dataesr/scientific-paragraphs-categorization huggingface dataset:
They operate at 16-17 Mhz. Observations are collected every 20 min and data have been edited and averaged daily over the period May 2012-September 2014. The surface current vectors represent the total current averaged over the upper 1 m of the ocean and include a significant ageostrophic component, not present in the altimetric currents. 3 Spectral analysis of along-track altimeter data Spectral analyses are performed on each of the three altimeter missions, with their tracks shown in Fig. 1. Only data more than 50 km from the coast are analysed to avoid the increased errors in the coastal zone. Each track and cycle is then selected along a common segment of 200 km. This segment length was chosen to allow a large number of altimeter segments in different regions in between the numerous islands and to be more than 50 km from the coast, to avoid the increased errors in the coastal altimeter data.
5 spectral slope. Shading represents the error bars, based on a chi-squared test with the number of degrees of freedom being wavenumber dependent. Table  1  gives the number of sections used. data period from 1 April 2013 to 30 April 2014. The unbiased estimate with the noise removed is in Fig. 3b. Recall that the space-time samplings of the three missions are different, and as such they may capture different dynamics at different regions. So we do not expect the spectra to be perfectly aligned. More distinctive are the different noise levels between 15 and 100 km wavelength. Jason-2 has the highest noise level in this region, followed by CryoSat-2 in SAR mode. SARAL/AltiKa in Ka-band exhibits the lowest noise of all. When a constant noise level is removed from each spectral PSD, the spectral slopes line up surprisingly well, given the different space-time sampling of the three missions over this 13-month period.
The spectral slope is again around k -2. 5 from a fit to the unbiased spectra over the wavelength range from 50 to 200 km. These spectral slopes in the offshore regions of the Mediterranean Sea are quite shallow compared to the k -5 slopes expected for quasi-geostrophic theory  (Stammer, 1997). The reason for this needs further investigation, but smaller slopes are also characteristic of openocean low-eddy-energy regions  (Xu and Fu, 2012). For the Mediterranean Sea, the dominant mesoscale energy at small Rossby radius scales tends to flatten the spectra, but internal waves or mean sea surface errors in the CryoSat-2 data could also contribute to higher SSH energy at small scales and flatter spectra  (Dufau et al. , 2016). The fact that the CryoSat-2 1 Hz data in SAR mode had a higher noise level than SARAL/AltiKa was unexpected.
We verified that the CryoSat-2 20 Hz data were consistent with the 1 Hz averages, so this is not an averaging problem. The CryoSat-2 20 Hz SAR mode does exhibit a spec-tral hump for this region and time period that was not present in other regions with SAR data (Agulhas or tropical Pacific; S. Labroue, personal communication, 2016). This warrants further analysis of the particular surface roughness conditions occurring in the NW Mediterranean during this year, and further expertise in SAR processing for the Mediterranean conditions is needed. These results reinforce the very low noise level associated with the 40 Hz Ka-band SARAL data, averaged here to 1 Hz. Seasonal spectra were also calculated from the longest time series possible, i. e. over 6. 5 years for Jason-2 data, over 22 months for SARAL/AltiKa, and for the shorter 13-month period for CryoSat-2 (see Table  1 ).
Co-located altimeter and glider observations The previous section highlighted that the altimetric noise was effectively masking the smaller-scale SLA signals in the along-track data. The smallest scales observable with a signal-to-noise ratio greater than 1 will vary from one altime-ter mission to another and seasonally. Statistically, we cannot observe structures less than 35-45 km wavelength with SARAL, or 50-60 km wavelength with the higher noise of Jason-2. However, individual energetic features may be revealed above the statistical noise. We will explore this with a series of co-located along-track altimeter-glider sections and compare the vertical structure observed by the gliders with their steric height and geostrophic velocities. In this section, the filtering of the along-track altimetry data is based on the standard Loess filtering applied to the CTOH coastal processed data  (Birol et al.
