dataesr/scientific-paragraphs-categorization huggingface dataset:
For this paper, data were obtained at three stations: an open-ocean station (Loyalty Channel), a large, deep and open bay (Santal Bay, Lifou Island) and a lagoon station (Ouinne, southeast coral reef lagoon). Taxonomic identification, measurements and counts of filamentous species were carried out by microscopy while data on picoplankton were analyzed by flow cytometry. The various estimators for determining filamentous cyanobacterial biomass were also compared. Nutrient data  (Van Den Broeck et al. 2004 , Moutin et al. 2005 ) and all N2 fixation rates measured under the Diapalis program had been published earlier  (Garcia et al. 2007). During cruises, CTD casts were carried out at each station with water sampling down to 200 m for the deepest stations and down to 30 m in the lagoon. Temperature (T°C) and salinity were measured with Sea-Bird SBE 911 and water samples were collected with 8L-Niskin bottles attached to a CTD-rosette system.
Nitrate + nitrite (NOx) and phosphate (PO # $% ) were preserved in Nalgene bottles with HgCl2 prior colorimetric assays on a Technicon autoanalyzer II  (Raimbault et al. 1990). Ammonium was measured immediately on board by fluorometry (Turner Design TD-700) according to the procedure described in  Holmes et al. (1999). More details on the methods and the full data set Diapalis are available on http://www. obsvlfr. fr/proof/vt/op/ec/diapazon/dia. htm. The mixed layer depth (MLD) was estimated by taking the first depth where potential density (sigma-t) was 0. 03 Kg m -3 greater than the density at 10 meters, based on  Montegut et al. (2004).
Influence of methodology on enumeration and biomass estimates! As part of the effort of collecting biomass and metabolic rates specific to different phytoplankton functional groups, a database on diazotrophic organisms in the global pelagic upper ocean was built by  Luo et al. (2012). Variability in counting and sampling procedures, however, hamper comparisons between studies around the world, especially for filamentous cyanobacteria. Significant error in the accuracy of low abundance measurements occurs depending on the sampling mode. In this case, the use of a plankton net increased the detection of Trichodesmium (0. 1 against 1 trichome. l -1 ) by a factor of 10 compared to water samples collected with 2. 5 L GO FLO bottles  (Chang et al. 2000). When bottle sampling, it is essential to filter all the contents to prevent uneven distribution of Trichodesmium, which can occur when trichomes (and colonies) migrate due to cell-controlled or uncontrolled buoyancy (Bergman et al.
! During the Diapalis program, Trichodesmium spp. abundance in the ocean varied from 1 to 4578 trichomes l -1 found at 0 to 60 meters (average 586 trichomes l -1 , n = 88), which was much higher than previously published data in New Caledonian waters at the end of the summer (250-1000 trichomes l -1 in April 1998;  Campbell et al. 2005). The only other abundance data available for our region around New Caledonia are nifH gene copies  (Moisander et al. 2010 ). In Ouinne lagoon, a maximum of 1200 trichomes l -1 was found in summer 2007 and more than 10,000 trichomes l -1 in accumulations  (Rodier & Leborgne 2010). These concentrations are, however, lower than measured in a surface accumulation in the Southwest Lagoon, namely up to 30 10 6 trichomes l -1 for a Chla value of 3 mg. l -1  (Neveux et al.
In particular, several Level-2 retrieved, Level-3 monthly gridded, and Level-4 assimilated nadir ozone profile data products have been im-proved and harmonized in the context of the ozone project of the European Space Agency's Climate Change Initiative (ESA Ozone_cci). To verify their fitness for purpose, these ozone datasets must undergo a comprehensive quality assessment (QA), including (a) detailed identification of their geographical, vertical, and temporal domains of validity; (b) quantification of their potential bias, noise, and drift and their dependences on major influence quantities; and (c) assessment of the mutual consistency of data from different sounders. For this purpose we have applied to the Ozone_cci Climate Research Data Package (CRDP) released in 2017 the versatile QA and validation system Multi-TASTE, which has been developed in the context of several heritage projects (ESA's Multi-TASTE, EUMETSAT's O3M-SAF, and the European Commission's FP6 GEOmon and FP7 QA4ECV).
