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opencc-by-4.0Aug 2022View details →

Radar and ground-level measurements collected during the POPE 2020 campaign at Princess Elisabeth Antarctica

This repository contain the datasets of radar and ground-level measurements collected in the vicinity of the Belgian research base Princess Elisabeth Antarctica (PEA). 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). 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> . Content of the archives The datasets have been divided into compressed archives. Each of them contains a series of data files, all saved in NetCDF4 format. The content of each archives is listed below. - WProf.zip This archive contains the radar variables collected by the W-band Doppler profiling cloud radar (WProf) deployed at PEA. 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. - MXPol_PPI.zip 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. - MXPol_sector_scans_2019.zip 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. - MXPol_sector_scans_2020.zip This archive contains the polarimetric radar variables collected by MXPol during the sector scans scans performed in January and February 2020. - MXPol_hydrometeor_types.zip 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. - MRR_PRO_06.zip, MRR_PRO_22.zip, and MRR_PRO_23.zip 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. - aws_radiometers.zip 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.   Content of the NetCDF4 files 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.   The following polarimetric variables are available for all the scans performed by MXPol, stored in the archives MXPol_PPI.zip, MXPol_hydrometeor_types.zip, MXPol_sector_scans_2019.zip, and MXPol_sector_scans_2020.zip: – the horizontal reflectivity factors (ZH), identified in the files by the short name “Zh”; – the vertical reflectivity factors (ZV), identified by the short name “Zv”; – the differential reflectivity (ZDR), identified by the short name “Zdr”; – the signal-to-noise ratio on the horizontal polarization channel (SNRH), identified by the short name “SNRh”; – the signal-to-noise ratio on the vertical polarization channel (SNRV), identified by the short name “SNRv”; – the mean Doppler radial velocity (V), identified by the short name “RVel”; – the spectral width (SW), identified by the short name “Sw”; – the total differential phase shift (ΨDP), identified by the short name “Psidp”; – the differential phase shift (ΦDP), identified by the short name “Phidp”; – the specific differential phase on propagation (KDP), identified by the short name “Kdp”; – the co-polar correlation coefficient (ρhv), identified by the short name “Rhohv”.   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 MXPol_hydrometeor_types.zip), producing the following variables: – the dominant hydrometeor type, identified by the short name “hydro”, – 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”; – 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); – the entropy computed by the demixing algorithm, identified by the short name “entropy”.   The following radar variables are available for all the profiles collected by WProf, stored in the archive WProf.zip: – the equivalent reflectivity factor (Ze), identified in the files by the short name “Ze” – the signal-to-noise ratio (SNR), identified in the files by the short name “SnR”; – the mean Doppler radial velocity, identified in the files by the short name “Mean-velocity”; – the spectral width, identified in the files by the short name “Spectral-width”; – the skewness, identified in the files by the short name “Spectral-skewness”; – the kurtosis, identified in the files by the short name “Spectral-kurtosis”; – the noise level at each range gate gate, identified in the files by the short name “Noise_level”; – the noise floor at each range gate gate, identified in the files by the short name “Noise_floor”.   The following retrievals (Billault-Roux et al., 2021) have been included in the WProf data files, stored in the archive WProf.zip: – the Integrated Water Vapor (IWV), identified in the files by the short name “Integrated-water-vapor”; – the Liquid Water Path (LWP), identified in the files by the short name “Liquid-water-path”.   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 WProf.zip: – the atmospheric pressure, identified in the files by the short name “Barometric-pressure”; – the air temperature, identified in the files by the short name “Environment-temp”; – the relative humidity with respect to liquid water, identified in the files by the short name “Rel-humidity”; – the horizontal wind direction, identified in the files by the short name “Wind-direction”; – the horizontal wind speed, identified in the files by the short name “Wind-speed”.   The following variables are available for all the profiles collected by the three MRR-PRO, stored in the archives MRR_PRO_06.zip, MRR_PRO_22.zip, and MRR_PRO_23.zip: – the attenuated equivalent reflectivity factor (Zea), identified in the files by the short name “Zea”; – the mean Doppler radial velocity, identified in the files by the short name “VEL”; – the spectral width, identified in the files by the short name “SW”; – the signal-to-noise ratio, identified in the files by the short name “SNR”; – 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”; – the noise floor computed by ERUO at each range gate gate, identified in the files by the short name “noise_floor”.   The following variables are available for all the measurements collected by the three weather stations, stored in the archive aws_radiometers.zip: – the atmospheric pressure, identified in the files by the short name “pressure”; – the air temperature, identified in the files by the short name “temperature”; – the relative humidity with respect to liquid water, identified in the files by the short name “relative_humidity”; – the horizontal wind direction, identified in the files by the short name “wind_speed”; – the horizontal wind speed, identified in the files by the short name “wind_direction”.   The following variables are available for all the measurements collected by the pyrgeometer and pyranometer, stored in the archive aws_radiometers.zip:: – the total downwelling irradiance in the shortwave, identified in the files by the short name “shortwave_irradiance”; – the total downwelling irradiance in the longwave, identified in the files by the short name “longwave_irradiance”.     References 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. 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 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 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

opencc-by-4.0Feb 2023View details →

Ground Penetrating Radar survey of San Quintin glacier, Northern Patagonia Icefield

