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25 results for “vertical velocity”
Vertical velocity field from the JCOPE-T-NEDO simulation
<p>Vertical velocity field from the "NEDO" version of the "JCOPE-T" ocean general circulation model (Varlamov et al 2015; Wang et al 2024).</p> <p><a href="../api/records/13132471/draft/files/wzm-2014.11.01-2014.11.11.nc.gz/content">wzm-2014.11.01-2014.11.11.nc.gz</a> contains the hourly-mean vertical velocity "wzm" from 133.3°E to 148°E, from 24°N to 28.3°N, and from 2014-11-01T00:00:00Z to 2014-11-11T00:00:00Z. The model uses the sigma coordinates and the data file contains the variable zed(x,y,sigma) that indicates the depth at which "wzm" is defined for each x and y.</p>
A data set of monthly global ocean vertical velocity from 1950-2014
<p>This data set provides monthly global ocean vertical velocity from 1950-2014. It was constructed from 41 CMIP6 models (historical experiment). It may be used for investigating the large-scale upwelling and downwelling.</p> <p>Note that this data set has not been widely tested. Please feel free to contact the author if you had any questions or concerns.</p> <p>It will be greatly appreciated if you could send the author an email when you used this data set, so that the author can better improve this data set, and more importantly, provide you with updated data sets or any modifications.</p> <p> </p> <p> </p>
Ground Vertical and east Velocities for the Western Gulf of Corinth, Greece, combining InSAR and GPS
<p><strong>Data group</strong> :<br> Ground Vertical and East Velocities for the Western Gulf of Corinth combining InSAR and GPS, for the period 2002-2010</p> <p><strong>Data identifiers</strong> : </p> <ol> <li>Best constrained 951 vertical and east PS-SBAS velocities for pixels of 200m</li> <li>Best constrained 4391 vertical and east PS-SBAS velocities for pixels of 200m</li> </ol> <p><strong>Version</strong> : 1.0</p> <p><strong>Coordinate Reference System</strong>: Geographic WGS84, EPSG:4326</p> <p><strong>Citation</strong>: Elias & Briole, 2018, Ground deformations in the Corinth rift, Greece, investigated through the means of SAR multi-temporal interferometry<br> </p>
Horizontal and vertical velocities in Ho Chi Minh city by Sentinel-1 radar interferometry
<p>Ho Chi Minh City (HCMC), the most crowded city and economic hub of Viet Nam, has been experiencing land subsidence over the past decades. This effort aims to contribute the spatial distribution of subsidence in HCMC in its horizontal and vertical components using synthetic aperture radar interferometry (InSAR) time series. To this purpose, an advanced Persistent Scatterers and Distributed Scatterers (PSDS) InSAR technique was applied to two European Space Agency (ESA) Sentinel-1 datasets consisting of 96 ascending and 202 descending images, acquired from 2014 to 2020 over the HCMC area. The combination of ascending and descending satellite passes is used to decompose the light of sight velocities into horizontal east-west and vertical components. The obtained results revealed that subsidence is most pronounced in the areas along the Sai Gon River, in the northwest-southeast axis, and in the southwest of the city, with a maximum value of 80 mm/yr, which is in accordance with the findings of the literature. The amplitude of east-west horizontal velocities is relatively small and large-scale eastward movement can be observed in the west of the city at a rate of 3-5 mm/yr.</p> <p>File "Dinh_HCMUD_v1.tif" is the 50-m vertical velocity in mm/year. Negative velocities represent movement subsidence.</p> <p>File "Dinh_HCMEW_v1.tif" is the 50-m east-west horizontal velocity in mm/year. Positive velocities represent movement Eastward.</p> <p>For more details on the technique, the reader can be found in [1].</p> <p>[1] Ho Tong Minh, D.; NGO, Y.; Lê, T.T.; Le, T.C.; Bui, H.S.; Vuong, Q.V.; Le Toan, T. Quantifying Horizontal and Vertical Movements in Ho Chi Minh City by Sentinel-1 Radar Interferometry. <em>Preprints</em> <strong>2020</strong>, 2020120382. Available: https://www.preprints.org/manuscript/202012.0382/v2</p> <p> </p> <p> </p>
