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115 results for “vertical profile”
Doppler lidar vertical wind profiles from Rzecin during POLIMOS 2018
<p>This is data set includes Doppler wind lidar quantities which were calculated from measurements performed between May and September 2018 at <em>PolWET </em>site in Rzecin, Poland (52.75°N, 16.30°E, 59 m a.s.l.) of the Poznan University of Life Sciences</p> <p>The system is a Doppler lidar Stream Line (Halo Photonics), which is part of ACTRIS-Cloudnet (Illingworth et al., 2007). The system laser emits at 1.5 μm and the detector is heterodyne using fiber-optic technology. The measurements for this data set consisted of continuous vertically pointing measurements with a temporal resolution of 2 s. A more detailed description of the instrument can be found in (Ortiz-Amezcua et al., 2022)</p>
WHOI prototype Vertical Temperature Profiler data from Quashnet River site QRSP28
<p>Vertical Temperature Profiler data from the WHOI prototype instrument, acquired at the Quashnet River site QRSP28 from 17 June, 2022 to 11 July, 2022. Data sampled at 5 minute intervals. Eighteen total temperature records, with the measurement depth listed in the first (header) row of the file. Depths are in cm (e.g., T8 is the temperature record from 8 cm depth). Note that absolute depths could be in error by as much as 2 cm due to uncertainty introduced by insertion process, but relative depths are highly accurate. Data have been calibrated based on water bath tests.</p>
Dataset on sub-daily vertical profiles of physicochemical parameters and chlorophyll concentration in El Val reservoir, together with its daily meteorological data, storage state and downstream flow (2018-2022).
This dataset contains the physicochemical parameters and chlorophyll concentration of El Val reservoir (province of Zaragoza, Spain), together with its meteorological conditions, the water level, the stored volume and the flow rate of the effluent, the Queiles River, a few meters downstream of the dam. These data are useful to feed deterministic, data driven or hybrid hydrological models with different purposes, like the identification of the impact of meteorological conditions on the physicochemical properties of the reservoir, like the thermal stratification, as well as the assessment of different management strategies in the reservoir. The original data were collected by the Confederación Hidrográfica del Ebro (CHE) and were published in real time through the web page of the Ebro Automatic Water Quality Information System (SAICA Ebro by its initials in Spanish) and the Ebro Automatic Hydrographic Information System (SAIH Ebro by its initials in Spanish). Then, the CHE curated the data that are finally available to the citizens under request by variable and date. In order to facilitate their availability and reuse, these data have been gathered, pre-processed and packaged in the form of datasets. Specifically, they are structured in four data tables: vertical profiles of physicochemical data in the reservoir, meteorological data in the same basin, water level and stored water volume in the reservoir and water flow rate of the Queiles River downstream of the reservoir.
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>
A dataset of ground-based vertical profile observations of aerosol, NO2 and HCHO from the hyperspectral vertical remote sensing network in China (2019-2023)
<p>Vertical <span>profile </span>observations of atmospheric composition are crucial for understanding the generation, evolution, and transport of regional air pollution. However, existing technological limitations and costs have resulted in a scarcity of vertical profil<span>e</span> data. This study <span>introduces </span>a high-<span>time-</span>resolution (approximately 15 minutes) dataset of vertical <span>profile </span>observations of atmospheric composition (aerosols, NO2, and HCHO) conducted using passive remote sensing technology across 32 sites in seven major regions of China from 2019 to 2023. The study meticulously documents the vertical distribution, seasonal <span>variations and </span>diurnal <span>pattern</span> of these pollutants, revealing long-term trends in atmospheric composition across various regions of China. This dataset provides essential scientific evidence for regional environmental management and policy-making. Its sharing <span>would </span>facilitate the scientific community <span>in </span>explor<span>ing</span> of source-receptor relationships, investigating the impacts of atmospheric composition on regional and global climate <span>and </span>feedback mechanisms.</p>
Data archive for: Exploring the use of machine learning to improve vertical profiles of temperature and moisture
