dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, since 2010 (the launch date of the SMOS mission is 2 November 2009), continuous research efforts have been made to improve SSS remote sensing in these missions and their associated developments. The heterogeneity of approaches developed is due to the diversity of scientific challenges faced at different processing levels by the various data center teams (e. g. , for SMOS, the instrument and swath data (called Level 1 and 2 products, respectively) are processed at ESA/DPGS, while the time-space composites of swath SSS products (Level 3 and 4 products) are generated at CATDS and BEC, etc. ). The Pi-MEP gathers all available satellite SSS products from Level 2 to Level 4 and is updated each time a new product version is made available by one of the above-listed data centers. The platform then systematically co-localizes all Level 2 to Level 4 satellite SSS products from all sensors and associated data centers with the various preprocessed and quality controlled in situ datasets.
For any given satellite/in situ SSS/region product triplet, the results of the co-location are stored in the so-called match-up database (MDB) files, provided in NetCDF format to the users. Match-up data consist of satellite and in situ SSS pair datasets but also of auxiliary geophysical parameters such as the local and history of wind speed, rain rates, and various pieces of additional information (salinity climatology, distance to coast, upper ocean hydrological conditions, etc. ), which are also included in the final match-up files. Match-up files are generated for each available satellite SSS product file and all available that of the input satellite data product files. Match-up files can be accessed on the web interface of the platform  [4]. The results of systematic analyses of the match-up database (MDB) files generated by the Pi-MEP platform are provided to the users in the form of specific Portable Document Format (PDF) reports  [5].
Match-up files are analyzed for all pairs of satellite/in situ SSS data and for an ensemble of 30 geographical predefined regions and covering the full operation period of each mission. A series of tools have been developed to facilitate the exploration and extraction of the SSS products (plot interface) and the match-up files (plot and MDB interfaces) with their associated metrics. This manuscript aims to describe the main characteristics of this satellite SSS-dedicated platform and is partitioned in the following sections: In Section 2, all available datasets stored by the platform are presented. Section 3 explores systematic validation processing of the different SSS products, while Section 4 gives an overview of the developed tools available on the platform. Lastly, Section 5 summarizes the main points and presents future perspectives.
In Situ SSS Datasets A large ensemble of in situ SSS data distributed by different data centers were used to infer SMOS, Aquarius and SMAP SSS data product quality. These include in situ data from the following sources: Argo floats (CORIOLIS), moored buoys (TAO, PIRATA, RAMA, STRATUS, NTAS, SPURS1-2, WHOTS), thermosalinographs installed on voluntary observing ships (LEGOS, SAMOS), research vessels (GOSUD, Polarstern, NCEI-0170743) and sailing ships (GOSUD), surface drifters (LOCEAN), marine mammals (MEOP), analyzed in situ data fields (IFREMER/LOPS), and dedicated campaign data (e. g. , SPURS-2). Figure  1  and Table  1  describe the in situ datasets in terms of spatial density and variability, as well as the temporal coverage of each dataset since January 2010. In situ SSS data have different quality control (QC), horizontal, vertical and temporal sampling characteristics, and they often need to be preprocessed to be properly compared to large footprint-size satellite SSS products.
The Pi-MEP centralizes, regularly updates, performs QC, and filters the input in situ datasets to be used for satellite product validation. The characteristics of the five major types of in situ datasets (Argo, TSG, moorings, surface drifters and marine mammals) used by the Pi-MEP to validate SMOS, SMAP and Aquarius satellite SSS products are summarized in a regularly updated "in situ data report", accessible via the website  [6]. For each in situ dataset, the QC used by the platform is detailed and a series of plots show the following features: the number of SSS data as a function of time and distance to coast, histograms of the shallowest salinity and pressure (if relevant), spatial maps of the temporal mean of the shallowest salinity and pressure (if relevant), spatial maps of the temporal standard deviation (STD) of the shallowest salinity, the spatial density of the shallowest salinity and the difference ∆SSS between the local in situ SSS data and the closest (in space and time) analyzed field value of the in situ analysis system (ISAS)  [7] , sorted as a function of an ensemble of geophysical conditions.
