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658 results for “doppler”
Vertical profiles of Doppler spectra of hydrometeors from a Micro Rain Radar recorded during the austral summer of 2016/2017 in the Southern Ocean on the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>This dataset includes vertical profiles of the Doppler spectra and derived quantities of hydrometeors using a Micro Rain Radar (MRR-2) during the Antarctic Circumnavigation Expedition in 2016/2017. The MRR is a frequency-modulated-continuous-wave (FMCW) Doppler radar working at 24 GHz (K band). It measures the Doppler spectrum at vertical incidence with 31 range gates of 100 m. The data are processed to calculate rainfall rates and standard radar moments. For rain events, the drop size distribution and the rainfall rate are derived using the method by Peters et al. (2005). For snow events, the moments are computed from the Doppler spectrum as described in Maahn & Kollias (2012).</p> <p><strong>Dataset contents</strong></p> <ul> <li>RawSpectra/${year}${month}/MRR_ACE_${year}${month}${day}_RAW.nc: raw data of Doppler spectra for rainfall events. Null values are denoted as -99900.</li> <li>ProcessedData/${year}${month}/MRR_ACE_${year}${month}${day}_PRO.nc: 10-second averages (highest resolution) for specific rain events, using the standard products from Metek (MRR physical basics, 2009).</li> <li>AveData/${year}${month}/MRR_ACE_${year}${month}${day}_AVE.nc: one-minute averages, using the standard products from Metek (MRR physical basics, 2009).</li> <li>Processed_IMProToo/${year}${month}/mrr_improtoo_0.101_ACE_${year}${month}${day}.nc: processed data for snow events using IMProToo (Maahn & Kollias 2012) in one-minute averages.</li> <li>RR_timeline/${year}${month}/RR_MRR_ACE_${year}${month}${day}.csv: 10-minute running mean with one-minute resolution of rain rate for [100-200]m and [200-300]m range gate. Null values are denoted as ‘nan’.</li> <li>RR_timeline/${year}${month}/RR_MRR_ACE_${year}${month}${day}.png: daily plots of rain rates.</li> <li>PrecipitationEvents.txt: text file classifying the precipitation events according to hydrometeors (rain, snow or hail).</li> <li>Graphs_Metek/${year}${month}/moments_MRR_ACE_${year}${month}${day}_AVEnc.png: daily plots of Radar reflectivity and Doppler velocity for rain events.</li> <li>Graphs_IMProToo/${year}${month}/moments_mrr_improtoo_0101_ACE_${year}${month}${day}.png: daily plots of Radar reflectivity and Doppler velocity for snow events.</li> <li>RawSpectra_data_file_header.txt, metadata, text format</li> <li>Metek_ProcessedData_data_file_header.txt, metadata, text format</li> <li>IMProToo_ProcessedData_data_file_header.txt, metadata, text format</li> <li>AveData_data_file_header.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> <li>change_log.txt, metadata, text format</li> </ul> <p><strong>Change log</strong></p> <ul> <li>v1.2 - Amended abstract text. Changed descriptions of files in dataset contents. Added dataset license to README. Updated README and change_log files.</li> <li>v1.1 - Added missing PrecipitationEvents.txt file. Added change_log.txt file.</li> <li>v1.0 - Initial version of dataset.</li> </ul> <p><strong>Dataset license</strong></p> <p>This vertical Doppler spectra profile dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Data presented in "Three-dimensional Doppler, polarization-gradient, and magneto-optical forces for atoms and molecules with dark states"
<p>These are the data presented in our paper "Three-dimensional Doppler, polarization-gradient, and magneto-optical forces for atoms and molecules with dark states" which has been accepted for publication in the New Journal of Physics (as of 07 November 2016).</p>
Presentation in ADANO 2022: Vibration measurements: Laser Doppler vibrometry (LDV)
<p><span>These data include Jae Hoon Sim's presentation in Conventus Herbsttagung in der Leopoldina mit wissenschaftlicher Unterstützung der ADANO, and related documents</span></p>
Presentation in HEARING: High-resolution structural and functional EAR imaging 2023: Three-dimensional vibration of the human tympanic membrane using a scanning laser doppler vibrometer
<p>This is for Bastian Baselt's poster presentation in HEARING: High-resolution structural and functional EAR imaging, Ascona, Switzerland in 2023, and the related data.</p>
DART: Doppler-Aided Radar Tomography
<p>Dataset for <em>DART: Implicit Doppler Tomography for Radar Novel View Synthesis;</em> see our <a href="https://wiselabcmu.github.io/dart/">project site</a> for details.</p> <p> </p> <p> </p>
Identification of Ionospheric Acoustic Wave Signatures from Conventional Surface Explosions Using MF/HF Doppler Sounding
<p>These HDF5 files contain complex time series data from HF receptions of a Digisonde Portable Sounder 4D (DPS4D). Each data point is the phase and amplitude of a decoded Sky Map mode pulse. </p>
WWV 10 MHz Doppler Recording, AD8Y
<p>10 MHz data collected by AD8Y during 2020 and 2021. Details at www.hamsci.org/grape1. Timelapse available here: https://www.youtube.com/watch?v=U-0qZsqzNFE</p> <p> </p> <p> </p>
Atmospheric visibility inferred from continuous-wave Doppler wind lidar, data set
