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1,337 results for “Antarctica”
Dataset for the paper "Ephemeral grounding on the Pine Island Ice Shelf, West Antarctica, from 2014 to 2023"
<p>This code and related datasets are used for generating the figures for the paper "Ephemeral grounding on the Pine Island Ice Shelf, West Antarctica, from 2014 to 2023". Includes corrected REMA DSM stripes at the central ice shelf region of Pine Island Ice Shelf, the double differential vertical displacement results from 2014 to 2023 which cazlculated from the offset tracking results output from GAMMA software. Other dataset for other analysis are also included in the ZIP file. Each MATLAB codes in MATLAB_function.zip includes the discriptions that guide the user how to used it and how to find the dataset that used for processing. Some sample files are provided in data_and_results.zip that can let user test the code easily. These data can be accessed after the paper is accepted.</p> <p> </p>
Air/Snow temperature vertical profiles at different nodes of the 'Limnopolar Lake' CALM site, in Byers Península Livingston Island, Antarctica (2013-2022)
<div> <div> </div> </div> <div> <p>Air or seasonal snow temperature data were collected at different heights above the ground between 2013 and 2022 using an array of temperature micro-loggers (iButton models by Maxim) mounted on vertical wooden masts. These measurements were conducted at various nodes within the 100x100 m 'Limnopolar Lake' CALM site (A25) grid of the PERMATHERMAL network, managed by the University of Alcalá, Madrid, Spain, to monitor active layer thickness on Byers Peninsula, Livingston Island, South Shetland Islands, Antarctica.</p> <p>In 2013, nine arrays were installed at nodes with relative coordinates (00,00), (00,05), (00,10), (05,00), (05,05), (05,10), (10,00), (10,05), and (10,10). Measurements were taken at heights of 2.5, 5, 10, 15, 20, 25, 30, and 40 cm above the ground surface using DS1921G iButton loggers, which recorded air/snow temperatures every 4 hours. This experiment, referred to as 'Mini', was active for only one year and is now discontinued.</p> <p>Between 2017 and 2022, three arrays were installed at nodes (00,00), (05,05), and (10,10). These arrays measured air/snow temperatures at heights of 2.5, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, and 160 cm above the ground surface using DS1922L iButton loggers, which recorded temperatures every 3 hours. This experiment, referred to as 'HR', has also been discontinued.</p> </div>
Air/Snow temperature vertical profiles at different sites in Livingston Island, Antarctica (2006-2023)
<p>Air or seasonal snow temperature data collected at different heights above the ground (2.5, 5, 10, 20, 40, 80, and 160 cm), generally recorded every 3 hours between 2006 and 2023, using an array of temperature micro-loggers (iButton models by Maxim) mounted along a vertical wooden mast. These measurements were taken at various stations of the PERMATHERMAL network, managed by the University of Alcalá, Madrid, Spain, to monitor the thermal dynamics of frozen soils on Livingston Island, South Shetland Islands, Antarctica.</p>
Air/Snow temperature vertical profiles at different sites in Deception Island, Antarctica (2008-2023)
<p>Air or seasonal snow temperature data collected at different heights above the ground (2.5, 5, 10, 20, 40, 80, and 160 cm), generally recorded every 3 hours between 2006 and 2023, using an array of temperature micro-loggers (iButton models by Maxim) mounted along a vertical wooden mast. These measurements were taken at various stations of the PERMATHERMAL network, managed by the University of Alcalá, Madrid, Spain, to monitor the thermal dynamics of frozen soils on Deception Island, South Shetland Islands, Antarctica.</p>
Air/Snow temperature vertical profiles at different nodes of the 'Crater Lake' CALM site in Deception Island, Antarctica (2012-2023)
<p>Air or seasonal snow temperature data were collected at different heights above the ground between 2012 and 2023 using an array of temperature micro-loggers (iButton models by Maxim) mounted on vertical wooden masts. These measurements were conducted at various nodes within the 100x100 m 'Crater Lake' CALM site (A16) grid of the PERMATHERMAL network, managed by the University of Alcalá, Madrid, Spain, to monitor active layer thickness in Deception Island, South Shetland Islands, Antarctica.</p> <p>In 2012, nine arrays were installed at nodes with relative coordinates (00,00), (00,05), (00,10), (05,00), (05,05), (05,10), (10,00), (10,05), and (10,10). Measurements were taken at heights of 2.5, 5, 10, 15, 20, 25, 30, and 40 cm above the ground surface using DS1921G iButton loggers, which recorded air/snow temperatures every 4 hours. This experiment, referred to as 'Mini', was active until early 2021.</p> <p>Between 2017 and 2023, four arrays were installed at nodes (00,010), (05,05), (06,00), and (10,00). These arrays measured air/snow temperatures at heights of 2.5, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, and 160 cm above the ground surface using DS1922L iButton loggers, which recorded temperatures every 3 hours. Three of the arrays of this experiment, referred to as 'HR', has also been discontinued in early 2021, althought one of them was active until early 2024.</p>