• The TSG (GOSUD-Research-vessel) dataset corresponds to French research vessels' SSS data that have been collected since early 2000 as a contribution to the Global Ocean Surface Underway Data (GOSUD) program. The set of homogeneous instruments is permanently monitored and regularly calibrated. Water samples are taken on a daily basis by the crew and later analyzed in the laboratory. The careful calibration and instrument maintenance, complemented with a rigorous adjustment on water samples, lead an accuracy of a few 10 -2 PSS in salinity. This delayed mode dataset is updated annually and freely available  [10]. Adjusted values when available and only collected TSG data that exhibit quality flags 1 or 2 were used.
• The TSG (GOSUD-Sailing-ship) dataset corresponds to observations of sea surface salinity obtained from voluntary sailing ships using medium-or small-sized sensors. They complement the networks installed on research vessels or commercial ships. This delayed mode dataset  [11]  is updated annually as a contribution to GOSUD (http://www. gosud. org, accessed on 30 April 2021) and freely available. Adjusted values when available and only collected TSG data that exhibit quality flags = 1 and 2 were used.
• The TSG (SAMOS) dataset corresponds to "Research" quality data from the US Shipboard Automated Meteorological and Oceanographic System (SAMOS) initiative  [12]. Data are available at http://samos. coaps. fsu. edu/html/ and has been accessed on 30 April 2021. Adjusted values when available and only collected TSG data that exhibit quality flags = 1 and 2 were used. After visual inspection, data from the NANCY FOSTER (ID = "WTER", IMO = "008993227") acquired on 21 March 2011 and all data from the ATLANTIS (ID = "KAQP", IMO = "009105798") for the year 2010 have been removed from this dataset.
• The TSG (LEGOS-Survostral) dataset corresponds to delayed mode regional data from the TSG installed on the Astrolabe vessel (IPEV) during the round trips between Hobart (Tasmania) and the French Antarctic base at Dumont d'Urville  [13]  over several cruises to both Antarctica and sub-Antarctic islands. On-board TSG devices were regularly calibrated and continuously monitored in between cruises; no appreciable sensor drift emerged. Independent water samples taken along the cruises were used to validate the data; salinity measurement error was a few hundredths of a unit on the practical salinity scale. A careful quality control allowed us to discard erroneous data for each single campaign.
Marine Mammals The instrumentation of southern elephant seals with satellite-linked CTD tags offers unique temporal and spatial coverage. This includes extensive data in the Antarctic continental slope and shelf regions during the winter months, which are outside the conventional areas covered by Argo autonomous floats and ship-based studies. The use of elephant seals has been particularly effective to sample the Southern Ocean and the North Pacific. Other seal species have been successfully used in the North Atlantic, such as hooded seals. The marine mammal dataset (MEOP-CTD database) is quality-controlled and calibrated using delayed-mode techniques, involving comparisons with other existing profiles as well as cross-comparisons similar to established protocols within the Argo community, with a resulting accuracy of ±0.
03 • C in temperature and ±0. 05 in salinity or better  [16]. The marine mammal data were collected and made freely available by the International MEOP Consortium and the national programs that contribute to it. This dataset was last updated in April 2018  [17]  and has been accessed on 30 April 2021. A preprocessing stage is applied to the database before being used by the Pi-MEP, which consists of keeping only profiles with salinity, temperature and pressure quality flags set to 1 or 2, if at least one measurement is in the top 10 m depth. Marine mammal SSS correspond to the top (shallowest) profile salinity data provided that the profile depth is 10 m or less.
