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28 results for “Weather radar”
Supplementary material for 'Revealing patterns of nocturnal migration using the European weather radar network'
<p>This package contains data, filters and visualizations from <a href="https://doi.org/10.1111/ecog.04003">Nilsson and Dokter et al. (2019)</a>.</p> <p><strong>Files</strong></p> <p><strong>radar_metadata.csv</strong>: Metadata for the 84 European radars considered for this study. Includes radar code (<code>odim_code</code> = <code>country</code> + <code>odim_code_3char</code> and alternative radar code <code>vp_radar</code>), radar site location (<code>location</code>, <code>latitude</code>, <code>longitude</code>), radar site elevation (<code>site_altitude_asl</code> in meters above sea level) and radar altitude range used in this study (<code>min_height_cut_asl</code> and <code>max_height_cut_asl</code> in meters above sea level).</p> <p><strong>vp.zip</strong>: Vertical profiles of birds (vp) data, processed from the radar volume data following procedures described by Dokter et al. (2011), using the vol2bird algorithm in the R package bioRad. Zip file includes vp data for the 84 European radars considered for this study from September 19 to October 9, 2016 (21 days). This time period is characterized by strong passerine migration throughout Europe. Files are organized in radar (= <code>odim_code</code>), date and hour directories and follow the <a href="https://github.com/adokter/vol2bird/wiki/ODIM-bird-profile-format-specification">ODIM bird profile format specification</a>. Data can be read with the <a href="https://github.com/adokter/bioRad/">R package bioRad</a>.</p> <p><strong>vp_processing_settings.yaml</strong>: Data selection setting for this study, based on data quality criteria. File lists for each radar the altitudes to include (<code>include_heights</code>), time periods to exclude (<code>exclude_datetimes</code>) and reasons for exclusion (comments). 70 of the 84 radars were retained after filtering.</p> <p><strong>vp_processed_70_radars_20160919_20161009.csv</strong>: Processed vp data for 70 radars. Is the result of processing <code>vp.zip</code> with <code>vp_processing_settings.yaml</code> and <code>radar_metadata.csv</code> using <a href="https://doi.org/10.5281/zenodo.1173544">vp-processing</a> (Desmet & Nilsson 2018). Note: includes all timestamps: day and night & those marked for exclusion (marked in <code>exclusion_reason</code>). This data file forms the basis for analysis in the study.</p> <p>Headers are:</p> <ul> <li><code>radar_id</code>: odim_code of the radar</li> <li><code>datetime</code>: timestamp</li> <li><code>HGHT</code>: lower altitude of altitude bin (m above sea level)</li> <li><code>u</code>: bird ground speed towards east (m/s)</li> <li><code>v</code>: bird ground speed towards north (m/s)</li> <li><code>dens</code>: bird density (birds/km3)</li> <li><code>dd</code>: bird flight direction (degrees from north)</li> <li><code>ff</code>: bird ground speed (m/s)</li> <li><code>DBZH</code>: reflectivity factor (dBZ) in horizontal polarisation</li> <li><code>mtr</code>: migration traffic rate (birds/km/h)</li> <li><code>day_night</code>: timestamp occurs during <code>day</code> or <code>night</code> (based on sunrise/sunset)</li> <li><code>date_of_sunset</code>: date at sunset, with night timestamps between midnight and sunrise belonging to the previous date</li> <li><code>exclusion_reason</code>: reason timestamp is excluded in vp_processing_settings.yaml (if applicable). Excluded timestamps have <code>NA</code> values for <code>u</code>, <code>v</code>, <code>dens</code>, <code>dd</code>, <code>ff</code>, <code>DBZH</code>, and <code>mtr</code>.</li> </ul> <p><strong>vp_flowviz.csv:</strong> Input data for visualizations. Is the result of processing <code>vp_processed_70_radars_20160919_20161009.csv</code> using <code>vp-to-flowviz.Rmd</code> in <a href="https://doi.org/10.5281/zenodo.1173544">vp-processing</a> (Desmet & Nilsson 2018). Aggregates data in hourly bins for 200-2000m (<code>altitude_band</code> = 1) and above (<code>altitude_band</code> = 2). Only altitude band 1 is used in visualizations.</p> <p><strong>flowviz.mov:</strong> Screencast of <code>vp_flowviz.csv</code> visualized with <a href="https://doi.org/10.5281/zenodo.57472">Bird migration flow visualization v2</a> (Desmet et al. 2016, Shamoun-Baranes et al. 2016). The visualization extrapolates the migration over the entire sampling range (cropped in the screencast due to technical limitations and thus excluding the Bulgarian radar), not taking topography or water bodies into account, and shows the ground speed (length of arrows) and direction of migration over time. Note that density is not shown: low density movements can therefore appear as strong as high density movements when ground speeds are similar.</p> <p><strong>cartoviz.mov:</strong> Screencast of <code>vp_flowviz.csv</code> visualized as an interactive map with <a href="https://carto.com">CARTO</a>. Visualization shows migration density (size of circles) and mean direction (colour) over time. The interactive map is available at <a href="https://inbo.carto.com/u/lifewatch/builder/8685140f-8d8c-4d06-9e1e-25d051d43748/embed">https://inbo.carto.com/u/lifewatch/builder/8685140f-8d8c-4d06-9e1e-25d051d43748/embed</a>.</p>
