dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysed RadioSoundings Archive The Analysed RadioSoundings Archive (ARSA) is a  41year (1979-2019)  database of radiosonde temperature profile measurements from different stations around the globe  (Scott et al. , 2015). ARSA provides 43 pressure level profiles (from the surface to 0. 0026 hPa) of temperature, water vapour and ozone, and surface temperature. The raw radiosonde observations go through severe multistep quality controls to eliminate gross errors. If the selected radiosonde measurement is unable to provide forward radiative transfer modellers with the required information (above 30 hPa for temperature), ARSA combines existing radiosonde measurements with other reliable data sources to complete the description of the atmospheric state as high as 0. 0026 hPa. Temperature profiles are thus extrapolated with ERA-Interim  (Dee et al.
, 2020). Based on these validations, incorrect or unreliable data inherent to the quality of the radiosondes were completed using measurement data of other relevant auxiliary datasets (in particular Level 2 results of ACE-FTS temperature profiles above 10 hPa). It is useful to recall that ARSA is being reprocessed to replace ERA-Interim with ERA5. This will allow, among other things, the period to be extended beyond summer 2019, when the production of ERA-Interim stopped.
EUMETSAT CDR of all-sky temperature profiles In 2020, EUMETSAT released a climate data record (CDR) of all-sky IASI temperature (https://doi. org/10. 15770/EUM_SEC_CLM_0027), so the temperature is homogeneous over the whole IASI time series  (EUMETSAT, 2020). The reprocessed temperatures were computed with a piecewise linear regression cube (PWLR 3 ) algorithm, using all IASI observations in input (clear and cloudy scenes) and observations from two other microwave instruments flying on board the Metop-A and Metop-B satellites: the Microwave Humidity Sounding (MHS) and the Advanced Microwave Sounding Unit (AMSU-A). The basic principle of this algorithm is a linear regression between IASI radiance observations and real atmospheric temperatures. To take into account the non-linearity between the observations and the temperatures, the training dataset is divided into several sub-datasets, resulting from a k-means clustering.
Artificial neural network We trained a two-layer artificial neural network (ANN) to estimate atmospheric temperature profiles. This method has been used for instance in  Aires et al. (2002) , using IASIsimulated radiances before its launch. A total of 450 000 IASI observations are used to train the ANN. These observations (3000 scenes per month) were selected randomly around the globe between January 2008 and December 2020. This training dataset is composed of day and night and clear-and cloudy-sky observations mixed together. The input data consist of the pseudo-normalized radiances (multiplied by 10 4 so that their order of magnitude is not too small compared to temperatures) in the selected channels as well as the scan angle of the observation. A monthly value for global CO 2 concentration was also added to take into account the CO 2 variations that affect the radiance values measured in the selected channels.
The CO 2 monthly values come from the NOAA Earth System Research Laboratories global monitoring dataset (https://gml. noaa. gov/ccgg/trends/, last access: 15 December 2021;  Dlugokencky and Tans, 2021). As the expected output of the training of the ANN, we use the ERA5 temperatures interpolated to the latitudes, longitudes, and time of the IASI observations. These temperatures are given on a static pressure level grid. We chose ERA5 because it is the most complete homogeneous dataset of temperatures available, allowing us to select observations in any year and any type of sky. A set of 50 000 different observations (selected the same way as the 450 000) is used to assess the quality of the ANN at the end of the training. The temperatures are computed at 11 fixed pressure levels: 2, 7, 10, 20, 30, 70, 100, 200, 400, 550, and 750 hPa.
Comparison with ERA5 Although the neural network was trained with ERA5, it does not reproduce the same temperatures. The output of the retrieval is mainly governed by the variations in observed radiances, and ERA5 can be used for validation. ERA5 temperatures are given on a 0. 25 • × 0. 25 • latitudelongitude grid. For the comparison with the IASI-ANN output, ERA5 temperatures were interpolated to the time, latitudes, and longitudes of IASI observations. We then computed the daily zonal mean of the IASI-based ANN temperatures and ERA5 and looked at the differences between the two datasets. Figure  3  illustrates the zonal mean differences between the ANN retrievals and ERA5 from 2008 to 2020 for the 11 pressure levels considered in this study and the time-averaged differences. Between 200 and 750 hPa, the differences are less than 0.