Moorings The Pi-MEP collects data from the Global Tropical Moored Buoy Array (GTMBA), a multinational effort to provide data in real-time for climate research and forecasting. The major components include the TAO/TRITON array in the Pacific, PIRATA in the Atlantic, and RAMA in the Indian Ocean. Data collected within TAO/TRITON, PIRATA and RAMA come primarily from ATLAS and TRITON moorings. These two mooring systems are functionally equivalent in terms of sensors, sample rates, and data quality. The data were directly downloaded from ftp://ftp. pmel. noaa. gov accessed on 31 May 2021 and stored in the Pi-MEP. Only salinity data measured at 1 or 1. 5 m depth of standard (predeployment calibration applied) and the highest qualities (pre/post calibration in agreement) are considered. A careful filtering of suspiciously erroneous mooring salinity data when compared with all satellite data was also performed (cf.
presentation). The Pi-MEP project acknowledges the GTMBA Project Office of NOAA/PMEL for providing the data. Data from the Ocean Station PAPA were also added to the Pi-MEP in situ database. SSS data from several moorings of the Upper Ocean Processes Group at Woods Hole Oceanographic Institution (WHOI) are also included in the Pi-MEP-namely, delayed mode surface mooring salinity records under the stratus cloud deck in the eastern tropical Pacific (Stratus), in the trade wind region of the northwest tropical Atlantic (NTAS), 100 km north of Oahu at the WHOI Hawaii Ocean Time-series Site (WHOTS), in the salinity maximum region of the subtropical North Atlantic (SPURS-1) and in the Pacific intertropical convergence zone (SPURS-2).
SPURS-2 Field Campaign Datasets Focused on a central mooring located near 10 • N 125 • W, the objective of SPURS-2 (a NASA-funded oceanographic process study) was to study the dynamics of the rainfalldominated surface ocean at the western edge of the eastern Pacific fresh pool. This location is subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. During this campaign, a number of in situ instruments were deployed to monitor the SSS in the first centimeters of the ocean surface layer. From this extensive set of measurements, we decided to include SSS data from the following instruments:.
In Situ SSS Analyses and Climatologies The Pi-MEP ingests the following analyzed in situ datasets: • The In Situ Analysis System (ISAS), as described in  [7] , is an optimal interpolation tool developed and run at the Laboratory for Ocean Physics and Satellite remote sensing The new version of the Roemmich-Gilson Argo Climatology as described in  [18]  and provided by Scripps Institution of Oceanography was also used.
SSS Satellite Products SMOS and SMAP are the ongoing satellite missions providing regular sea surface salinity (SSS) measurement capabilities at the global scale from space since November 2009 and May 2015, respectively. A third NASA satellite-dedicated salinity mission, the Aquarius-SAC/D, has also provided SSS data from space between 2011 and 2015. For SMOS, Aquarius, and, SMAP, the PI-MEP collects an ensemble of three types of satellite SSS products, namely: • Level 2: swath SSS data (50-minute half orbit for SMOS, and 98-minute orbit for SMAP and Aquarius); • Level 3: space-time SSS composite of one single sensor swath data; • Level 4: space-time SSS composite of multisensor SSS data. These products are collected from all official data centers (cf introduction) and both last and before-last releases are considered by the platform in order to monitor algorithms' evolution and their associated products' quality.
Thematic Datasets Additional EO datasets are used to characterize the geophysical conditions at the co-localized in situ/satellite SSS pairs' measurement locations and times, as well as 10 days prior to the measurements, to obtain an estimate of the geophysical concomitant condition and recent history. As discussed in  [19] , the presence of near-surface vertical gradients and the horizontal variability of sea surface salinity indeed complicates comparisons of satellite and in situ measurements. The additional EO data are used here to obtain a first estimate of conditions for which L-band satellite SSS measured in the first centimeters of the upper ocean within a 50-150 km diameter footprint might differ from pointwise in situ measurements performed between 10 and 5 m depths below the surface.
Precipitation Precipitation is estimated using the CMORPH 3-hourly products at 1/4 • resolution  [20]. CMORPH (CPC MORPHing technique) produces global precipitation analyses that cover a global belt (-180 • W to 180 • E) extending from 60 • S to 60 • N latitude. Data are available over the complete period of the Pi-MEP core datasets (January 2010-now).
