dataesr/scientific-paragraphs-categorization huggingface dataset:
Phase The first test was a simple one, mainly used to check the speed, feasibility and accuracy of the analysis. In contrast, the second part of the test was devoted to comparing the FPSM procedure with the classical search-match routines. For this reason we used the datafiles and tests provided by the searchmatch round robin (Le  Meins et al. , 2002). The round robin considered four samples, and we used two procedures, as in the previous test. In the first procedure, no composition restriction was applied, while in the second, we used the possible elemental composition given by the round-robin proposers to restrict the number of phases during the search. The analysis could not be completed for sample 2, because it contained one unknown crystal structure. There was no similar structure in the COD and the correct phase is only reported in the JCPDS-PDF database as a card.
The structure is described as a silicon oxide quinuclidine fluoride octadecasil and we have not found it even in the Cambridge Structural Database  (Groom et al. , 2016). So we will report the test results on the other three samples: two inorganics and one organic. Sample 1. This sample analysis was performed using the standalone version of the program without compositional restrictions and the web form with restrictions. Both gave the same results, so we report here only the output of the web version. The final results, in line with the round-robin solution, are reported in Fig. 4  and Table  2. The main discrepancy in the fit visible in the diagram arises from a small difference between the souzalite structure of the real sample and that available in the database. Souzalite, or the analogue gormanite, has a high variability in composition and structure.
In the analysis reported, we limited the phases to no more than five. Otherwise, FPSM will also find and add several other minor phases, all around 1%, that are indeed possible, as the roundrobin organizers acknowledge, but we preferred to omit them from the results as there is no information available on them to check for correctness. Sample 3. This sample is an organic, and according to the notes on the round-robin results it should be composed of about 50:50% of two polymorphs,and -thalidomide. However, the JCPDS-PDF only contains the polymorph, and consequently identification with only the form was accepted. In fact, none of the round-robin participants identified the other polymorph. In our case, we did have both the and the form in the COD database and FPSM found both correctly (Fig.
5  and Table  3 ). But in this case, even with the compositional restrictions, we could not run the search on the web interface as we have nearly 40 000 organic phases in the database having similar composition. So we only used the   Table  3  Results of the FPSM analysis on sample 3 of the search-match round robin (see Fig. 5 ). Phase standalone version for this test. FPSM took about 3300 s to find the solution on the workstation. The calculation time seems relatively high, but if we consider that we went directly from the raw pattern to the final correct phase quantification, this is reasonable in absolute terms. Sample 4 was the easiest one, being composed of only leadbased compounds, but it showed some particularities. Again we report here only the analysis done via the web form using the restricted composition.
One example (TiO 2 rutile nanoparticles), where we used an early version of FPSM, has already been reported in a paper dealing with TEM diffraction ring analysis  (Boullay et al. , 2014). We used another TiO 2 sample prepared in different conditions  (Ceccato et al. , 2003) , containing more than one polymorph. The sample has been measured in a capillary using silver radiation and an INEL (now Thermo Fisher Scientific) CPS 120 detector, to reach a large range in Q space, the data originally being intended for a pair distribution function analysis. For both the traditional search-match and FPSM we had to impose restrictions using the known chemical elements. It was easy to identify the anatase phase by both procedures (nearly 67 wt% of the sample), while the rutile and brookite phases were ranked very low by the traditional search-match.
Instead, the qandilite phase was identified, and we found it has a similar structure to the magnetite, the only difference being possible substitution in the Fe 3+ and Fe 2+ sites with other atomic species like Mg 2+ , Ti 3+ or Al 3+. The diamond phase, which was wrongly found by the algorithm, contributes only to lowering the R wp by better fitting of the background and some strong reflections of calcite and magnetite with high graininess. The final quantification may be wrong owing to the carbon phase and graininess, but if we ignore the carbon phase and take qandilite as a replacement for magnetite the result can be considered a partial success. We compared this with the results obtained using the traditional search-match program QUALX  (Altomare et al. , 2015) , also using the COD database.
