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55 results for “atmospheric profiles”
Atmospheric profiling data collected from radiosondes in the Southern Ocean in the austral summer of 2016/2017 during the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>The data set consists of the vertical profiles of the atmospheric variables measured using radiosondes (i-Met) during the Antarctic Circumnavigation Expedition from November 2016 to April 2017. The data include the raw variables measured directly by the radiosondes and derived parameters: altitude (km), air pressure (mb), air temperature (ºC), relative humidity (%), frostpoint (ºC), potential temperature (ºK), water vapour mixing ratio (ppmv), total column water (mm w.e.), wind speed (m/s) and wind direction (deg).</p> <p><strong>Dataset contents</strong></p> <ul> <li>aceNNN_yyyymmdd, directory <ul> <li>aceNNN_yyyymmdd.csv, data file, comma-separated values</li> <li>aceNNN_yyyymmdd.kml, metadata, XML</li> <li>aceNNN_yyyymmdd.raw, data file, raw, ASCII DOS</li> <li>aceNNN_yyyymmdd.raw_config, metadata, XML</li> <li>aceNNN.de1, metadata, ASCII text format</li> <li>aceNNNflt.dat, data file, ASCII text format</li> <li>aceNNNpre.dat, data file, ASCII text format</li> </ul> </li> <li>plots, directory <ul> <li>Sounding_ACENNN.png, metadata, portable network graphics</li> </ul> </li> <li>data_file_header_csv.txt, metadata, text format</li> <li>data_file_header_dat.txt, metadata, text format</li> <li>data_file_header_launches.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> <li>overview_radiosonde_launches.csv, metadata, comma-separated value</li> </ul> <p>where NNN is the launch number yyyy is the year, mm is the month and dd is the day. Dates are in UTC.</p> <p>json files make up a Frictionless Data package.</p> <p><strong>Dataset citation</strong></p> <p>Please cite this dataset as:</p> <p>Gorodetskaya, I.V., Thurnherr, I., Tsukernik, M., Graf, P., Aemisegger, F., Wernli, H. and Ralph, F.M. (2021). Atmospheric profiling data collected from radiosondes in the Southern Ocean in the austral summer of 2016/2017 during the Antarctic Circumnavigation Expedition. (Version 1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4382460</p>
Digitized Particulate Matter Size Distribution Profiles from Literature Sources for Improved Size Representation of PM Emissions in Atmospheric Chemical Transport Models
<p>Processing particulate matter (PM) emissions for use in a chemistry transport model (CTM) such as GEM-MACH (Global Environmental Multiscale Modelling Air-Quality and Chemistry) requires detailed information about particle size distribution and chemical speciation for different PM emissions source types. The current PM size distribution and speciation profile library used at Environment and Climate Change Canada (ECCC) for preparing model-ready emission files for GEM-MACH contains very detailed chemical speciation profiles for PM emissions from 91 source types but only has three generic PM size disaggregation profiles, one each for mobile, point, and area sources. These generic profiles are used to disaggregate bulk PM emissions to a 12-bin sectional size representation, where PM<sub>2.5</sub> emissions are split into size bins 1-8 and PM<sub>10‑2.5</sub> emissions are split into bins 9 and 10. Since there is wide variability in the particle size distribution depending on the source type, the inclusion of source-type-specific PM size disaggregation profiles should lead to better representation of PM particle size for emissions from different source types in the model.</p> <p>A presentation entitled “Expansion of a Size Distribution Profile Library for Particulate Matter (PM) Emissions Processing from Three to 32 Source Categories” was given recently at the Community Modeling and Analysis System (CMAS) conference in Chapel Hill, North Carolina in October 2019 (<a href="https://www.cmascenter.org/conference/2019/slides/1300_zhang_expansion_size_2019.pptx">https://www.cmascenter.org/conference//2019/slides/1300_zhang_expansion_size_2019.pptx</a>) . This presentation described work carried out at ECCC to improve the PM size disaggregation profile library used to generate model-ready emissions. In particular, the number of PM size disaggregation profiles in the library was increased from three generic