Output from the Glacier Energy and Mass Balance (GEMB v1.0) forced with 3-hourly ERA5 fields and gridded to 10km, Greenland and Antarctica 1979-2024
<p>These model output of firn air content (FAC) and surface mass balance (SMB) are from version 1.0 of the open-source Glacier Energy and Mass Balance model. GEMB is a column model of ice sheet and glacier surface-atmospheric energy and mass exchange as well as firn state. GEMB has been integrated into the open-source Ice-Sheet and Sea-level System Model which can be downloaded at https://issm.jpl.nasa.gov/. Here, GEMB is forced with 3-hourly ERA5 output from 1979 through end of 2024. For Greenland and its periphery, the ERA5 surface temperature and downwelling longwave radiation forcing are spatially bias-corrected for each month. All values are adjusted by the difference between the RACMO2.3 and the ERA5 1980-2015 monthly means. The GEMB output is bilinearly interpolated onto a 10km grid, from the native ISSM grid, and the output is given as 5-day output or as monthly.</p>
Bathymetry beneath the Amery ice shelf, East Antarctica, revealed by airborne gravity
<p>We estimated the seafloor topography beneath the Amery Ice Shelf, East Antarctica, from airborne gravity anomaly through a nonlinear inversion method called simulated annealing. The estimation results provide a view of the seafloor beneath the Amery Ice Shelf, where direct bathymetric observations are rare. The model, 'gravity_estimated_seafloor_topography_beneath_the_Amery_Ice_Shelf.nc', is in NetCDF format which can be read through MATLAB commands "ncdisp" and "ncread". Contents of the model can be found in "contents.txt". The MATLAB program "nc2mat.m" reads the NetCDF ".nc" format model and saves the variables in the model to a MATLAB ".mat" format file.</p>
First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS
<p>This dataset is relative to the paper entitled: "First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS" publishing in journal Diversity (MPDI).</p> <p>Abstract:</p> <p>Zooplankton is a fundamental group in all aquatic ecosystems located the base of the food chain. It forms a link between the lower trophic levels with secondary consumers and shows marked fluctuations of populations with environmental change, especially reacting to heating and water acidification. At sea copepod crustaceans account for app. 70% in abundance of zooplankton and are a target of monitoring activities in key areas such as the Southern Ocean. In this study we have used FAIR-inspired legacy data (dating back to the ‘80s) collected in the Ross Sea by the Italian National Antarctic Program in GBIF.org. Together with other open-access GIS data sources and tools it allows generating, for the first time, three-dimensional predictive distribution maps for twenty-six copepod species. These predictive maps were obtained by applying machine learning techniques to grey literature data, which were visualized in open-source GIS platforms. In a Species Distribution Modeling (SDM) framework we used machine learning with three types of algorithms (TreeNet, RandomForest and Ensemble) to analyze the presence and absence of copepods at different areas and depth classes in function of environmental descriptors obtained from the Polar Macroscope Layers present in Quantartica. The models allow for the first time to map-predict the food chain in quantitative terms showing the relative index of occurrence (RIO) and identified the presence for each copepod species analyzed in the Ross Sea. Our results show marked geographical preferences that vary with species and trophic strategy. This study demonstrates that machine learning is a successful method in accurately predicting Antarctic copepod presence, also providing useful data to orient future sampling and management of wildlife and conservation.</p>
Evaluating Automated Seismic Event Detection Approaches: An Application to Victoria Land, East Antarctica
