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136 results for “outflow”
Manually-collected discharge data for multiple inflow and outflow tributaries at Falling Creek Reservoir, Beaverdam Reservoir, and Carvins Cove Reservoir, Virginia, USA from 2019-2025
Discharge rates at multiple inflow streams into Falling Creek Reservoir (Vinton, Virginia, USA), Beaverdam Reservoir (Vinton, Virginia, USA), and Carvins Cove Reservoir (Roanoke, Virginia, USA), and one outflow at Falling Creek Reservoir were measured manually using multiple methods from 2019-2025. Falling Creek Reservoir, Beaverdam Reservoir, and Carvins Cove Reservoir are owned and operated by the Western Virginia Water Authority as drinking water sources for Roanoke, Virginia. The dataset consists of discharge rates calculated using one of four methods: handheld flowmeter, salt injection, velocity float, or bucket method. Data were collected weekly to monthly from February through October 2019 at Falling Creek and Beaverdam Reservoir, and approximately weekly to seasonally at Falling Creek and Carvins Cove from 2020 to 2025. The dataset is accompanied by a maintenance log and quality assurance/quality control analysis scripts.
Directed Outflow Project Lower Trophic Study
The upper San Francisco Bay Estuary and Sacramento San Joaquin Delta is critical habitat for the endemic Delta Smelt (Hypomesus transpacificus), an endangered planktivorous fish with an annual, semi-anadromous life cycle. Freshwater outflow actions during the fall season are hypothesized to improve Delta Smelt habitat and therefore Delta Smelt condition before the spawning life stage. As part of efforts to evaluate the effectiveness of such actions to benefit Delta Smelt populations, various studies were initiated by the U.S. Bureau of Reclamation under the Directed Outflow Project (DOP). One component of the DOP was to evaluate how the Fall X2 freshwater outflow action changed the lower trophic prey available to Delta Smelt. The action is hypothesized to change abiotic and biotic aspects that can benefit Delta Smelt habitat by using freshwater outflow to maintain the position of X2 (defined as the distance in kilometers from the Golden Gate Bridge to the tidally averaged 2 ppt salinity isohaline) at around 70 km during wet or above normal precipitation water years. This moves the low salinity zone (0.5 - 6 ppt) further seaward into Suisun Bay which increases the area of preferred Delta Smelt habitat. Field sampling began the September of 2017 and occurred bi-weekly through the end of November, paired with the U.S. Fish and Wildlife Service Enhanced Delta Smelt Monitoring program. Three random sites were sampled per target region (Suisun Bay, Suisun Marsh, the Lower Sacramento River, the Cache Slough Complex, and the Sacramento Deep Water Ship Channel) per week. Sites were chosen using a Generalized Random Tessellation Stratified design. During 2018, sampling was increased to weekly and in 2019-2020 sampling began in April and ended in November. Sampling occurs for three different habitat types: shoal, channel surface and channel deep at each site depending on the depth of the water column. The shoal habitat is less than 10 feet, channel habitat is greater than 10 f
In situ observations of Ganymede's outflowing ionosphere
<p>The file contains values for ionospheric moments from the Juno flyby of Ganymede on 7 June 2021, during the period between 16:50 to 17:00, UTC. A description of the flyby and the data set is found in Valek et al. (2022), <em>In situ ion composition observations of the Ganynmede's outflowing ionosphere</em>, GRL DOI:10.1029/2022GL100281.</p>
Thunderstorm outflows in the Mediterranean Sea area
<p>In the context of the European projects “Wind and Ports” (grant No. B87E09000000007) and “Wind, Ports and Sea” (grant No. B82F13000100005), an extensive in-situ wind monitoring network was installed in the main ports of the Northern Mediterranean Sea. An unprecedent number of wind records has been acquired and systematically analyzed. Among these, a considerable number of records presented non-stationary and non-Gaussian characteristics that are completely different from those of synoptic extra-tropical cyclones, widely known in the atmospheric science and wind engineering communities. The cross-checking with meteorological information allows to identify which of these events can be defined as thunderstorm winds, i.e., downbursts and gust fronts.