3 to 41. 3 • N, then a westward return current from 41. 3 to 42 • N at the northern end, when the third Jason pass is co-located. The filtered glider data and the filtered Jason data are also shown for the southbound section (Fig. 6b ) and the northbound section (Fig. 6d ). The instant of the Jason-2 passage is marked by a vertical line -identifying the latitude where the glider and the Jason observations coincide exactly in time. The geostrophic currents from the AVISO 2-D maps are also shown for reference. The southbound section crosses a series of small reversing currents around small SST structures of 30-50 km (Fig. 6a ). The glider and along-track Jason-2 data show cross-track currents in phase, although the Jason-2 amplitudes are stronger (correlation, r = 0. 5; RMSE = 0. 06 m s -1 ).
This may be real (due to deeper baroclinic or barotropic structures not observed by the glider's upper 1 km observations) or in-duced by the effects of filtering higher noise. The mapped AVISO data have similar amplitude to the glider data but are not in phase, which reduced their statistical correlation (r = 0. 4; RMSE = 0. 06 m s -1 ). Adding the glider "drift" reference currents introduces little change to these results. Three weeks later, the northbound section crosses a strong mesoscale eddy. The three data sets present similar eastward currents across the mesoscale eddy, and although the amplitude of the westward current near 42 • N is similar, along-track altimetry positions the return flow 30 km further north than is detected by the glider. For this larger eddy, 100 km in diameter, the AVISO 2-D maps and the 50 km filtered along-track data both provide a good estimate of the glider's geostrophic currents (r = 0.
25 • N. These nar-row currents are the limit of the observability with the gliders, given the filtering cutoff at 30 km wavelength. In comparison, the altimeter data show a broad intense westward flow over the entire section, except for the return eastward flow in the south. The along-track comparison of their amplitudes (Fig. 7b ) shows that the two systems measure similar currents at the exact time of the SARAL passage (vertical line), but otherwise the broad, intense westward flow cap-tured by altimetry is not observed by the gliders. The mapped AVISO data are halfway between. If the glider and altimeter observations are overlaid on a daily time series of satellite SST maps, the differences between these two observations becomes clearer. Figure  8  shows the 5 days needed by the glider to complete this 77 km section to 1000 m depth and the evolving SST conditions during this period.
CryoSat-2-glider comparisons The third example concerns two gliders deployed at 1-day intervals along the CryoSat-2 track 493, which passed on 27 April 2015. CryoSat-2 SAR data are filtered at 35 km (see Sect. 3). Figure  9  shows that the two gliders and the CryoSat-2 data detect well the westward-flowing Northern Current near 42. 5 • N as well as an eastward return flow around 41. 5 • N. In contrast, the CryoSat-2 data overlay a weak cyclonic eddy centred on 42 • N, which is also apparent in the mapped AVISO data but is not detected by the gliders. The CryoSat-2 data are included in the AVISO maps, so the two products show consistent results, though AVISO is smoother. The along-track geostrophic currents (Fig. 9b ) show that the two gliders, separated by 1 day, observe the same features.
, 2010). However, the real improvement in altimetric signal-to-noise levels expected with SARAL and CryoSat-2 are not revealed in these glider comparisons, mainly because at the time of these altimeter observations, rather weak signals were de-tected or the small-scale meanders were moving rapidly. In these cases, our observations approach the error levels of the two systems. Small offsets in the structure of the Northern Current could also be introduced by the removal of a mean sea surface from the CryoSat-2 data sets, which could induce errors on these small space scales (up to 80 km wavelength,  Dufau et al. , 2016). Although gliders can observe energetic small-scale structures in dedicated campaigns in the Mediterranean Sea (e. g. Bosse et al. , 2015) , the chance is small that these occur at the precise position and time when the gliders and altimeter tracks coincide.
This comparison highlights the difficulty in setting up a validation campaign for altimetric observations of small-scale rapidly moving dynamics. 5 Co-located HF radar and SARAL altimeter HF radar data provide an additional observation of the oceanic surface currents. In comparison to the geostrophic component of the flow obtained with altimetry and gliders, HF radars measure the total surface current, due to balanced geostrophic and unbalanced ageostrophic currents (winddriven, inertial, tidal currents, etc. ). The daily data set we used has been processed to remove the HF tides and inertial currents, retaining the geostrophic and wind-driven currents. Figure  10  shows an example of the HF radar total currents for one date, 20 October 2013 near Toulon, with the two coastal radar locations marked.