<p>This repository contains the waveform data used by Ho et al. (2024), along with all generated fine-tuned models and event catalogs. See the README file for a summary. The corresponding software packages are available on GitHub at <a href="https://github.com/jakewalter/easyQuake.git">https://github.com/jakewalter/easyQuake.git</a>, <a href="https://github.com/seisbench/seisbench">https://github.com/seisbench/seisbench</a>, and <a href="https://github.com/longmho/Transfer_Learning_and_Seisbench">https://github.com/longmho/Transfer_Learning_and_Seisbench</a>. See the README.md file at <a href="https://github.com/longmho/Transfer_Learning_and_Seisbench">https://github.com/longmho/Transfer_Learning_and_Seisbench</a> for additional details.</p>
Data used in the manuscript entitled "Turbulent heat flux dynamics along the Dotson and Getz ice-shelf fronts (Amundsen Sea, Antarctica)"
<p>Data files used in the analysis in the manuscript entitled "Turbulent heat flux dynamics along the Dotson and Getz ice-shelf fronts (Amundsen Sea, Antarctica)".</p> <p>Data were collected during the RV NB Palmer NBP2202 cruise, during the 2022 TARSAN campagine in the Amundsen Sea.</p> <p>Underway data provides daily files from the underway and meteorology sensors in JGOFS format. CTD data collected from the cruise. Information about sensors and data formats is included in the data report.</p> <p>Glider data was processed through the UEA Seaglider Toolbox (https://bitbucket.org/bastienqueste/uea-seaglider-toolbox/src/toolbox/) and is provided in Matlab format.</p> <p> </p> <p>Manuscript abstract:</p> <p>In coastal polynyas, where sea–ice formation occurs, it is crucial to have accurate estimates of heat fluxes in order to predict future rates of sea–ice formation. The Amundsen Sea Polynya is the fourth largest coastal polynya around Antarctica, yet remains poorly observed because of its remoteness. Consequently, we rely on models and reanalysis that are unvalidated to study the effect of atmospheric forcing on polynya dynamics. We use summer ship-board data from the NBP22/02 cruise to understand the turbulent heat flux dynamics in the Amundsen Sea Polynya and evaluate our ability to represent these dynamics in ERA5. We show that cold and dry air outbreaks from Antarctica enhance air–sea temperature and humidity gradients, triggering episodic heat loss events. The heat loss is larger along the ice shelves, and it is also where the ERA5 turbulent heat flux exhibits the largest biases, underestimating the flux by up to 141~W~m$^{-2}$ due to its coarse resolution and misrepresentation of ice-shelf location. By reconstructing a turbulent heat flux product from ERA5 variables using a nearest neighbour approach to obtain sea surface temperature, we decrease the bias to 107 W m$^{-2}$. Using a 1D-model, we show that the mean co-located ERA5 heat loss underestimation of -28~W~m$^{-2}$ led to an overestimation of the summer evolution of sea surface temperature (heat content) by +0.76~°C (+8.2e+07~J) over 35-days. By obtaining the reconstructed flux, the reduced heat loss bias (12 W~m$^{-2}$) reduced the seasonal bias in sea surface temperature (heat content) to -0.17~°C (-3.30e+07~J) over the 35-days. This study shows that caution should be applied when retrieving ERA5 turbulent flux along the ice shelves, and that a reconstructed flux using ERA5 variables shows better accuracy.</p> <p> </p> <p> </p>
Water temperature measurements collected during austral summer 2017/2018 on lakes located in the Schirmacher oasis, East Antarctica.
<p>Lakes’ water temperature are measured on lakes of three types (epiglacial, epishelf and land-locked) located in the Schirmacher oasis, East Antarctica. The temporal hydrological network is equipped by 11 temperature sensors, which are measured both surface and bottom water temperature of lakes. The surface temperature is recorded with the temperature loggers iButton DS1922L/DS1922T (https://www.maximintegrated.com/en/datasheet/index.mvp/id/4088) on 8 lakes. The sensors are deployed within a distance of 1–3 m from a lake’s coast, on a depth of 0.02 m. The lake’s surface temperature is also measured on two lakes with the temperature sensors by HOBO Water Level U20L (https://www.onsetcomp.com/products/data-loggers/u20l-01), which are deployed on the depth of 0.2 m. One HOBO sensor is installed to be attached to a lake’s ground on a depth approximately 0.5 m. The measurements cover the period of over 12–36 days depending on a lake. The data set includes the field campaign’s report of 63 RAE in the Schirmacher oasis (a text, in Russian) as a pdf-file, the metadata for the measurements (name, elevation, lon/lat of the temperature sensors deployed; name of the lakes; period with measurements; comments) as a shp-file, and the tables with water temperature measured for each lakes (as files of CSV format). Also, the deployment of the temperature sensor on the Lake Pomornik is presented in the jpg-file.</p> <p> </p>
3D wind speed and CO2/H20 concentration measurements collected during austral summer 2017/2018 over an ice free surface of a shallow lake located in the Schirmacher oasis, East Antarctica.
