dataesr/scientific-paragraphs-categorization huggingface dataset:
Solid precipitation measurements in Antarctica are crucial as snowfall represents the main water input term for the polar cap, and its probable increase in the coming century can mitigate sea-level rise caused by global warming. This paper presents 7 years of Micro Rain Radar (Metek MRR-2) data at the Dumont d'Urville station in coastal Adélie Land, East Antarctica. Statistics are calculated on 3 radar variables (equivalent reflectivity, mean Doppler velocity and signal-to-noise ratio) to outline the main characteristics of the radar dataset. Seasonal and interannual variabilities are also investigated, but no significant temporal trends are detected except for the seasonal mean Doppler velocity which is higher in summer and lower in winter. We then use the snowfall rate (S) data from a colocated snow-gauge to estimate the MRR precipitation profile from the radar equivalent reflectivity (Ze) through a locally derived Ze-S relation.
This involves hypotheses and tuning. In addition, these radars have been initially developed, and are provided with processing tools, designed for liquid precipitation. Obviously, for Antarctica, this has to be revised to access solid precipitation. In this paper, we present 7 years of vertical profiling of precipitation at the Dumont d'Urville station in Adélie Land, East Antarctica using a Metek Micro-Rain Radar (MRR-2) precipitation profiler. The setting, instruments, data processing methods and datasets are presented in Sect. 2. The main characteristics of the MRR dataset, including variability, statistics and extremes are presented in Sect. 3. 1. In Sect. 3. 2, the mean MRR snowfall profile is derived from an empirical and local Ze-S relation, enabling a vertical comparison with two climate models in section 3.
As these variations remain within 10% relative difference, this result supports that the derived Ze-S relation is also robust to interannual changes in the recordings. (3) with the parameters found for the whole dataset with an integration time of 1 hour. Doing so, we assume that the Z-S relationship derived using the MRR data at 300 m is representative for the entire profile (up to 3 km), which may not hold true when there is a large change in the hydrometeors structure and type along the vertical.
Example of application to models evaluation In this section, we show how the hourly MRR snowfall computed from the Ze-S relation described in Sect. 3. 2 can be used to evaluate the vertical profiles of precipitation as simulated by numerical models. The MRR is compared to the ERA5 reanalysis and the LMDZ model (described in Sect. 2. 4) for the period ranging from 2015-12-01 to 2022-01-01. Firstly, the median and quantiles profiles of the three datasets in coastal Adélie Land are investigated. The model profiles are precipitation threshold-sensitive, as they produce a large number of very small precipitation events (below 0. 01 mm hr -1 ). Palerme et al. (2014)  proposed a threshold of 0. 07 mm per 6 hours to optimize the comparison of ERA-Interim precipitation rates with CloudSat observations, which was also used in  Roussel et al.
(2023)  for models evaluation. Converted to mm hr -1 , the models' threshold is 0. 012 mm hr -1. The MRR snowfall threshold is derived from the equivalent reflectivity sensitivity of -5 dBz, i. e. +1 dBz taking into account radome attenuation (see Sect. 2. 1) and 10 1/10 = 1. 3 mm 6 m -3 in linear units. Equation (3) with the parameters a and b found in Sect. 3. 2 gives a MRR threshold of 0. 019 mm hr -1. To avoid sampling period biases due to MRR missing data, only precipitating times (i. e. , with a precipitation rate above the thresholds defined above) for the three datasets have been retained, corresponding to 3717 data points. Figure  8  shows the profiles medians (solid lines with dots) and the 10th and 90th quantiles (dotted lines). The MRR 90th quantile stands out rather strikingly, peaking more than twice as large as the corresponding ERA5 and LMDZ profiles, while its median and 10th quantile remain of similar magnitude.
Although the MRR data do not extend below 300 m, the slopes of the model profiles below 800 m substantially overestimate surface precipitation because of too weak sublimation. If sublimation is defined as the relative difference of the median snowfall rate between the altitude of maximum precipitation and 300 m (including for the models), the MRR sublimation is 40%, whereas and LMDZ (in blue) for the 3717 hourly data points of common precipitating timesteps. ERA5 (LMDZ) sublimation is only 12% (13%). The underestimation of strong events intensity by the models has a big impact on total snow accumulation. Indeed, Fig. 1  of  Turner et al. (2019)  indicates that extreme precipitation events contributes by more than 50% to total accumulation near the At the end of the period, after having removed the gaps in the MRR dataset, the MRR yearly average accumulation at 300m is 1060 mm yr -1.