For this date (20 October 2013), the amplitude of the HF radar currents, projected in the altimetric cross-track direction (in red), is similar to the SARAL cross-track currents (in black), reaching 0. 7-0. 8 m s -1 within the Northern Current. Further offshore, the HF radar currents decrease gradually whereas the geostrophic altimetric currents are much weaker outside of the jet. The presence of ageostrophic currents in the HF radar data could contribute to this difference. Our statistical estimate of the spatial observability of SARAL observations in autumn is around 35 km wavelength (Sect. 3), representing feature structures across the current of around 17 km. Clearly at these scales, www. ocean-sci. net/13/13/2017/ Ocean Sci. , 13, 13-29, 2017 the 20 km wide Northern Current can be observed by the SARAL altimeter.
The advantage of the HF radar data set is its daily 2-D coverage at fine resolution, so we should not have the space-time offsets in the sampling of small-scale features that plagued the glider-altimeter comparisons. The disadvantage is that altimeter data in the last 10-50 km from the coast are noisy, and the ageostrophic wind-driven component of the HF radar surface currents can be strong here, in the region with strong mistral winds. We have compared the observability of these near-shore currents with the finer-resolution SARAL altimeter time series, filtered at 35 km (see Sect. 3). SARAL data are available along this track every 35 days, and Fig. 11  shows the 18-month time series of cross-track surface velocities from the HF radar. The upper panel shows the full time series of HF radar currents projected perpendicular to the altimeter track; the middle panel shows the HF radar currents sampled at the same dates as the SARAL altimeter passes, and spatially sampled at 7 km as for the 1 Hz altimeter data.
The bottom panel shows the SARAL 1 Hz geostrophic currents (mean and anomalies), filtered at 35 km. SARAL clearly detects more of the offshore return flow than the HF radar can but covers a similar data range as the HF radar to the coast. Along-track correlations of the HF radar and altimetric currents for this cross-track velocity component are between 0. 7 and 0. 9 for these 16 tracks, except for four dates, where the correlations drop below 0. 5. The RMSE between the crosstrack HF radar current amplitudes and the SARAL current amplitudes is shown in Fig. 12. Dates with low correlations (< 0. 5) are marked with the vertical dashed line, and these have a higher RMSE. The RMSE is generally lower in the summer months when the wind is lower and increases in winter. Wind forcing of the ageostrophic currents may explain part of the difference.
If we consider the daily time series of HF radar data (Fig. 11a ) and extract the outliers in cross-track velocity having > 1σ standard deviation from the mean, we find that these outliers are correlated at 0. 84 with the crosstrack wind at the same date (not shown). For the dates with weak correlations, wind may play a role for one date (December 2013), but the other dates have relatively low wind. The differences with SARAL are often associated with 10 km wide structures and close to the coast. This could be due to errors in either measurement system (e. g. for SARAL: the nearshore wave height bias, wet tropospheric corrections, mean sea surface errors) but also from rapid events that are detected by the altimeter 8 s "snapshot" but viewed differently with the HF radar 1-day averages (rapid meander, internal waves, etc.
Discussion The along-track altimeter spectral analysis allows us to estimate the mean dynamical scales that can be observed today with different altimeter technology and associated processing, and in different seasons. In winter, when the mixed layers are deepest and the submesoscale is energetic, all of the altimeter missions can observe wavelengths down to 40- 50 km (individual feature diameters of 20-25 km). In summer when the submesoscales are weaker, SARAL can detect ocean scales down to 35 km wavelength, whereas the higher noise from Jason-2 and CryoSat-2 blocks the observation of scales less than 50-55 km wavelength. This is a statistical view. There are limits in applying this too assiduously, especially as these statistics are calculated from relatively short records for SARAL, and only 13 months of reprocessed SAR data for CryoSat-2.
We chose to analyse the longest time series possible for the seasonal calculations since the records are relatively short. However, entire years should be analysed to remove any sampling biases in these statistics. Given the long repeat time for CryoSat-2, we also measure different geographical regions in each season, which can introduce biases in our basin-scale averages. Interannual variations also occur in the dynamics in response to interannual atmospheric changes, which can lead to different deep convection events from one season to another  (Adloff et al. , 2015). Analysing a longer time series of SARAL and CryoSat data should improve the significance of these early results. One application of this type of analysis is to improve the altimetric data post-processing to be adapted to the regional conditions.