</p> <p>The scientific literature of the last few decades has demonstrated that downbursts, and especially micro-bursts, are extremely dangerous for the natural and built environment. Furthermore, recent trends in climate change seem to preview drastic future scenarios in terms of intensification and frequency increase of this type of extreme events. However, the limited space and time structure of thunderstorm outflows makes them still difficult to be measured in nature and, consequently, to build physically reliable and easily applicable models as in the case of extra-tropical cyclones. For these reasons, the collection and publication of events of this type represents a unique opportunity for the scientific community.</p> <p>The dataset here presented was built in the context of the activities of the project THUNDERR “Detection, simulation, modelling and loading of thunderstorm outflows to design wind-safer and cost-efficient structures”, financed by the European Research Council (ERC), Advanced Grant 2016 (grant No. 741273, P.I. Prof. Giovanni Solari, University of Genoa). It collects 29 thunderstorm downbursts that occurred between 2010 and 2015 in the Italian ports of Genoa (GE) (4), Livorno (LI) (14), and La Spezia (SP) (11), and were recorded by means of ultrasonic anemometers (Gill WindObserver II in Genoa and La Spezia, Gill WindMaster Pro in Livorno). All thunderstorm events included in the database were verified by means of meteorological information, such as radar (CIMA Research Foundation is gratefully acknowledge for providing with most of the radar images), satellite, and lightning data. In fact, (i) high and localized clouds typical of thunderstorm cumulonimbus, (ii) precipitations, and (iii) lightnings represent reliable indicators of the occurrence of the thunderstorm event.</p> <p>Some events were recorded by multiple anemometers in the same port area – the total number of signals included in the database is 99. Despite the limited number of points (anemometers), this will allow the user to perform cross-correlation analysis in time and space to eventually retrieve size, position, trajectory of the storm, etc.</p> <p>The ASCII tab-delimited file ‘Anemometers_location.txt’ reports specifications of the anemometers used in this monitoring study: port code (Port code – Genoa-GE, Livorno-LI, La Spezia-SP); anemometer code (Anemometer code); latitude (Lat.) and longitude (Lon.) in decimal degree WGS84; height above the ground level (h a.g.l.) in meters; Instrument type. Bi-axial anemometers were used from the ports of Genoa and La Spezia, recording the two horizontal wind speed components (<em>u</em>, <em>v</em>). Three-axial ultrasonic anemometers were used in the port of Livorno, also providing the vertical wind speed component <em>w </em>(except bi-axial anemometers LI06 and LI07). All anemometers acquired velocity data at sampling frequency 10 Hz, sensitivity 0.01 m s<sup>-1</sup> (except anemometers LI06 and LI07 with sensitivity 0.1 m s<sup>-1</sup>) and were installed at various heights ranging from 13.0 to 75.0 m, as reported in the file ‘Anemometers_location.txt’.</p> <p>The ASCII tab-delimited file ‘List_DBevents.txt’ lists all downburst records included in the database, in terms of: event and record number (Event | record no.); port code (Port code); date of event occurrence (Date) in the format yyyy-mm-dd; approximate time of occurrence of the velocity peak (Time [UTC]) in the format HH:MM; anemometer code (Anemometer code). </p> <p>The database is presented as a zip file (‘DB-records.zip’). The events are divided based on the port of occurrence (three folders GE, LI, and SP). Within each folder, the downburst events that were recorded in that specific port are reported as subfolders (name format ‘[port code]_yyyy-mm-dd’) and contain the single anemometers signals as TAB-delimited text files (name format ‘[port and anemometer code]_yyyy-mm-dd.txt’). Each sub-dataset (file) contains 3(4) columns and 360.000 rows. The first column shows the 10-h time vector (<em>t</em>, ISO format) in UTC, while the remaining 2(3) columns report the 10-h time series of 10-Hz instantaneous horizontal (zonal west-to-east <em>u</em>, meridional south-to-north <em>v</em>) and, where available, vertical (positive upward <em>w</em>) wind speed components, centred around the time of maximum horizontal wind speed (vectorial sum of <em>u</em> and <em>v</em>). The choice of representation of the wind speed in a large time interval (10 hours) allows the user to perform a more comprehensive and detailed analysis of the event by taking into account also the wind conditions before and after the onset of the downburst phenomenon. 