• Zonal and meridional components of the surface geostrophic current and the Ekman current at 15 m depth from GlobCurrent. The data are interpolated and collocated to a common grid with a spatial resolution of 25 km and a temporal resolution of 1 day for the geostrophic current and three hours for the Ekman currents. Multimission altimeter satellite gridded sea level anomalies (SLA) computed with respect to a twenty-year mean (1993-2012) and using an optimal and centered computation time window (6 weeks before and after the date). This product is processed by the SL-TAC multimission altimeter data processing system and distributed by CMEMS (https://resources. marine. copernicus. eu/products with product identifier SEALEVEL_GLO_PHY_L4_REP_OBSERVATIONS_008_047 and SEALEVEL_GLO_PHY_L4_NRT_OBSERVATIONS_008_046 accessed on 30 April 2021.
In Situ/Satellite Data Preprocessing The first step consists of filtering in situ data using the quality flags as described in Section 2. 1 so that only valid salinity data remain in the final match-up files. For high-spatial resolution in situ SSS measurements such as the thermosalinograph (TSG) SSS data, as well as SSS data from surface drifters, an additional spatial filtering step is performed on the in situ data that will be eventually compared to the satellite SSS products. If R sat is the spatial resolution of the satellite SSS product (L2 to L3-L4), the in situ data are spatially low-pass filtered using a running median filter with a window width = R sat to try to minimize the spatial representation uncertainty when compared to the lower spatial resolution of the satellite SSS product.
• For L2 SSS swath data: If R sat is the spatial resolution of the satellite swath SSS product, for each in situ data sample collected in the Pi-MEP database, the platform searches for all satellite SSS data found at grid nodes located within a radius of R sat /2 from the in situ data location and acquired with a time-lag from the in situ measurement date that is less than or equal to ± 12 h. If several satellite SSS samples are found to meet these criteria, the final satellite SSS match-up point is selected to be the closest in time from the in situ data measurement date. The final spatial and temporal lags between the in situ and satellite data are stored in the MDB files. Recently, in the context of the evolving partnership with NASA, the Pi-MEP also provides an additional co-localization criterion that is applied only to L2 products, namely the "L2-Averaged" product match-ups.
• If the in situ data are located within the 60 • N-60 • S band, we select the CMORPH 3-hourly product that is closest in time from t in situ and found at the CMORPH 1/4 • grid node that is the closest distance from the in situ data location. We then store the time series of the CMORPH rain rate at the same node for 10 days prior to the in situ measurement time. For the given month/year of the in situ data, we selected the ISAS and WOA fields for the same month (and same year for ISAS fields) and take the SSS analysis (monthly mean, STD) found at the closest grid node from the in situ measurement. The distance from the in situ SSS data location to the nearest coast is evaluated and provided in km. We use a distance-to-coast map at 1/4 • resolution where small islands have been removed. When vertical profiles of salinity (S) and temperature (T) along depth (z) are made available from the in situ measurements used to build the match-up (Argo or marine mammals), the following variables are also included in each satellite/in situ match-up file: 1.
Match-Up Characteristics for Each Specific In Situ/Satellite Pair Several features/metrics of the MDB classified for each in situ/satellite/region triplet available are exhaustively described in each report generated. To illustrate what's available in the reports, a sketchy listing of some of the latter are described below for a specific in situ dataset  (Argo) , satellite product (CCI SSS L4 Merged-OI v3. 2-30-day running (ESA)) and Pi-MEP region (Global). The following section focuses on a general description of some of the available metrics, rather than on the geophysical interpretation of the obtained results.
Match-Up Analysis Report In each report, the platform then provides an ensemble of results of analyses on the selected paired satellite/in situ SSS MDB. The figures presented are examples of the results obtained from the automated analysis of the MDB. The database will also enable additional analyses by users. At the bottom of Figure  8 , the temporal mean (left) and standard deviation (right) of the differences between the satellite SSS product and in situ data found at match-up pairs, namely ∆SSS (Satellite-Argo), is also gridded over the full satellite product period and over spatial boxes of size 1 • × 1 •.