5 K at all latitudes. We see slight seasonal variations in the differences at 200, 400, and 550 hPa. At 750 hPa, the seasonal variations are more pronounced and more often negative than at the other pressure levels. Note that due to orography at this pressure level, there are no data over the South Pole, Greenland, and in the major mountain ranges. Between 7 and 100 hPa, the differences between the two datasets are less than 0. 5 K at mid-latitudes and at the poles. Around the Equator (30 • S to 30 • N), the differences are slightly larger (1 K). Figure  4  shows the monthly mean of the differences between 10 • S and 10 • N from 100 to 7 hPa as well as the monthly zonal wind from ERA5 in the same latitude range. Positive differences (ANN temperatures larger than ERA5) are correlated to negative zonal wind, and negative differences are correlated to positive zonal wind.
We interpolated IASI temperatures to the latitude, longitude, and time of each considered station, and we computed the daily mean IASI temperature in each region. We then computed the differences between the two datasets. Figure  7  shows the daily differences between IASI retrievals and ARSA mean regional temperature in the eight selected regions between 2008 and 2018 and the time-averaged difference profiles. We only show differences between 750 and 30 hPa as ARSA data above 30 hPa do not always come from radiosounding measurement but from the extrapolation datasets. Between 7 and 100 hPa, the differences are small and mostly negative (about 0. 5 K). At 200 hPa and below, the differences remain small and negative in the Pacific, Oceania, and East Asia. In Greenland, North America, and Europe, the differences at these pressure levels are slightly larger and more often positive (about 0.
The differences between ERA5 and ARSA are very similar to those between IASI retrievals and ARSA, but slightly smaller (less than 0. 3 K between 750 and 7 hPa). Figure  S2  shows the differences between IASI and ERA5 temperatures interpolated to the time and locations of ARSA observations. In most regions, the differences are less than 0. 5 K. In Antarctica, in Europe (troposphere only), and in the Arabian Peninsula and Oceania (stratosphere only), the differences can reach 1 K. Figure  8  shows the standard deviation of the daily differences between IASI-ANN and ARSA temperatures and the correlation between the two datasets in the eight regions. In most regions, the standard deviation ranges from 0. 5 to 1 K, except in the Arabian Peninsula and East Asia, where they reach 1. 3 K at 750 hPa, and in Antarctica and Europe, where they range from 1 to 2 K.
The correlations between IASI-ANN and ARSA temperatures show that there is no significant bias between the two datasets. Figures  3, 5, 7, and 8  show that between 7 and 750 hPa, the IASI-ANN product can be considered to be good-quality temperatures, very consistent with the temperatures of the ERA5 and ARSA datasets (differences smaller than 1 K at most latitudes, 2 K at the Equator). At 2 hPa, the quality of the ANN product decreases as it was reflected in the lower count of weighting functions of IASI (Fig. 2 ). This means that at 2 hPa, the temperatures are not accurate enough to follow the long-term evolution of atmospheric temperatures. However, they can still be used to study large variations in temperature (during extreme events for example).
At 7, 10, and 70 hPa, the differences are positive and larger at the Equator, and they decrease over time. This bias can also be seen at higher latitudes and other pressure levels, although it is less obvious. This bias might be due to the fact that EU-METSAT's algorithm does not use CO 2 in input, and the retrieval is impacted by the variations in the CO 2 over time, which we account for. Note that although the ANN and EUMETSAT retrievals are both based on IASI radiances, the two temperature records are not redundant. The two datasets use different observations (IASI radiances and CO 2 concentrations for the ANN and IASI, AMSU, and MHS radiances for EUMET-SAT) and different methods of retrieval (ANN and PWLR 3 ). When needed, our dataset can constantly be enhanced and rapidly reprocessed for the entire time series.
Conclusions We use an artificial neural network to construct a homogeneous temperature record from IASI radiances. Validation of the IASI-ANN product with ERA5-, ARSA-, and EUMETSAT-reprocessed temperatures shows a good agreement between the four datasets, especially between 7 and 750 hPa. The differences between IASI-ANN temperatures and ERA5 are less than 0. 5 K at most latitudes and most pressure levels, and the differences between IASI and ARSA are similar, demonstrating that our IASI product can be used to assess local variation in temperature and to compute trends. We used these temperatures to compute trends over the 2008-2020 period. We found a warming trend in tropospheric temperatures, stronger at mid-latitudes (0. 5 K/decade) and at the poles (1 K/decade due to Arctic amplification).