'Not-a-Number' (‘NaN’) values are reported in wind velocity signals when the instrument did not record valid data. Some wind speed records show noise in discrete intervals of the signal, which reflects in an increase of the wind speed standard deviation. A modified Hampel filter was employed to remove measurement outliers. For each wind speed signal, every data sample was considered in ascending order, along with its adjacent ten samples (five on each side). This technique calculated the median and standard deviation within the sampling window using the median absolute deviation. Elements deviating from the median by more than six standard deviations were identified and replaced with 'NaN'. The tuning of the filter parameters involved finding a balance between overly agressive and insufficient removal of outliers. Residual outliers were subsequently manually removed through meticulous qualitative inspection. The complexity and subjectivity of this operation provide users with the opportunity to explore alternative approaches. Consequently, the published dataset includes two versions: an initial version (v1) comprising the original raw data with no filtering applied, and a second "cleaned" version (v2).</p> <p>The presented database can be further used by researchers to validate and calibrate experimental and numerical simulations, as well as analytical models, of downburst winds. It will also be an important resource for the scientific community working in the wind engineering field, in meteorology and atmospheric sciences, as well as in the risk management and reductions of losses related to thunderstorm events (i.e., insurance companies).</p>
VMP and SADCP at Dotson Ice Shelf outflow (2022)
<p>Matlab files of vertical microstructure profiler (VMP) 2000 and shipboard acoustic doppler current profiler (SADCP) transects at the Dotson Ice Shelf (DIS) outflow measured during the NBP2202 expedition between 22 Jan 2022 and 06 Feb 2022, used in the manuscript Dotto et al. (in prep).</p> <p> </p> <p>Please, read the ReadMe for an explanation of the variables and dataset.</p> <p> </p> <p>These data were collected onboard Nathaniel B Palmer, and are part of the the Thwaites-Amundsen Regional Survey and Network Integrating Atmosphere-Ice-Ocean Processes (TARSAN) project, a component of the International Thwaites Glacier Collaboration (ITGC; https://thwaitesglacier.org/).</p> <p> </p> <p>Citing paper:</p> <p>Tiago S. Dotto, Peter M. F. Sheehan, Yixi Zheng, Rob A. Hall, Gillian M. Damerell and Karen J. Heywood (in prep.) Heterogeneous Mixing Processes Observed in the Dotson Ice Shelf Outflow, Antarctica</p> <p> </p>
Dataset for: DECaPS and SMA discovery of a highly-inclined class I YSO with an outflow: IRAS 08235-4316
<p>This repository provides the dataset for the paper "DECaPS and SMA discovery of a highly-inclined class I YSO with an outflow: IRAS 08235-4316" by Joshua Bennett Lovell et al. It is associated with manuscript number AJ AAS57883.</p> <p>All data and code for the project stored here.</p> <p>A jupyter notebook file is included which provides the script to reproduce all presented figures in the paper.</p> <p>A set of .fits extension files are provided, which include the SMA 12CO cube, associated moment map images, the SMA continuum image, and the DECaPS RGB cube.</p>
IFU Observations of Collimated Bipolar Outflows in Wolf-Rayet Planetary Nebulae
<p><strong>IFU Observations of Collimated Bipolar Outflows in Wolf-Rayet Planetary Nebulae</strong></p> <p>A large fraction of planetary nebulae have been found to have aspherical morphologies with collimated outflows. We do not fully comprehend how these axisymmetric morphologies are formed. I recently conducted kinematic studies of a sample of Galactic planetary nebulae around Wolf-Rayet stars using integral field unit (IFU) observations collected with the ANU 2.3-m telescope. I constrained their 3D morpho-kinematic models with position-velocity (P-V) diagrams and velocity channel maps extracted from IFU observations, together with archival high-resolution imaging observations, using the kinematic modeling program SHAPE (<a href="https://arxiv.org/abs/2107.03994">arXiv:2107.03994</a>). Collimated bipolar outflows are noticeable in the P-V diagrams of many of these objects, while their main shells have elliptical morphologies based on IFU velocity channel maps and high-resolution images. Further studies are required to explore possible links between their bipolar outflows and hydrogen-deficient Wolf-Rayet central stars.</p> <p>Pitch Talk presented at <em>IAU Symposium 366: The Origin of Outflows in Evolved Stars</em> (<a href="https://iaus366.be/">iaus366.be</a>), Virtual Meeting, 1-5 November 2021</p>
Fig. 6 in Influence of Dardanelles outflow induced thermal fronts and winds on drifter trajectories in the Aegean Sea
Fig. 6: Same as Figure 4, but of the drifters during the 2009 experiment: A) drifter d1 and B) drifter c1 (red), drifter c2 (magenta) and drifter d3 (yellow).
