dataesr/scientific-paragraphs-categorization huggingface dataset:
The 3-years of repeat data will allow a better estimate of the mean sea surface and marine geoid; the high latitude coverage should allow a better assessment of the ice caps up to 78 • N and S, and may even be used for estimating sea-ice freeboard and other parameters. The science objectives covering all disciplines are outlined in the SWOT Mission Science Document  (Fu et al. , 2012)  and in  Morrow et al. (2018a). The SWOT Science Team has been preparing for this mission and its technical and scientific challenges since 2008, and the mission was introduced in the OceanObs09 whitepaper  (Fu et al. , 2009). In the first part of this 2019 white paper, we will give a brief introduction to the SWOT science objectives and its observing system and errors. We will highlight how SWOT observations may be used in conjunction with other observations to improve our understanding of the ocean energy cascade, internal gravity waves, and ocean fronts, and then consider the challenges in reconstructing the fine-scale upper ocean circulation with SWOT and models.
SWOT's capability to observe the 2-D structure of small mesoscale processes down to 15-30 km in wavelength (i. e. , eddy diameters of 7-15 km) will greatly improve our understanding of the generation and dissipation phases, and of mesoscale dynamics in small Rossby radius regions (high latitudes, regional seas, and coastal zones). Ocean circulation models now have both the computational power and the theoretical support for simulating basin-scale regions with fine-scale resolving capabilities (e. g. ,  Arbic et al. , 2018; Qiu et al. , 2018;  http://meom-group. github. io/swot-natl60/ science. html). Recent high-resolution modeling studies have highlighted the importance of the smaller scales generated, for example, by energetic instabilities in the deep winter mixed layers  (Callies and Ferrari, 2013; Qiu et al.
These model results need to be validated by 2D observations which will be available from the SWOT mission. Improving our understanding of these small mesoscale fields and submesoscale fronts and filaments is essential not only for quantifying the kinetic energy of ocean circulation, but also for the ocean uptake of heat and carbon that are key factors in climate change. Traditional altimeters combined with in situ data have revealed the fundamental role of larger mesoscale eddies in the horizontal transport of heat and carbon across the oceans  (Dong et al. , 2014). The vertical transport of heat, carbon, and nutrients is mostly accomplished by the submesoscale fronts with horizontal scales 1-50 km (e. g. ,  Lévy et al. , 2012a). The SWOT mission will open a new window for studying the SSH signature of these processes.
SWOT MEASUREMENT SYSTEM Measurement Technique, Ocean Data Products Surface Water and Ocean Topography will use SARinterferometric technology to observe 2D images of SSH and surface roughness over two 50-km wide swaths with a 20 km nadir gap (see Figure  2A ). A conventional Jason-class nadir altimeter provides measurements in the central gap. SAR-interferometry has been demonstrated with SRTM on the Space Shuttle, and with Cryosat-2 SARIN mode for the polar ice caps. SWOT improves on these concepts: it is designed to have lower noise and uses different polarizations and antenna patterns to clearly distinguish the signals coming from the left-right swaths. The details of the SAR-interferometric technique are explained in  Rodriguez et al. (2018). Surface Water and Ocean Topography uses SAR processing to refine the alongtrack resolution of the return signal; the interferometric processing refines the cross-track resolution.
The SWOT SAR-KaRIn instrument provides a basic measurement resolution of 2. 5 m alongtrack, ranging from 70 m in the near-nadir swath to 10 m in the far swath. Over the 70% of the Earth's surface covered by oceans, SWOT's huge volume of data cannot be downloaded from the satellite. Instead, socalled "low-resolution" data will be pre-processed onboard, and building blocks of nine interferograms at 250 m posting (and 500 m resolution) will be downloaded from each antenna. Other parameters that are useful for the surface roughness and front detection, such as the 250 m resolution backscatter images, will also be downloaded. The interferometric data will be combined through a geolocation and height calculation into a 250 m × 250 m expert SSH product in swath coordinates. Most scientists will use a 2 km × 2 km product available in geographically fixed coordinates.