Model snowfall events are also longer with a median of 13h (14h) for ERA5 (LMDZ) compared to 7h for the MRR. However, this higher occurrence of model precipitation is not enough to compensate for the larger MRR accumulation. A Ze-S relation has been derived from the dataset to retrieve precipitation profiles, thus allowing to refine the relation found by  Grazioli et al. (2017a)  for the same instrument but built on one summer season only. Despite a large amount of noise, the 7-year period made it possible to apply restrictive filters robust to integration time and interannual variability. The uncertainty of the Ze-S relation is estimated as its RMSE in log space. The results have been compared with literature, and particularly with the relation of  Grazioli et al. (2017a)  with whom an offset probably due to the sampling period has been found, leading to smaller MRR snowfall rates than this study.
However, Ze-S relations in the literature still present a significant degree of uncertainty and makes it difficult to draw final conclusions. Although we chose to be very cautious, the R 2 score remains low and other processing methods may be applied to the raw data (see data availability below) by future users. For instance, other explanatory variables such as temperature, wind speed, or the mean Doppler velocity of hydrometeors could be combined to the equivalent reflectivity to better constrain the Ze-S relation, but such considerations are beyond the scope of this study. This Ze-S relation allowed the evaluation of two climate models (ERA5 and LMDZ) along the vertical as an application example of the dataset. Models showed profiles too smooth, both in altitude and time, with a large underestimation of intense snowfall events compared to the MRR leading to an accumulation twice as small.
The weaker sublimation of precipitation by the models is not enough to compensate for their smaller accumulation. We believe that the 7 years of data presented in this paper are a great opportunity to evaluate and optimize climate models by fostering future studies on the parameterization of snowfall along the vertical in Antarctica, as well as the representation of the katabatic layer and its impact on precipitation sublimation. The dataset can also be used to complement and validate satellite products by providing ground-based information, for instance to evaluate the effect of the blind range near the ground level to obtain more accurate surface precipitation estimates.
Acknowledgements. The LMDZ simulation was performed using HPC resources from the  IDRIS (Institut du Développement et des Ressources en Informatique Scientifique, CNRS, France) , projects  RLMD AD010107632R1. This study benefited from the ESPRI computing and data center (https://mesocentre. ipsl. fr), which is supported by  CNRS ,  Sorbonne Université ,  Ecole Polytechnique , and  CNES  as well as through national and international grants. We thank  Claudio Durán-Alarcón  for sharing his script for the MRR raw data processing, and  Jean-Louis Dufresne  for sharing the DDU snow-gauge data, with an instructive critical insight. We also thank  IPEV  (the  French polar institute)  for support to program  CALVA , and  CNES  for support to program EECLAT. Finally, we gratefully thank  Pauline Jaunet ,  Laurent Baudchon  and  Météo-France  for preparing and sharing the DDU weather station data.
Data availability. Data are available here : https://web. lmd. jussieu. fr/~vwiener/MRRDATA/  (Wiener et al. (2023) ), and are furthermore currently under review on PANGAEA. A DOI and full data citation will be provided at the end of the process. The MRR 1-min profiles of the source variables (equivalent reflectivity, mean Doppler velocity, SNR and quality flag) are stored in zipped netCDF files from 2015-11-23 to 2023-07-01 (one file per year). The MRR hourly snowfall profiles computed in Sect. 3. 2 from the relation Ze = 43. 3 S 0. 88 along with the gauge 1-min snowfall accumulation and the hourly wind speed and temperature from Météo-France observations are also available for the same period. The processing code in python used for the derivation of the Ze-S relation is also attached as a Jupyter Notebook.
Age constraints for the obduction of the ophiolites The Vedi natural reserve and the Erakh anticline hold some key bio-chronostratigraphic elements to constrain the age of obduction of ophiolites in the Lesser Caucasus. The first type of data comes from the uppermost carbonate sequences of the SAB. Hakobyan [1978]  mentions the presence of upper Cenomanian layers with Bicarinella bicarina and Pyrazopsis quinquecostatus (both gastropods) and lower Turonian layers with Radiolites peroni (a rudist bivalve) and Omphaloacteonella ovata (a gastropod). In addition to these old data, which are difficult to be confirmed,  Danelian et al. [2014]  studied the 150 uppermost metres of the carbonate sequence of the SAB at Vedi and found benthic foraminifera suggesting a Cenomanian age, especially based on the presence of Daxia cenomana.