Time Series of the Monthly Median and STD of In Situ and Satellite SSS and of Their Difference (∆SSS) In the top panel of Figure  9 , we show the time series of the monthly median SSS estimated over the full Global Pi-MEP region for both CCI SSS L4 Merged-OI v3. 2-30-day running (ESA) satellite SSS product (in black) and the Argo in situ dataset (in blue) at the collected Pi-MEP match-up pairs. In the middle panel of Figure  9 , we show the time series of the monthly median of the SSS difference ∆SSS (Satellite-Argo) for the collected Pi-MEP match-up pairs and estimated over the full Global Pi-MEP region. In the bottom panel of Figure  9 , we show the time series of the monthly standard deviation of the SSS difference ∆SSS (Satellite-Argo) for the collected Pi-MEP match-up pairs and estimated over the full Global Pi-MEP region.
Zonal Mean and STD of In Situ and Satellite SSS and of Their Difference (∆SSS) In Figure  10  on the left panel, we show the zonal mean SSS considering all Pi-MEP match-up pairs for both CCI SSS L4 Merged-OI v3. 2-30-day running (ESA) satellite SSS product (in black) and the Argo in situ dataset (in blue). The full satellite SSS product period is used to derive the mean. In the right panel of Figure  10 , we show the zonal mean of ∆SSS (Satellite-Argo) for all the collected Pi-MEP match-up pairs estimated over the full satellite product period.
Syntool Syntool is a web application to explore and visualize collections of satellite, in situ and model data (Figure  14 ). By rendering various sources of data on the same map, it aims to reveal synergies and coherencies/incoherencies between multiple datasets. The main component of the Syntool application is a cartographic view which takes most of the available screen space. This view is built upon the widely used OpenLayers library which has been tested on most platforms and web browsers. This library can load data using most GIS protocols (TMS, WMS, etc. ), render them on a 2D map and let users move the map around by dragging it with the mouse or zoom to a specific area by using the mouse wheel. Pi-MEP core datasets (satellite, model and in situ) are made available on Syntool as well as the listed ocean surface currents, sea surface temperature, wind speed and precipitation products.
Match-Up Interface Searching match-ups and extracting the associated data is made easy using this tool (Figure  16 ). The match-up database (MDB) interface contains a form to define the search filters (spatial box, temporal window, satellite product, in situ dataset, distance to coast, etc. ) and provides means to visualize the results through specific plots, also allowing one to download them as CSV files. Before performing a search or an extraction, the interface requires the user to select an in situ/satellite couple, an area (from the list of preset Pi-MEP regions) and a time range. A user-defined spatial domain can also be selected using the rectangle and zoom functions on the map. More advanced filters are also available but are locked until the in situ/satellite couple has been selected since some options are specific to a satellite or an in situ sensor.
Jupyter Notebooks A Jupyter notebook is a web interface where users can write documents that contain code, text, equations, plots and rich media. By default, code is executed remotely on the web server but the notebook server can be configured to delegate code execution to machines which have both access to the data and to the web server, which is a better option for scalable infrastructures. By default, only Python code is supported, but Jupyter has a modular design and other languages can be added by installing plugins. The notebooks server offered by the Pi-MEP gives users the possibility to develop and execute their own algorithms within a Python environment with preinstalled modules for scientific processing and with access to a large amount of data. Due to the remote execution mechanism, code written by users has direct access to the Pi-MEP files and can produce results without requiring the transfer of a potentially high volume of input data.
, the Amazon and Orinoco, Congo and Niger, Mississippi, Ganges and Brahmaputra rivers (Figure  18 ). Users can generate their own SSS time series (satellite/in situ) and plots on a given spatial domain around each river mouth, or at a virtual buoy (point) in these regions. Time series are compared to the amplitude of the annual cycle of the river discharges. The visualization of the seasonal and interannual co-variabilities (correlation) between spatial patterns of satellite SSS, absorption coefficient of CDOM at 443 nm, precipitation, and surface winds are provided either after averaging all years or year by year. The coherency between the different satellite SSS products and surface currents from combined altimetry, wind, and SST data (Globcurrent products) in the river plume regions can also be monitored using the Syntool visualization software.