dataesr/scientific-paragraphs-categorization huggingface dataset:
Raw bathymetric data were processed and corrected for tide and salinity-induced celerity bias using IFREMER CARAIBES software, while the seafloor reflectivity was processed and corrected using IFREMER SonarScope software. Ten short cores were sampled in Pago Pago Bay (Fig. 1 ) using a custom-made gravity box-coring device adapted from the CASQ (CAllypso SQuare) box corer. This coring device, with a maximum penetration of 30-60 cm, allows a slower and gentler penetration of the sediment surface, permitting an intact sampling of the superficial sediment record with preservation of sediment structures and laminae. Cores were sampled in water depth ranging from 27 to 47 m and their locations were chosen based on the raw seismic, bathymetric and reflectivity surveys. Cores were retrieved in topographic lows of the inner domain (Figs.
1  and 2 ), and in the outer domain, where the 2009 SPT backwash deposits were believed most likely to be found. Cores were split, photographed and logged in detail, noting all physical sedimentary structures and the vertical facies succession. Grain size analysis of samples collected from all cores in 5 mm intervals was conducted using a Malvern Mastersizer S laser particle size analyzer. Statistical analysis of the grain size data was conducted using the Gradistat 8. 0 software (Kenneth Pye Associates Ltd. ). All cores were analysed for XRF using an Avaatech core scanner with a 1 mm measuring step. Two complementary runs were carried out for each core in order to count the full element spectrum: a first run at 10 kV and 1500 µA, and a second run at 30 kV and 2000 µA. XRF data was analyzed as element ratios and as separate elements normalized by the total counts.
The quality of the Data Unification and Altimeter Combination System (DUACS) Sentinel-3A altimeter data in the coastal area of the European seas is investigated through a comparison with in situ tide gauge measurements. The comparison was also conducted using altimetry data from Jason-3 for inter-comparison purposes. We found that Sentinel-3A improved the root mean square differences (RMSD) by 13% with respect to the Jason-3 mission. In addition, the variance in the differences between the two datasets was reduced by 25%. To explain the improved capture of Sea Level Anomaly by Sentinel-3A in the coastal band, the impact of the measurement noise on the synthetic aperture radar altimeter, the distance to the coast, and Long Wave Error correction applied on altimetry data were checked. The results confirmed that the synthetic aperture radar altimeter instrument onboard the Sentinel-3A mission better solves the signal in the coastal band.
cls. fr). The DUACS system provides directly usable, high-quality near-real-time and delayed time (DT) global and regional altimeter products  [1, 5]. The main processing steps include product homogenisation, data editing, orbit error correction, reduction in Long Wavelength Errors (LWE), and the production of along-track and mapped sea level anomalies. The DUACS processing  [3]  is based on the altimeter standards given by L2P (Level-2 Plus) products (see e. g. ,  [16] ). They include the most recent standards recommended for altimeter global products by agencies and expert groups such as the Ocean Surface Topography Science Team (OSTST) and the ESA Quality Working groups. More than 25 years of level-3 (L3) and level-4 (L4) altimetry products were reprocessed and recently delivered as the DUACS DT 2018 version  [5].
These elements introduce errors in the measurements  [18]. To minimise their impact, DUACS-DT2018 re-processing considers the most up-to-date altimetry corrections, such as (a) dry and wet troposphere corrections, (b) ionospheric correction, (c) sea state bias correction, (d) dynamic atmospheric correction (DAC), and (e) the ocean tide. Some of these instrumental and environmental errors remain in the along-track products delivered to final users, mainly due to the imprecision of the corrections applied. Altimeter data are widely calibrated and validated by comparison with in situ timeseries  [19]. Tide gauge measurements are commonly used. In situ and altimetric observations are complementary and are often assumed to observe the same signals (e. g. ,  [20] ). The comparisons with in situ observations allow us to obtain altimetry errors relative to the external measurements and provide an improved picture of SSH.