(2018)  and  Chelton et al. (2019). Most importantly, it is the random errors in the KaRIn measurements over short wavelengths that eventually determines the SWOT resolution. Surface Water and Ocean Topography random errors across the swath are not uniform (Figure  2B ) being larger near the edges, smaller in the center. These errors vary spatially and temporally, influenced by the surface wave and roughness conditions, as with all altimeter missions. Predictions of the SWOT sampling and noise levels are available for ocean studies using a simulator at: https://github. com/SWOTsimulator/ swotsimulator. git. Since the total SWOT error has different causes and space-time structure, techniques are being developed to estimate the noise patterns using cross-spectral methods  (Ubelmann et al. , 2018)  and remove them using advanced denoising techniques (e.
g. , Argo). (iv) SWOT data should be integrally assimilated. It is common, when dense data are assimilated, to thin the observation data set. A main objective of data thinning is to avoid data with correlated errors, simply because data assimilation systems are not designed for such data. Different groups in the SWOT Science Team are working on these issues. To address (i) and (ii) above, the approach followed by Mercator Ocean is to develop/test innovative data assimilation methods and analyze the impact of simulated SWOT data through OSSEs. The long term plan is to ingest SWOT data in near real time (<2 days) in the operational Mercator Ocean and Copernicus Marine Service global and regional data assimilation systems. First OSSEs in the North East Atlantic have been carried out with a 1/12 • (assimilated run) and a 1/36 • (nature run) North Atlantic regional models that include tides.
SWOT data were simulated using the SWOT ocean simulator (see section "Measurement Errors, SWOT Simulator, and Effective Spatial Resolution"). Several experiments have been made using the new R&D versions of the Mercator Ocean assimilation scheme (SAM-2) (e. g. , 4D scheme) and the latest version of the NEMO model code. Our initial results demonstrate the high potential of SWOT observations to constrain ocean analysis and forecasting systems (Figure  11 ). Methods to deal with spatially correlated errors in SWOT [challenge (iv) above] have been developed by  Ruggiero et al. (2016)  and  Yaremchuk et al. (2018). The common strategy consists of assimilating spatial derivatives of SWOT (along and across-track, first and second order) in addition to the original SWOT data itself. This is equivalent to.
Many of these questions are difficult to answer because they involve not only the 2D information that SWOT will provide, but also require more information than is currently available about the full spectrum of ocean variability in three spatial dimensions and time. In addition, the relationship between surface pressure fluctuations (SSH anomalies) and the many other dynamical quantities of interest (particularly horizontal and vertical velocity and property fluxes) is itself complicated at wavelengths below 150 km. SWOT data alone will not fully address the many pressing scientific questions concerning ocean variability at horizontal wavelengths below 150 km, but there are exciting opportunities to make advances on these questions by combining SWOT data with other measurements. Over the last 25 years, many studies have analyzed the larger scale ocean dynamics using a combination of satellite altimetry and collocated in situ observations.
This is a one-off opportunity -other planned wide swath missions for 2030+ are all on a sun-synchronous orbit, and will not resolve the full range of tidal constituents available with SWOT. This means that the SWOT era from 2022 to 2025 will be an exciting and unique opportunity to explore the role of internal tides and internal waves interacting with the "balanced" ocean circulation, and modifying the eddy energy, evolution and mixing on a global scale. Disentangling the balanced and unbalanced signals in SSH remains a challenge though, if we want to use SWOT or other fine-resolution alongtrack altimetry data to calculate balanced surface currents. Mapping the fine-scale SWOT SSH swath data onto regular 2D fields presents many challenges, which are being explored using dynamical rather than statistical interpolation techniques, different vertical projection schemes, and full assimilation techniques.
The improved small-scale sea surface height and surface currents from SWOT can also be analyzed in synergy with fine-scale satellite tracer data (SST, ocean color) and surface parameters (surface roughness, sun-glitter, etc. ) that are strongly modified across fronts and filaments, in order to link the deeper dynamics with the surface fronts. Finally, the altimetric mesoscale era was accompanied by a global Argo program that allowed us to collocate largescale dynamics and eddies with vertical profiles. The question of how to collocate the rapidly evolving fine-scales observed by SWOT data with in situ data poses new challenges, that are actively being explored. Data mining of historical ADCP or glider data to re-analyze the high-frequency, fine-scales is one option. Developing new, rapid, fine-scale ocean profilers is another.