<p>Visibility data from Pershore, UK, between 2018 and 2020</p>
Application of inverse theory for high spatial resolution reconstructions of thermospheric vector wind fields from Doppler shifts measured by a ground-based network of all-sky Fabry-Perot interferometers
<p>Several types of all-sky viewing Fabry-Perot Interferometers (FPI) have been developed since the 1990s for ground-based remote sensing of thermospheric winds. The Scanning Doppler Imager (SDI) is one such instrument, which provides temporally simultaneous line-of-sight observations from hundreds of independent look directions per instrument exposure. A geographically distributed network of such instruments increases spatial coverage and, at many locations, also provides overlapping observations along multiple independent lines-of-sight. Together, these characteristics significantly increase the density and fidelity that is possible for reconstructed thermospheric vector wind fields, compared to a traditional narrow-field FPI, but at the cost of complexity and difficulty.<br><br></p> <p>Presently, we describe an application of inverse theory to reconstruct three-component vector thermospheric neutral wind fields using data from multiple SDI instruments. The salient features of the method used here are the ability to reconstruct three-component winds on a dense grid that is sampled regularly in latitude, longitude, and time, without assuming any a-priori underlying structure of the winds. This requires solving an inverse problem that does not in general yield a unique solution unless additional constraints are enforced. We describe this step, also known also as regularization, along with the strategy used to maximize the spatial resolution of the derived wind fields by automatically determining the minimum level of regularization that can produce stable inversions. We present example results obtained from applying this technique to one night of data from a network of SDIs in Alaska, and discuss the implications of these results for current understanding of thermospheric dynamics.</p>
Distribution of blood flow oscillation across the Doppler shift evaluated by the proposed approach with local pressure test.
<p>A step-wise increase in local pressure is known to cause a gradual change in the parameters of capillary blood flow in the<br> upper layers of the skin, and can also affect the measurement results of various optical methods. The impact of the procedure has several subsequent effects, such as mechanical compression of vessels, and neurological and metabolic compensating mechanisms like pressure-induced vasodilation, which maintain the homeostasis of the skin during moderate levels of external pressure and tissue hypoxia. To examine how those effects are translated to the blood flow registering in different ranges of the Doppler spectra, we have developed a 3D-printed pressure distribution tool compatible with the developed sensor which was used, and equipped with a set of weights. </p> <p>During the main series of measurements, the weights were placed into the PDT in a step-wise manner to achieve the<br> following values of pressure applied: 10 mmHg, 30 mmHg, 90 mmHg, 150 mmHg, 210 mmHg. At the end of the procedure, the load was reduced back to 30 mmHg. Experiments were conducted with the participation of 7 healthy volunteers with 10 min LDF recording for each step. To estimate the prominence of the observed effects and substantiate the measuring routing, several preliminary experiments were also conducted where the set of values of pressure was applied with a step-wise increase and then decrease with about 2 min of LDF recordings for each step.</p> <p>Published in IEEE Transactions on Biomedical Engineering "Diagnosis of skin vascular complications revealed by time-frequency analysis and laser Doppler spectrum decomposition", Zherebtsov et al.</p>
Doppler lidar wind profiles from Kumpula
<p>This data set contains Doppler lidar wind profiles calculated from VAD (Velocity-Azimuth Display) scans by a Halo Photonics Streamline Doppler lidar between 10 April 2018 and 30 September 2020 at Kumpula, Finland (60.333 N, 25.6 E, 45 m.a.s.l.). A more detailed description of the instrument specification and operating parameters is given in Hirsikko et al. (2014).</p>
Radarize: Enhancing Radar SLAM with Generalizable Doppler-Based Odometry
<p>This is the dataset release for the <strong><a href="https://www.sigmobile.org/mobisys/2024/" target="_blank" rel="noopener">ACM MobiSys 2024</a> </strong>paper "<strong>Radarize: Enhancing Radar SLAM with Generalizable Doppler-Based Odometry</strong>".</p> <ul> <li><strong>Project Website:</strong> <a href="http://radarize.github.io" target="_blank" rel="noopener">https://radarize.github.io</a></li> <li><strong>Project Code:</strong> <a href="http://github.com/ConnectedSystemsLab/radarize_ae" target="_blank" rel="noopener">github.com/ConnectedSystemsLab/radarize_ae</a></li> </ul> <p>If you found this useful, please cite </p> <pre><code>@inproceedings{sie2024radarize, author = {Sie, Emerson and Wu, Xinyu and Guo, Heyu and Vasisht, Deepak}, title = {Radarize: Enhancing Radar SLAM with Generalizable Doppler-Based Odometry}, booktitle = {The 22nd ACM International Conference on Mobile Systems, Applications, and Services (ACM MobiSys '24)}, year = {2024}, doi = {https://doi.org/10.1145/3643832.3661871}, }</code></pre>