The paper  [5]  assessed gridded products in coastal areas through a comparison with monthly tide gauge measurements from the Permanent Service for Mean Sea Level (PSMSL) Network  [21]. The procedure described in  [19, 22]  was used. The paper  [5]  reported a global reduction of 0. 6% in variance when using the DUACS-DT2018 data with respect to the previous DUACS-DT2014 dataset  [3] , with a clear improvement along the Indian coast, Oceania, and Northern Europe. The consistency between altimeter gridded products and tide gauge data in the coastal region was also investigated at global  [23, 24]  and regional (Mediterranean basin) scales  [25, 26]. Mean square differences between tide gauge and gridded altimetry (see Section 3. 1 in the text) ranging between 30% and 90% were obtained by  [23]  in the European coasts, whilst these differences were reduced to around 40%  [24]  thanks to improved geophysical corrections (i.
e. , a new DAC) applied to altimetry data with root mean square differences (RMSD) between gridded altimetry and tide gauges of 4. 43 cm in the Atlantic Ocean. The paper  [25]  found a median value for the correlation altimetry-a tide gauge of 0. 79 in the Mediterranean Sea. These works used low-pass filtered (monthly averaged) tide gauge records from PSMSL and the Global Sea Level Observing System (GLOSS)/Climate Variability and Predictability (CLIVAR)  [27]  to remove high frequencies not resolved by the altimetry gridded products used for inter-comparisons. Thus, a comparison at higher frequencies between a specific regional product for the whole European coast and a high-density tide gauge dataset is, to our knowledge, still lacking. Here, we present an assessment of the Copernicus Marine Environment Monitoring Service (CMEMS)/DUACS along-track (L3) altimeter regional operational products in the European seas using in situ tide gauges from the Copernicus Marine Environment Monitoring Service (CMEMS) catalogue.
The aim is to validate the Sentinel-3A SAR mode SLAs against the equivalent in situ tide gauge measurements. Six-hour time series of tide gauge measurements will be compared with the 1 Hz along-track altimetry data from the Sentinel-3A satellite mission, this strongly enhancing both the spatial and temporal resolution of the results reported by  [5]. We expect to obtain a more detailed assessment of DUACS-DT2018 products in the European seas at the both regional and sub-regional level. This inter-comparison has been also conducted by using SLA from the Jason-3 satellite mission to investigate the improvements of the Sentinel-3A mission over the latter in the coastal band.
Tide Gauge Observations The sea-level records used to compare with satellite altimetry were obtained from the CMEMS In Situ Thematic Assembly Centre (TAC) data repository. The Copernicus catalogue provides data of 485 tide gauge stations along the World Ocean coasts, which are updated within a few weeks or a few months. This dataset covers the time period spanning from January 2010 to the present. Sixhourly tide gauge records were used according to the following procedure (Figure  1 ): the 445 tide gauge stations located in the European seas' domain were initially considered for this study. The quality flags of the tide gauge records were checked and observations with anomalous SSH data.
Tide Gauge Observations The sea-level records used to compare with satellite altimetry were obtained from the cmEMS In Situ Thematic Assembly Centre (TAC) data repository. The Copernicus catalogue provides data of 485 tide gauge stations along the World Ocean coasts, which are updated within a few weeks or a few months. This dataset covers the time period spanning from January 2010 to the present. Six-hourly tide gauge records were used according to the following procedure (Figure  1 ): the 445 tide gauge stations located in the European seas' domain were initially considered for this study. The quality flags of the tide gauge records were checked and observations with anomalous SSH data (values larger than three times the standard deviation of the time series) or changes in the vertical reference of the tide gauge were rejected.
Additionally, tide gauge time series exhibiting a large variance (more than 20 cm 2 ) with respect to altimetry data were removed, as they are considered not representative of ocean sea level changes and are likely related to local features (e. g. , river discharge). Only tide gauges with at least a 70% yearly data coverage were selected in order to allow the analysis of the seasonal signal. The final set consists of 370 stations (Figure  2 ). The stations and their information are listed in Table  A1. Before they can be compared with altimeter data, tide gauge measurements have to be processed  [7, 19]  to remove oceanographic signals whose temporal periods are not resolved by altimetry, thus avoiding important aliasing errors  [6]. First, tidal components were removed from the sea level records using the u-tide software  [28].