Fig. 7 in Influence of Dardanelles outflow induced thermal fronts and winds on drifter trajectories in the Aegean Sea
Fig. 7: Wind progressive vector diagrams for the 2008 (A) and 2009 (B) experiments colour coded with the time. The axis represents the displacements in km of a pure wind-driven particle having a speed equal to 1% of wind speed.
Fig. 2 in Influence of Dardanelles outflow induced thermal fronts and winds on drifter trajectories in the Aegean Sea
Fig. 2: (A) Central points chosen as reference for the meteorological conditions of the Aegean Sea (black dot in the middle of the basin). Wind time series during the 2008 (B) and 2009 (C) experiments.
Fig. 5 in Influence of Dardanelles outflow induced thermal fronts and winds on drifter trajectories in the Aegean Sea
Fig. 5: Same as Figure 4, but of the southern triplet drifters during the 2008 experiment: A) drifter a1; B) drifter a2 and C) drifter a3.
Fig. 1 in Influence of Dardanelles outflow induced thermal fronts and winds on drifter trajectories in the Aegean Sea
Fig. 1: Geographical references and deployment positions during 2008 (yellow dots) and 2009 (magenta dots). The bathymetry is saturated at - 600 m. SB: Singitikos Bay; SM: Samothraki Island; L: Lemnos Island; DS: Dardanelles Strait; AE: Agios Efstratios Island; S: Skyros Island; A: Andros Island; T: Tinos Island; MY: Mykonos Island; M: Milos Island.
Fig. 4 in Influence of Dardanelles outflow induced thermal fronts and winds on drifter trajectories in the Aegean Sea
Fig. 4: Six-hourly interpolated trajectories of the northern triplet drifters during the 2008 experiment superimposed on the bathymetry: A) drifter b1 (red curve) and drifter b2 (yellow curve) and B) drifter b3. The dots show the drifter position every 6 hours and the black arrows, indicating the direction of the drifters, are depicted every 5 days. See Table 1 for drifter attributes. Depth colourbar is the same as in Figure 1.
Fig. 8A in Influence of Dardanelles outflow induced thermal fronts and winds on drifter trajectories in the Aegean Sea
Fig. 8A: Three-day drifter trajectories superimposed on the SST composite of the period 28-30 August 2008. The dot symbols correspond to the end of the 3-day trajectories.
Fig. 3 in Influence of Dardanelles outflow induced thermal fronts and winds on drifter trajectories in the Aegean Sea
Fig. 3: Drifter trajectories during the 2008 (A) and 2009 (B) experiments. The drifters of the northern triplets are indicated in magenta colour, while the drifters of the southern triplets are depicted in yellow colour. Depth colourbar is the same as in Figure 1.