<p>The data set includes measurements collected by the integrated CO2 and H2O open-path gas analyzer and 3-D sonic anemometer (Irgason by Campbell Scientific with serial number 1243, https://www.campbellsci.com/irgason). The instrument was operated from 01.01.2018 to 07.02.2018. It was deployed on the north-west shore of the Lake Zub/Priyadarshini (S70° 45′ 41.5″, E011° 44′ 16.6″) on the distance of 10 m from the coast. The instrument was placed on the aluminum tripod on the height of 2 m, and directed to south-eastwards (137 SE). Six metal guidelines were linked to anchors, and the boom was fixed on the tripod. Two rechargeable batteries (12V/33Ah) were used in additional to two solar panels to power supply of the instrument (irgason_deployment.jpg). The format of the output files is given in Irgason_output.pdf. The raw data are packed into the *.dat files (one per day) and then compressed (bz2). The calibration of the Irgason was done 21.08.2017 in the lab of the Finnish Meteorological Institute with standard zero-and-span procedure, and then the instrument is adjusted accordingly.</p>
MARv3.10 outputs: What is the Surface Mass Balance of Antarctica? An Intercomparison of Regional Climate Model Estimates
<p>MARv3.10 outputs used in:</p> <p><em>Mottram, R., Hansen, N., Kittel, C., van Wessem, M., Agosta, C., Amory, C., Boberg, F., van de Berg, W. J., Fettweis, X., Gossart, A., van Lipzig, N. P. M., van Meijgaard, E., Orr, A., Phillips, T., Webster, S., Simonsen, S. B., and Souverijns, N.: What is the Surface Mass Balance of Antarctica? An Intercomparison of Regional Climate Model Estimates, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2019-333, accepted, 2020.</em></p> <ul> <li>MARv3.10 forced by ERA-Interim outputs with monthly values of SMB and components (kg m<sup>-2</sup> month<sup>-1</sup>), and (near-) surface temperature (°C) over the Antarctic ice sheet (1981--2018)</li> <li>Grid file used in MAR simulation</li> </ul> <p>Be carreful that the unit metadata in the netcdf files from SMB and its components are uncorrect. <strong>Values are in kg m<sup>-2</sup> month<sup>-1</sup></strong> instead of kg m<sup>-2</sup> day<sup>-1</sup>.<br> <br> If you need other variables or output frequencies from MAR, write me (c2kittel@gmail.com) and I will be glad to help you. I will also be happy to share the scripts I have developed to analyse the outputs and make the figures in this paper if needed. Please cite the paper if you use these MAR outputs. However, note that these outputs are now considered as deprecated since new outputs using a more recent model version (MARv3.11) and forcing (ERA5) have been published (see Kittel et al., 2021: https://tc.copernicus.org/articles/15/1215/2021/).<br> <br> Data usage notice:</p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgements should have language similar to the below that contained informations related to MAR. In order to document MAR scientific impact and enable ongoing support of the model, users are likely encouraged to contact C. Kittel and C. Agosta to add their works in the list of MAR-related publications. </p> <p>"We thank the MAR team which make available the model outputs, as well agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR simulations."</p> <p>You should also refer to and cite the following paper:</p> <p><em>Mottram, R., Hansen, N., Kittel, C., van Wessem, M., Agosta, C., Amory, C., Boberg, F., van de Berg, W. J., Fettweis, X., Gossart, A., van Lipzig, N. P. M., van Meijgaard, E., Orr, A., Phillips, T., Webster, S., Simonsen, S. B., and Souverijns, N.: What is the Surface Mass Balance of Antarctica? An Intercomparison of Regional Climate Model Estimates, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2019-333, accepted, 2020.</em></p>
Rutford Ice Stream, Antarctica M_sf tidal velocity components derived from COSMO-SkyMED SAR data
<p>This repository provides rasters for velocity components of a tidal (periodic) model for Rutford Ice Stream (RIS), Antarctica. The tidal model consists of a secular (constant) term and a single sinusoidal component corresponding to the M_sf tidal cycle (14.76529 days). The tidal model is fit to time-dependent velocity fields over RIS derived from speckle tracking of COSMO-SkyMed SAR data, collected over 9 months beginning in August 2013. The original methodology and source dataset are described in the publication:</p> <p>Minchew, B. M., Simons, M., Riel, B., & Milillo, P. (2017). Tidally induced variations in vertical and horizontal motion on Rutford Ice Stream, West Antarctica, inferred from remotely sensed observations. <em>Journal of Geophysical Research: Earth Surface</em>, <em>122</em>(1), 167-190. doi: <a href="https://doi.org/10.1002/2016JF003971">10.1002/2016JF003971</a></p> <p>The rasters are provided in GeoTIFF format in the Polar Stereographic South (EPSG: 3031) coordinate system. The velocity components are also referenced to Polar Stereographic South coordinates. The pixel spacing is 400 meters (in both the X- and Y-directions). The individual files are:</p> <ol> <li>vx_secular.tif: secular velocity in X-direction in meters/day.