Yet all L2 observations also show sensitivity outliers in the UTLS and are strongly correlated vertically due to substantial averaging kernel fluctuations that extend far beyond the kernel's 15 km FWHM. The CRDP typically does not comply with the GCOS user requirements in terms of total uncertainty and decadal drift, except for the UV-visible L4 dataset. The drift values of the L2 GOME and OMI, the L3 IASI, and the L4 assimilated products are found to be overall insignificant, however, and applying appropriate altitude-dependent bias and drift corrections make the data fit for climate and atmospheric composition monitoring and modelling purposes. Dependence of the Ozone_cci data quality on major influence quantities -resulting in data screening suggestions to users -and perspectives for the Copernicus Sentinel missions are additionally discussed.
The present work therefore adopts the more exhaustive seven-step evaluation approach established in  Keppens et al. (2015) , including (1) satellite data collection and postprocessing, (2) dataset content study, (3) information content study, (4) FRM data selection, (5) co-located datasets study, (6) data harmonization, and (7) comparative analyses and their dependences on physical influence quantities of relevance. Section 2 first introduces the vertical profile retrieval schemes that have been used to generate the ESA Ozone_cci nadir profile (NP) Climate Research Data Package (CRDP). These are namely the Rutherford Appleton Laboratory (RAL, UK) version 2. 14 for the backscatter UV-VIS instruments and the FORLI (Fast Optimal Retrievals on Layers for IASI) version 20151001 for the thermal infrared mission instruments, developed at the RAL and by the cooperation of the Belgian ULB (Université Libre de Bruxelles, Belgium) and the French LATMOS (Laboratoire Atmosphères, Milieux, Observations Spatiales, Paris, France), respectively.
L2 TIR retrieval algorithm The Ozone_cci Metop-A and Metop-B IASI nadir ozone profile data for 2008-2015 and 2013-2015, respectively, were generated in a near-real-time mode using the FORLI-O 3 (Fast Optimal Retrievals on Layers for IASI Ozone) latest version 20151001 (see  Hurtmans et al. , 2012  for a full description of the retrieval parameters and performances). FORLI-O 3 relies on a fast radiative transfer and a retrieval algorithm based on the optimal estimation method  (Rodgers, 2000). In the current version of FORLI-O3, look-up tables (LUTs) were precomputed to cover a larger spectral range (960-1105 cm -1 ) using the HITRAN 2012 spectroscopic database  (Rothman et al. , 2013)  and correcting numerical implementation, especially with regard to the LUTs at higher altitude compared to the previous version.
L3 monthly gridded data For the thermal infrared IASI instrument on Metop-A, a tropospheric L3 product (prefix TTC instead of NP in Table  1 ) has been generated by the ULB-LATMOS team from their quality-screened L2 nadir ozone profile retrievals directly. This product consists of horizontally gridded (1 • latitude by 1 • longitude) monthly averages of the 0 to 6 km vertically integrated IASI-A ozone observations. Monthly averaged L3 profile products are produced from the filtered RAL v2. 14 GOME, GOME-2A, SCIAMACHY, and OMI data by the KNMI. Version 0004 of the KNMI L3 products has been used in this work (see Table  1 ). The KNMI L3 data consist of monthly ozone profile averages, also on a 1 × 1 • latitude-longitude grid, containing 19 layers between 20 fixed pressure levels at each grid point.
This correction is applied to the L2 data before the assimilation, meaning that the ozonesonde measurements involved (from 64 stations) cannot be used for the Ozone_cci L4 comparative validation exercise (see Sect. 5. 6) as FRM used for comparisons have to be independent of the validated product. 3 Validation approach and reference data 3. 1 Quality assessment of atmospheric satellite data This work adopts the exhaustive seven-step satellite data QA approach presented in  Keppens et al. (2015) , as schematized in its Appendix A. This approach includes (1) satellite data collection and post-processing, (2) dataset content study, (3) information content study, (4) FRM data selection, (5) colocated datasets study, (6) harmonization of data representation in terms of vertical sampling and units, and (7) comparative analyses including dependences on physical influence quantities of relevance.