profiles to 32 source-type-specific profiles. After the conference, four more profiles were added to the library for a total of 36 PM size disaggregation profiles. In order to carry out this study, over 100 PM size distribution profiles from various PM emissions sources were gathered from literature publications, analyzed, and transformed into size disaggregation profiles that correspond to the GEM-MACH 12-bin sectional configuration. The 36 PM size disaggregation profiles that were obtained were then combined with detailed PM chemical speciation data to compile a new PM size disaggregation and chemical speciation library for emissions processing using the SMOKE (Sparse Matrix Operator Kernel Emissions) emissions processing system.</p> <p>This Excel workbook provides the digitized particle size distribution data for PM emissions from 36 different source types that were used as input to calculate the PM size disaggregation profiles for the GEM-MACH 12-bin sectional configuration. The digitized particle size distribution profiles were obtained by digitizing images of size distribution plots obtained from the literature publications using graph digitizing software such as Engauge Digitizer (<a href="http://markummitchell.github.io/engauge-digitizer/">http://markummitchell.github.io/engauge-digitizer/</a>) and WebPlot Digitizer (<a href="https://directory.fsf.org/wiki/WebPlotDigitizer">https://directory.fsf.org/wiki/WebPlotDigitizer</a>). By manually defining the axes and selecting points along the curve by computer mouse, a comma-separated-values file was generated for each size distribution profile image. From there, a series of transformations were carried out as required, including particle diameter conversions from aerodynamic diameter to Stokes diameter, and conversion of number-weighted size distributions to volume-weighted size distributions, in order to obtain a harmonized set of profiles. This Excel workbook contains the raw digitized data for all literature size distributions included in the compilation of the new library, as well as the diameter and size distribution weighting conversions. There are 39 worksheets: the first is an introductory worksheet entitled “Spreadsheet_Info” while the next 36 worksheets are ordered alphabetically and correspond to each of the 36 emissions source types for which a PM size disaggregation profile was generated. The final two worksheets contain digitized particle penetration data for common PM control devices.</p> <p>These digitized profiles may be used and adapted for use with other emissions processing systems and other CTMs with a size-resolved representation for PM. More details are provided in the following publication:</p> <p>Elisa I. Boutzis, Junhua Zhang & Michael D. Moran (2020) Expansion of a size disaggregation profile library for particulate matter emissions processing from three generic profiles to 36 source-type-specific profiles, <em>Journal of the Air & Waste Management Association</em>, 70:11, 1067-1100, DOI: <a href="https://doi.org/10.1080/10962247.2020.1743794">10.1080/10962247.2020.1743794</a></p>
Aircraft profiles of stable isotope ratios in atmospheric total and condensed water from the NASA ORACLES mission.
<p>Aircraft in-situ measurements of water concentration and heavy water isotope ratios D/H and 18O/16O of cloud water and total water (water vapor plus condensed water) were collected during the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) project. Aircraft sampling took place in the southeast Atlantic marine boundary layer and lower troposphere (equator to 22 degrees south) over the months of Sept. 2016, Aug. 2017, and Oct. 2018. Isotope measurements were made using cavity ring-down spectroscopic analyzers integrated into the Water Isotope System for Precipitation and Entrainment Research (WISPER). The WISPER data are processed into mean latitude-altitude curtains and individual vertical profiles for each sampling period.</p> <p> </p> <p>The WISPER data accompanied a suite of other variables including standard meteorological quantities (wind, temperature, moisture), trace gas and aerosol concentrations, radar, and lidar remote sensing, which can be accessed through the DOIs listed further down. The ORACLES campaigns are described by Redemann et al., (2021). The water isotope measurements are further described in Henze et al., (2021). The absolute error with respect to the SMOW-SLAP scale is explained in detail by Henze et al., (2021).