Regarding the age of the terrigenous sequences that are situated underneath the obducted ophiolites,  Hakobyan [1978]  considers the mélange unit at Erakh as early Coniacian in age; he mentions the presence of ammonites (Barroisiceras onilahyense) and several gastropod species found in siltstones and quartz-feldspar sandstones of the mélange unit. Sosson et al. [2010]  presented some calcareous nannofossil biostratigraphic results from the Vedi sector based on identifications by Carla Müller (see their Table  1 ). Here, we attempt to discuss their results based on the biostratigraphic scheme of  Burnett [1998]  and biostratigraphic information available in the Nannotax online database (https://www. mikrotax. org/Nannotax3/index. php? dir=ntax_mesozoic). At the Vedi valley, the ARS16 sample was collected from the base of the flysch capping the SAB carbonate sequence.
Acknowledgments Funding from the  DARIUS program  and the  GDRI-CNRS « South Caucasus  » is gratefully acknowledged. Fieldwork was also facilitated by the logistic support of the  Institute of Geological Sciences  and the Project  21T-1E119 , supported by the  Committee of Science of the Ministry of ESCS of  RA. The Erasmus + ICM allowed TD to do additional fieldwork during one of his educational visits. The  University of Lille  is thanked for funding MT as an invited  Professor  to  Lille. Sylvie Regnier  and  Valentin De Wulf  are thanked for help with radiolarian chert sample processing, radiolarian picking, and SEM photography. Constructive remarks by  A. H. F. Robertson  ( Edinburgh ) and  S. Goričan  ( Ljubljana ) helped improve the initial manuscript.
, 2017; Frank and Brodsky, 2019) , Cascadia  (Michel et al. , 2019a; Ducellier et al. , 2022; Itoh et al. , 2022) , along the San Andreas Fault  (Khoshmanesh and Shirzaei, 2018; Rousset et al. , 2019; Michel et al. , 2022) , the Haiyuan fault in Tibet  (Jolivet et al. , 2015a; Li et al. , 2021) , on the Alto Tiberina and Pollino fault systems in Italy  (Gualandi et al. , 2017; Cheloni et al. , 2017; Essing and Poli, 2022) , or Japan  (Nishimura et al. , 2013; Takagi et al. , 2019; Nishikawa et al. , 2019; Uchida et al. , 2020). All observations suggest the importance of accounting for aseismic slip in our understanding of earthquake cycle dynamics. However, the underlying physics controlling aseismic slip is still debated, mainly due to the lack of good, dense observational databases. Nowadays, observations of aseismic slip in subduction zones are frequently documented over a wide range of slip amplitudes and at different stages of the earthquake cycle  (Avouac, 2015; Obara and Kato, 2016; Bürgmann, 2018; Kato and Ben-Zion, 2021, and references therein).
, 2016)  in Peru (5. 5 mm/yr) to the convergence rate. In the Arica bend (16 o S -18 o S), at the boundary of the Chilean and Peruvian slivers, we build a gradient to make a smooth transition between the two slivers. We solve for the distribution of models that satisfy the geodetic data. The forward problem is written as d = Gm, with d the geodetic data (GNSS and InSAR velocities), m the vector of parameters to solve for and G the Green's functions (Section 2. 2). Parameters include coupling at each mesh node and geometric transformations akin to those in  Jolivet et al. (2020). We adopt a probabilistic approach to estimate the parameters in order to evaluate the associated uncertainties. The a posteriori Probability Density Function (PDF) of a model m given a dataset d, p(m|d), writes as p(m|d) ∝ p(m)p(d|m), (2) where p(m) is the a priori model PDF and p(d|m) is the data likelihood.
Detected slow slip events occur mostly in regions of intermediate coupling. This observation is not as clear for the Peruvian region, probably because of the sparsity of the data used here, although the same tendency is suggested on Figure  8. This result aligns with  Frank (2016)  findings in the Mexico subduction zone, where a database of slow slip events seems to compensate the lack of slip deficit in transition zones with respect to coupled regions of the megathrust. Materna et al. (2019)  describe a comparable behavior over longer periods where coupling variations seem to occur in regions of transitional coupling  (Michel et al. , 2019a). In addition, events offshore Peru tend to cluster spatially around locked asperities, areas that are generally of intermediate coupling (Figure  9 ).