Рис. 3. ПоΔразΔеΛение Обжоровского участка на зоны и поΔзоны районирования низовьев ΔеΛьты ВоΛги. Обозначения: 1 — верхняя часть русΛовой зоны; 2 — среΔняя часть русΛовой зоны; 3 — нижняя часть русΛовой зоны; 4 — куΛтучная зона и вытечки протоков Fig. 3. Obzhorovsky transect by zones and subzones of the lower reaches of the Volga delta. Designations: 1 — upper part of the streambed; 2 — middle part of the streambed; 3 — lower part of the streambed; 4 — cultuk zone and the streambed outflow in The number and distribution of the raccoon dog (Nyctereutes procyonoides Gray) and the common jackal (Canis aureus L.) in 2021-2022 in Astrakhan Nature Reserve under the influence of hydrological changes
Рис. 3. ПоΔразΔеΛение Обжоровского участка на зоны и поΔзоны районирования низовьев ΔеΛьты ВоΛги. Обозначения: 1 — верхняя часть русΛовой зоны; 2 — среΔняя часть русΛовой зоны; 3 — нижняя часть русΛовой зоны; 4 — куΛтучная зона и вытечки протоков Fig. 3. Obzhorovsky transect by zones and subzones of the lower reaches of the Volga delta. Designations: 1 — upper part of the streambed; 2 — middle part of the streambed; 3 — lower part of the streambed; 4 — cultuk zone and the streambed outflow
Рис. 2. ПоΔразΔеΛение Αамчикского участка на зоны и поΔзоны районирования низовьев ΔеΛьты ВоΛги (часть 2). Обозначения: 1 — верхняя часть русΛовой зоны; 2 — среΔняя часть русΛовой зоны: 3 — нижняя часть русΛовой зоны; 4 — куΛтучная зона и вытечки протоков Fig. 2. Damchiksky transect by zones and subzones of the lower reaches of the Volga delta (part 2). 1 — upper part of the streambed; 2 — middle part of the streambed; 3 — lower part of the streambed; 4 — cultuk zone and the streambed outflow in The number and distribution of the raccoon dog (Nyctereutes procyonoides Gray) and the common jackal (Canis aureus L.) in 2021-2022 in Astrakhan Nature Reserve under the influence of hydrological changes
Рис. 2. ПоΔразΔеΛение Αамчикского участка на зоны и поΔзоны районирования низовьев ΔеΛьты ВоΛги (часть 2). Обозначения: 1 — верхняя часть русΛовой зоны; 2 — среΔняя часть русΛовой зоны: 3 — нижняя часть русΛовой зоны; 4 — куΛтучная зона и вытечки протоков Fig. 2. Damchiksky transect by zones and subzones of the lower reaches of the Volga delta (part 2). 1 — upper part of the streambed; 2 — middle part of the streambed; 3 — lower part of the streambed; 4 — cultuk zone and the streambed outflow
Рис. 1. ПоΔразΔеΛение Αамчикского участка на зоны и поΔзоны районирования низовьев ΔеΛьты ВоΛги (часть 1). Обозначения: 1 — верхняя часть русΛовой зоны; 2 — среΔняя часть русΛовой зоны: 3 — нижняя часть русΛовой зоны; 4 — куΛтучная зона и вытечки протоков Fig. 1. Damchiksky transect by zones and subzones of the lower reaches of the Volga delta (part 1). Designations: 1 — upper part of the streambed; 2 — middle part of the streambed; 3 — lower part of the streambed; 4 — cultuk zone and the streambed outflow in The number and distribution of the raccoon dog (Nyctereutes procyonoides Gray) and the common jackal (Canis aureus L.) in 2021-2022 in Astrakhan Nature Reserve under the influence of hydrological changes
Рис. 1. ПоΔразΔеΛение Αамчикского участка на зоны и поΔзоны районирования низовьев ΔеΛьты ВоΛги (часть 1). Обозначения: 1 — верхняя часть русΛовой зоны; 2 — среΔняя часть русΛовой зоны: 3 — нижняя часть русΛовой зоны; 4 — куΛтучная зона и вытечки протоков Fig. 1. Damchiksky transect by zones and subzones of the lower reaches of the Volga delta (part 1). Designations: 1 — upper part of the streambed; 2 — middle part of the streambed; 3 — lower part of the streambed; 4 — cultuk zone and the streambed outflow
Investigating the asymmetry of young stellar outflows: A combined MUSE-X-Shooter study of the Th 28 jet