</li> <li>vy_secular.tif: secular velocity in Y-direction in meters/day.</li> <li>vx_amp.tif: M_sf velocity amplitude in X-direction in meters/day.</li> <li>vy_amp.tif: M_sf velocity amplitude in Y-direction in meters/day.</li> <li>vx_phase.tif: M_sf velocity phase delay in X-direction in days.</li> <li>vy_phase.tif: M_sf velocity phase delay in Y-direction in days.</li> </ol>
Freshwater sources from Antarctica and Greenland
<p>Freshwater sources from Greenland and Antarctica. For details see https://github.com/NASA-GISS/freshwater-forcing-workshop and https://doi.org/10.5194/egusphere-2025-1940</p> <p>v7 update: See changelog at <a href="https://github.com/NASA-GISS/freshwater-forcing-workshop/compare/de5779e8b454a7d432b0159aebfe289665ced3c8...f4250709080c46d82fd96a082a80e4f18ceaa604">https://github.com/NASA-GISS/freshwater-forcing-workshop/compare/de5779e8b454a7d432b0159aebfe289665ced3c8...f4250709080c46d82fd96a082a80e4f18ceaa604</a></p>
Geophysical and physical oceanography data and acoustic facies mapping results of the Central Basin in the northwestern Ross Sea margin, Antarctica
<p>Multi-channel seismic (MCS), sub-bottom profiler (SBP), multi-beam echosounder (MBES) and expandable conductivity-temperature-depth (XCTD) data and acoustic facies mapping results of the Central Basin in the northwestern Ross Sea margin, Antarctica. The Coordinate Reference System (CRS) for the MCS, SBP, MBES data and acoustic mapping results is WGS 84 / Antarctic Polar Stereographic (EPSG:3031). ). The geophysical data (MCS, SBP, MBES) and oceanographic measurements (XCTD) collected by the RV <em>Araon</em> are provided by the Korea Polar Data Center (<a href="https://kpdc.kopri.re.kr">https://kpdc.kopri.re.kr</a>).</p>
Doppler spectra collected by a transect of three MRR-PRO during the POPE 2020 campaign at Princess Elisabeth Antarctica
<p>This repository contain the datasets of Doppler spectra collected by three K-band Doppler profiling radars (MRR-PRO) deployed in a transect across the Sør Rondane Mountains, in the vicinity of the Belgian research base Princess Elisabeth Antarctica (PEA).</p> <p>The measurement campaign has been conducted by the Environmental Remote Sensing Laboratory (LTE) of the École Polytechnique Fédérale de Lausanne (EPFL), with the logistical support of the International Polar Foundation (IPF).</p> <p>The datasets are described in the article “Radar and ground-level measurements of clouds and precipitation collected during the POPE 2020 campaign at Princess Elisabeth Antarctica”, by Alfonso Ferrone and Alexis Berne. The article was submitted to Earth System Science Data in August 2022, and is available at the following URL: <a href="https://doi.org/10.5194/essd-2022-295">https://doi.org/10.5194/essd-2022-295</a> .</p> <p>This repository complements "Radar and ground-level measurements collected during the POPE 2020 campaign at Princess Elisabeth Antarctica", uploaded on Zenodo at: <a href="https://doi.org/10.5281/zenodo.7428690">https://doi.org/10.5281/zenodo.7428690</a> . The radar variables in the MRR-PRO data files contained in the latter have been computed from the raw spectra stored in the current repository.</p> <p> </p> <p><strong>Content of the archives</strong></p> <p>- <em>MRR_PRO_23_raw_spectra.zip,</em><br> This archive contains the Doppler spectra collected by the MRR-PRO 23, deployed at the lowest altitude in the transect, at1543 m above mean sea level (a.m.s.l.), at the following coordinates: latitude 72° 6’ 50.4” S, longitude 23° 30’ 50.4” E.</p> <p>- <em>MRR_PRO_06_raw_spectra.zip,</em><br> This archive contains the Doppler spectra collected by the MRR-PRO 06, deployed at approximately 2000 m a.m.s.l. of altitude, at the following coordinates: latitude 72° 7’ 4.8” S, longitude 23° 20’ 49.2” E.</p> <p>- <em>MRR_PRO_22_raw_spectra.zip,</em><br> This archive contains the Doppler spectra collected by the MRR-PRO 22, deployed at the highest location in the transect (2360 m a.m.s.l.), at the following coordinates: latitude 72° 13’ 37.2” S, longitude 23° 11’ 27.6” E.</p> <p> </p> <p><strong>Content of the NetCDF4 files</strong></p> <p>A “short name” is associated to each variables in the NetCDF4 files stored in the three archives.