CONUS crustal vertical velocities 2007-2017
<p>This dataset includes vertical velocity fields derived from Global Positioning System (GPS) daily positions from 2007-2017, in the IGS08 reference frame. Residual velocities after removing glacial isostatic adjustment model (ICE6GD), elastic deformation due to GRACE-derived hydrologic loading, and Earth's geocenter motion are also presented. Description of data, methods, and results are documented in the following peer-reviewed publication:</p> <p>Lau, N., Borsa, A.A., & Becker, T.W. (2020). Present-day crustal vertical velocity field for the contiguous United States. Journal of Geophysical Research: Solid Earth, 125, e2020JB020066. https://doi.org/10.1029/2020JB020066</p> <p>This NETCDF file contains the following nine subsets:</p> <ol> <li>'lat', latitude, centerd in 0.25-degree grid cell.</li> <li>'lon', longitude, centered in 0.25-degree grid cell.</li> <li>'gps_vu', gps derived vertical velocities, in millimeters.</li> <li>'gps_vu_smooth', gps derived vertical velocities smoothed by 300-km Gaussian filter; in millimeters.</li> <li>'gps_uncertainties', one-sigma uncertainty estimates for 'gps_vu'; in millimeters.</li> <li>'gps_vu_poro', gps derived vertical velocities, including stations strongly affected by poroelastic effect; in millimeters.</li> <li>'grace_vu', velocities due to hydrologic elastic loading estimated by GRACE; in millimeters.</li> <li>'net_vu', velocities after removing GRACE hydrolog, ICE6GD glacial isostatic adjustment model, and Earth's geocenter motion from 'gps_vu'; in millimeters.</li> <li>'net_vu_smooth',velocities after removing GRACE hydrolog, ICE6GD glacial isostatic adjustment model, and Earth's geocenter motion from 'gps_vu_smooth'; in millimeters.</li> </ol> <p> </p>
Radar high resolution vertical velocity
<p>High temporal resolution vertical velocity profiles from 205 MHz wind profiler radar at Cochin university of science and technology</p>
Turkish Straits System - Vertical Velocity
<p>Vertical Velocity daily mean estimates from a six-year simulation of Turkish Straits System (TSS) using high-resolution unstructured triangular mesh ocean model FESOM between 2008-2013. Other variables are provided separately.</p> <p>The mesh files are appended to the dataset for processing purposes.</p> <p>Aydogdu, A., Pinardi, N., Ozsoy, E., Danabasoglu, G., Gurses, O., and Karspeck, A.: Circulation of the Turkish Straits System under interannual atmospheric forcing, Ocean Sci., 14, 999-1019, doi:10.5194/os-14-999-2018, 2018.'</p>
Vertical velocity maps for the Nice Côte d'Azur airport measured by InSAR
<p>The present datasets consists in three InSAR velocity maps in mm/yr (vertical land velocity) that shows the ongoing Nice Côte d'Azur airport subsidence from 1992 until today. The three velocity maps have been computed from SAR data acquired by three succesive ESA missions (ERS, Envisat, and Sentinel-1).</p>
North Atlantic vertical velocity estimates from OGCM geostrophic meridional velocities
<p>Annual vertical velocity (w_GLVB) estimates from OGCM geostrophic velocities within the North Atlantic ocean during the 1960-2015 period.</p> <p>The w fields are computed following the methodology described in <em>Cortés-Morales and Lazar, submitted.</em></p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: North Atlantic Ocean [107ºW-25ºE/12ºS-75ºN]</p> <p>Grid and horizontal spatial resolution: 1/4º (ORCA025 grid)</p> <p>Vertical levels: 58 levels from sigma level 21 to 28.05 kg m<sup>-3</sup></p> <p>Temporal resolution: Annual (1960-2015)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>nav_lon (2D)</p> <p>nav_lon (2D)</p> <p>isop_level (1D)</p> <p>time (1D)</p> <p>w_GLVB (4D)</p>
Wind velocity vertical profile in a 50 m wind mast at Sisal, Yucatán, Mexico