The satellite data collection and post-processing (mainly L2 profile screening) is described by the previous section. The L2 datasets have, however, been reduced to 300 km ground station overpass datasets for the quality assessment in this work in order to reduce the total amount of data processing (i. e. satellite pixels must be within a 300 km radius from a FRM station). The FRM data selection, co-located dataset study, and data harmonization are therefore included as the successive subsections within this section. The satellite data content studies and informa-tion content studies are discussed in Sect. 4. These include statistics on the L2 station overpass data screening and spatiotemporal coverage as well as averaging-kernel-based information content measures. The comparative analysis with both spatially and temporally co-located FRM data follows later in Sect.
Data content The nadir ozone profile CRDP L2 data content study focuses on the spatiotemporal distribution and the effect of screening of the retrieved satellite profiles in the first place, next to the regular file structure, file content, and value checks for the quantities of highest relevance (also see Table  3 ). Figure  3  displays the latitude-time distribution per 10 • latitude band and per month of the percentage of screened profiles for all NP L2 station overpass (300 km) datasets (except for IASI on Metop-B). The data that are screened fail the filtering criteria suggested to data users as described in Table  3  and are therefore omitted from further analysis. Where the screening goes from 0 % (all data passes, in blue) to 100 % (no data passes, in red), one could equally insightfully interpret the plots as showing the spatiotemporal coverage of the satellite data ranging between 100 % (full coverage, in blue) and 0 % (no coverage left, in red), respectively.
The screening for the GOME and SCIAMACHY instrument retrievals is quite high (60-80 % on average), mainly due to the cloud screening that rejects all effective cloud fractions above 20 %. The lack of GOME data in the southern mid-latitudes from 2003 onwards is due to severe screening of L2 overpass data for ground stations that are all located near the South Atlantic Anomaly (SAA). The ECF has less impact on the GOME-2 and OMI instruments, but the SZA screening (if higher than 80 • ) still causes meridian and seasonal coverage variations. Moreover, a latitudinal striping can be observed for all UV-VIS instrument distributions, although this is partially due to the satellite pixel co-adding before retrieval and the 300 km station overpass data selection afterwards. The decreased GOME-2B availability from June 2015 onwards points at a retrieval issue and justifies additional screening, as shown in Table  3.
Degrees of freedom in the signal Figure  4  displays the latitude-time distribution per 10 • latitude band and per month of the median DFS for all NP L2 datasets (except for IASI on Metop-B). RAL's UV-VIS DFS is typically around 5, with the lowest values for SCIA-MACHY (4 to 5) and the highest for OMI (5 to 5. 5), and quite stable in time, reflecting the signal degradation correction that is incorporated within the RAL v2. 14 retrieval algorithm. This correction maintains the instrument's signalto-noise ratio close to its initial level and hence reduces the effect of the instrument degradation on the retrieval's DFS. Seasonal DFS variations amount to about 0. 5, which is approximately the same as the DFS decrease per decade, except for the more stable OMI retrieval. The temporal DFS behaviour is also reflected in the AKMs' eigenvalues and eigenvectors (not included).
More exceptional are the two to three DFS outliers for SCIAMACHY, which typically occur in the SAA due to stratospheric intrusion of high-energetic particles (the tropospheric DFS is mostly maintained). Such SAA outliers also occur in other instrument retrievals, but to a lesser extent (also see next sections). Note that the area of missing GOME data in the tropics from 2003 due to the SAA is larger than in Fig. 3 , as the DFS and other information content values are empty when all data are screened (100 % values in Fig. 3 ). Also note that the decreased retrieval performance for GOME-2B from June 2015 (eventually resulting in its total screening) actually has little effect on its DFS behaviour. Due to its stronger meridian and seasonal dependence, the FORLI TIR median DFS for IASI-A ranges between 2 towards the poles and 4 towards the Equator.
To avoid spurious effects due to a seasonal cycle in the differences, only time series of 5 years or longer are used for this drift assessment. Therefore Metop-B GOME-2 and IASI instruments are excluded from the drift studies (indicated with an asterisk in Table  4 ). Moreover, only fully available years of the satellite datasets have been considered for comparative analysis in order not to introduce seasonal effects at the beginning and the end of each time series. Due to the availability of assimilated global ozone fields every 6 h, the L4 comparative validation approach is fully similar to the L2 statistics described above. The strongly reduced amount of parameters in the L4 data product files, however, reduces the number of influence quantity dependences that can be studied. These have therefore been limited to the latitude, quarter, and time (drift).
e. a groundbased L3-type dataset). Yet both approaches introduce similar spatial and temporal representativeness errors into the difference statistics because taking (monthly) averages as a bias estimator x yields comparable outcomes: x = N -1 m x L3 s -x r,1 + x L3 s -x r,2 +. + x L3 s -x r,m = x L3 s -x r. (9) For sufficiently fine-gridded L3 data, the comparisons can therefore be limited to direct differences with ground-based reference measurements, if one additionally only considers ground-based stations with a sufficient number N m of valid measurements per month. This number has been set to six (per month, or about at least one measurement every 5 days) in the L3 validation presented in this work. As such, an implicit averaging of at least six ozonesonde or lidar measurements per month is introduced in the comparison statistics.