Measured ice thickness, Residual Bedrock Reflection Power (BRP) and Internal Reflection Power (IRP) of San Quitin glacier, Northern Patagonia Icefield.   Data for the preprint  "Frontal collapse of San Quintín glacier (Northern Patagonia Icefield), the last piedmont glacier lobe in the Andes" in review for The Cryosphere (https://doi.org/10.5194/tc-2023-10)

opencc-by-4.0Mar 2023View details →

Data archive for Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning

This dataset contains the machine learning training data files, pretrained model weights and results for the paper Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning, submitted to Natural Hazards and Earth System Sciences, 2023. The radar dataset can be found at the following Zenodo repository: <a href="https://doi.org/10.5281/zenodo.6325370">https://doi.org/10.5281/zenodo.6325370</a> For instructions for using the data, please see the GitHub code repository at <a href="http://github.com/meteoswiss/c4dl-polar">https://github.com/meteoswiss/c4dl-polar</a>. Download all the files here and extract the contents to the following subdirectories in the ML code directory: <ul> <li>Training data (patches_quality-index_2020.zip or patches_*_2020.nc) -> data/2020/</li> <li>Results: (results.zip) -> runs/run*/results/</li> <li>Pretrained models (models_run*) -> runs/run*/</li> </ul>

opencc-by-4.0Apr 2023View details →

SkiYMET meteor radar data at OLAP (2008-2009)

SkiYMET meteor radar data from OLAP observatory at São João do Cariri (7.23º S; 36.32º W; dip lat. -22.22), Brazil. The files summarize the parameters of the diurnal, semidiurnal, and terdiurnal tides, such as amplitude and phase, for the zonal and meridional wind components. The winds were estimated for the months of April, July and October of 2009, and December of 2008. These data were used as input to the MIRE model in the study of Fontes et al. (2022).

opencc-by-4.0Apr 2023View details →

ALO Meteor Radar Processed Data in Jan 2022

Meteor radar data include: Horizontal wind (vel) and detected meteors (met).  Both original format and MatLab format (mat) files are included.  Wind data are in three temporal resolutions, 1-hr, 30-hr, and 15-min.

TEAMx-PC22 (TEAMx pre-campaing 2022) - KITcube cloud radar vertical winds in Kolsass

The RPG FMCW dual-pol dual-frequency cloud radar was operated in Kolsass. This data set contains vertical wind speed in 10 s temporal resolution for both frequencies, 94 GHz and 35 GHz. Vertical wind measurements are interrupted by PPI scans, thus there are regular gaps. The data set covers the period from May, 18th, through Sept., 22th, 2022.There were technical problems which caused partly very long measurement gaps especially in the second half of the period. Data are stored as one NetCDF file.

TEAMx-PC22 (TEAMx pre-campaing 2022) - KITcube cloud radar horizontal winds (from PPI) in Kolsass

The RPG FMCW dual-pol dual-frequency cloud radar was operated in Kolsass. This data set contains wind speed and wind direction in 10 min. temporal resolution for both frequencies, 94 GHz and 35 GHz. The wind is determined from PPI at 70 degree elevation via an unfolding VAD algorithm (see Pierre Tabary, Georges Scialom, and Urs Germann. Real-time retrieval of the wind from aliased velocities measured by doppler radars. J. Atmos. Oceanic Technol., 18 (6):875–882, June 2001.) PPIs were performed from May, 31st, through Aug, 26th, 2022. There were technical problems which caused partly very long measurement gaps especially in the second half of the period. Data are stored as one NetCDF file.

RaNDT Radar Benchmark

© 2023. This work is licensed under a CC BY-NC-SA 4.0 license by INSTITUTE OF MECHANISM THEORY, MACHINE DYNAMICS AND ROBOTICS - RWTH AACHEN UNIVERSITY. The data set contains multimodal sensor data generated by a tracked mobile robot in an outdoor and an indoor environemnt. Sensors include radar (indurad iSDR-C), LiDAR (SICK TiM), and IMU (Phidgets IMU). The data is used for benchmarking the RaNDT SLAM available at <a href="https://github.com/IGMR-RWTH/RaNDT-SLAM">IGMR Github</a>. The data is related to a publication accepted at IEEE IROS 2024. The pre-print is available at <a href="https://www.arxiv.org/abs/2408.11576" target="_blank" rel="noopener">arxiv</a>.