<p>A meteorological mast, instrumented with sonic anemometers was implemented during the years 2010-2014, in order to study high frequency wind data at five different heights above the ground. The mast is 50 m height, located about 100 m from the shoreline at the Sisal campus of UNAM. An automated acquisition system recorded raw data in a database (server) directly through a RF link.</p> <p>For more information visit: http://ocse.mx/en/experimento/torre-sisal</p> <p> </p>
Global Observation-based LInear Vorticity Vertical Velocities (OLIV3) over isopycnal levels
<p>Observation-based Linear Vorticity Vertical Velocities (OLIV3) estimates from observation-based geostrophic velocities within the global themocline during the 1993-2019 period at annual frequency.</p> <p>The beta-plane geostrophic OLIV3 fields are computed following the Indefinite depth-integrated geostrophic linear vorticity balance methodology described in <em>Cortés-Morales and Lazar, 2024, </em><em>Diego Cortés Morales, 2023 [thesis] and Cortés-Morales et al., (submitted) </em>applied to the ARMOR3D [<em>Mulet et al., 2013; https://data.marine.copernicus.eu/product/MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012</em>] geostrophic meridional velocities. The boundary condition used is the Ekman pumping vertical velocities computed from ERA5 wind stress [DOI: 10.24381/cds.f17050d7]. The velocity field is quality-flagged based on the relative error and interannual correlation coefficient between w_g and w_tot in an OGCM perfect model test (<em>Cortés-Morales et al., (submitted)</em>).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Global Ocean</p> <p>Grid and horizontal spatial resolution: Evenly spaced 0.25º grid</p> <p>Vertical levels: 71 levels from sigma level 21 to 28.17 kg/m^3.</p> <p>Temporal resolution: Annual (1993-2019)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (2D): Longitude</p> <p>lat (2D): Latitude</p> <p>isolev (1D): Isopycnal level</p> <p>time (1D): Year</p> <p>w_oliv3(4D): Beta-plane geostrophic vertical velocities</p> <p>h_depth (4D): Depth isopycnal surfaces</p> <p>flag_time_var (3D): Flag based on correlation coeffcient between geostrophic and total vertical velocties from OGCM</p> <p>flag_time_mean (3D): flag based on relative error between geostrophic and total vertical velocties from OGCM</p>
UKV Lee Waves 700 hPa vertical velocities and hand labels
<p>Data to train models to segment trapped lee waves from vertical velocity NWP data.</p> <p>Full details in QJRMS paper <a href="https://doi.org/10.1002/QJ.4592">https://doi.org/10.1002/QJ.4592</a>.</p> <p>Met Office UKV data available under the <a href="https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">Open Government Licence</a>.</p>
Liquid water content and vertical velocity from RICO-based LES simulations
<p>The RICO-based simulations by implementing the LES module of WRF model 4.0 generate the raw data. The simulation continued for 40 hours with a time step of 0.5 second. The domain size is 12.8×12.8×4 km<sup>3</sup>, with a resolution of 50 m and 40 m in the horizontal and vertical orientation, respectively. The outputs during 8~40 hour were retained every 20 minutes. Two parameters (LWC and vertical velocity) were then extracted by using MATLAB to create the following dataset. Additionally, LWC at 1 min before each moment was also uploaded for tracking. The "q" and "w" in the filename represent LWC and vertical velocity, respectively, and the number in the filename represents the corresponding hour and minute (e.g. "q24_20" means LWC value at 24h20m). The unit of "q" and "w" are "kg/kg" and "m/s' respectively.</p>
Supporting figures: Seasonality and trend of the global upper-ocean vertical velocity over 1998–2017
<p>These uploaded figures are results of seasonal variations and trend of the global upper-ocean vertical motions, based on BRAN2020, OFES, OMEGA3D and SODA3.3.1. This is to support our revised manuscript entitled '<strong>Seasonality and trend of the global upper-ocean vertical velocity over 1998</strong>–<strong>2017'</strong>, under consideration in <em>Progress in Oceanography</em>. </p>