SARAL could have a finer-scale along-track filtering applied, to retain wavelengths greater than 35 km in summerautumn and 45 km in winter. A filter cutoff of 50 km yearround could be suitable for CryoSat-2. Knowing how this statistical signal-to-noise ratio varies from one mission to another, and seasonally, is very useful for regional applications, for local process studies or for data assimilation. The in situ validation remains very limited in space and time and did not allow us to confirm whether these smaller scales are realistic ocean features. For the glider comparison with SARAL, small-scale structures were detected by both systems, but their rapid movement prevented us from giving a precise along-track co-location except for the short scales close to the temporal crossing point. Indeed, for advective dynamics to be resolved correctly, they should conform to the Friedrichs-Lewy condition, i.
e. U t / x < 1. If we follow small structures with typical advection speeds of U = 0. 3 m s -1 (typical of the Northern Current), then we need time differences, t, of less than 1. 35 days to resolve the smaller SARAL wavelengths at 35 km, and within 2 days for the Jason-2 and CryoSat-2 data to resolve 50 km wavelength structures. With the slow-moving gliders, we can only cover 30 km per day, and so our along-track intercomparisons should be limited to the ±30 km around the altimeter-glider crossing point. This places a very strong constraint on our in situ validation. The SARAL intercomparison with the Toulon HF radar data was quite promising. Despite the apparent nearshore errors in the SARAL data, and the periods with strong wind-driven currents, the correlation between the SARAL geostrophic currents and HF radar total currents remained high.
Data availability Altimeter data: the unfiltered along-track Jason-2 and SARAL altimeter SLA data sets are available from the AVISO website (http://aviso. altimetry. fr/) and the CMEMS website (http://marine. copernicus. eu/). The unfiltered along-track CryoSat-2 CPP data are an experimental product provided by the CNES. These Level 2 (GDR) input data are provided by CNES, ESA, and NASA. CryoSat SLAs are calculated relative to a gridded mean sea surface (MSS_CLS2011, http://www. aviso. altimetry. fr/en/data/ products/auxiliary-products/mss. html). Glider data are available as part of the MOOSE project (http://www. moose-network. fr/gliders). HF radar data are also available as part of the MOOSE observing system (http://hfradar. univ-tln. fr/HFRADAR).
, 2015b). Additionally Arrhenius models are highly sensitive to the input temperature field, which itself is poorly constrained. Despite this evidence for spatial variation in attenuation, radar algorithms, which use the relationship between bed-returned power and ice thickness to identify an attenuation trend, make the assumption that the attenuation rate is locally constant (e. g. Gades et al. , 2000; Winebrenner et al. , 2003; Jacobel et al. , 2009; Fujita et al. , 2012). Due to this constancy assumption, these radar algorithms are suspected to yield erroneous values  (Matsuoka, 2011; Schroeder et al. , 2016). Moreover, these radar algorithms are not tuned for automated application over the scale of an ice sheet. In this study we introduce a new ice-sheet-wide framework for the radar inference of attenuation and apply it to IPR data from the Greenland Ice Sheet (GrIS).
Constraining the algorithm sample region Radar inference of the depth-averaged attenuation rate, using the relationship between bed-returned power and ice thickwww. the-cryosphere. net/10/1547/2016/ The Cryosphere, 10, 1547-1570, 2016 ness, requires sampling IPR data from a local region of the ice sheet  (Gades et al. , 2000; MacGregor et al. , 2007; Jacobel et al. , 2009; Fujita et al. , 2012; Matsuoka et al. , 2012b ). An implicit assumption of the method is that the depth-averaged attenuation rate is constant across the sample region (Layberry and  Bamber, 2001; Matsuoka et al. , 2010a). However, as was shown in Sect. 2. 4, the depth-averaged attenuation rate is predicted to have pronounced spatial variation, and therefore an ice-sheet-wide radar attenuation algorithm must take this into account.
the-cryosphere. net/10/1547/2016/ The Cryosphere, 10, 1547-1570, 2016 where (x i , y i ) corresponds to the position of the ith data point within the target window and (x 0 , y 0 ) corresponds to the central point. This power correction represents an estimate of the difference in attenuation loss between an ice column of the actual measurement (loss estimate 2 < B(x i , y i ) > h i ), and a hypothetical ice column with the same thickness as the measurement but with the attenuation rate of the central point (loss estimate 2 < B(x 0 , y 0 ) > h i ). An example of a [P C ] vs. h regression plot pre-and postpower correction, Eq. (  9 ), is shown in Fig. 8. In this example, ice columns that are thinner than the central point have (< B(x i , y i ) > -< B(x 0 , y 0 ) >) > 0 and the power values are increased by Eq.