BALTRAD_VPTS - Vertical profiles of biological targets derived from European weather radars
<p><em>BALTRAD_VPTS - Vertical profiles of biological targets derived from European weather radars</em> is a vertical profile time series dataset published by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal movement data derived from 151 European weather radars in 18 countries, with varying coverage from 2012 to 2023. These data were created by processing weather radar data - provided by the Operational Programme for the Exchange of Weather Radar Information (<a href="https://www.eumetnet.eu/activities/observations-programme/current-activities/opera/">OPERA</a>) - with methods optimized for extracting bird targets. The resulting data are vertical profile time series (VPTS), containing the density, speed and direction of biological targets within a weather radar (<code>radar</code>) volume, grouped into altitude bins (<code>height</code>) and measured over time (<code>datetime</code>). The data are also available in the <a href="https://aloftdata.eu/browse/?prefix=baltrad/">Aloft bucket</a>.</p> <div> <div>See Desmet et al. (2025, <a href="https://doi.org/10.1038/s41597-025-04641-5">https://doi.org/10.1038/s41597-025-04641-5</a>) for a more detailed description of this dataset.</div> </div> <h2>Files</h2> <p>VPTS data in this deposit are organized per country (.tgz file), radar (directory), year (directory) and month (.csv.gz file). Fields in the data follow the <a href="https://aloftdata.eu/vpts-csv/">VPTS CSV</a> format and are described in <code>vpts-csv-table-schema.json</code>. An overview of what data are available is provided in <code>coverage.csv</code>. Radar metadata can be found at <a href="https://aloftdata.eu/radars/">https://aloftdata.eu/radars/</a>.</p> <ul> <li><strong>coverage.csv</strong>: coverage of the VPTS data, representing the number of unique hours, heights, source files and records for each radar and date combination.</li> <li><strong>vpts-csv-table-schema.json</strong>: technical description of the fields in the VPTS data.</li> <li><strong>be.tgz</strong>: VPTS data from 2 radars in Belgium.</li> <li><strong>ch.tgz</strong>: VPTS data from 5 radars in Switzerland.</li> <li><strong>cz.tgz</strong>: VPTS data from 2 radars in Czechia.</li> <li><strong>de.tgz</strong>: VPTS data from 20 radars in Germany.</li> <li><strong>dk.tgz</strong>: VPTS data from 5 radars in Denmark.</li> <li><strong>ee.tgz</strong>: VPTS data from 2 radars in Estonia.</li> <li><strong>es.tgz</strong>: VPTS data from 15 radars in Spain.</li> <li><strong>fi.tgz</strong>: VPTS data from 13 radars in Finland.</li> <li><strong>fr.tgz</strong>: VPTS data from 26 radars in France.</li> <li><strong>hr.tgz</strong>: VPTS data from 7 radars in Croatia.</li> <li><strong>il.tgz</strong>: VPTS data from 1 radar in Israel.</li> <li><strong>nl.tgz</strong>: VPTS data from 3 radars in the Netherlands.</li> <li><strong>no.tgz</strong>: VPTS data from 11 radars in Norway.</li> <li><strong>pl.tgz</strong>: VPTS data from 8 radars in Poland.</li> <li><strong>pt.tgz</strong>: VPTS data from 3 radars in Portugal.</li> <li><strong>se.tgz</strong>: VPTS data from 22 radars in Sweden.</li> <li><strong>si.tgz</strong>: VPTS data from 2 radars in Slovenia.</li> <li><strong>sk.tgz</strong>: VPTS data from 4 radars in Slovakia.</li> </ul> <h2>Acknowledgements</h2> <p>This dataset was processed using infrastructure provided by the University of Amsterdam, SURF Cooperative, Ghent University and the Research Institute for Nature and Forest (INBO). It was mainly supported by the <a href="https://globam.science/">GloBAM project</a>, funded through the 2017-18 Belmont Forum and BiodivERsA joint call for research proposals under the BiodivScen ERA-Net COFUND programme.</p>
UVA_VPTS - Vertical profiles of biological targets derived from weather radars in Belgium, Germany and the Netherlands