Conclusions We have described a new method to perform a search-match based on full-profile fitting and the Rietveld/DDM algorithms. It works quite well for highly crystallized samples, but the method looks promising also for nanosized particles and nanomaterials. In the latter case it is generally more difficult to determine unambiguously the peak positions required by the traditional search-match routines. More testing is necessary to determine the algorithm's efficiency with nanocrystalline systems of greater complexity. The method relies heavily on brute force in computation and the availability of databases of crystal structures. In the present work we used the COD database, but potentially any other structure database can be used. The main drawback of the method, at present, is that the computation time is much longer than usual search-match programs, unless some compositional restriction can be used.
The last major problem we found arises from the completeness of the database used. If the JCPDS-PDF database is considered quite complete, at least for the inorganic case, including both the classical experimental reflections and the  ones calculated from the crystal structure databases, the same cannot be said for other crystal structure databases, especially the free ones like the COD. Using a more complete database like the ICSD for inorganics or the CSD for organics may well improve its performance, but it has not been possible for us to test them up to now. Further improvements could be achieved by integrating a pre-search using a traditional position-based search-match to reduce the initial number of phases. We are also planning to add the ability to deal with preferred orientations as they affect the intensities.
Funding information The authors warmly thank the  Conseil Regional de Basse-Normandie  and  FEDER  for financing  L. Lutterotti , Chair of  Excellence at CRISMAT , the  Universitede Caen Normandie , for two months as invited professor and the European Project  SOLSA  (funded by the  European Union 's  Horizon 2020 research and innovation programme ; grant agreement No. 689868 ) for later support. INEL-Thermo Fisher Scientific and  L. Lutterotti  wish to thank the  European Community  for the initial support of this work through the project  Nanoair  (funded by the  European Union 's REA  FP7 ; grant agreement No. 222333 ).
04 are constrained by direct lidar measurements at an altitude greater than 7. 5 km and where a low aerosol concentration is found. An aerosol source apportionment using the Lagrangian FLEXPART model is used in order to determine the lidar ratio of the remaining 48 % of the lidar database. Backscatter ratio vertical profile, aerosol type and AOD 808 derived from micropulse lidar data are compared with sun-photometer AOD 808 and satellite observations (CALIOP space-borne lidar backscatter and extinction profiles, Moderate Resolution Imaging Spectroradiometer (MODIS) AOD 550 and Infrared Atmospheric Sounding Interferometer (IASI) CO column) for three case studies corresponding to the main aerosol sources with AOD 808 > 0. 2 in Siberia. Aerosol typing using the FLEXPART model is consistent with the detailed analysis of the three case studies.
, 1998 ) and located at the same site. The objective is then to use the micropulse lidar database to characterize the sources of aerosols that can be transported over the measurement site and to verify how they contribute to the vertical distribution of aerosols and to the optical thickness of the atmospheric column. Analysis of satellite observation (CALIPSO, MODIS, VIIRS, etc. ) measurements provide additional information on aerosol source variability and aerosol plume transport processes. The lidar system, signal processing and AOD retrieval method are described in Sect. 2, while Sect. 3 presents the aerosol transport model and the aerosol sources. Section 4 described the results of the AOD retrieval using the lidar-calibrated signal, AERONET sun-photometer data and aerosol type from Sect.
Lidar data analysis An eye-safe CIMEL CE372 lidar was installed in Tomsk in April 2015 to obtain continuous measurements of cloud and aerosol backscatter vertical profiles. The lidar was first installed on the roof of the Institute of Atmospheric Optics (IAO) for 4 months (April-August 2015) before being moved to a thermostatically controlled box at Fonovaya Observatory, 50 km west of Tomsk (September 2015 to August 2016). It was then re-installed on the IAO roof for 1 month in September 2016 before being shut down for several months of maintenance. The lidar was installed near the local AERONET sun photometer to obtain an independent measurement of the total AOD. This is necessary when no proper calibration can be applied nor molecular scattering identified above aerosol layers  (Welton et al.
