dataesr/scientific-paragraphs-categorization huggingface dataset:
These averaging schemes and their bias corrections have been tested on scenes modelled from real satellite data in terms of altitude, surface pressure, weighting functions and relative variations of reflectivity. The three scenes present interesting characteristics, as they show different geophysical variations that could impact averaging biases. Besides, the signals and random noise are simulated from geophysical parameters and instrument parameters. The simulation shows that the lowest biases are obtained for the AVS scheme using appropriate bias corrections and averaging weights. Furthermore, this scheme is robust to low reflectivity values unlike the AVX and AVD schemes, which are highly sensitive to the accuracy of the SNR estimation. The best scheme, AVS, is compliant with the allocated averaging bias requirements (RSE) of 0.
Acknowledgements. This work was funded by  CNES  as part of the  CNES  and  DLR  project  MERLIN. We thank  Frédéric Chevallier  ( LSCE ) for the kind support he provided to this work. The authors would also like to thank the following LMD collaborators working on the  MERLIN  project (in alphabetical order):  Raymond Armante ,  Vincent Cassé ,  Olivier Chomette ,  Cyril Crevoisier ,  Thibault Delahaye ,  Dimitri Edouart  and  Frédéric Nahan. Edited by:  Joanna Joiner Reviewed  by: three anonymous referees.
Data availability. SPOT-5 data can be accessed at https://earth. esa. int/web/guest/data-access (last access: 22 October 2018). SRTM data can be accessed at http://srtm. csi. cgiar. org/ (last access: 22 October 2018). ECMWF data can be accessed at https://www. ecmwf. int/en/forecasts/datasets (last access: 22 October 2018). www. atmos-meas-tech. net/11/5865/2018/ Atmos. Meas. Tech. , 11, 5865-5884, 2018.
HFRs are designed to measure the local ocean surface dynamics with a high time and space resolution. However, altimetry and HFR do not capture exactly the same dynamics. Altimetry detects surface currents that are in geostrophic equilibrium (by excluding the direct response of the surface layer to the wind and then part of the high-frequency variations), whereas HFRs measure surface total currents, i. e. the geostrophic and ageostrophic components (like wind-driven and inertial currents or the waveinduced Stokes drift; e. g. Graber et al. , 1997; Law, 2001; Ardhuin et al. , 2009). Besides the effort made for collecting data from different platforms, methods for combining these data are under development. Recent studies focused on the evaluation of the performance of altimetry using HFRs, concluding that HFRs offer a way to improve the validation of altimetry products for coastal areas  (Chavanne and Klein, 2010; Liu et al.
, 2012; Pascual et al. , 2015; Troupin et al. , 2015; Roesler et al. , 2013). One of the most extended approaches found in the literature to study the synergy between altimetry and HFR data consists in the comparison of the total across-track currents in the along-track direction (e. g. Morrow et al. , 2017; Troupin et al. , 2015; Pascual et al. , 2015). The combination of HFR and altimetry could help to potentiate their strengths by, for example, expanding the spatial and temporal coverage of the HFR systems or evaluating and correcting the altimetric signal near the coast. In this study, we focus on the south-eastern Bay of Biscay (SE-BoB), which is characterized by the presence of canyons (e. g. Capbreton canyon), by an abrupt change in the orientation of the coast, and by a narrow shelf and slope.
HFR data HFRs are remote-sensing instruments that send radio waves to the ocean surface and use the signal backscattered by the waves to infer the radial velocity of the surface current (toward or away from each HFR antenna). They can measure surface currents over wide areas with high spatial (300 m-10 km) and temporal (≤ 1 h) resolution. In this study, surface currents were obtained by means of two long-range HFR antennae. These antennae emit at a central frequency of 4. 5 MHz and with an operational 30 kHz bandwidth. They are located at Matxitxako and Higer capes (Fig. 1 ) and have provided operational data since 2009 (with some interruptions mostly due to maintenance stops or malfunctioning related to severe atmospheric conditions). The averaged Doppler backscatter spectrum obtained from the received signal (in a 3 h window) is processed to obtain hourly radial currents using the MUSIC algorithm  (Schmidt, 1986).