In the stratosphere, we found a strong cooling trend in the tropics between 30 • S and 30 • N, while poleward of 40 • S there are two regions with important warming due to the ozone hole recovery and a SSW that happened in 2019. This work shows that artificial neural networks are efficient to retrieve atmospheric temperatures from huge datasets of radiance. With this method, temperature profiles from all 10 billion observations from Metop-A and Metop-B can be computed in 2 d. With the short computation time and with IASI radiances being available a few hours after the observations, we can obtain temperature profiles in near real time. We now have a homogeneous product to study seasonal and climatological variations in temperatures. It can also be used to study extreme events such as El Niño-Southern Oscillation, volcanic eruptions, heatwaves, and sudden stratospheric warmings as well as their link with climate change.
Acknowledgements. We would like to thank the reviewers for their thoughtful comments and efforts towards improving our manuscript. IASI has been developed and built under the responsibility of the  Centre National d'Etudes spatiales (CNES, France ). It is on board the Metop satellites as part of the EUMETSAT Polar System. The IASI L1C data are received through the EUMETCast near-real-time data distribution service. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 742909 ). Lieven Clarisse  is a research associate ( Chercheur Qualifié ) supported by the  Belgian F. R. S. -FNRS. The LATMOS team is grateful to CNES for scientific collaboration. We thank  EUMETSAT  for providing us with a fully reprocessed dataset of radiances with the latest version of the L1C.
Data availability. EUMETSAT-reprocessed L1C and L2 data are available at https://doi. org/10. 15770/EUM_SEC_CLM_0014 (EUMET-SAT, 2018) and https://doi. org/10. 15770/EUM_SEC_CLM_0027 (Doutriaux-Boucher and August, 2020), respectively. ERA5 data can be downloaded from the Copernicus Climate Change Service Climate Data Store: https://cds. climate. copernicus. eu/cdsapp#!/ dataset/reanalysis-era5-pressure-levels?tab=overview (Copernicus, 2018). Requests for ARSA temperatures can be made at https: //ara. lmd. polytechnique. fr/index. php?page=arsa  (Scott, 2019). The temperatures retrieved with the ANN can be downloaded at https://iasi-ft. eu/products/atmospheric-temperature-profiles (Bouillon, 2021a) (URL for Metop-A temperatures: https://iasi-ft. eu/metadata/metadata_ATP_A; Bouillon, 2021b; and URL for Metop-B temperatures: https://iasi-ft.
Observations of the predominantly cross-shore forcing of the beach sediment have suggested that these beaches function as semi-enclosed sediment compartments most of the time, with sediment bypasses occurring in the subtidal zone during major storms  28, 32. Monitoring programs of pocket and embayed beaches have consisted mainly in video-derived shoreline positions and beach profiling  30, 31  , which can produce high-frequency data over long periods of time (i. e. , several years or decades), but provide limited quantitative hindsight on sediment transfers at the full embayment scale. In this paper, we describe for the first time the long-term dataset of beach topography and nearshore bathymetry at Porsmilin, a macrotidal embayed beach situated in Brittany, northwest France (Fig. 1 ). The dataset represents one of the longest records of continual beach surveys along Europe's Atlantic seaboard  33, 34.
Other studies interested in the methods for collecting and processing these geomorphic data present detailed information and validation of the techniques implemented in the survey program  [40] [41] [42]. Starting in 2019, after reaching sixteen years of running the Porsmilin survey program, the dataset was completely revamped with the aim that unrestricted access to the complete and archived data would follow. Doing so allowed for the incorporation of recent advances in geomorphic processing and validation techniques, providing consistent and well-documented data that will be easily reused by others for their analyses. Accompanying the results of our field surveys, we provide hourly wave hindcasts and tidal levels, obtained with the help of external organisations, for the period 2000-2019.