We are aware it is a crude assumption for a true aerosol modelling exercise but it is a reasonable criteria for testing the potential effect of urban aerosol on the lidar data. The biomass-burning emission zones are derived from the daily fire radiative power (FRP) maps provided by NASA Fire Information for Resource Management System (FIRMS) using MODIS  (Giglio et al. , 2003)  and the Visible Infrared Imaging Radiometer Suite (VIIRS)  (Schroeder et al. , 2014). The FRP is estimated from both MODIS and VIIRS hot spots of the brightness temperature measurements. MCD14ML collection 6 standard quality products and VNP14IMGTDLNRT are used for, respectively, MODIS and VIIRS. The FIRMS data set then provides daytime (MODIS, VIIRS) and night-time (VIIRS) measurements with spatial resolutions of 1 km (MODIS) or 0.
375 km (VI-IRS). Only FRP values > 0. 3 GW for MODIS and > 0. 1 GW for VIIRS are used to identify biomass-burning zones. To identify continental regions covered by forests and deserts, we use the built-in United States Geological Survey (USGS) 24 category land-use database in the WRF (Weather Research and Forecasting) model. This global land cover database is derived from the Advanced Very High Resolution Radiometer (AVHRR) data with a resolution of 1 km spanning a 12-month period (April 1992-March 1993)  (Sertel et al. , 2010). The role of dust plumes can be overestimated when using only the land-use map, so it is only considered if neither urban pollution nor biomass burning have been identified. Russia and Nigeria are the two biggest contributors to gas flaring used at oil and gas production and processing sites.
The location of flaring sources is based on the anthropogenic emissions ECLIPSEv4 database (Evaluating the Climate and Air Quality Impacts of Short-Lived pollutants) described in  Klimont et al. (2017). This inventory includes, in particular, the gridded methane emissions from gas flaring in the Russian Arctic at a 0. 5 • × 0. 5 • degrees horizontal resolution. A threshold of 50 moles km -2 h -1 has been applied to the methane emissions to select areas that could potentially be defined as flaring sources. Owing to the strong variability of flaring emissions, the role of flaring may be overestimated, so, as for the dust emission, it is only considered if anthropogenic and biomass-burning sources are not identified. The map of the main aerosol emission sources are shown in Fig. 5  for 2015 (top) and 2016 (bottom).
Description of data products Available satellite observations for these three periods were selected to identify the aerosol source regions. The horizontal distribution of strong AOD is documented by the 550 nm MODIS AOD maps averaged over 5 days. AOD maps are made using the Level-3 MODIS Atmosphere Daily Global Product, which contains roughly 600 statistical data sets sorted into 1 by 1 • cells on an equal-angle grid that spans a 24 h interval  (Platnick et al. , 2015; Levy et al. , 2013). The role of biomass burning or fuel combustion can be described with a satellite tropospheric CO column measured, for example, by the Infrared Atmospheric Sounding Interferometer (IASI) instrument on Metop A and B. Because a large  fraction of atmospheric CO is also related to the oxidation of hydrocarbons including methane, flaring will be a source of CO.
atmos-meas-tech. net/12/147/2019/ Atmos. Meas. Tech. , 12, 147-168, 2019 low (< 1. 5 km) between 55 and 60 • N, i. e. an aerosol plume thickness similar to the Tomsk lidar measurement. The overall conclusion of this section (Sect. 5) is that (1) our approach to attributing an aerosol type to Tomsk lidar observations is validated by a more in-depth study of aerosol sources based on available satellite observations, (2) the AOD and lidar ratio calculated for the Tomsk lidar observations are comparable to the sun-photometer daily AOD variability and satellite AODs in the aerosol source regions identified by the FLEXPART analysis. This will allow the statistical analysis of the lidar measurements according to aerosol types for the 18-month database. 6 Contribution of aerosol sources to aerosol optical depth distribution In this section, all observations from April 2015 to October 2016 will be analysed by taking into account the type of aerosol source attributed to each aerosol layer in Sect.
Conclusions In conclusion, this study complements several publications  (Huang et al. , 2010; Sicard et al. , 2016)  showing that a micropulse lidar is capable of characterizing the variability of the optical properties of aerosols (AOD, vertical profile of the backscatter ratio) at a remote site such as a measuring station in Siberia. In this work, 540 vertical profiles can be used to characterize aerosol sources in Siberia, i. e. 7 times larger than that of the largest lidar database used for dating in Siberia  (Samoilova et al. , 2012). A total of 300 daytime and 240 night-time profiles of backscatter ratio and AODs have been retrieved over periods of 30 min, after a careful calibration factor analysis. Lidar ratio and AODs are constrained with sun-photometer AODs for 70 % of the daytime lidar measurements, while 26 % of the night-time lidar ratio and AODs greater than 0.