</p> <p> </p> <p>Total water concentration and isotope ratios were binned and averaged onto latitude-altitude grids using a kernel estimation approach, with weighting designed to estimate the mean during the approximate month-long duration of each sampling period. Standard deviations for each bin are also computed using kernel density estimation.</p> <p> </p> <p>Time intervals during aircraft vertical profiling are isolated and averaged onto 50-meter vertical levels. The files include water concentration and isotope ratios for both total water and cloud water in addition to temperature, pressure, latitude, and longitude.</p> <p> </p> <p>See included file README.txt for additional details.</p> <p> </p> <p>References</p> <p>---------------</p> <p>Henze, D., Noone, D., and Toohey, D.: Aircraft measurements of water vapor heavy isotope ratios in the marine boundary layer and lower troposphere during ORACLES, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2021-238, in review, 2021.</p> <p> </p> <p>Redemann, J., Wood, R., Zuidema, P., Doherty, S. J., Luna, B., LeBlanc, S. E., Diamond, M. S., Shinozuka, Y., Chang, I. Y., Ueyama, R., Pfister, L., Ryoo, J.-M., Dobracki, A. N., da Silva, A. M., Longo, K. M., Kacenelenbogen, M. S., Flynn, C. J., Pistone, K., Knox, N. M., Piketh, S. J., Haywood, J. M., Formenti, P., Mallet, M., Stier, P., Ackerman, A. S., Bauer, S. E., Fridlind, A. M., Carmichael, G. R., Saide, P. E., Ferrada, G. A., Howell, S. G., Freitag, S., Cairns, B., Holben, B. N., Knobelspiesse, K. D., Tanelli, S., L'Ecuyer, T. S., Dzambo, A. M., Sy, O. O., McFarquhar, G. M., Poellot, M. R., Gupta, S., O'Brien, J. R., Nenes, A., Kacarab, M., Wong, J. P. S., Small-Griswold, J. D., Thornhill, K. L., Noone, D., Podolske, J. R., Schmidt, K. S., Pilewskie, P., Chen, H., Cochrane, S. P., Sedlacek, A. J., Lang, T. J., Stith, E., Segal-Rozenhaimer, M., Ferrare, R. A., Burton, S. P., Hostetler, C. A., Diner, D. J., Seidel, F. C., Platnick, S. E., Myers, J. S., Meyer, K. G., Spangenberg, D. A., Maring, H., and Gao, L.: An overview of the ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) project: aerosol–cloud–radiation interactions in the southeast Atlantic basin, Atmos. Chem. Phys., 21, 1507–1563, https://doi.org/10.5194/acp-21-1507-2021, 2021.</p> <p> </p> <p>The complete archive of ORACLES data are accessible via the digital object identifiers (DOIs) provided under ORACLES Science Team references as follows:</p> <p> </p> <p>ORACLES Science Team: Suite of Aerosol, Cloud, and Related Data Acquired Aboard P3 During ORACLES 2018, Version 3, NASA Ames Earth Science Project Office, https://doi.org/10.5067/Suborbital/ORACLES/P3/2018_V3, 2020a. </p> <p> </p> <p>ORACLES Science Team: Suite of Aerosol, Cloud, and Related Data Acquired Aboard P3 During ORACLES 2017, Version 3, NASA Ames Earth Science Project Office, https://doi.org/10.5067/Suborbital/ORACLES/P3/2017_V3, 2020b. </p> <p> </p> <p>ORACLES Science Team: Suite of Aerosol, Cloud, and Related Data Acquired Aboard P3 During ORACLES 2016, Version 3, NASA Ames Earth Science Project Office, https://doi.org/10.5067/Suborbital/ORACLES/P3/2016_V3, 2020c. </p> <p> </p> <p>ORACLES Science Team: Suite of Aerosol, Cloud, and Related Data Acquired Aboard ER2 During ORACLES 2016, Version 3, NASA Ames Earth Science Project Office, https://doi.org/10.5067/Suborbital/ORACLES/ER2/2016_V3, 2020d.</p>
Electron concentration profiles calculated using eight-component model of the ionospheric D-region and two different set of input atmospheric data
<p>The files contain electron concentration <i>Ne</i> profiles during solar X-ray flares that occurred on 9-11 June 2014. The altitude range is 50-90 km.</p><p>Values of electron concentration were calculated using eight-component model of the ionospheric D-region and two different set of input atmospheric data (MSIS neutral atmosphere model and Aura satellite measurements). Results are obtained for ten VLF paths: from European transmitters ICV, TBB, GQD, GBZ, and DHO to Mikhnevo geophysical observatory (55°N 38°E) and A118 SID station (43°N 1°E).</p><p>The data is presented as MATLAB files. Each .mat file contains data and variable "description" with data's structure information.</p>