Data associated with "Lightning and radar characteristics of tornadic cells in landfalling tropical cyclones"

These data include all tropical cyclone tornado reports from 2013–2020, as part of all data from 1995–2022, included in the Storm Prediction Center (SPC) Tropical Cyclone TORnado database (TCTOR; Edwards and Mosier, 2022) used in the following publication: Schenkel, B., K. Calhoun, T. Sandmael, M. Ake, Z. Fruits, B. Kassel, and I. Schick, 2023: Lightning and radar characteristics of tornadic cells in landfalling tropical cyclones. J. Geophys. Res.: Atmospheres, accepted. Each specific tropical cyclone tornado record has been extracted from the broader SPC tornado database, for all Atlantic and Gulf of Mexico tropical cyclones impacting the continental United States from 1995–2022. The tornado records were analyzed individually to determine their presence within the circulation envelope of either a classified or remnant tropical cyclone, without regard to fixed radii from tropical cyclone center, inland extent, temporal cutoffs before or after landfall, or other such arbitrary thresholds that may either exclude tropical cyclone events or include non-tropical cyclone tornadoes unnecessarily. These data will not be updated regularly. Citation for SPC TCTOR dataset: Edwards, R., & Mosier, R. M. (2022). Over a quarter century of TCTOR: Tropical cyclone tornadoes in the WSR-88D era [Dataset]. In Proc., 30th conf. on severe local storms (p. 171). Santa Fe, NM.

opencc-by-4.0Aug 2023View details →

Datasets and trained diffusion models for "Diffusion Models for Interferometric Satellite Aperture Radar"

A set of trained Probabilistic Diffusion Models (PDMs) and corresponding training datasets for the paper "<a href="https://doi.org/10.48550/arXiv.2308.16847">Diffusion Models for Interferometric Satellite Aperture Radar</a>", by Tuel, Kerdreux et al. The code for this paper can be found at <a href="https://github.com/thomaskerdreux/PDM_SAR_InSAR_generation">this link</a>. Training datasets - "InSAR_noise_32x32.zip": a dataset of 32x32 ground deformation scenes obtained from InSAR interferograms over New Mexico with the small baseline subset (SBAS) algorithm. Images were normalised to [0, 1]. - "insar_unwrapped_phase_normalised.zip": a dataset of 128x128 InSAR interferograms obtained from Sentinel-1 acquisitions over Nex Mexico. Images were normalised to [0, 1]. Trained Models We provide 6 trained PDMs in separate .zip files. Each .zip file contains the model weights (in *.pt format) and the model metadata file (in *.json format). - "mnist_32_cond_sigma_100.zip": a class-conditional model trained with 100 diffusion time steps on 32x32 MNIST images; - "mnist_32_no_cond_sigma_100.zip": an unconditional model trained with 100 diffusion time steps on 32x32 MNIST images; - "SAR_lowres_128_cond_sigma_2000.zip": a low-resolution (256 to 128) model trained with 2000 diffusion time steps on 128x128 TenGeoP-SARwv images; - "SAR_superres_128_to_256_cond_sigma_2000.zip": a super-resolution (128 to 256) model trained with 2000 diffusion time steps on TenGeoP-SARwv images; - "insar_phase_128_sigma_2000.zip": an unconditional model trained with 2000 diffusion time steps on 128x128 Sentinel-1 InSAR interferograms over New Mexico; - "insar_noise_32_sigma_1000.zip": an unconditional model trained with 1000 diffusion time steps on 32x32 Sentinel-1 InSAR ground deformation scenes over New Mexico.

opencc-by-4.0Dec 2019View details →

TAASRAD19 Radar Scans 2010-2016

TAASRAD19 (Trentino-Alto Adige/Südtirol Radar 2019) is a high-resolution radar reflectivity dataset collected by the Civil Protection weather radar of the Trentino South Tyrol Region, in the Italian Alps. The dataset includes 894,916 scans of precipitation from more than 9 years of data, offering a novel resource to develop and benchmark analog ensemble models and machine learning solutions for precipitation nowcasting. Data are expressed as 2D images, considering the maximum reflectivity on the vertical section and 5 minutes sampling rate, covering an area of 240km of diameter at 500m horizontal resolution. The TAASRAD19 distribution also includes a curated set of 1,732 sequences, for a total of 362,233 radar images, labeled with precipitation type tags assigned by expert meteorologists. We validated TAASRAD19 as a benchmark for nowcasting using deep learning model to forecast reflectivity and a procedure based on the UMAP dimensionality reduction method for interactive exploration. Software methods for data pre-processing, model training and inference, and a pre-trained model are publicly available at https://github.com/MPBA/TAASRAD19 for replication and reproducibility.