ACKNOWLEDGMENTS The authors would like to acknowledge the work performed by the  SWOT Science Team  in the preparation of this mission. The authors thank  D. Chelton  and  R. Samelson  for careful reading and comments on the manuscript. The work of L-LF and JW was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration (NASA).
FUNDING The authors were mostly funded through the  NASA Physical Oceanography Program  and the  CNES/TOSCA  programs for the SWOT and OSTST Science teams. AnP acknowledges support from the  Spanish Research Agency  and the  European Regional Development Fund  (Award No. CTM2016-78607-P ). AuP acknowledges support from the  ANR EQUINOx  ( ANR-17-CE01-0006-01 ).
Satellite altimeters have been used to monitor river and reservoir water levels, from which water storage estimates can be derived. Inland water altimetry can therefore play an important role in continental water resource management. Traditionally, satellite altimeters were designed to monitor homogeneous surfaces such as oceans or ice sheets, resulting in a poor performance over small inland water bodies due to the contribution from land contamination in the returned waveforms. The advent of synthetic aperture radar (SAR) altimetry (with its improved along-track spatial resolution) has enabled the measurement of inland water levels with a better accuracy and an increased spatial resolution. This paper presents three specialized algorithms or retrackers to retrieve water levels from SAR altimeter data over inland water bodies dedicated to minimizing land contamination from the waveforms.
However, thanks to satellite altimetry, we possess a powerful tool for monitoring the water levels both within inland water systems where no in situ data are available and within transboundary river basins. Space-borne radar altimeters are essential tools for monitoring the oceans, a task they have performed for over 20 years  [1]. Satellite altimeters have also proven to be valuable tools for monitoring the water levels within inland water systems, including lakes and rivers  [2] [3] [4]. A list of satellite altimeters and their resolutions is shown in Table  1. Sentinel-3, the newest in-orbit satellite, has a temporal resolution of approximately 27 days and an inclination angle of approximately 98. 6 degrees, which means that it can cover all Earth latitudes (i. e. , the entire globe). Operating in highresolution synthetic aperture radar (SAR) mode  [5] , the ground track separation at the equator is approximately 104 km, which is better than that (approximately 315 km) of the Jason satellite mission.
3 Topography Experiment/Poseidon. 4 GEOSAT Follow-On satellite. 5 Environmental Satellite. 6 Earth Explorer Opportunity Mission. 7 Haiyang-2 / Ocean-2. 8     Satellite with Argos (Data Collection System) and ALtiKa (Altimeter in Ka-band). 9 Chinese-French Oceanography Satellite. 10 Surface Water Ocean Topography Mission. 11 Jason Continuity of Service Mission. Most previous studies focused on relatively large water bodies with a scale of several kilometres. The first study that employed satellite altimetry for inland water bodies was performed by Koblinsky et al. [6] , who processed Geodetic Satellite (Geosat) waveforms to estimate the water levels at four sites in the Amazon basin. A root-mean-square error (RMS) of 70 cm was estimated between the satellite and in situ measurements. Since then, with new generations of satellites, the spatial resolution and the accuracy of the orbit determination have both increased, thereby increasing the accuracies of the estimation results.
Accordingly, several studies focused on lakes and large rivers with scales ranging from more than 1000 km 2 to 80,000 km 2 and demonstrated good results  [6] [7] [8] [9] [10] [11] [12] [13]. For example, Nielsen et al. [11]  used Environmental Satellite (Envisat) and CryoSat-2 SAR data to retrieve the water levels over Vaenern (Sweden, 5650 km 2 ) and Lake Okeechobee (Florida, 1900 km 2 ) and obtained very good accuracies with RMSEs varying from 4 cm to 9 cm between the satelliteretrieved water levels and in situ measurements. Currently, several online data hubs, such as the Database for Hydrological Time Series of Inland Waters (DAHITI) (https://dahiti. dgfi. tum. de/en/) [14] , Rivers and Lakes (http://tethys. eaprs. cse. dmu. ac. uk/RiverLake/)  [15] , Hydroweb (http://hydroweb. theia-land.