<p><em>UVA_VPTS - Vertical profiles of biological targets derived from weather radars in Belgium, Germany and the Netherlands</em> is a vertical profile time series dataset published by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal movement data derived from 24 weather radars in Belgium, Germany and the Netherlands, with varying coverage from 2008 to 2023. These data were created by processing weather radar data - provided by the Royal Meteorological Institute of Belgium (<a href="https://www.meteo.be/">RMI</a>), German Meteorological Service (<a href="https://www.dwd.de/">DWD</a>) and Royal Netherlands Meteorological Institute (<a href="https://www.knmi.nl/">KMNI</a>) - with methods optimized for extracting bird targets. The resulting data are vertical profile time series (VPTS), containing the density, speed and direction of biological targets within a weather radar (<code>radar</code>) volume, grouped into altitude bins (<code>height</code>) and measured over time (<code>datetime</code>). The data are also available in the <a href="https://aloftdata.eu/browse/?prefix=uva/">Aloft bucket</a>.</p> <p>See Desmet et al. (2025, <a href="https://doi.org/10.1038/s41597-025-04641-5">https://doi.org/10.1038/s41597-025-04641-5</a>) for a more detailed description of this dataset.</p> <h2>Files</h2> <p>VPTS data in this deposit are organized per country (.tgz file), radar (directory), year (directory) and month (.csv.gz file). Fields in the data follow the <a href="https://aloftdata.eu/vpts-csv/">VPTS CSV</a> format and are described in <code>vpts-csv-table-schema.json</code>. An overview of what data are available is provided in <code>coverage.csv</code>. Radar metadata can be found at <a href="https://aloftdata.eu/radars/">https://aloftdata.eu/radars/</a>.</p> <ul> <li><strong>coverage.csv</strong>: coverage of the VPTS data, representing the number of unique hours, heights, source files and records for each radar and date combination.</li> <li><strong>vpts-csv-table-schema.json</strong>: technical description of the fields in the VPTS data.</li> <li><strong>be.tgz</strong>: VPTS data from 3 radars in Belgium.</li> <li><strong>de.gz</strong>: VPTS data from 18 radars in Germany.</li> <li><strong>nl.gz</strong>: VPTS data from 3 radars in the Netherlands.</li> </ul> <h2>Acknowledgements</h2> <p>This dataset was processed using infrastructure provided by the University of Amsterdam, SURF Cooperative, Ghent University and the Research Institute for Nature and Forest (INBO). It was mainly supported by the <a href="https://globam.science/">GloBAM project</a>, funded through the 2017-18 Belmont Forum and BiodivERsA joint call for research proposals under the BiodivScen ERA-Net COFUND programme.</p>
JRC Phased Array Weather Radar Observational Data
<p>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.</p>
IT-RAD weather radar data collected on 5 September 2015 during an exceptionally intense hailstorm in the Gulf of Naples
<p>On 5 September 2015 a violent hailstorm hit the Gulf and the city of Naples in Italy. The storm originated over the Tyrrhenian Sea dropping 7–10 cm diameter hailstones along its path. The event was observed by a dual-polarization Doppler C-band, namely the Monte il Monte radar operated by the Italian Department of Civil Protection, being part of the Italian radar network (IT-RAD). </p> <p>The dataset is composed by high-resolution polar volumes of the following radar parameters: Uncorrected reflectivity Z (UZ), Corrected reflectivity Z (CZ), differential reflectivity (ZDR), uncorrected differential phase shift (PHIDP), co-polar correlation coefficient (RHOHV), Doppler Velocity (V), Doppler Width (W). The scan strategy is based on inverse elevation mode (from the highest to the lowest elevation angle) with variable PRF. The different phases of the event were well captured by the weather radar of “Monte il Monte” (lat = 41.9394 °, lon = 14.6208 °, altitude = 710 m) which, at the time of the event, was affected by a differential bias (on ZDR) of about 0.8 dB to be algebraically subtracted from the measured quantities. The user should be informed about some spot regions of missing data that might be present in the radar variables, especially on the V and differential phase. This is caused by the thresholding approach applied at RSP level on the signal quality index (SQI) to compensate for W-LAN interferences.</p> <p>The dataset, consisting of 44 raw radar data volumes with 5 minutes sampling rate, is provided in a proprietary format (“Datamet”) that is manageable by using the python code named read_datamet.py that is available at <a href="https://zenodo.org/record/4897245">https://zenodo.org/record/4897245</a>.</p>
Data from: Learning to predict spatio-temporal movement dynamics from weather radar networks