Data for "Evaluating vertical velocity retrievals from vertical vorticity equation constrained dual-Doppler analysis of real, rapid-scan radar data"
<p>This archive contains data from the Rapid Scanning X-Band Polarimetric (RaXPol) radar, Atmospheric Imaging Radar (AIR), and Shared Mobile Atmospheric Research and Teaching radar (SMART-R) for 4 September 2018 in central Oklahoma. This is a rapid-scan dual-Doppler dataset of a convective storm. RaXPol and AIR were the two radars that can be used for dual-Doppler retrievals and the SMART-R data can be used for verification of vertical velocity.</p> <p>The AIR and RaXPol data have been quality controlled and are provided in cfRadial format. The SMART-R data has not been quality controlled and are available in its raw data format. All data can be read using the Python ARM Radar Toolkit.</p> <p>This dataset was used for the manuscript:</p> <p>Gebauer, J. G., A. Shapiro, C. K. Potvin, N. A. Dahl, M. I. Biggerstaff, and A. A. Alford, 2021: Evaluating vertical velocity retrievals from vertical vorticity equation constrained dual-Doppler analysis of rapid-scan radar data. <em>J. Atmos. Meas. Tech., </em>in review.</p>
Animation of the vertical velocity and potential temperature for the reference simulation rf08_003b
<p>This animation shows the 12 h temporal evolution of the simulated 2D vertical wind field and potential temperature over the Scandinavian mountains for 28 January 2016. The numerical simulation is initialized with one-hourly ECMWF IFS upstream profiles taken at 61°N and 1°E and averaged from 12 to 18 UTC. The IFS input data are spectrally truncated to T21 in order to filter gravity waves out. The animation shows the computational domain extending from 0 to 20 km altitude (model top is at 80 km) and the whole horizontal domain excluding the lateral absorbers. Horizontal and vertical resolution is 500 m and 250 m, respectively, the time step is 2 s and the output for this animation is every 120 s.</p>
Liquid water content and vertical velocity from RICO-based LES simulations
<p>The RICO-based simulations by implementing the LES module of WRF model 4.0 generate the raw data. The simulation continued for 40 hours with a time step of one second. The domain size is 12.8×12.8×4 km3, with a resolution of 100 m and 40 m in the horizontal and vertical orientation, respectively. The outputs during 8~40 hour were retained every 10 minutes. Two parameters (LWC and vertical velocity) were then extracted by using MATLAB to create the following dataset. The "q" and "w" in the filename represent LWC and vertical velocity, respectively, and the "a"~"e" in the filename represents the corresponding minute in each hour (e.g. "q24a" means LWC value at 24h10m). The unit of "q" and "w" are "kg/kg" and "m/s' respectively. </p>
Total column moisture and vertical velocity for two cases of extreme precipitation
<p>Total column water (filled) and vertical velocity at 500 hPa (red—descending motions, blue—ascending) for 1st case (8-9 July 1994) and 2nd case (6-7 July 2001)<strong>.</strong></p>
Machine Learning Estimation of Maximum Vertical Velocity from Radar (Test Data)
<p>Inside this archive is the testing dataset for the paper titled: <i>Machine Learning Estimation of Maximum Vertical Velocity from Radar, </i>currently under review in AMS AIES. The files are compressed using tar.gz, so know that decompressed this data is about 20 GB in size. The training and validation sets are much larger. If you need those, please reach out to me and we will work on getting you the data. </p><p>Please see the github repo for how to use this data: https://github.com/ai2es/hradar2updraft </p>
Dataset to accompany the manuscript "Drifter observations reveal intense vertical velocity in a surface ocean front"
<p>This dataset has been used in the publication "Drifter observations reveal intense vertical velocity in a surface ocean front"</p> <p>The dataset contains data from the following platforms<br> · Drifters<br> · Underway-CTD</p>
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