L3 UV-VIS monthly gridded ozone product Median relative differences and 68 % interpercentile spreads for comparison of L3 GOME, SCIAMACHY, GOME-2A, and OMI data with ground-based reference measurements are presented in Fig. 11. The same difference statistics are redistributed for each instrument over two influence quantity ranges, with the influence quantities being the latitude and quarter. Note the high numbers of co-locations in the title of each plot, as for each ground-based reference measurement an overlapping L3 data grid cell can be identified. As can be expected, the median relative differences roughly follow the bias features of the respective L2 datasets for their comparison with ozonesonde and lidar data. These features, together with the corresponding spreads, seem to be enlarged due to larger differences in spatiotemporal representativeness.
The latter results from the lack of averaging kernel smoothing that reduces vertical smoothing difference errors and the limited amount of reference data measurements per month (although at least six, see previous sections). Note, however, that the lack of kernel smoothing instead reduces the L3 spread for the lowest level, which has a strongly reduced sensitivity in comparison with the levels above. GOME L3 data show an above-tropopause bias of 5-10 % positive to negative, with strong outliers around 70 and 8 hPa, especially in the tropical UTLS and Antarctic local spring (up to 50 %) due to ozone hole's vortex conditions. The corresponding spread is of the order of 10-30 %, with again outliers at the same two scenes. Especially during Antarctic spring (SON) the spread explodes to the order of 100 %.
Below the tropopause (100-200 hPa), GOME L3 data show stronger negative and positive biases ranging between 10 and 30 %. Exceptions can be observed in the Arctic winter (DJF) and Antarctic spring (SON), with outliers ranging up to 60 and -50 %, respectively. Corresponding spread values are of the order of 20-40 %, with the highest values again in Arctic winter. The SCIAMACHY L3 bias and spread values are very similar to those of the GOME L3 comparison results. Only exceptions are the strong positive Arctic spring (MAM) bias in the troposphere (up to 40 %) and the availability of Antarctic winter (JJA) data showing a strong negative bias in the UTLS and above (-30 to -40 %). Also the GOME-2 instru-A. Keppens et al. :  Quality assessment of the Ozone_cci CRDP (release 2017) -Part 2 ment on board Metop-A shows a performance that is very similar to the GOME instrument in of L3 bias and spread.
UV-VIS L2 and L3 drift studies Relative decadal drift and 68 % interpercentile spreads for comparisons of L2 and L3 GOME, SCIAMACHY, GOME-2A, and OMI data with ground-based reference measurements are collected in Fig. 12. As discussed in the previous section for their bias and spread behaviour, the similarity between the L2 and L3 UV-VIS drift results for the same instrument appears very clearly. Again, however, features in the L2 statistics are enlarged for the L3 data due to larger differences in spatiotemporal representativeness (except for the lowest-level spread, see previous section). The GOME L2 and L3 stratospheric drift typically do not exceed 10 % decade -1 values, with the exception of an almost 20 % decade -1 positive drift near the South Pole lower stratosphere and an equally large L3 peak around 35 km.
L4 assimilated data The L4 1996-2013 data, constructed by data assimilation at KNMI from merged RAL v2. 14 GOME and GOME-2A observations, can be compared with ground-based reference profiles directly. The single 2 × 3 • box that overlaps with the ground measurement within 3 h is thereby taken as the colocated measurement. The number of co-locations and stations, however, is smaller than for the L3 data, as data from 64 ozonesonde stations (that have been used for satellite bias correction during assimilation) are omitted from the comparative analysis. Median relative differences and 68 % interpercentile spreads for comparison of the L4 assimilated nadir ozone profile data with ground-based reference measurements are collected in Fig. 13 , redistributed over two influence quantity ranges (latitude and quarter).