</p> <p>The relevant variables in each data file are:<br> – the raw spectral power, identified in the files by the short name “spectrum_raw”;<br> – the transfer function, used to convert the raw spectral power to spectral reflectivity, as described in Ferrone et al. (2022), and identified in the files by the short name “transfer_function”.</p> <p> </p> <p><strong>References</strong></p> <p>Ferrone, A., Billault-Roux, A.-C., and Berne, A.: ERUO: a spectral processing routine for the Micro Rain Radar PRO (MRR-PRO), Atmospheric Measurement Techniques, 15, 3569–3592, https://doi.org/10.5194/amt-15-3569-2022, 2022</p> <p>Ferrone, A., and Berne, A., Radar and ground-level measurements collected during the POPE 2020 campaign at Princess Elisabeth Antarctica (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7428690, 2023</p>
Radar and ground-level measurements collected during the POPE 2020 campaign at Princess Elisabeth Antarctica
<p>This repository contain the datasets of radar and ground-level measurements collected in the vicinity of the Belgian research base Princess Elisabeth Antarctica (PEA).</p> <p>The measurement campaign has been conducted by the Environmental Remote Sensing Laboratory (LTE) of the Scole Polytechnique Fédérale de Lausanne (EPFL), with the logistical support of the International Polar Foundation (IPF).</p> <p>The datasets and their processing are described in the article “Radar and ground-level measurements of clouds and precipitation collected during the POPE 2020 campaign at Princess Elisabeth Antarctica”, by Alfonso Ferrone and Alexis Berne. The article was submitted to Earth System Science Data in August 2022, and is available at the following URL: <a href="https://doi.org/10.5194/essd-2022-295">https://doi.org/10.5194/essd-2022-295</a> .</p> <p><br> <br> <strong>Content </strong><strong>of the archives</strong><br> The datasets have been divided into compressed archives. Each of them contains a series of data files, all saved in NetCDF4 format.</p> <p>The content of each archives is listed below.</p> <p>- <em>WProf.zip</em><br> This archive contains the radar variables collected by the W-band Doppler profiling cloud radar (WProf) deployed at PEA.<br> The liquid water path and integrated water vapor (retrieved thanks to the 89 GHz radiometer included in the instrument) has also been included in the files.</p> <p>- <em>MXPol_PPI.zip</em><br> This archive contains the polarimetric radar variables collected by the X-band Doppler dual-polarization scanning weather radar (MXPol) during the nearly-vertical PPI scans.</p> <p>- <em>MXPol_sector_scans_2019.zip</em><br> This archive contains the polarimetric radar variables collected by MXPol during the sector scans (PPI scans limited to a sector of the full azimuth circle) scans performed in December 2019. Sector scans collected in the following months have been stored separately, due to a limitation on the maximum number of files in input to the creation of the zip archive.</p> <p>- <em>MXPol_sector_scans_2020.zip</em><br> This archive contains the polarimetric radar variables collected by MXPol during the sector scans scans performed in January and February 2020.</p> <p>- <em>MXPol_hydrometeor_types.zip</em><br> This archive contains the polarimetric radar variables collected by MXPol during the RHI scans. The files also contain information on the proportion of hydrometeor classes and the dominant hydrometeor type computed from the measurements of MXPol.</p> <p>- <em>MRR_PRO_06.zip, MRR_PRO_22.zip, and MRR_PRO_23.zip</em><br> The three archives contain the radar variables collected by the K-band Doppler profiling radars (MRR-PRO) deployed in a transect across the mountain range south of PEA.</p> <p>- <em>aws_radiometers.zip</em><br> This archive contains the measurements collected by the Automated Weather Station (AWS) installed alongside each of the three MRR-PRO. The data from the two radiometers deployed at the MRR-PRO 06 site have also been included in the archive.</p> <p> </p> <p><strong>Content of the NetCDF4 files</strong></p> <p>A “short name” is associated to each variables in the NetCDF4 files. This section provides a list of all the relevant variables collected by each instruments, alongside their short name.