The coverage of radial data is up to 150 km, and the range cell and angular resolution are set to 5 km and 5 • , respectively. Radial data are quality controlled using advanced procedures based on velocity and variance thresholds, signal-to-noise ratios, and radial total coverage. Since the deployment of the HFR system, the receiving antenna pattern of the two HFR sites has been calibrated at least every 2 years. A more detailed description of the system and of the HFR data validation ex-ercises are provided by  Solabarrieta et al. (2014 Solabarrieta et al. ( , 2015 Solabarrieta et al. ( , 2016) )  and  Rubio et al. (2011 Rubio et al. ( , 2018)). To obtain total currents gridded onto a 5 km resolution regular orthogonal mesh, a least mean square algorithm (spatial interpolation radius of 10 km) was applied by using the HFR_Progs Matlab package (https://cencalarchive.
org/~cocmpmb/COCMP-wiki/index. php/Main_Page#HFR_Progs_Documentation, last access: 1 October 2018), based on  Gurgel (1994)  and  Lipa and Barrick (1983). Then, using the same grid, radial velocities were processed with HFR_Progs to generate spatially gap-filled open-mode analysis (OMA) total currents  (Kaplan and Lekien, 2007). In order to generate hourly total fields, 85 OMA modes, built setting a minimum spatial scale of 20 km, were used. A first analysis of the comparisons between HFR and altimetry showed that the results obtained using total currents generated by least mean squares and OMA were very similar. Thus, only results using OMA currents are presented in this work. The typical spatial scales resolved by the HFRs depend on the resolution of the data and thus mainly on the operation frequency of the systems  (Rubio et al.
Altimetry data The basic principle of the altimetry technology is to send a radar signal to the sea surface and then to measure the reflected return echo. The time needed for the signal to go and come back determines the distance between the altimeter and the sea surface (called the range). A physically based model  (Brown, 1977)  is adjusted to the resulting signal, called waveform, providing different parameters, including range. To reduce the measurement noise, the result is averaged and the final data rate is classically (and in our case) 1 Hz (i. e. one datum every ∼ 7 km along the satellite track). By subtracting the range to the satellite orbit altitude (with respect to the reference ellipsoid) and by applying several corrections (e. g. tropospheric and ionospheric effects on the radar wave, sea surface bias), the sea surface height (SSH) is obtained (SSH = orbit -range -corrections).
I. Manso-Narvarte et al. : Joint analysis of coastal altimetry and HF radar data The two different along-track SLA time series used herein come from Jason-2's 248 and 213 tracks from cycle 18 to 259 and with a revisit period of ∼ 10 days. Track 248 covers the HFR footprint area, whereas track 213 only crosses a small area at the NW of the HFR total-current coverage (Fig. 1 ); therefore, track 213 is only useful for the comparison with HFR radial data. One of the altimetry data sets used in this study is the CTOH (Center for Topographic studies of the Ocean and Hydrosphere)-XTRACK product (http://ctoh. legos. obs-mip. fr/products/coastal-products/coastal-products-1/sla-1hz, last access: 1 October 2018), which provides SLA data specifically processed for coastal areas. The filtered version of the product was used, meaning that a 40 km cut-off spatial Loess filter  (Cleveland and Devlin, 1988)  was also applied in order to reduce the remaining noise in the along-track SLA.
For the same Jason-2 altimeter measurements, a differently processed data set was also used to assess possible discrepancies between the two altimetric products. This product is a preliminary version of the 2018 update of the reprocessed global ocean along-track level-3 data provided by the Copernicus Marine Environment Monitoring Service (CMEMS). As for the CTOH-XTRACK product, data filtered with a 65 km cutoff spatial Lanczos filter  (Pujol et al. , 2016)  were used. Note that, on the one hand, the accuracy of altimetry data is lower in the 20-30 km coastal band, so it might be a source of differences between altimetry and HFR data, and that, on the other hand, the larger the oceanic signal (larger signal-tonoise ratio), the lower this effect will be observed to be. In the SE-BoB, part of the slope (characteristically narrow) is located within the 20-30 km coastal band; therefore, the altimetry data will be affected by this inaccuracy in this area.