5 km. Aerosols from forest fire emissions are observed to be the opposite, both within and above the PBL. Code availability. The FLEXPART code version 9. 2 was downloaded from the FLEXPART wiki homepage (https://www. flexpart. eu/downloads, last access: 9 January 2019; see also  Stohl et al. , 2002). Data availability. The CIMEL lidar 372 processed data (AOD, backscatter ratio) are available on the LATMOS data server and can be provided on request. The 18-month calibrated lidar data for Tomsk are available on request at the following ftp address: ftp://ftp. icare. univ-lille1. fr/GROUND-BASED/Tomsk/ (last access: 23 October 2018) The daily MODIS and VIIRS information from the fires were provided by LANCE FIRMS operated by NASA/GSFC/EOSDIS and are available at https://firms. modaps. eosdis.
nasa. gov/download/ (last access: 9 January 2019). The level 3 gridded MODIS aerosol parameter data collection 6 were provided in hdf format by https: //ladsweb. modaps. eosdis. nasa. gov/search/order/1/MYD08_D3--61 (see https://doi. org/10. 5067/MODIS/MYD08_D3. 061,  Platnick et al. , 2017)  and by https://ladsweb. modaps. eosdis. nasa. gov/search/order/1/MOD08_D3--61 (see https://doi. org/10. 5067/MODIS/MOD08_D3. 061,  Platnick et al. , 2017)  The sun-photometer data for TOMSK have been downloaded from the AERONET database (https://aeronet. gsfc. nasa. gov, last access: 9 January 2019). CALIOP L1 and L2 data were downloaded from the ICARE database (http://www. icare. univ-lille1. fr, last access: 9 January 2019). The IASI CO data were downloaded from the https://doi. org/10. 25326/16 web site  (Clerbaux, 2018).
Acknowledgements. This work was supported by the  CNES   EECLAT  project, the  iCUPE H2020  project and the  Chantier Arctique Français (PARCS). The work was also supported in part by  Ministry of Education of Science of RF  (agreement no. 14. 616. 21. 0104 , unique identifier RFMEFI61618X0104). We thank the  European Centre for Medium Range Weather Forecasts (ECMWF)  for the provision of  ERA-Interim reanalysis data  and the FLEXPART development team for the provision of the FLEXPART 9. 2 model version used in this publication. The authors thank the AERIS infrastructure and  NASA/GSFC  for providing the satellite data used in this paper (CO, AOD, CALIOP and fire FRP). Edited by: Thomas Eck Reviewed by: two anonymous referees.
2017) , or adapt the maximum likelihood technique to take missing data into account  (Baghi et al. 2015 (Baghi et al. , 2016)). Existing techniques are suboptimal, as they may affect the noise characteristics. Moreover, transients could be considered as conveying useful information. They are created by an external impulse; if this is assumed to be instantaneous (Dirac), the shape and relaxation time of the observed signal is a measurement of MICROSCOPE's transfer function. 1D exponential shapelets are well-matched to these transient signals. The upper panel of Fig. 3  shows a transient in the time domain: the observed data are shown in black, a model (fitted between t = 10s and t = 25s) and its 68% confidence interval are shown in red, and residuals consistent with noise are shown in green.
1D exponential shapelets can be used to model (and subtract or understand) spurious transients in time series, such as measurements by the MICROSCOPE or GRACE satellites, or the LIGO search for gravitational waves. Thanks to their exponential decay, 2D exponential shapelets overcome the main criticism aimed at Gaussian shapelets (though at the cost of losing some simplicity) and can be used to measure the brightness, shape and size of distant galaxies in astronomical imaging. The characteristics of exponential shapelets should make them well-suited to data compression and analysis in a wide range of fields; their convenient mathematical properties should see them frequently adopted. Figure  9. Convergence speed as a function of shapelets' nmax (upper panels) and number of coefficients (lower panels), for exponential (solid lines) and Gaussian (dashed lines) shapelets, for the same Sersic galaxies as in Fig.