</p> <p> </p> <p>The following polarimetric variables are available for all the scans performed by MXPol, stored in the archives <em>MXPol_PPI.zip</em>, <em>MXPol_hydrometeor_types.zip</em>, <em>MXPol_sector_scans_2019.zip</em>, and <em>MXPol_sector_scans_2020.zip</em>:</p> <p>– the horizontal reflectivity factors (Z<sub>H</sub>), identified in the files by the short name “Zh”;</p> <p>– the vertical reflectivity factors (Z<sub>V</sub>), identified by the short name “Zv”;</p> <p>– the differential reflectivity (Z<sub>DR</sub>), identified by the short name “Zdr”;</p> <p>– the signal-to-noise ratio on the horizontal polarization channel (SNR<sub>H</sub>), identified by the short name “SNRh”;</p> <p>– the signal-to-noise ratio on the vertical polarization channel (SNR<sub>V</sub>), identified by the short name “SNRv”;</p> <p>– the mean Doppler radial velocity (V), identified by the short name “RVel”;</p> <p>– the spectral width (SW), identified by the short name “Sw”;</p> <p>– the total differential phase shift (Ψ<sub>DP</sub>), identified by the short name “Psidp”;</p> <p>– the differential phase shift (Φ<sub>DP</sub>), identified by the short name “Phidp”;</p> <p>– the specific differential phase on propagation (K<sub>DP</sub>), identified by the short name “Kdp”;</p> <p>– the co-polar correlation coefficient (ρ<sub>hv</sub>), identified by the short name “Rhohv”.</p> <p> </p> <p>The hydrometeor classification (Besic et al., 2016) and the retrieval of the proportion of each hydrometeor category in the radar volume (Besic et al., 2018) has been applied to all RHI scans of MXPol (archive <em>MXPol_hydrometeor_types.zip</em>), producing the following variables:</p> <p>– the dominant hydrometeor type, identified by the short name “hydro”,</p> <p>– the entropy computed by the classification algorithm, which provides an estimate of the confidence on the label assigned to the volume, identified by the short name “hydroclass_entropy”;</p> <p>– the proportion of each hydrometeor type in the volume, stored in the variables “proportion_AG” (aggregates), “proportion_CR” (ice crystals), “proportion_LR” (light rain), “proportion_RP” (rimed particles), “proportion_RN” (rain), “proportion_VI” (vertically-aligned ice), “proportion_WS” (wet snow), “proportion_MH” (melting hail);</p> <p>– the entropy computed by the demixing algorithm, identified by the short name “entropy”.</p> <p> </p> <p>The following radar variables are available for all the profiles collected by WProf, stored in the archive <em>WProf.zip</em>:</p> <p>– the equivalent reflectivity factor (Z<sub>e</sub>), identified in the files by the short name “Ze”</p> <p>– the signal-to-noise ratio (SNR), identified in the files by the short name “SnR”;</p> <p>– the mean Doppler radial velocity, identified in the files by the short name “Mean-velocity”;</p> <p>– the spectral width, identified in the files by the short name “Spectral-width”;</p> <p>– the skewness, identified in the files by the short name “Spectral-skewness”;</p> <p>– the kurtosis, identified in the files by the short name “Spectral-kurtosis”;</p> <p>– the noise level at each range gate gate, identified in the files by the short name “Noise_level”;</p> <p>– the noise floor at each range gate gate, identified in the files by the short name “Noise_floor”.</p> <p> </p> <p>The following retrievals (Billault-Roux et al., 2021) have been included in the WProf data files, stored in the archive <em>WProf.zip</em>:</p> <p>– the Integrated Water Vapor (IWV), identified in the files by the short name “Integrated-water-vapor”;</p> <p>– the Liquid Water Path (LWP), identified in the files by the short name “Liquid-water-path”.</p> <p> </p> <p>The following atmospheric variables, recorded by the automated weather station integrated in the radar, have been included in the WProf data files, stored in the archive <em>WProf.zip</em>:</p> <p>– the atmospheric pressure, identified in the files by the short name “Barometric-pressure”;</p> <p>– the air temperature, identified in the files by the short name “Environment-temp”;</p> <p>– the relative humidity with respect to liquid water, identified in the files by the short name “Rel-humidity”;</p> <p>– the horizontal wind direction, identified in the files by the short name “Wind-direction”;</p> <p>– the horizontal wind speed, identified in the files by the short name “Wind-speed”.</p> <p> </p> <p>The following variables are available for all the profiles collected by the three MRR-PRO, stored in the archives <em>MRR_PRO_06.zip, MRR_PRO_22.zip,</em><em> and</em><em> MRR_PRO_23.zip</em>:</p> <p>– the attenuated equivalent reflectivity factor (Z<sub>ea</sub>), identified in the files by the short name “Zea”;</p> <p>– the mean Doppler radial velocity, identified in the files by the short name “VEL”;</p> <p>– the spectral width, identified in the files by the short name “SW”;</p> <p>– the signal-to-noise ratio, identified in the files by the short name “SNR”;</p> <p>– the noise level computed by ERUO (Ferrone et al., 2022) at each range gate gate, identified in the files by the short name “noise_level”;</p> <p>– the noise floor computed by ERUO at each range gate gate, identified in the files by the short name “noise_floor”.