<p>This dataset contains the following:</p> <ul> <li><strong>data</strong>: <ul> <li><em>preprocessed</em>: hourly European weather radar data (here: <em>radar</em>) and aggregated simulation outputs (here: <em>abm</em>), combined with ERA5 reanalysis data and Voronoi tessellation details</li> <li><em>shapes: </em>geographical shapes used for plotting</li> </ul> </li> <li><strong>results</strong><em>:</em> trained models and corresponding results for both simulated data (here: <em>abm</em>) and weather radar data (here: <em>radar</em>).</li> <li><strong>figures</strong><em>: </em>final figures presented in our paper "Learning to predict spatio-temporal movement dynamics from weather radar networks" to summarize the results</li> </ul> <p>The corresponding code used to train and evaluate models is archived here: <a href="https://doi.org/10.5281/zenodo.6921595">10.5281/zenodo.6921595</a>.</p>
A Deep Learning Approach to Quantitative Precipitation Estimation Mainly Using Weather Radar Data: Taking the Plum Rain Season in Taiwan as an Example
<p>The following partial dataset is provided by the authors for the Quantitative Precipitation Estimation method presented in the paper.</p> <p>wissdom_out_Taiwan_mosaic_202105010000.bin is wind data, the number means year, data, and time.</p> <p>CAPPI_COMP_DR_202005100000.bin is radar data.</p> <p>qpepre_202005010000-202005010100_1_h.txt is rain data.</p> <p> </p> <p> </p>
Tornado Detection From Full-Resolution Polarimetric Weather Radar Data (SAMPLE)
<p>This dataset contains a small sample of the tornado dataset described in the talk "A Tornado Detection Algorithm using Deep Neural Networks, Full-Resolution Polarimetric Weather Radar Data, and Explainable AI" presented at the 40th Conference on Radar Meteorology on Aug 31st 2023.</p> <p>The files contained in this data represent approximately 1% of the full dataset that will be released upon final publication.</p> <p>For questions please contact</p> <p>James.Kurdzo@ll.mit.edu and mark.veillette@ll.mit.edu</p>
Analysis of mixtures of birds and insects in weather radar profile data
Open the record for dataset details and reuse information.
Discrimination of biological scatterers in polarimetric weather radar data: opportunities and challenges
<p>The associated dataset represents the base training/test data used for analyses by S. Gauthreax and R. Diehl in “Discrimination of biological scatterers in polarimetric weather radar data: opportunities and challenges.”</p>
Observation of Electrical Alignment Signatures in An Isolated Thunderstorm by Dual-Polarized Phased Array Weather Radar and the Relationship with Intracloud Lightning Flash Rate
<div> <p>The data of the Dual-polarized Phased Array Weather Radar (DP-PAWR), from 2019-08-20 16:10:15 to16:41:04 on August 20, 2019. </p> <p> </p> </div>
Using weather radar to help minimize wind energy impacts on nocturnally migrating birds
<p>As wind energy rapidly expands worldwide, information to minimize impacts of this development on biodiversity is urgently needed. Here we demonstrate how data collected by weather radar networks can inform placement and operation of wind facilities to reduce collisions and minimize habitat-related impacts for nocturnally migrating birds. We found over a third of nocturnal migrants flew through altitudes within the rotor-swept zone surrounding the North American Great Lakes, a continentally important migration corridor. Migrating birds concentrated in terrestrial stopover habitats within 20-km from shorelines, a distance well beyond the current guidelines for construction of new land-based facilities, and their distributions varied seasonally and at local and regional scales, creating predictable opportunities to minimize impacts from wind energy development and operation. Networked radar data are available across the U.S. and other countries and broad application of this approach could provide information critical to bird-friendly expansion of this globally important energy source.</p>
University of Queensland portable weather radar (UQ-XPOL) dataset for 20231025 and 20231031
<p>Two packages are included: 20231025_scan includes the raw scan files generated by the UQ-XPOL Furuno WR2100 for observations made near Tara, South-East Queensland, Australia. 20231031_scan includes the raw scan files generated by the UQ-XPOL Furuno WR2100 for observations made near Wallangarra at the border between Queensland and NSW, Australia. Both packages include observations of clouds and fire plumes of large fires happening during these two days. Observations were made with the help of Dr Jordan Brook from the Australian Bureau of Meteorology and Kathryn Turner, enrolled as PhD student at The University of Queensland at the time of the observations.</p> <p>This work was funded by Google.org, the charity branch of the company Google.</p>
Data for the manuscript "The impact of dealiasing biases on bird and insect data products of C-band weather radars and consequences for aeroecological applications" by Weisshaupt et al.