<p>Vertical profiles of temperature and dewpoint are useful in predicting deep convection that leads to severe weather that threatens property and lives. Currently, forecasters rely on observations from radiosonde launches and numerical weather prediction (NWP) models. Radiosonde observations are, however, temporally and spatially sparse, and NWP models contain inherent errors that influence short-term predictions of high-impact events. This work explores using machine learning (ML) to postprocess NWP model forecasts, combining them with satellite data to improve vertical profiles of temperature and dewpoint. We focus on different ML architectures, loss functions, and input features to optimize predictions. Because we are predicting vertical profiles at 256 levels in the atmosphere, this work provides a unique perspective at using ML for 1-D tasks. Compared to baseline profiles from the Rapid Refresh (RAP), ML predictions offer the largest improvement for dewpoint, particularly in the mid- and upper-atmosphere. emperature improvements are modest, but CAPE values are improved by up to 40%. Feature importance analyses indicate that the ML models are primarily improving incoming RAP biases. While additional model and satellite data offer some improvement to the predictions, architecture choice is more important than feature selection in fine-tuning the results. Our proposed deep residual UNet performs the best by leveraging spatial context from the input RAP profiles; however, the results are remarkably robust across model architecture. Further, uncertainty estimates for every level are well-calibrated and can provide useful information to forecasters.</p>
Vertical profiles of leaf photosynthesis and leaf traits, and soil nutrients in two tropical rainforests in French Guiana before and after a three-year nitrogen and phosphorus addition experiment
<p>We provide a comprehensive dataset of vertical profiles of photosynthetic capacity and important leaf traits, including leaf N and P concentrations, from two three-year, large-scale fertilisation experiments conducted in two tropical rainforests in French Guiana. These data present a unique source of information to further improve model representations of the roles of N, P, and other leaf nutrients, in photosynthesis in tropical forests. To further facilitate the use of our data in syntheses and model studies, we provide an elaborate list of ancillary data, including important soil properties and nutrients, along with the leaf data. As environmental drivers are key to improve our understanding of carbon (C)-nutrient cycle interactions, this comprehensive dataset will aid to further enhance our understanding of how nutrient availability interacts with C uptake in tropical forests.</p>
Global 1° × 1° map of vertical POC flux profiles simulated by the MSPACMAM
<p>Model output data generated by two different configurations of the MSPACMAM: (1) the standard model configuration (so-called <em>std</em> configuration) and (2) the model configuration including large particle fragmentation (so-called <em>frag</em> configuration). The MSPACMAM simulates the climatological annual mean state of sinking particulate organic carbon (POC) fluxes in the contemporary global ocean. Model variables include small particle POC concentration, large particle POC concentration, small particle sinking speed, large particle sinking speed, sinking POC flux, and POC transfer efficiency at 1000 m (so-called <span class="math-tex">\(T_{eff}\)</span>).</p>
Measurement report: Vertical profiling of particle size distributions over Lhasa, Tibet: Tethered balloon-based in-situ measurements and source apportionment
<p>Particle size distribution data in summer 2020 in Lhasa, Tibet for https://doi.org/10.5194/acp-2021-810</p>
Vertical temperature profiles obtained from Venus Express and Akatsuki radio occultation data using FSI
<p>Vertical temperature profiles of Venusian atmosphere obtained from selected radio occultation data taken in ESA's Venus Express and JAXA's Akatsuki missions. The temperatures were retrieved using a radio holographic method, Full Spectrum Inversion (FSI). The data list and format are given in two Excel files and the data are given in text files with an extension ".dat".</p>
Stratified vertical sediment profiles increase burrowing crab effects on salt marsh edaphic conditions
Open the record for dataset details and reuse information.
Tree functional trait variation along the vertical canopy profile in central Amazonian forests
Open the record for dataset details and reuse information.
Data archive for: Exploring the use of machine learning to improve vertical profiles of temperature and moisture
Open the record for dataset details and reuse information.