<h3>This record contains supplementary tables and figures for the article <em>'Investigating the asymmetry of young stellar outflows: A combined MUSE-X-Shooter study of the Th 28 jet' </em>by A. Murphy, E. T. Whelan, F. Bacciotti, D. Coffey, F. Comeron, J. Eisloffel, B. Nisini, S. Antoniucci, J. M. Alcala and T. P. Ray, accepted for publication by Astronomy & Astrophysics.</h3> <p> </p> <p><strong>Abstract: </strong></p> <p>Characterising stellar jet asymmetries is key to providing robust constraints for jet launching models, and hence to understanding the underlying mechanisms of jet launching. This study aims to characterise the asymmetric properties of the bipolar jet from the Classical T Tauri Star Th 28. We combine data from integral field spectroscopy with VLT/MUSE and high-resolution spectra from VLT/X-Shooter to map optical emission line ratios in both jet lobes. We carry out a diagnostic analysis of these ratios to compare the density, electron temperature, and ionisation fraction within both lobes. The mass accretion rate is derived from the emission lines at the source, and compared with the mass outflow rate derived in both lobes using the estimated densities and measured [O I]λ6300 and [S II]λ6731 luminosities. The blue-shifted jet shows a significantly higher electron temperature and moderately higher ionisation fraction than the red-shifted jet. In contrast to previous studies we also estimate higher densities n H in the blue-shifted jet by a factor ∼2. These asymmetries are traced to within 1′′ (160 au) of the source in the line ratio maps. We find Ṁacc = 2.4 × 10^−7 M⊙ yr ^−1 , with an estimated obscuration factor of ∼54 due to grey scattering around the star. Estimated values of Ṁout range between 0.66 – 13.7 × 10^−9 M⊙ yr^−1 in the blue-shifted jet and 5-9 × 10^−9 M⊙ yr^−1 in the red-shifted jet.<em> </em>The emission line maps and diagnostic results suggest that the jet asymmetries originate close to the source and are likely intrinsic to the jet. Furthermore, the combined dataset allows access to a broad array of accretion tracers. This in turn enables a more accurate estimation of the mass accretion rate, revealing Ṁacc higher by a factor > 350 than would otherwise be determined.</p> <p> </p> <p><strong>Summary of supplemental material:</strong></p> <p>Table 1: Emission lines detected in X-shooter observations of the jet. Fluxes are measured from the red-shifted jet lobe.</p> <p>Tables 2 and 3: Mass accretion rates measured from MUSE and X-Shooter observations of Th 28, respectively, assuming an on-source extinction of 2.5 mags.</p> <p>Tables 4 and 5: As in Tables 2 and 3, for an on-source extinction of 1.26 mags.</p> <p>Figures 1-4: Position-velocity maps of detected emission lines from the UV and VIS arms of the X-shooter observations.</p> <p>Figure 5: Accretion luminosities measured from MUSE and X-Shooter data, before and after correction for on-source obscuration. Left and right panels show the corresponding values if the fluxes are corrected for a wavelength-dependent extinction of 2.5 and 1.26 mags, respectively.</p> <div> </div> <div> </div>
Dataset for mammatus-like radar echoes along the bases of upper-tropospheric outflow-layer clouds of typhoons.
<p>PPI and RHI data of radar observations of mammatus-like echoes at the cloud bases of upper-level clouds of typhoons observed by the Nagoya-University cloud radar. The observations were carried out at Okinawa, Japan in 2016 and 2019, and Kobe, Japan in 2018 This dataset includes raw data at polar coordinates and grid data interpolated to Cartesian coordinates. This dataset also includes upper-level sounding observation data during the appearance of mammatus-like echoes at Okinawa, Japan in 2016.</p> <p>In this version 2, all RHI data obtained in 3 October 2016 were added.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.