</p> <p> </p> <p>The following variables are available for all the measurements collected by the three weather stations, stored in the archive <em>aws_radiometers.zip</em>:</p> <p>– the atmospheric pressure, identified in the files by the short name “pressure”;</p> <p>– the air temperature, identified in the files by the short name “temperature”;</p> <p>– the relative humidity with respect to liquid water, identified in the files by the short name “relative_humidity”;</p> <p>– the horizontal wind direction, identified in the files by the short name “wind_speed”;</p> <p>– the horizontal wind speed, identified in the files by the short name “wind_direction”.</p> <p> </p> <p>The following variables are available for all the measurements collected by the pyrgeometer and pyranometer, stored in the archive <em>aws_radiometers.zip</em>::</p> <p>– the total downwelling irradiance in the shortwave, identified in the files by the short name “shortwave_irradiance”;</p> <p>– the total downwelling irradiance in the longwave, identified in the files by the short name “longwave_irradiance”.</p> <p> </p> <p> </p> <p><strong>References</strong></p> <p>Besic, N., Figueras i Venturra, J., Grazioli, J., Gabella, M., Germann, U., and Berne, A.: Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach, Atmospheric Measurement Techniques, 9, 4425–4445, https://doi.org/10.5194/amt-9-4425-2016, 2016.</p> <p>Besic, N., Gehring, J., Praz, C., Figueras i Ventura, J., Grazioli, J., Gabella, M., Germann, U., and Berne, A.: Unraveling hydrometeor mixtures in polarimetric radar measurements, Atmospheric Measurement Techniques, 11, 4847–4866, https://doi.org/10.5194/amt-11-4847-2018, 2018<strong> </strong></p> <p>Billault-Roux, A.-C. and Berne, A.: Integrated water vapor and liquid water path retrieval using a single-channel radiometer, Atmospheric Measurement Techniques, 14, 2749–2769, https://doi.org/10.5194/amt-14-2749-2021, 2021</p> <p>Ferrone, A., Billault-Roux, A.-C., and Berne, A.: ERUO: a spectral processing routine for the Micro Rain Radar PRO (MRR-PRO), Atmospheric Measurement Techniques, 15, 3569–3592, https://doi.org/10.5194/amt-15-3569-2022, 2022</p>
Surface sediment Nd isotope compositions from the Ross Sea, Antarctica
<p>The dataset contains seafloor surface sediment neodymium isotope compositions for several sites in the Ross Sea, Antarctica. The sediments were supplied by Helen Bostock from NIWA, Wellington, New Zealand, plus two samples from International Ocean Discovery Program (IODP) Expedition 374.</p>
Extreme precipitation records in Antarctica [Dataset]
<p>This is the dataset associated to the research 'Extreme precipitation records in Antarctica' published in <em>International Journal of Climatology</em>.</p> <p>This repository contains:</p> <ul> <li>Precipitation extremes for each <em>model</em> at every grid point for a duration of <em>xxx</em> days. Files named: <ul> <li>[<em>model</em>]_PCP_max_[<em>xxx</em>]d.csv <ul> <li>Dimensions for ERA5: [lons, lats]</li> <li>Dimensions for RACMO2: [grid_x, grid_y]</li> <li>Units: mm</li> </ul> </li> </ul> </li> <li>Dimensions to plot the precipitation extremes: lons (longitudes), lats (latitudes) and duration. Files named: <ul> <li>[<em>model</em>]_PCP_max_lats.csv <ul> <li>Dimensions for ERA5: [lats]</li> <li>Dimensions for RACMO2: [grid_x, grid_y]</li> <li>Units: degrees</li> </ul> </li> <li>[<em>model</em>]_PCP_max_lons.csv <ul> <li>Dimensions for ERA5: [lons]</li> <li>Dimensions for RACMO2: [grid_x, grid_y]</li> <li>Units: degrees</li> </ul> </li> <li>[<em>model</em>]_PCP_max_duration.csv <ul> <li>Dimensions: [time]</li> <li>Units: days</li> </ul> </li> </ul> </li> <li>World Precipitation Records from 1 day. File named: <ul> <li>Max_WR_from1day.csv (first row duration [days]; second row precipitation [mm])</li> </ul> </li> </ul> <p> </p> <p><strong>How to cite</strong></p> <p>If you use this dataset, please cite the accompanying paper as:</p> <p>González-Herrero, S.,Vasallo, F., Bech, J., Gorodetskaya, I., Elvira, B., & Justel, A. (2023). Extreme precipitation records in Antarctica.International Journal of Climatology, 43(7), 3125–3138. <a href="https://doi.org/10.1002/joc.8020">https://doi.org/10.1002/joc.8020</a></p> <p> </p> <p><strong>Complementary code</strong></p> <p>You can find the jupyter notebooks to complement the research in: <a href="https://github.com/sergigonzalezh/Extreme_PCP_Scaling_Antarctica">https://doi.org/10.1002/joc.8020</a></p> <p> </p> <p><strong>Contact</strong></p> <p>If you have any question, please contact with Sergi at <a href="mailto:sergi.gonzalez@slf.ch">sergi.gonzalez@slf.ch</a></p>
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