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>
Data for paper "Effects of inflow conditions on plunge points and vertical profiles of turbidity currents on sloping flume based on 3D numerical simulation"
<p>This set of xlsx files, metadata of model results and Matlab code accompanies the manuscript "Effects of inflow conditions on plunge points and vertical profiles of turbidity currents on sloping flume based on 3D numerical simulation" by Ruoyin Zhang, Baosheng Wu, and Bangwen Zhang. This is the first release of the data.</p>
The vertical profile of the dynamic TOI from double to single, TIEGCM
<p>The simulation from TIEGCM to explore the roles of IMF By and Bz on the dynamic evolution of TOI from double to single at 300 and 500 km.</p>
Vertical profiles of 3D cluster properties
<p>The two zip files contain the properties of the 3D clusters used in the submission version of the Paper "Size-dependence of surface-rooted three-dimensional convective objects in continental shallow cumulus simulations". The paper was submitted to JAMES in May 2021.</p> <p>When unzipped, the data is stored in python panda dataframes in pkl files. Warning! Unzipping the files increases the data size by a factor of 20.</p> <p>The two python scripts contain the functions used to segment the 3D snapshots into individual objects. The most interesting things are in proc_watershed, cusize_functions is just a collection of mostly abandoned functions. The functions in proc_watershed are reasonably well commented.</p>
Pyroconvection Classification based on Atmospheric Vertical Profiling Correlation with Extreme Fire Spread Observations
<p>1. Isochrones for Martorell, Santa Coloma Queralt, Torroella, Pobla Massaluca and Sierra Bermeja fires, in shapefile format. Each file has associated an attribute table identifying the hour (in UTC), the affected area, the rate of spread, and direction. Source: Catalan Fire and Rescue Service (Bombers de la Generalitat de Catalunya)</p> <p>2. ERA5 reanalysis data obtained for each fire, hourly and at different pressure levels (37) from the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). The files are in netCDF format, and the variables requested were temperature, relative humidity, U-component of wind, V-component of wind. Source: Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2018): ERA5 hourly data on pressure levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). 10.24381/cds.bd0915c6</p> <p>3. Data from sondes launched in fires during the 2021 Spain wildfire campaign. The files are in CSV format, and there are two per fire: the sounding data corrected and the raw flight history. The information provided is Hour (UTC), Wind speed (m/s), Wind direction (true deg), Dew point (C), Latitude, Longitude, Altitude (in m MSL and m AGL), Pressure (Pascal), Speed (m/s), Heading (degrees), Temperature (C), Relative humidity (%), Internal temperature (C), Latitude, Longitude, Rise speed (m/s). Source: Catalan Fire and Rescue Service (Bombers de la Generalitat de Catalunya)</p> <p>4. Data from the closest weather station to each fire. The file is an Excel file. The table fields are: fire name, weather station name, day, hour, average temperature (°C), maximum temperature(°C), minimum temperature (°C), average relative humidity (%), precipitation (mm), wind speed (10 m, km/h), wind direction (10 m, degrees), wind gusts (10 m, km/h), pressure (hPa), radiation (W/m). Source: Meteo.cat, Servei Meteorològic de Catalunya</p> <p>5. Fire behavior resume for Martorell, Santa Coloma Queralt, Torroella, Pobla Massaluca, Llançà, Alfarràs and Sierra Bermeja fires (Spain). The differences in the data shown respond to the possibility of launching sondes, recreating isochrones, and observing the plume column during each fire. In those cases where the information was obtained through these three ways, the variables available are: column type, ABL and LCL height (m), sonde ID, rate of spread (km/h), ROS observed / ROS expected ratio, fireline intensity expected and observed (kW/m), and affected area (ha).</p> <p>6. Photographic registry of the fire plume evolution and a brief description of the pyroconvective moments in the Alfarràs, Martorell, Llançà, Torroella, Santa Coloma de Queralt, Pobla Massaluca, and Sierra Bermeja fires (Spain). Pictures sources: Catalan Fire and Rescue Service (Bombers de la Generalitat de Catalunya)</p>
Supplementary Data from manuscript entitled: Cloud processing dominates the vertical aerosol profiles in marine air masses over the Great Barrier Reef
<p>The data used in the manuscript "Cloud processing dominates the vertical aerosol profiles in marine air masses over the Great Barrier Reef" is available. It contains information from different instruments on board the research aircraft. The manuscript describes the instrumentation details. There is a read_me file in each folder with further information.</p>
An ocean turbulence data reduction scheme for autonomous, vertically profiling floats: datasets
<p>See <a href="https://doi.org/10.5281/zenodo.5719505">https://doi.org/10.5281/zenodo.5719505</a> instead</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.