Nevertheless, different retrackers need to be developed and compared for inland water bodies. In our study, we modelled three different retrackers, namely, the threshold retracker, the Offset Centre of Gravity (OCOG) retracker, and the 2-step physical-based retracker; the parameters of these retrackers were adjusted for inland water bodies, and they were applied directly over Sentinel-3 Level-1 data. Additionally, we attempted to limit land contamination within the received waveforms and to improve the accuracy for the monitoring of middle-sized and small-sized water bodies by including digital elevation model (DEM) information to select the waveform portion from nadir. The remainder of this paper is organized as follows. In Section 2, the studied area and the database are presented. Section 3 introduces the methodologies, including the selection of the waveform portion, different retrackers and waveform filtering approach.
In situ data In situ data for the Ebro River basin are available in the Automatic Hydrological Information System (SAIH) Ebro data hub  [28]. SAIH Ebro is an online system providing hourly and daily hydrological information, including river gauge data, reservoir levels, rainfall amounts and temperatures, over the Ebro River basin. Together with the Sentinel-3 passes, we studied eight water bodies in our research, as listed in near-global scale from 56°S to 60°N  [29]. The SRTM provides global data at two resolutions: 1 arcsecond (~30 m) and 3 arc-seconds (~90 m). In our study, 1 arc-second global elevation data, which offer a worldwide coverage of void-filled data at a resolution of approximately 30 m, are used.
For the OCOG retracker, the COG when using the selected waveform portion is located inside the first peak, while that when using the whole waveform is located in between peaks. ( The differences between the performance using the waveform portion selection method and the method using the whole waveform are also shown in the time series results over the Ebro Reservoir in Figure  6. The Level-2 data were obtained from the ESA Level-2 product using the ocean retracker without the waveform portion selection method. The waveform portion selection method eliminated most outliers compared with the method of using the whole waveform.
Discussion The water levels of all water bodies tracked in closed loop mode can be tracked, while three Reservoir with satellite data varies from 400 m to more than 2 km depending on the different satellite tracks, which drift on a daily basis. In addition, the Irabia Reservoir is a very small water body whose width is only approximately 130 m, making the retrieval of its surface height more challenging. The results of the three retrackers, namely, the 2-step physical-based retracker, OCOG retracker and threshold retracker, in combination with the waveform portion selection method are always better than the results of the Level-2 ocean retracker; the results of both sets of retrackers are shown in Figure  5  and Figure  6. Therefore, given the lack of an ice retracker over inland water bodies, retrieving the water levels by retracking the Level-1 waveforms and using the waveform portion selection method constitutes an alternative approach to retrieving more accurate water levels.
Acknowledgements This study was financed in part by the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES) -Finance Code 001. The authors would like to thank the  Instituto Nacional de Ciência e Tecnologia  ( INCT-TMCOcean 573601/20089 ). The authors are also grateful for the support of the  FEEDBACKS-PRINT-UFF  Project (grant  CAPES 88887. 310301/2018-00 ). E. Silva-Filho  is senior researcher of the  National Council for Research and Development (CNPq, Brazil)  and the  Foundation for Research Support of the State of Rio de Janeiro  ( FAPERJ E-26/203. 037/2017 ).
(2013). The ROMS-Rutgers was forced by a climatological atmospheric forcing. The air temperature, shortwave radiation, longwave radiation, precipitation, cloud cover, and freshwater flux used to force the model came from the ERA-40 reanalysis  (Uppala et al. , 2005). Surface pressure came from the ERA-Interim reanalysis  (Dee et al. , 2011). All these variables had a spatial resolution of one degree and a time resolution of 6 hours. QuikSCAT blend data was used for wind forcing (both zonal and meridional). The wind had a spatial resolution of a quarter of a degree and a time resolution of 6 hours. The boundary conditions were obtained from NEMO (available from http://www. nemo-ocean. eu; these simulations were reported in  Adani et al. (2011)  and interpolated to the ROMS grid to define a sponge layer of 10 horizontal grid points with a nudging relaxation time of 30 days.
org/10. 5194/os-2019-61 Preprint. Discussion started: 4 July 2019 c Author(s) 2019. CC BY 4. 0 License. tions was MPDATA (recursive flux corrected 3D advection of particles  (Smolarkiewicz and Margolin, 1998) ) and Large-McWilliams-Doney (LMD) mixing as a sub-grid scale turbulent mixing closure scheme  (Large et al. , 1994) , also known as the K-profile parameterization (KPP) scheme. The air-sea interaction used for the boundary layer in ROMS was based on the bulk parameterization of  (Fairall et al. , 1996). We ran the ROMS-Rutgers model version 3. 6 using climatological atmospheric forcing and boundary conditions. The initial conditions to start the simulations were obtained using the same interpolated fields as the ones used for the boundary conditions for all variables. After an 8-year spin-up period with a baroclinic timestep of 120 seconds, we used the ninth year as the study period.