opencc-by-4.0Dec 2019View details →

TAASRAD19 Radar Scans 2017-2019

opencc-by-4.0May 2020View details →

Data and Code for: Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals

This repository contains the datasets and scripts used to obtain the figures of the paper "Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals". The repository is organized as follows: - Part I) Monte Carlo simulation codes - Part II) Monte Carlo simulations using the parameter configuration "Param. 1" of Shi et al. (1999) - Part III) Monte Carlo simulation using the parameter configuration "Param. 1" of Shi et al. (1999) and a limited ion composition search space - Part IV) Monte Carlo simulations using the parameter configuration "Param. 2" of Wang et al. (2012) - Part V) Monte Carlo simulation using the parameter configuration "Param. 2" of Wang et al. (2012) and a limited ion composition search space - Part VI) Codes to generate all figures of the manuscript All datasets and scripts were generated and tested using Matlab 2017. Simulations have been executed in parallel on a SLURM cluster, compilation and running scripts are provided.

opencc-by-4.0May 2020View details →

JRC Phased Array Weather Radar Observational Data

These datasets were observed by Phased Array Weather Radar from 0755 to 0810 JST on October 12, 2019. Data type is NetCDF. Data variables are radar reflectivity and Doppler velocity.

opencc-by-4.0Sep 2020View details →

Conjunctions between ICON-MIGHTI and 4 meteor radars, used in "Validation of ICON-MIGHTI thermospheric wind observations: 2. Greenline comparisons to meteor radars" by Harding et al. (2020, Submitted)

<pre>This dataset was used to generate the figures in the paper mentioned above and is being made available for the sake of reproducibility and future analysis. The primary variables are los_wind (the line of sight wind profiles observed by ICON-MIGHTI) and los_wind_r (the wind profiles observed by the meteor radar, interpolated in time and altitude to the MIGHTI sample, and projected onto the MIGHTI line of sight). Dimensions are "time" and "row" (which refers to the row of the MIGHTI CCD, roughly equivalent to altitude. Velocity units are m/s, distances are km, and lat/lon are in degrees. More information can be found in the paper.</pre>

opencc-by-4.0Nov 2020View details →

SKiYMET Meteor Radar Horizontal Wind at Andes Lidar Observatory 2009-2014

This is horizontal wind measured by a SKiYMET Meteor Radar near Andes Lidar Observatory in Cerro Pachón, Chile (30.05 S, 70.82 W) from Sep 2009 to Aug 2014.  The radar was previously installed at Maui, Hawaii and is described in the paper Franke, S. J., X. Chu, A. Z. Liu, W. K. Hocking (2005), Comparison of meteor radar and Na Doppler lidar measurements of winds in the mesopause region above Maui, Hawaii, J. Geophys. Res., 110, D09S02, doi:10.1029/2003JD004486. The data is in NetCDF format, at 1 hr and 2 km resolution from 80 to 100 km altitude.  Time is in UT.  Both time and altitude refer to the center of the 1 hr bin.  Wind rms errors and numbers of meteor detections used for wind retrieval are also inicluded.

opencc-by-4.0Dec 2020View details →

Supplementary data: What millimeter-wavelength radar reflectivity reveals about snowfall: An information-centric analysis

This dataset includes supplementary data used in the analyses described in Wood, N. B., and T. S. L'Ecuyer, 2020:  What millimeter-wavelength radar reflectivity reveals about snowfall:  An information-centric analysis.  Atmospheric Measurement Techniques, doi:10.5194/amt-2020-216.

opencc-by-4.0Apr 2017View details →

Integrated field-aligned radar data and analysis results

Dataset used in "A statistical survey of heat input parameters into the cusp thermosphere" J. Geophys. Res. 2017, doi:10.1002/2016JA023594.

opencc-by-4.0Nov 2023View details →

STREAM - Sub-THz Radar sensing of the Environment for future Autonomous Marine platforms: RLG Dataset Coniston A

This dataset contains the files corresponding to which results have been included in the journal paper. The full descirption of conducted trials and data structure is mentioned in the attached pdf document.STREAM trials were conducted by the University of Birmingham (UoB) and the University of St. Andrews from 29/08/2022 - 02/09/2022 at Coniston Lake in the UK. The primary aim was to gather propagation data across lakes and measure the returns from the lake surface. The data will be used to develop algorithms to extract the information needed for pilotage.The experiments were performed with radars operating in the 79, 150, and 300 GHz bands to investigate the Doppler and imaging capabilities of these radars.This report describes the measurement scenarios and data structure of INRAS Radarlog (76 GHz – 81 GHz) used for data collection campaign.Contact: a.a.a.pirkani@bham.ac.uk or m.s.gashinova@bham.ac.uk