<p>Netcdf files of the analyses in the manuscript "The impact of dealiasing biases on bird and insect data products of C-band weather radars and consequences for aeroecological applications" by Weisshaupt et al. containing flight directions, flight speeds and animal densities in 200-m height layers between 0-1 km from four single PRF and one dual-PRF scans from 10 Finnish polarimetric C-band weather radars between 1-30 Sept 2022 and from the Kankaanpää weather radar between 10 Sept – 30 Oct 2023 and 10 April - 10 June 2024. Bird and insect echoes were identified by the classifier developed by Mäkinen, T., J. Ritvanen, S. Pulkkinen, N. Weisshaupt, and J. Koistinen, 2022: Bayesian Classification of Nonmeteorological Targets in Polarimetric Doppler Radar Measurements. <em>J. Atmos. Oceanic Technol.</em>, <strong>39</strong>, 1561–1578, <a href="https://doi.org/10.1175/JTECH-D-21-0177.1" target="_blank" rel="noopener">https://doi.org/10.1175/JTECH-D-21-0177.1</a>.</p> <p>File names indicate the PRF mode, processing mode (either untreated ("raw") or dealiased ("no_classif")), date and a 5-letter radar id.</p>
Revealing Key Dynamical Mechanisms of a Severe Supercell within a QLCS using Rapid Update 4DVar Assimilation of C-band Phased Array Weather Radar Data
<div> <p>These are data supporting the conclusions of the paper. The observation dataset including sounding data, automatic weather station data and radar data. And Vdras outputs are also provided here. The corresponding scripts are based on python(version 3.9) and NCL (version 6.6.2).</p> <p> </p> </div> <h2>Files</h2>
Bird strikes at commercial airports explained by citizen science and weather radar data
<p>1. Aircraft collisions with birds span the entire history of human aviation, including fatal collisions during some of the first powered human flights. Much effort has been expended to reduce such collisions, but increased knowledge about bird movements and species occurrence could dramatically improve decision support and proactive measures to reduce them. Migratory movements of birds pose a unique, often overlooked, threat to aviation that is particularly difficult for individual airports to monitor and predict: the occurrence of birds vary extensively in space and time at the local scales of airport responses.</p> <p>2. We use two publicly available datasets, radar data from the US NEXRAD network characterizing migration movements and eBird data collected by citizen scientists to map bird movements and species composition with low human effort expenditures but high temporal and spatial resolution relative to other large scale bird survey methods. As a test case we compare these results from weather radar distributions and eBird species composition with detailed bird strike records from three major New York airports.</p> <p>3. We show that weather radar based estimates of migration intensity can accurately predict probability of bird strikes, with 80% of the variation in bird strikes across the year explained by the average amount of migratory movements captured on weather radar. We also show that eBird based estimates of species occurrence can, using species' body mass and flocking propensity, accurately predict when most damaging strikes occur.</p> <p>4. Synthesis and applications: Our results highlight the power of federating datasets with movement and distribution data for developing better and more taxonomically and ecologically tuned models of likelihood of strikes occurring and severity of strikes. By better understanding when, and where, different species occur, airports across the world can predict seasonal periods of collision risks with greater temporal and spatial resolution; such predictions include potential to predict when the most severe and damaging strikes may occur.</p>
Electrical Alignment Signatures of Ice Particles before Intracloud Lightning Activity Detected by Dual-polarized Phased Array Weather Radar
<p>lightning data from LIDEN system and the data of the Dual-polarized Phased Array Weather Radar (DP-PAWR) on August 20, 2019. </p>
Data from: A characterization of autumn nocturnal migration detected by weather surveillance radars in the northeastern US
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Using weather radar to help minimize wind energy impacts on nocturnally migrating birds
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Bird strikes at commercial airports explained by citizen science and weather radar data
Open the record for dataset details and reuse information.
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Allen Brain Atlas
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OpenNeuro
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