Acknowledgements The authors thank the  Laboratoire International Associé "Montessus de Ballore" (LIA-MB) ,  International Plate Boundary Observatory Chile  (IPOC, www. ipoc-network. org),  Central Andean Tectonic Observatory Geodetic Array  (CAnTO, www. tectonics. caltech. edu/resources/continuous_gps. html), and  Instituto Geofísico del Perú  (www. igp. gob. pe) for making the raw GNSS data available, as well as all researchers, technicians, and students involved in the installation and maintenance of such networks. J. Jara  acknowledges a PhD scholarship granted by  Chilean National Science Cooperation (CONICYT)  through " Becas Chile" Program  and  MSCA Postdoctoral Fellowship  ( MSCA-101066069 , project  ERASMUS )  A. Socquet  and  J. Jara  benefited from a grant from  ANR  ( ANR-17-CE31-0002-01 , project  AtypicSSE ).
Data and code availability GNSS data are processed using GAMIT software  (Herring et al. , 2015)  (http://geoweb. mit. edu/gg/), while the reference frame is defined using PYACS  (Nocquet, 2018)  (https://github. com/JMNocquet/pyacs36). GNSS time series used in this work can be found at: https://doi. org/10. 5281/zenodo. 7898656. The modeling has been performed using the Classic Slip Inversion library (Jolivet et al. , 2015b)  (CSI, https://github. com/jolivetr/csi) and AlTar  (Minson et al. , 2013)  (https: //github. com/AlTarFramework/altar). All plots are made using Matplotlib  (Hunter, 2007)  and Cartopy  (Office, 2010)  Python packages.
The results of  Ferrer et al. (2004)  suggested limited dispersal of ELS anchovies towards the open ocean, while  Allain et al. (2001 Allain et al. ( , 2007) )  and  Huret et al. (2010)  suggested a stronger offshore transport, varying by spawning location and period considered. Despite the different transport patterns observed in these studies, none of them provided a comprehensive understanding of their variability throughout multiple years or how various coastal processes affected them. The extensive historical datasets existing today in the southeastern Bay of Biscay (SE-BoB, Fig. 1a ) allow performing new Lagrangian diagnostics using observational data. The SE-BoB is characterised by an abrupt change in the orientation of the coast and the presence of the steep Capbreton submarine canyon, which separates the narrower Spanish shelf from the wider French shelf (Fig.
g. Capó et al. , 2021; Tarry et al. , 2022) ) in the study area  (Caballero et al. , 2016) , computations at 3 (surface level) and 10 m (subsurface level) were considered. Surface computations utilized HFR data, while velocities for the subsurface ones were obtained by reconstructing HFR and acoustic Doppler current profiler (ADCP) data by a reduced order optimal interpolation (ROOI) method. Despite also using model data, this method strongly constrains the reconstructions to the observations and has been successfully tested in the area  (Manso-Narvarte et al. , 2020 , 2021). Despite the assumptions made and limitations of the used approach, this study provides a novel insight into the effects of circulation on ELS anchovies' distribution. Transport patterns were analysed by a multiyear analysis and from a process-oriented perspective supported by additional advanced Lagrangian diagnostics such as Lagrangian Coherent Structures (LCS).
Observational velocity data Observational velocities were provided by the EuskOOS HFR system and the ADCPs located in two slope moorings. The HFR system consists of two sites located at Cape Higher and Cape Matxitxako (Fig. 1b ). It works at a central frequency of 4. 46 MHz, with an operational bandwidth of 30 kHz, providing hourly surface velocity fields gridded in a regular mesh of 5 km resolution. It covers ~150 km off the coast and represents the first ~1. 5 m of the water column. This system has been providing surface currents since 2009  (Rubio et al. , 2011 (Rubio et al. , , 2013 (Rubio et al. , , 2018 (Rubio et al. , , 2020;; Solabarrieta et al. , 2014 Solabarrieta et al. , , 2015 Solabarrieta et al. , , 2016) )  with some interruptions mostly due to severe atmospheric conditions. Two downward-looking ADCPs (located at the Donostia and Matxitxako moorings; Fig.
1b ) working at a central frequency of 150 kHz provided time series of hourly velocities from 12. 26 m to 200 m in the water column with bins every 8 m since 2007  (Rubio et al. , 2013; Solabarrieta et al. , 2014). HFR and Donostia ADCP velocities were used as input observations for the ROOI method. Additionally, HFR velocities were used as forcing data in surface computations and the estimation of LCSs. Matxitxako ADCP velocities were used in the sensitivity tests to select the optimal parameters for applying the ROOI method, shown in Supplementary Data (SD) 1. Furthermore, they were used in the ROOI validation exercises in SD 2. HFR velocities were spatially gap-filled to ensure the spatiotemporal continuity in the velocity fields needed to perform accurate Lagrangian diagnostics. The data-gap filling was carried out by the open-mode analysis (OMA) method  (Kaplan and Lekien, 2007) , which provides reliable velocities for these diagnostics  (Hernández-Carrasco et al.