Method for Comparing Altimeter and In Situ Tide Gauge Records The comparison method of altimetry with tide gauges consisted of co-locating both datasets in time and space. It was based on a particular track point selected for each tide gauge location as follows: we computed the correlations between each tide gauge record (tidal residuals) and SLA time series corresponding to track points within a radius of 1 degree around the tide gauge site and choose the most correlated track point. A minimum length of time series of 10 months (corresponding to approximately 10 cycles of Sentinel-3A) was set up to allow statistical significance  [14]. Statistical analyses were performed between both datasets using all available data pairs (altimetry-tide gauge) for a given region. The co-located altimeter and tide gauge measurements were analysed in terms of the RMSD and variance of the time series.
The RMSD metric is commonly used to examine along-track altimeter data quality  [14]. In addition, the robustness of the results was investigated according to  [33]  using a bootstrap method  [34] , which allows us to estimate quantities related to a dataset by averaging estimates from multiple data samples. To do that, the dataset is iteratively resampled with replacement. A total of 10 3 iterations were used to ensure that meaningful statistics such as standard deviation could be calculated on the sample of estimated values, thus allowing us to assign measures of accuracy to sample estimates.
Ancillary Data The Global, Self-consistent, Hierarchical, and High-resolution Geography database (GSHHG) was used to estimate the nearest distance to the coast of the altimetry track points used to compare with tide gauges. The aim was to investigate the degradation of the altimetric signal as we approach the coast. The shorelines in the GSHHG database are constructed entirely from hierarchically arranged closed polygons and are available in five geographical resolutions. The early processing and assembly of the shoreline data is described in  [35]. We used the latest data files for version 2. 3. 7 presently available and released on 15 June 2017 with the original full data resolution.
Comparison of Sentinel-3A and Tide Gauges along the European Coasts This section presents the statistics of the comparisons performed between the Sentinel-3A altimetry data and the tide gauge observations from the cmEMS catalogue in the coastal region in terms of errors (RMSD) and the variance of the differences between both datasets. The analysis has been conducted for the entire European coast and the following sub-regions: the Mediterranean Sea, the IBI and NWS regions, and the Baltic Sea (Figure  2 ). SLA measurements without filtering (Figure  1 ) were used. The bootstrapping technique  [34]  was applied to gain an estimation of the standard errors of the differences between both datasets. The mean value of the RMSD between the Sentinel-3A satellite altimetry and tide gauges is 6. 97 cm.
The mean distance between the location of the tide gauge and the location of the corresponding altimeter data with the highest correlation is 80 km with a standard deviation of 33 km. Data from 342 tide gauge stations were compared with the Sentinel-3A data. Thus, 28 stations were rejected from the computation according with the selection criteria described in the previous section. These stations are located in the NWS region, the Mediterranean Sea, and the Arctic Sea (Table  A1 ). The rejected tide gauge time series showing a variance much larger than that found in the corresponding altimetry time series (RMSD between both datasets larger than 20 cm) were further investigated. We checked the shape of their time series, together with the quality flag data related to SSH, tide gauge position, and recorded atmospheric pressure.
The aim was to investigate the presence of outliers in the tide gauge time series due to poor quality control not captured by satellite altimetry responsible for such large discrepancies, which could be corrected by the data providers. A subset of twenty-four tide gauge stations (Table  A2 ) showed abnormal values in variance due to poor quality control that induced substantial RMSD when compared to the Sentinel-3A and Jason-3 altimetry data. This represents 5% of the tide gauge dataset in the European coasts. Figure  3  shows the consistency between the altimetry and tide gauge data computed as follows: variance(tide gaugealtimeter) variance(tide gauge) × 100, where the variance of the tide gauge is associated with the variance of the signal. Consistency is expressed here as the mean square differences between both datasets, computed as the variance of the differences (altimetry-tide gauge) in terms of percentage of the tide gauge variance.