Wind data and sea surface temperature images Hourly wind data from the Weather Research and Forecasting model (WRF; http://mandeo. meteogalicia. es/ thredds/catalogos/WRF_2D/catalog. html, last access: 1 October 2018) were provided by the meteorological agency of Galicia (MeteoGalicia). This model, with a native resolution of 12 km, reproduces the offshore wind fields of the SE-BoB  (Ferrer et al. , 2010)  with reasonable accuracy. In this study, the WRF gridded fields were interpolated to the Jason-2 along-track points. Due to the cloudy weather in the SE-BoB, the most appropriate infrared sea surface temperature (SST) images were selected one by one from the AVHRR (Advanced Very High Resolution Radiometer) sensor series of 1 km resolution to process level-2 SST maps.
Methods Since the time resolution of the altimetry (∼ 10 days) is lower than that of the HFR (hourly), the HFR current data were filtered using a low-pass filter, based on a 10-day running average. The objective was to remove the high-frequency (HF) signals contained in the data (see, for instance,  Solabarrieta et al. , 2014)  and part of the ageostrophic signals to make the HFR data closer to the measurements from altimetry. Several tests were carried out to determine the sensibility of the results to the temporal window chosen for the filter. For that purpose, 2-, 5-, 10-and 15-day low-pass filter configurations were tested, and the resulting currents were compared to those derived from the SLA. The 10-day filtered HFR currents provided the highest correlation with the altimetry data. It is worth noting that the 10-day running average filtered out a significant part of the windinduced currents, except for the low-frequency (LF) Ekman component.
Since from the altimetry data used here we can only obtain sea surface anomaly currents, as explained in Sect. 2. 1. 2, the comparisons with the radar data were carried out in terms of anomaly. In order to obtain the HFR anomaly currents (AC HFR ), the temporal average of the HFR currents for the study period was subtracted to the series of low-pass HFR currents (Eq. 1). Then, to obtain SLA relative to the same period, the average SLA for the study period was subtracted to the SLA series (Eq. 2). AC HFR = V -V t 1 -t 2 (1) SLA = SLA -SLA t 1 -t 2 (2) Hence, the time referenced SLA and AC HFR were obtained as suggested in  Pujol et al. (2016) , where t 1 = 1 January 2009 and t 2 = 24 July 2015. Hereafter, when referring to AC HFR computed using Eq. (  1 ), we will use AC HFR,R to refer to current anomalies computed directly from the radial HFR components and AC HFR,T to refer to current anomalies computed from total OMA HFR currents.
Point E is located near the Capbreton canyon at 43. 75 • N, 2. 05 • W, 46. 86 km from the coast and at a depth of 500 m (on the slope), whereas point W is located at 43. 80 • N, 3. 58 • W, 40. 73 km from the coast and at a depth of 3000 m (on the abyssal plain). It is worth noting that by using directly radial currents, additional errors that propagate in the combination of HFR radials into HFR OMA currents are avoided. In order to make the computations more robust to the potential absence of HFR or altimetry data at points W and E, nearby points were considered to obtain the across-track currents (Fig. 2 ). With regard to AC HFR,R , radial directions of ±5 • , away from the orthogonal radial direction were also considered. For each of them, the points in a range of ±5 km from the track along the central and adjacent radials were selected.
Then, the across-track AC HFR,R in our point was obtained by firstly averaging the values for each radial, so that only three values along the track were obtained. Finally, the three values were again averaged to get the corresponding across-track AC HFR,R. On the other hand, the AC G in the three along-track points considered for the HFR case were averaged to obtain the AC G in the central point  (E and W ). This permitted us to ensure a similar spatial smoothing for both data sets. It must be mentioned that at point W and for the CTOH altimetry product, the points were located between the along-track HFR points, so instead of considering three along-track points, two points on each side of the central point (W ) were selected.