Reanalysis model velocity data The IBI_REANALYSIS_PHYS_005_002 data product (hereinafter IBI) provided by Copernicus Marine Environment Monitoring Service (CMEMS) was used as velocity data input to compute the covariance matrix for the ROOI method. Additionally, this dataset was used in the ROOI validation exercises shown in SD 2. IBI is based on a realistic configuration of the NEMO model for the Iberian Biscay Irish region and assimilates in-situ and satellite data. For more information, the reader is referred to  Sotillo et al. (2015). IBI provides daily data with unevenly distributed vertical levels with separations between 1 and 3 m in shallow depths and increasing separations with depth. The horizontal resolution is of 0. 083 • x 0. 083 • (~6-9 km).
Wind and chlorophyll-a data Wind data and chlorophyll-a (Chl-a) images were used to complement the process-oriented analyses. Hourly wind data were extracted at 43. 86 • N-2. 14 • W from the Weather Research and Forecasting model (WRF) of the meteorological agency of Galicia (MeteoGalicia). This model is able to reproduce the wind fields of the SE-BoB with reasonable accuracy  (Ferrer et al. , 2010). On the other hand, daily level 3 Chl-a images (for dates sufficiently free of clouds) with a 1 km resolution were obtained from the OCEANCOLOUR_ATL_CHL_L3_REP_OBSE RVATIONS_009_067 data product of CMEMS.
Transport computations set-up To compute the transport of ELS anchovies as infinitesimal passive particles, the Lagrangian module of the MOHID Water software  (Leitão, 1996; Neves, 2013)  was used. The computation grid was set to 2. 25 × 3. 1 km with a slip boundary condition at the South and East sides of the domain (coastal areas) to avoid the removal of particles. The slip condition was set due to the scarce resolution nor coverage of observational currents in the coastal area to properly parametrize the complex beaching processes. At the North and West areas (open boundaries) particles were allowed to leave the domain. The module uses an Euler scheme with a time step of 15 min to integrate the equation of motion, and a bilinear interpolation in space and linear interpolation in time of the velocity fields at the particle positions.
The equation of motion includes a diffusion term with a diffusivity value of 4 m 2 s -1 , based on  Okubo (1971). Transport computations were performed for seven periods corresponding to the yearly BIOMAN surveys from 2011 to 2018, excluding 2015 due to the lack of available velocity data. The data used and the scheme followed in the transport computations are summarized in Fig. 2. In each period 10,000 particles were initially distributed according to the observations from CUFES at 3 m and PairoVET at 10 m, for surface and subsurface computations, respectively. An example of the initial particle distributions for 2011 and 2013 is shown in Fig. 3. The starting dates were determined as the mean date of the BIOMAN survey in the study area (which represents a period of 3 days where the abundance distributions are stable): 10 May 2011; 13 May 2012; 13 May 2013; 8 May 2014; 9 May 2016; 9 May 2017 and 13 May 2018.
Data availability The datasets used in this study are available at the following links: -HFR: https://www. euskoos. eus/en/data/basque-ocean-meteoro logical-network/high-frequency-coastal-radars/ -ADCP: https://www. euskoos. eus/en/data/basque-ocean-mete orological-network/donostia-deep-water-buoy/ -IBI: https://resources. marine. copernicus.
Acknowledgments We thank the  Emergencies and Meteorology Directorate (Security department) of the Basque Government  for public data provision from the  Basque Operational Oceanography System EuskOOS. This study has been conducted using  EU Copernicus Marine Service  information. Wind data were obtained from the meteorological agency of  Galicia (Meteo-Galicia). The processing of HFR data was supported by  JERICO-S3  project, funded by the  European Union 's  Horizon 2020 Research and Innovation Program  under grant agreement no. 871153. This study has also been undertaken with the financial support of the " Departamento de Medio Ambiente, Ordenación del Territorio, Agricultura y Pesca" of the Basque Government (MarcoProgram). This work has been done partially with financial support from the project  LAMARCA  ( PID2021-123352OB-C31 ;  PID2021-123352OB-C33 ) funded by  MICIN/AEI /10.