As a consequence of this, the Arctic region will not be investigated here due to the lack of valid data (Figure  A1 ). Table  1. Inter-comparison of the satellite altimetry and tide gauge data from the European coasts in terms of the RMSD (cm) and variance (cm 2 ) of the differences between both datasets. The number of tide gauge stations used in the comparison, the mean distance between tide gauges and the most correlated along-track altimetry points, and the number of total data pairs (altimetry-tide gauge) used The largest errors, which reach 100%, are mainly found in the Atlantic shore of the IBI region. This could be due to the imprecisions of the corrections applied (i. e. , ocean tide) to the altimeter data.
As a consequence of this, the Arctic region will not be investigated here due to the lack of valid data (Figure  A1 ). The RMSD between the Jason-3 and tide gauge time series shows a mean value of 7. 97 cm, whereas it is reduced to 6. 89 cm for the inter-comparison using the Sentinel-3A dataset. Overall, the results from the Jason-3 satellite mission are consistent with those obtained for Sentinel-3A in terms of spatial distribution (Figure  A1 ). In the IBI and NWS regions, 81 and 55 common tide gauge stations to Sentinel-3A and Jason-3 missions were respectively identified from the whole tide gauge dataset (Table  2 ). The analysis conducted with these stations shows a mean RMSD of 6. 62 cm and 10. 31 cm, respectively, for the comparison with Sentinel-3A, whilst the mean values for the inter-comparison using the Jason-3 dataset are 7.
31 cm for the IBI region and 12. 22 cm for the NWS region. Thus, the Sentinel-3A satellite mission improves, respectively, the errors with tide gauges in both regions by 9% and 15%. Table  1. Inter-comparison of the satellite altimetry and tide gauge data from the European coasts in terms of the RMSD (cm) and variance (cm 2 ) of the differences between both datasets. The number of tide gauge stations used in the comparison, the mean distance between tide gauges and the most correlated along-track altimetry points, and the number of total data pairs (altimetry-tide gauge) used in the computation are displayed. The common tide gauge stations for the Sentinel-3A and Jason-3 satellite missions were used. Values in parenthesis show the uncertainties (error bars) computed for the RMSD and variance from the bootstrap method using 10 3 iterations.
The variance of the Sentinel-3A altimetry data diminished by 2% when using the filtered data (Table  3 ). This is an expected result due to higher frequencies being subtracted from the SLA time series in the filtering procedure. This fact decreased the RMSD by 0. 3% when comparing the filtered SLA with tide gauge records with respect to that obtained when using the SLA without filtering. The variance of the differences (altimetry-tide gauge) was also reduced by 1% when using the Sentinel-3A filtered data. However, it is worth noting that the improvements in the inter-comparisons (RMSD reduction) when using filtered SLA are negligible. This procedure was repeated using the Jason-3 dataset (Table  3 ). A reduction threefold larger (6%) in the variance of the filtered SLA with respect to the SLA without filtering is observed.
Effects of the Coastal Distance on Altimeter Data The quality of retrieved altimeter signal decays with closer distance to the coast, because radar return from the land and bright target causes the typical shape of waveform to deviate  [14, 39]. To investigate the degradation of the altimeter signal and its performance as we approach the coast, an additional comparison between satellite altimetry from both Sentinel-3A and Jason-3 and in situ tidal records in the European coasts was conducted. First, we estimated the distance to the coast of all track points within a radius of 1 degree around a given tide gauge by using the GSHHG dataset. Then, the closest altimetry track point to the coast (ctp hereafter) and the most correlated altimetry point (mcp hereafter) along the track of the ctp were identified.
European Coasts Sentinel The mean distance to tide gauges is lower for the Sentinel-3A dataset for both the mcp and ctp due to the reduction in the cross-track distance in the Sentinel-3A orbit with respect to Jason-3. The RMSD increased from 6. 78 cm to 7. 95 cm when we approach the coast (from the mcp location to the ctp location) for the Sentinel-3A dataset and from 7. 91 cm to 8. 74 cm for the Jason-3 dataset. These results suggest an impact of the distance to coast on the data quality for both missions. The degradation of the altimeter signal, estimated here as the difference in the percentage of statistics between the altimetry mcp and ctp computations for a single mission, shows a mean value for the RMSD of 15% for the Sentinel-3A mission when we approach the coast from around 13 km to 5 km and of 10% for the Jason-3 mission.