8  (ns = 1, 2, 4 from left to right). ing Kummer functions and the literature of the 2D hydrogen atom. We also acknowledge helpful and motivating discussions with Jason Rhodes, Richard Ellis, Alexandre Refregier, Sandrine Pires, James Nightingale, Bernard Foulon, Bruno Christophe, Gilles Métris, Manuel Rodrigues, Alain Robert and Nicolas Touquoy. This work makes use of technical data from the CNES-ESA-ONERA-CNRS-OCA Microscope mission, and has received financial support from ONERA and CNES. JB acknowledges the financial support of the UnivEarthS Labex program at Sorbonne Paris Cité (ANR-10-LABX-0023 and ANR-11-IDEX-0005-02) and of CNES through the APR programme (LISA project). RM is supported by a Royal Society University Research Fellowship and through STFC grant ST/N001494/1.
Monitoring Sea Surface Salinity (SSS) is important for understanding and forecasting the ocean circulation. It is even crucial in the context of the intensification of the water cycle. Until recently, SSS was one of the less observed essential ocean variables. Only sparse in situ observations, mostly closer to 5 meters depth than the surface, were available to estimate the SSS. The recent satellite ESA Soil Moisture and Ocean Salinity (SMOS), NASA Aquarius SAC-D and Soil Moisture Active Passive (SMAP) missions have made possible for the first time to measure SSS from space and can bring a valuable additional constraint to control the model salinity. Nevertheless, satellite SSS still contain some residual biases that must be removed prior to bias correction and data assimilation. One of the major challenges of this study is to estimate the SSS bias and a suitable observation error for the data assimilation system.
It was made possible by modifying a 3D-Var bias correction scheme and by using the analysis of the residuals and errors with an adapted statistical technique. This article presents the design and the analysis of an Observing System Experiment (OSE) conducted with the ¼° resolution Mercator Ocean global analysis and forecasting system during the El-Niño 2015/16 event. The SSS data assimilation constrains the model to be closer to the near-surface salinity observations in a coherent way with the other datasets already routinely assimilated in an operational context. This also shows that the overestimation of E-P is corrected by data assimilation through salting in regions where precipitations are higher. Globally, the SMOS SSS assimilation has a positive impact in salinity over the top 30 meters.
Comparisons to independent salinity data sets show a small but positive impact and corroborate the fact that the impact of SMOS SSS assimilation is larger in the ITCZ and SPCZ regions. There is little impact on the Sea Surface Temperature (SST) and Sea Surface Height (SSH) error statistics. Nevertheless, the SSH seems to be impacted by the Tropical Instability Waves (TIWs) propagation, itself linked to changes in Barrier Layer Thickness (BLT). Finally, this study helped us to progress in the understanding of the biases and errors that can degrade the SMOS SSS data assimilation performance.
To evaluate the impact of SSS observations from satellites on ocean monitoring and forecast systems in a realistic context, Observing System Experiments (OSEs) were conducted with the UK Met Office and Mercator Ocean global ocean forecast systems. Two simulations are compared, one with and the other without SSS data assimilation. The differences between the two simulations highlight the "impact" of the withheld observations. Similar OSE approaches are generally used to evaluate observation networks in the ocean data assimilation community of GODAE OceanView  (Oke et al. , 2015 , Lea et al. , 2014). Experiments conducted within the SMOS Niño15 project to test the impact of the satellite SSS data were carefully designed and analyzed to ensure robust conclusions on the impact of SSS measurements on ocean analysis.
The system used for the OSE is based on the operational ocean monitoring and forecasting system operated at Mercator Ocean. The use of such system ensures that conclusions are relevant for operational applications. To assess the benefit of assimilating SSS from satellite in a realistic context, all observations from the Global Ocean Observing System (GOOS) that are assimilated in real time ocean analysis or reanalysis are also assimilated. SST, in-situ temperature and salinity observations (from moorings, drifting platforms, ships) and along track Sea Level Anomalies are assimilated in the REF simulation and OSEs. OSEs conducted were designed to assess the impact of weekly SSS products as the system has a weekly assimilation cycle. It is recommended to withhold part of the usually assimilated observations from the OSEs to have fully independent data to compare with, see  (Fuji et al.