Discussion Table  5  summarizes the major QA and validation quantities discussed throughout this work, their corresponding typical values as discussed in the previous sections, and provides associated GCOS user requirements for the entire Ozone_cci nadir ozone profile CRDP, meaning that UV-VIS and TIR measurement and retrieval-based products are combined. These 13 ozone ECV datasets together cover the 1995 to 2015 time period globally, which is sufficiently long for (drift-corrected) ozone trend studies according to the GCOS user requirements. Yet the ongoing and upcoming satellite observations of both the GOME type (GOME-2 on Metop-A/B, Sentinel-5 Precursor TROPOMI, and the upcoming Copernicus Sentinel-5 series) and the IASI type (IASI on Metop platforms and IASI-NG on Metop-SG platforms) will even extend the available time series.
The higher drift values are found to be mostly insignificant for the L2 GOME and OMI instrument retrievals and for the L3 TIR data. The SCIAMACHY and GOME-2A products have a strong positive drift (up to 40 %) in the troposphere, and GOME-2A moreover shows a 20 % decade -1 negative drift around the tropopause. The FORLI IASI-A instrument retrieval shows an order of 25 % significant negative drift in the Antarctic UTLS and northern hemispheric troposphere only. Together with the systematic uncertainty studies, these drift results call for an appropriate altitude-dependent bias and drift correction of the L2 Ozone_cci nadir ozone profile products by data users for climate and atmospheric composition monitoring and modelling purposes. Applying bias and drift corrections to the nadir ozone profile CRDP presented in this work straightforwardly might not yield optimal results.
Next to the L2 data screening recommended by the respective data providers as summarized in Table  3 , the validation results presented in the pre-   1995-2015 1996-2015 1996-2013 2008-2015 2008-2012 (1996-2010)  L2  vious sections point at additional data screening options. In the UV-VIS instrument datasets (except for GOME-2B), some satellite profiles with very low DFS, nearly zero stratospheric sensitivity, and high bias occur, mainly around the SAA. By inserting a DFS < 3 flag, for example, these profiles could be fully screened or considered for tropospheric ozone monitoring only. The latter would be equivalent to an altitude-dependent screening, which could also be used along with the full-profile effective cloud screening advised by the RAL team. Comparison results have shown that one could apply a layer screening up to the UTLS instead, as the stratospheric ozone retrieval is hardly affected by the ECF (or surface albedo).
Conclusions This work, the second in a series of four Ozone_cci papers, reports for the first time on data content studies, information content studies, and comparisons with co-located groundbased reference observations for all 13 nadir ozone profile data products that are part of the CRDP on atmospheric ozone of the European Space Agency's Climate Change Initiative. These products consist of five L2 UV-VIS instrument retrieval datasets, two L2 TIR retrieval datasets, four UV-VIS L3 monthly gridded data series, a merged UV-VIS L4 product, and a 0 to 6 km integrated tropospheric L3 product based on IASI-A data. To verify their fitness for purpose and especially their compliance with the requirements identified for the GCOS, these ozone datasets were subjected to a comprehensive quality assessment system developed in several heritage projects.
The ground-based reference measurements have thereby been taken from the well-established NDACC, SHADOZ, and WMO GAW ozonesonde and lidar networks. All nadir ozone profile products under study fulfil the GCOS user requirements in terms of observation frequency and horizontal and vertical resolution. Yet all L2 nadir ozone profile observations also show sensitivity outliers in the UTLS and are strongly correlated vertically due to substantial averaging kernel fluctuations that extend far beyond the (typically tropospheric) kernel's 15 km FWHM. However, the required observation period for climate modelling is only fully covered when several instrument time series are combined. Moreover, the nadir ozone profile CRDP typically does not comply with the GCOS user requirements in terms of total uncertainty and decadal drift (except for the UV-VIS L4 dataset).
The drift values of the L2 GOME and OMI, the L3 IASI, and the L4 assimilated products are found to be overall insignificant, and applying appropriate altitude-dependent bias and drift corrections make the data fit for climate and atmospheric composition monitoring and modelling purposes. The nadir ozone profile product validation in terms of several influence quantities presented in this work correspondingly calls for the introduction of one or more L2 profile flags in addition to those recommended by the data providers, majorly based on a lower DFS threshold.