The regional ROMS-Rutgers implementation has been daily saved and validated. First, we compared the simulations with the coarser model used for the initial and boundary conditions (NEMO). Second, the model was compared to the ARGO floats vertical data profiles of temperature and salinity to test the correct structure of the simulated water column within the year. For the ARGO tests, we selected 1900 casts in the area from October 2003 to December 2012. The data collected covers depths from the surface down to a maximum of 720 m. The ARGO vertical profile resolution was 5 m from the surface to 200 m and 25 m from 200 m to 720 m. We grouped the ARGO casts by months and by six subareas dividing the domain according to the Coriolis force divided by the bottom depth (Fig  2 ). Then, we selected the subareas that have more than 30 ARGO profiles for each month, we calculated a monthly averaged profile of ARGO, and we compared them with the model climatologic profile (monthly average).
9 in both variables. The month of September was selected because the thermocline in our domain disappears and the mixing process at that period of the year is likely to vary the most. Consequently, it shows that the circulation over different depths has been well reproduced in the model. model of 4 km. Bathymetry was derived from the ETOPO2-arcminute model from  Smith and Sandell (1997). Surface forcing and initial and boundary conditions were built with the ROMSTOOLS package  (Penven et al. , 2008)  before running a ten-year simulation of the model. In ROMS-Rutgers configuration, the bottom circulation followed a southward direction along the bottom slope, except in the area south of Blanes canyon  (Fig 4). Over the bottom floor of the continental slope, the highest velocities reached 8. 5 cm/s on the southern mouth of Palamós canyon.
Biophysical model Particle dispersal was simulated with the open-source IBM code Ichthyop  (Lett et al. , 2008)  version 3. 3, which is a free userfriendly toolset used in numerous individual dispersal studies in the western Mediterranean Sea  (Ospina-Álvarez et al. , 2013; Palmas et al. , 2017). Displacements of virtual particles are computed by integrating the differential equation using a Runge-Kutta 4th order (RK4) advection scheme: dX= U. dt (2) where dX is the three-dimensional displacement vector of the particles during a time step dt of 30 minutes under the velocity vector U from the hydrodynamic models. RK4 is a stable and reliable multi-step method for numerical integration  (North et al. , 2009; Qiu et al. , 2011) , and is commonly used in dispersal Lagrangian models  (North et al.
Acknowledgements The authors are supported by a  Young Thousand Talents Plan of China , a  Swedish Research Council  Grant ( 2017-05183 ) and previously a  Marie Curie COFUND Fellowship  to T. P. T. , a  Swedish Research Council Grant  to  C. B. S. ( VR2016-04610 ) and a grant from  Spanish Ministry of Economics, Finance and Competitiveness   CGL2017-87631-P  to  S. C. Financial  support from the  National Natural Science Foundation of China  ( 41425008 ,  41621003 , and  41720104002  to Z. Z. , 41772002 to C. B. S. , and  41472015  to G. J. ), the  Strategic Priority Research Program  of the  Chinese Academy of Sciences  to the  Early Life Institute  ( XDB26000000 ) and the 111 project ( D17013 ) for the continuous fossil collections of the Xi'an group are sincerely acknowledged. Nobumichi Tamura  is thanked for his reconstructions of the taxa illustrated in this paper.
Please see Supplementary Note 1 for further details. Image acquisition. Specimens were photographed using both a Canon EOS 5D Mark III and a stereophotographic Zeiss Smart Zoom 5. Some images were taken when the specimens were coated with ammonium chloride to enhance contrast, and others were imaged using low vacuum scanning electron microscopy. Figures were constructed using Adobe Photoshop and Illustrator CS6 and any measurements were taken using ImageJ. Nomenclatural acts. This published work has been registered in ZooBank, the proposed online registration system for the International Code of Zoological Nomenclature (ICZN). The ZooBank LSIDs (Life Science Identifiers) can be resolved and the associated information viewed through any standard web browser by appending the LSID to the prefix "http://zoobank.