Along-track current comparison In order to assess the variability in the comparison between HFR and altimetry from the coast to the open ocean, the comparison between the across-track AC HFR,T and AC G along track 248 was performed. For that purpose, AC HFR,T was interpolated into the along-track altimetry points, and it was rotated to the across-track direction. Then, for each along-track AC G point, the average with its four adjacent points was calculated. As in the previous case, this permitted us to ensure a similar spatial smoothing for both data sets. The sensitivity to the number of adjacent points considered was tested, and this approach was the one that provided the best adjustment to the HFR data.
e. CMEMS and CTOH). The mean and the standard deviation (SD) are also estimated for each data set. E and W are the points where HFR radial velocities are orthogonal to tracks 248 and 213, respectively (see Fig. 1 ). E T means that the parameters are estimated at point E but using the HFR current velocity anomaly fields rotated to across-track direction instead of using the HFR radial velocity anomalies. That HFR data have also been used for Max T , which is the track point where the correlation r is maximal (point 2. 10 parameter, θ is the phase parameter, and τ is the wind stress vector (meridional and zonal components) that was obtained by means of the bulk-flux formula  (Stewart, 2009) : τ = ρ air C d |w|w, (5) where ρ air is the density of the air (1. 22 kg m -3 ), w is the wind vector, and C d is the drag coefficient proposed by  Large and Pond (1981).
Data availability. The CTOH-XTRACK altimetry product is available on the CTOH website (http://ctoh. legos. obs-mip. fr/products/ coastal-products/coastal-products-1/sla-1hz, last access: 8 October 2018). The CMEMS along-track level-3 altimetry product is available on the CMEMS website (http://marine. copernicus. eu/ services-portfolio/access-to-products/?option=com_csw&view= details&product_id=SEALEVEL_GLO_PHY_L3_REP_ OBSERVATIONS_008_045, last access: 8 October 2018). The SST data used were retrieved from the NERC Earth Observation Data Acquisition and Analysis Service website (http://www. neodaas. ac. uk/data, last access: 8 October 2018). The HF radar data are available at http://www. euskoos. eus/en/ radar-higer-en/ (last access: 8 October 2018) and at http://www. emodnet-physics. eu/map/platinfo/piradar.
DNA sequences were filtered using the OBITools software  (47) , as described in the study by Pansu et al. (16). Using the ecotag program, mammal sequences were assigned to the relevant taxa by comparing them with a global mammal database, generated from EMBL release 117; plant sequences were assigned using an exhaustive database of the vascular plants found on Kerguelen  (13). We only kept sequences with a match >97% with a mammal or a plant genus. Vertical migration (leaching) of sedDNA has not been observed in lake sediments  (48) , suggesting that it allows accurate temporal reconstruction. In ancient DNA studies, the pattern of DNA postmortem damage has often been used to authenticate DNA molecules  (17). This approach is particularly efficient with metagenomics, but it is not suitable with metabarcoding studies; thus, we used extensive controls and replicated analyses to ensure the quality of the sedDNA results  (17, 49).
We then assessed whether shifts in plant communities are significantly related to rabbit occurrence using RDA  (58). In RDA, the relative abundance of plant communities was the independent matrix. As predictor, we used a matrix composed by two proxies of rabbit presence: accumulation rate of Sporormiella spores and detection of rabbit sedDNA. Plant abundance data of the sample corresponding to 1941-1948 were outliers (Figs. 3  and 4  and table  S1 ), as in this sample almost only the DNA of A. selago was detected, and this can be explained by a different process determining plant DNA transport (rabbit feces; see Discussion). Therefore, the 1941-1948 sample was excluded from the RDA analyses, but we obtained identical results when we repeated the analyses including this sample (fig. S2 ). Temporal autocorrelation of the residuals was not significant (P > 0.
05 for all the species). Significance of ANOSIM and RDA was tested through 30,000 permutations using the vegan package in R (www. r-project. org). For analysis of erosion rate, we first used a dynamic programming algorithm to detect and date the occurrence of breakpoints in the erosion time series  (59). The best number of breakpoints was assessed using Bayesian information criterion. Subsequently, we used generalized least squares to assess the relationship between rabbit abundance (measured as the abundance of Sporormiella spores) and erosion rate, including a first-order autocorrelation term to take into account potential temporal autocorrelation issues. Results were identical if rabbit sedDNA was used as a proxy of rabbit presence, instead of the Sporormiella influx (appendix S2). Climate change is an alternative mechanism that might have major impacts on communities and erosion.