This is broadly equivalent to saying that the horizontal gradients in the depth-averaged temperature field of the ice sheet models are more robust than the absolute values of the depth-averaged temperature. Similarly, our use of local differences for the attenuation rate estimate is also robust to systematic biases in the Arrhenius model. We have yet to consider an explicit classification of the subglacial materials and quantification of regions of basal melting. In future work, we aim to combine IPR data from preceding CReSIS field campaigns to produce a gridded data product for basal reflection values and basal melt. It is anticipated that, as outlined by  Oswald and Gogineni (2008, 2012)  and  Schroeder et al. (2013) , the specularity properties of the basal waveform, and how this relates to basal melt detection, could also be incorporated in this analysis.
As the regions of algorithm coverage are sensitive to uncertainty, we suggest that these data products could have spatially varying uncertainty incorporated. Additionally, for the basal reflection and basal melt data sets, uncertainty in the measurements of [P C ] will have to be incorporated in the uncertainty estimate for [R]. Establishing a procedure for the interpolation of these data sets where either: (i) the algorithm coverage is poor due to low attenuation solution accuracy, or (ii) the IPR data are sparse, will form part of this framework. Regions of lower solution accuracy generally correspond to the interior of the ice sheet where spatial variation in the attenuation rate is much less pronounced (primarily the northern interior). Due to this lower spatial variability (and despite the caveats in the paragraph above), these regions could potentially have their basal reflection values derived by using a forward Arrhenius temperature model for the attenuation.
Finally, we envisage that the framework introduced in this paper could be used for radar inference of radar attenuation, basal reflection and basal melt for the Antarctic Ice Sheet. Given that for high solution accuracy the radar algorithm requires high topographic roughness and relatively warm ice, we suggest that IPR data in rougher regions toward the margins should be analysed first (refer to  Siegert et al. , 2005  for an overview of topographic roughness in East Antarctica). Additionally, the prediction of the model temperature field bias using the attenuation rate solution could be extended to the Antarctic Ice Sheet. are the molar concentrations of the chemical constituents (µM)  (MacGregor et al. , 2007 (MacGregor et al. , , 2015b)). The model parameters are summarised in tabular form by  MacGregor et al.
Cell cycle and subG1 analyses Both procedures were performed independently, described elsewhere  (24)  and are detailed in Supplementary Material. Briefly, for cell cycle analysis, cells were incubated with 10 M bromodeoxyuridine (BrdU, Sigma-Aldrich) for 20 min prior to harvesting at 37 • C. Replicating cells were stained with anti-BrdU (Dako, Glostrup, Denmark) and DNA content was stained with propidium iodide (PI) solution (20 g/ml, 200 g/ml RNase A (Invitrogen, Life Technologies), 0. 1% Triton X-100). For SubG1 analysis, the supernatant with detached cells and attached cells were stained with PI solution (above). Samples were loaded on a Guava Flow Cytometer (Millipore, Billerica, MA, USA) and analyzed with CytoSoft Data Acquisition and Analysis Software (Millipore), and approximately 7000 events were acquired for cell cycle analyses and 10 000 cells were counted for SubG1 analyses.
6, 10 mM zinc acetate and 5% glycerol) with or without S1 nuclease (Invitrogen, Life Technologies) at 20 U/ml for 30 min at 37 o C. See Supplementary Material for more details. DNA fibers were imaged using a fluorescent microscope (Axiovert 200, Zeiss, Jena, Germany) at a magnification of 1000×. Analyses were performed using Zeiss LSM Browser Software. All experiments were performed at least twice independently, and at least 100 fibers were counted for each slide. Results repeated in separate figures represent independent experiments.