  A. Kampf ,  E. A. Kim ,  D. LeBoeuf ,  M. LeTacon ,  M. Mali ,  P. Monceau ,  J. Orenstein ,  C. Proust , and especially S. Kivelson, for useful exchanges. This work was funded by  Université J. Fourier -Grenoble  and by the  French Agence Nationale de la Recherche (ANR)  under reference  AF-12-BS04-0012-01  (Superfield).
In 2007, Deagle et al. (Deagle et al. , 2007)  studied the diet of Macaroni penguins (Sphenisciformes:Spheniscidae) using faecal samples. Among the six species of fish for which 16S mtDNA sequences were retrieved there was one that did not match any sequence in GenBank and was about 20% divergent from any other fish species for which 16S sequences were available. This species could not be classified further than to the superorder of Acanthopterygii. Despite the great interest and comprehensive body of knowledge existing for this group, and very high rates of DNA sequencing in the recent years  (Ward et al. , 2009; Becker et al. , 2011; Trivedi et al. , A C C E P T E D M A N U S C R I P T 2015) , the unidentified species still displayed 16% divergence from any other fish species sequenced to date.
This strategy is ideal for biodiversity inventories. However, to guarantee prey identification at the species level a comprehensive DNA library for the chosen molecular marker is required. Currently, such a library exists only for the DNA barcoding region of the COI gene with more than 3. 7 million sequences available in the barcode of life database (BOLD) and 154,085 animal species formally barcoded (http://www. barcodinglife. com). However, for a number of communities, particularly those that have been poorly studied, the current BOLD database will not support complete resolution at the species level. For taxonomic groups that are largely unknown, there is a risk of overestimating or underestimating biodiversity in situations of exceptionally high intraspecific variation or exceptionally low interspecific divergence  (Shearer & Coffroth, 2008).