Data for Doppler Dimming and Brightening Effects in Solar Prominences
<p>This is the data used in Doppler Dimming and Brightening Effects in Solar Prominences by Peat, Osborne, and Heinzel. Published in the Monthly Notices of the Royal Astronomical Society (Letters) 2024.</p> <p>The data is sorted as follows,</p> <p>It is a dictionary of keys 'vlos', 'vrad', 'models'.<br>Models is a 2D array of shape (3333,2). The first axis is the model number, and the second axis is velocity and alititude (in SI).</p> <p><br>'vlos' and 'vrad' both are dictionaries with the keys 'pctr' and 'iso'.<br>'pctr' and 'iso' are lists of length 3333. Each of these entries corresponds directly with the parameters in 'models'.<br>Each of the entires in the list are dictionaries. Which contian 'lya', 'ha', 'mgiih'. These are Lyman Alpha, H Alpha and MgII h, respectively.<br>'lya', 'ha', 'mgiih' contain dictionaries with keys 'int' and 'wvl'. In 'int' is a numpy array of the line, and 'wvl' is a numpy array of the wavelength grid (with 0 being line centre).</p>
Large repeated measures experiments of acoustic Doppler current profiler (ADCP) streamflow measurements under steady flow conditions (Chauvan 2016 regatta)
<p>From 8 to 10 November 2016, 50 laboratories or teams (from 8 different countries) with 50 ADCPs, simultaneously conducted more than 600 ADCP discharge measurements in steady flow conditions (around 14 m<sup>3</sup>/s released by a dam), during three half-days, over 500 m along the Taurion River at Saint-Priest-de-Taurion, France. 26 cross-sections with various shapes and flow conditions, and more or less favourable conditions, were distributed along the river. A specific experiment procedure, which consisted of circulating every team over half of the cross-sections, was implemented in order to quantify the impact of site selection on the discharge measurement<br> uncertainty.</p> <p>The experimental design is presented in Despax et al (2017) available at <a href="https://irsteadoc.irstea.fr/cemoa/PUB00055007">https://irsteadoc.irstea.fr/cemoa/PUB00055007</a></p> <p>Despax, A., Hauet, A., Le Coz, J., Dramais, G., Blanquart, B., Besson, D., & Belleville, A. (2017). Inter-laboratory comparison of discharge measurements with Acoustic Doppler Current Profilers Chauvan field experiments. 8, 9 and 10th November 2016. (Technical report). Lyon, France: Groupe Doppler. (92 p.)</p> <p> </p>
Micro-power Pulse-Doppler Radar Clutter and Displacement Source Classification Dataset
<p>This is the official dataset for the ACM BuildSys 2019 publication <em>One Size Does Not Fit All: Multi-Scale, Cascaded RNNs for Radar Classification.</em></p> <p>The training code for MSC-RNN can be found at <a href="https://github.com/dhruboroy29/MSCRNN">https://github.com/dhruboroy29/MSCRNN</a></p> <p>Kindly cite this work as:</p> <pre><code>@inproceedings{roy2019one, title={One size does not fit all: Multi-scale, cascaded RNNs for radar classification}, author={Roy, Dhrubojyoti and Srivastava, Sangeeta and Kusupati, Aditya and Jain, Pranshu and Varma, Manik and Arora, Anish}, booktitle={Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation}, pages={1--10}, year={2019} }</code></pre> <p> </p>
TTU-Ka Mobile Doppler Radar - KTaL 2015-2016
<p>Mobile Doppler weather radar data collected near Lubbock, Texas as part of the Kinematic Texture and Lightning (KTaL) field campaign in 2015-2016. Radial velocity data have been unfolded and other quality control measures applied. Files are in NetCDF format with the CF-Radial metadata convention. Data are a subset of the full record, corresponding to the storms studied in a forthcoming publication.</p>
CLAMPS2 Doppler Lidar VAD Data
<p>These files contain 24 hour periods of data collected from the CLAMPS2 Halo Streamline XR+ Doppler lidar. The Doppler lidar conducts regular conical scans at a set elevation angle. These data are then passed through a typical VAD algorithm to retrieve horizontal wind speed and direction profiles. These data were collected during the SPLASH project.</p>
CLAMPS2 Doppler Lidar Vertical Stare Data
<p>These files contain 24 hour periods of data collected from the CLAMPS2 Halo Streamline XR+ Doppler lidar. While not conducting other scans, the lidar directs the beam to zenith, allowing for the measurement of vertical velocity. These data were collected during the SPLASH project.</p>
Seawater velocities from ship acoustic Doppler current profiler during PolarFront 2022-05 cruise
<p>Seawater velocities captured by Ocean Surveyor ADCP, RD Instruments, were recorded (blanking distance 8 m, bottom track on) during our entire study from 18-26 May.</p>
High-rate GNSS Raw Doppler Positive Impact on Cascading Filter-based Approach for Improving Real-time Transient Coseismic Velocities Modeling
<p>The high-rate GNSS average and instantaneous coseismic velocity waveforms for the 2016 Mw 6.6 Norcia earthquake and the 2011 Mw 9.1 Tohoku earthquake are included in this repository.</p>
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