Field surveys. Beach profile surveys consists in measuring positions along a central cross-shore profile, following a line from the dune-embankment down to the low-tide waterline. Positions are recorded using high-accuracy RTK-GNSS (simply named 'GPS' in the dataset). Topographic surveys are timed to coincide with low tides. Survey frequency was approximately weekly for the first year then fortnightly between January 2004 and June 2005. Thereafter, survey frequency was reduced to a more sustainable near-monthly approach, although gaps in the data eventually occurred, such as in 2013 (Fig. 2a ). Daily surveys over approximately one-week periods were also carried out at a few instances during the course of the program (e. g. , in April 2011 and October 2014). The cross-shore spacing between survey points is adapted to the terrain morphology, ranging from near-continuous (i.
g. , rock outcrops and human structures) for accuracy verification (refer 'Technical Validation'). Coordinates used for the dataset are metric and referenced to the legal coordinate system for metropolitan France, i. e. , RGF93-Lambert 93 horizontal and NGF-IGN69 vertical (EPSG:5698). Elevation zero (m NGF) corresponds to approximately 0. 5 m below mean sea level (MSL). In addition to line surveys along the central cross-shore profile, surveys of the whole beach have also been undertaken. Between March and April 2004, a total of six 3D point clouds (Fig. 2b ) of the subaerial beach excluding the reef, corresponding to surveyed areas between 15,000 and 30,000 m 2 (cf. Online-only Table  1 ), were obtained using the GPS survey method. From 2009 onwards, time-of-flight terrestrial laser-scanning (survey method 'TLS') and unmanned aerial vehicle (UAV) photogrammetry (survey method 'PHO') were progressively implemented in parallel to the on-going profile surveys, with the intention to better capture geomorphic changes and underlying processes through increasing coverage and spatial resolution.
04° and a vertical scanning range of 100°. In either case, this corresponds to a maximum ground sampling distance of approximately 0. 1 m at a distance of 100 m. Obtained point clouds are processed using the laser proprietary software RiSCAN PRO v1. 7-2. 0 for scan registration and geo-referencing (http://www. riegl. com/products/ software-packages/riscan-pro/), and CloudCompare (http://www. cloudcompare. org/) for data interpolation into a DEM. The manufacturer stated positional accuracy and precision for the VZ-400 (LMS-Z390i) is 5(6) and 3(4) mm, respectively. This is an order of magnitude below the 3D root-mean square errors (RMSE) we estimated using GCP coordinates during processing, which yield overall scan precision of 0. 026 ± 0. 01 m (μ σ ± 1 , n = 74, cf. Online-only Table  2 ). Drone imagery and GCPs have been processed using the popular Structure-from-Motion (SfM) method implemented in Agisoft Photoscan (now Metashape; https://www.
To be consistent with other surveys, these datasets were later reprocessed using the SfM method. In total, 24 bathymetric DEMs have been obtained over the period 2008-2019 from hydrographic surveys using a boat-mounted multi-beam echo sounder (survey method 'MES' , Online-only Table  5 ). Bathymetric surveys are generally undertaken during spring high tides to achieve good coverage of the upper shoreface. A topographic survey (DEM or profile) is often planned the same day at low tide (Fig. 2 ), providing independent data for quality verification and data fusion. Fusion DEMs and profiles (survey method 'FUS') are seamless subaerial and subtidal DEMs and transect lines obtained through averaging elevations at the overlap between concurrent topographic and bathymetric surveys (cf. Online-only Table  6  & 7 ).
The hydrographic equipment aboard the ship consists in a multi-beam echo sounder (RESON SeaBat 8160 until the end of 2011, KONGSBERG EM 3002 thereafter) connected to RTK-GNSS and inertial measurement unit sensors. In addition, one or multiple sound speed profiles of the water column are acquired during the survey using a dedicated celerity probe. Using the EM 3002 (similar specifications prevail for the SeaBat), data are recorded by 254 equidistant beams emitting sound pulses at 300 kHz. Angular resolution is set to 120°, resulting in a maximum lateral coverage of approximately 50 m per track and inter-beam distances of 0. 2 m at 15 m water depth (the maximum depth at the study area). Planning, collection and processing of bathymetric data are done using QPS Qinsy software suite (https://qps.
nl/qinsy/), which after guided correction of inertial movements of the boat, sound celerity gradients and tidal effects, allows producing bathymetric DEMs with sub-metre spatial resolutions and theoretical accuracies/precisions ~0. 1 m. Using quality-controlled DEMs (cf. explanation below), elevation transects were systematically extracted corresponding to the central cross-shore profile line, hence producing bathymetric and fusion profiles, and increasing the number of subaerial beach profiles for the period 2003-2019 from initially 253 to 280 (Table  1 , Fig. 2a ). an important effort was devoted to revamp the dataset, particularly DEMs, which were obtained using different survey methods, in order to produce a suite of consistent observations easily comprehensible and reusable.