The degradation in the variance of the differences (altimetry-tide gauge) was 22% for Sentinel-3A and 20% for the Jason-3 dataset. Despite this lower degradation in the Jason-3 dataset, a superior performance of the Sentinel-3A dataset in terms of the lower along-track RMSD and a lower variance of the differences (altimetry-tide gauge) against the same tide gauges was obtained, also showing a mean distance of the ctp 300 m closer to coast than that of the Jason-3 dataset (Table  4 ). The number of points used for both altimeters is similar, at 456 for Sentinel-3A and 442 for Jason-3, this suggesting a reasonable comparison. The altimetry variance exhibited an enhancement of 5% for the Sentinel-3A dataset when we approached the coast, whilst it reached 10% for the Jason-3 mission (Table  4 ).
In this section, we investigate the possible impact of the LWE correction applied to Sentinel-3A and Jason-3 datasets on both the retrieval of SLA in the coastal zone and the inter-comparisons with in situ measurements performed. To do that, LWE correction applied to SLA was subtracted from the altimetry time series to obtain uncorrected SLA as follows: SLA uncorr = SLA -LWE (2) Then, the SLA uncorr time series were compared with tide gauge records according to the procedure described in previous sections. Finally, the outcomes from this new computation were compared with the inter-comparison conducted by using the corrected SLA. In this analysis, SLA measurements without filtering have been used. The variance associated with the LWE correction applied on SLA from Sentinel-3A mission (Figure  4 ) shows low values close to 0 cm 2 for most of the tide gauge sites located in the Baltic and Mediterranean Seas and in the southernmost part of the IBI region; whereas a larger variability exhibiting values larger than 25 cm 2 was found in the north-easternmost part of the latter and in the NWS region.
Such variability is associated with the LWE absorbing part of the residual errors in ocean tide correction and DAC and also part of the remaining "long-wavelength" signal that can contribute to the SLA discrepancy between neighbouring tracks. Similar results were obtained for the LWE correction applied to SLA from the Jason-3 mission (figure not shown). The RMSD between the corrected SLA from Sentinel-3A (Jason-3) and the tide gauge records (Table  5 ) diminished by 10% (3%) with respect to that obtained when using uncorrected SLA. In addition, the variance of the differences between both datasets reduced by 18% (5%) when using corrected SLA. Thus, LWE correction leads to a better agreement between the altimeter datasets and the tide gauges. As we did for the comparisons conducted in the previous sections, here we have considered the same tide gauge sites and data points for the inter-comparisons using corrected and uncorrected SLA from the Sentinel-3A mission to make the outcomes comparable.
Thus, the statistics for corrected SLA displayed in Table  5  slightly differ from those shown in Table  1  due to the different tide gauge sites and data points used. The same applies to the Jason-3 dataset. Table  5. Inter-comparison of the LWE-corrected and -uncorrected SLA from the Sentinel-3A and Jason-3 satellite missions and tide gauge data in the European coasts in terms of the RMSD (cm) and variance (cm 2 ) of the differences between the datasets. The number of tide gauge stations used in the comparison, the mean distance between tide gauges and the most correlated along-track altimetry The RMSD between the corrected SLA from Sentinel-3A (Jason-3) and the tide gauge records (Table  5 ) diminished by 10% (3%) with respect to that obtained when using uncorrected SLA. In addition, the variance of the differences between both datasets reduced by 18% (5%) when using corrected SLA.
Thus, LWE correction leads to a better agreement between the altimeter datasets and the tide gauges. As we did for the comparisons conducted in the previous sections, here we have considered the same tide gauge sites and data points for the inter-comparisons using corrected and uncorrected SLA from the Sentinel-3A mission to make the outcomes comparable. Thus, the statistics for corrected SLA displayed in Table  5  slightly differ from those shown in Table  1  due to the different tide gauge sites and data points used. The same applies to the Jason-3 dataset. Table  5. Inter-comparison of the LWE-corrected and -uncorrected SLA from the Sentinel-3A and Jason-3 satellite missions and tide gauge data in the European coasts in terms of the RMSD (cm) and variance (cm 2 ) of the differences between the datasets.