Abstract. The Amazon Basin is a unique region to study atmospheric aerosols, given their relevance for the regional hydrological cycle and the large uncertainty of their sources. Multi-year datasets are crucial when contrasting periods of natural conditions and periods influenced by anthropogenic emissions. In the wet season, biogenic sources and processes prevail, and the Amazonian atmospheric composition resembles preindustrial conditions. In the dry season, the basin is influenced by widespread biomass burning emissions. This work reports multi-year observations of high time resolution submicrometer (10-600 nm) particle number size distributions at a rain forest site in Amazonia (TT34 tower, 60 km NW from Manaus city), between 2008 and 2010 and 2012 and 2014. The median particle number concentration was 403 cm -3 in the wet season and 1254 cm -3 in the dry season.
The multi-year observations reported here confirm that NPF is uncommon, at least at this forest site in central Amazonia. Some hypotheses to explain the scarcity of NPF and subsequent growth events in the Amazon are as follows: (i) the concentration of inorganic precursors like SO 2 and NH 3 are relatively low, typically < 0. 1 ppb  (Andreae et al. , 1990; Rizzo, unpublished dataset)  and < 0. 8 ppb  (Trebs et al. , 2004)  in the wet season, respectively; (ii) NPF is inhibited by high emissions of isoprene  (Kanawade et al. , 2011; Kiendler-Scharr et al. , 2009; Lee et al. , 2016) ; (iii) NPF is inhibited by high relative humidity levels  (Bonn and Moortgat, 2003; Hamed et al. , 2011; Hyvönen et al. , 2005) ; (iv) there are point sources of new particle formation, instead of regional sources, so that subsequent growth to the accumulation mode cannot be followed  (Vana et al.
Housing for the researchers and a 60 kW diesel generator (that provided the power supply) were located 0. 33 and 0. 72 km to the west of the sampling site (downwind), respectively. A detailed description of the measurement site and surroundings can be found in  Martin et al. (2010a). In this work we define wet season as the period from January to June, and the dry season from July to December, in line with  Rizzo et al. (2013). This definition may not be strictly correct from a climatological point of view, as the start date of each season changes from one year to the next, depending on precipitation patterns. Nevertheless, for the purpose of the current aerosol dataset interpretation, this is a reasonable choice. A detailed description of climatological conditions can be found in  Marengo et al.
5 % for geometric mean diameter (D pg ). Those values can be considered a lower limit for the uncertainties in long-term field measurements, especially at locations with difficult maintenance conditions. In total, 184 040 submicrometer particle number size spectra were measured between February 2008 and October 2014. It was detected that 2. 4 % of the samples were contaminated by the previously mentioned diesel generator, and these were removed from the dataset. Contamination was detected based on fast variations of the particle number concentration (> 100 % in 5 min) associated with northwestern winds (270-360 • ).
Cluster analysis A clustering method was applied to the particle number size distribution data, in order to group particle size spectra with similar shapes and to unveil relationships with other variables, such as time of occurrence, season, local wind direction and particle absorption coefficient. For this reason, particle concentration variability was removed via the normalization of each size spectra with respect to its maximum concentration. Cluster analysis has been applied on particle number size distribution data in previous studies (e. g. ,  Beddows et al. , 2009; Dall'Osto et al. , 2010; Tunved et al. , 2004). Here, the MATLAB built-in function kmeans. m was used, which is available in the statistical package of version 2014a. This function uses a two-phase iterative algorithm to minimize the sum of point-to-centroid distances, summed over all clusters.
The dataset is a combination of particle number concentration measurements and integrated particle size distribution measurements (10-600 nm). The red line represents the median, the extremes of the blue boxes represent the 25 and 75 percentiles and the black whiskers extend to the most extreme data points that are not considered outliers. et al. , 2017; Roldin et al. , 2011)  was implemented as a stationary 1-D column model for the measurement site. In the model, the lower atmosphere was divided into 50 vertical layers, each 50 m thick, extending from the surface up to 2500 m a. g. l. The vertical mixing was simulated with the first-order closure scheme (K-theory), represented by a turbulent diffusion process. The vertical turbulent diffusion coefficient (K) was calculated based on a slightly modified Grisogono scheme  (Jericevic et al.
This suggests that the Manaus urban plume influence is occasional, and that particles are processed on their way to the forest site. The particles then merge with the aerosol population above forest areas without any apparent significant differences concerning particle size distributions. Rizzo et al. (2013)  identified 43 episodes of Manaus plume influence over the TT34 tower site between 2008 and 2011, which comprised 1. 5 % of their dataset.