Data harmonization. To facilitate reuse of the data and to allow direct comparisons, a standard sampling resolution of 0. 5 m and consistent DEM grids were chosen to fit the entire dataset. Using a grid spacing of 0. 5 m preserved high data quality, particularly for photogrammetry and laser-scanning allowing higher resolutions, while maintaining file size small for efficient handling. Consequently, some surveys were resampled (using linear interpolation) to a resolution higher than their initial sampling distances. This is the case of nine bathymetric surveys until end 2011 initially produced at 1 m and the six GPS point clouds obtained in 2004. Although small-scale bed features (<1 m) may not be detectable using extrapolated DEMs, the negative impact of up-sampling a small portion of the DEMs over the duration of the dataset on change detection analyses (e.
Reef/sand classification was performed using a combination of orthoimage and roughness analyses. The results of post-processing are reported in the metadata accompanying each DEM and are summarized in Online-only tables. As a final step, all DEMs were assessed for systematic and random errors, with eventual corrections applied (cf. 'Technical Validation'). Doing so enabled for the provision of total error estimates for each survey and ensured altimetric consistency throughout the duration of the monitoring program, these two steps being important prerequisites for allowing reliable change detection using long-term morphological datasets  55. Waves. Continuous hourly wave characteristics directly offshore of Porsmilin in approximately 15 m water depth are provided for the period January 2000-December 2019.
The wave dataset starts three years before the field surveys to provide antecedent conditions that may be useful for understanding and modelling beach state at the start of the field surveys  22, 56. Wave parameters provided are the significant wave height (Hs), peak (Tp) and mean (T02) wave periods, and peak wave direction (Dir). They were derived from sea-states hindcast databases developed by Ifremer, using the node point 47554 (48°20'47. 76" N, 4°40'52. 32" W, Fig. 1 ). Databases are based on Wavewatch III (WW3) model (version 4. 11) using the same unstructured grid covering the English Channel and Bay of Biscay with a resolution at the coast of 200 m. The first database called HOMERE is a global hindcast produced in 2017 and covering the period 1994-2016 (23 years)  57. The second database, NORGAS-UG, is updated monthly with archived data that currently spans 2008-2019  58.
Tides. Continuous tidal levels at 10-min intervals are provided over the same time period as the combined wave dataset (i. e. , January 2000-December 2019). They were derived using the tidal analysis package for MATLAB Utide 59 based on hourly observed water-level data  60  (22/12/1970-31/12/2019) at the nearby Shom (Service hydrographique et océanographique de la marine) tide gauge of Le Conquet (48°21'32. 753"N, 4°46'50. 7"W, Fig. 1b ), situated less than 8 km from the field site.
Data Records The archived datasets presented herein, spanning 2003-2019 for field surveys and 2000-2019 for waves and tides can be accessed at Indigeo  52. With the monitoring program continuing, we intend to update the data on an annual basis through DOI versioning. Field survey products as they continue to be updated can also be accessed and visualised at https://www. dynalit. fr/La-carte-des-sites/Porsmilin#/map.
The analysis was performed on a composite DEM (Fig. 4a ), to which gaps of variable size provided from other DEMs were artificially created (resulting in the deletion of approximately 12,000 cells). Using a maximum radius for search of 10 m and a minimum number of known elevations set to three enabled to fill most data gaps ( >99%) without impacting the reliability of interpolated sand levels (mean error (ME) and standard deviation of error (SDE) ~10 -6 and 0. 01 m, respectively). Importantly, this traduced to a sand volume error of only 0. 4 m 3 over the complete DEM size, which is several orders of magnitude below the uncertainty that would be associated to a typical beach survey precision of 0. 1 m. In line with recent studies advocating for quantitative statements regarding data quality, to ensure reliable morphological analyses using coastal topographic data  28  , technical validation of this dataset was possible using a variety of error statistics adapted to the survey methods and all reported in this data descriptor.