Continuous snow temperature profiles from the Snow Ice Mass Balance Apparatus (SIMBA) (level 1 Raw), Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH), November 2022-June 2023
<p>Raw (Level 1) measurements from the Snow Ice Mass Balance Apparatus (SIMBA) deployed at the Avery Picnic site (~ 38°58.345' N, 106°59.811' W) during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign near Gothic, Colorado, from November 2021 through June 2023. The SIMBA, originally designed for observing the mass balance of sea ice, is comprised of a thermistor chain with 2 cm spacing (Jackson et al., 2013). This system was configured for terrestrial snowpack by the manufacturer, SAMS Enterprise, to the specifications for SPLASH. The chain was installed suspended from a tripod and fixed to a rigid plastic bar near in time to the onset of snowpack in November 2022. The lowest 10 cm of the chain were buried within the soil. The top of the chain reached approximately 180 cm above the soil surface and snow was permitted to accumulate around the chain throughout the winter of 2022-2023. In the files, negative values of the "height" vector are below the soil surface and positive levels are above, which may be either snow or air depending on the snow depth. The system also uses a low-power heating cycle to measure thermistor's temperature response time for aiding in determining material interfaces: see Jackson et al. (2013) for details. </p><p>There are several cautions to be aware of when using these data. The data has been ingested into daily netCDF and metadata (in attributes) have been provided but no quality control has been carried out on this raw version of the data set. From 1 November through 22 December 2022, the sensor obtained profiles every 10 min after which corruption of the configuration file reverted the profiles to every 6 hours (0, 6, 12, and 18 UTC). After 1 January a problem in the firmware caused the system to lose connection to the time-synching GPS network and therefore the clock drifted from January through June 2023 (the maximum potential time stamping error is likely < 81 sec). Finally, from 23 March through 4 April 2023, the depth of the snow at the location of the sensor was deeper than 180 cm and thus measurements in the upper part of the snowpack were not observed then.</p><p>Jackson, K., J. Wilkinson, T. Maksym, D. Meldrum, J. Beckers, C. Haas, and D. Mackenzie (2013) A novel and low-cost sea ice mass balance buoy. Journal of Atmosphere and Oceanic Technology, 30(11), 2676-2688, https://doi.org/10.1175/JTECH-D-13-00058.1.</p>
Dataset for "An innovative pure rotational Raman lidar for accurately profiling atmospheric temperature and aerosol/cloud backscatter coefficients"
<p>This is the dataset used in the paper "<span>An innovative pure rotational Raman lidar for accurately profiling atmospheric temperature and aerosol/cloud backscatter coefficients"</span></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>
The processed atmospheric wind profiles dataset in near space from ERA5 (20-50km)
<h2>experimental data</h2>
MUA PRR_lidar temperature profiles for atmospheric refraction correction in lunar laser ranging
<p>This PRR lidar temperature data are used for atmospheric refraction corrrection in millimeter-level precision lunar laser ranging.</p>
Scalar flux profiles in the unstable atmospheric surface layer under the influence of large eddies: Implications for eddy covariance flux measurements and the non-closure problem
<p>This dataset contains the data used in the submitted manuscript of Liu, Liu, Huang, Desai, Zhang, Ghannam, and Katul 2023. Please refer to the manuscript for the detailed description of the dataset.</p>
atmosphere profile data
Open the record for dataset details and reuse information.
Wind profile in the wave boundary layer and its application in a coupled atmosphere-wave model
<p>The simulation data for the study</p>
RFM atmospheric profiles input
Open the record for dataset details and reuse information.
Prestorm atmospheric dynamic variable profiling dataset in Beijing as observed from the Radar wind profiler mesonet
<p>This dataset contains prestorm atmospheric dynamical variables of 30 minutes before rainfall onset in summer (June ~ August) for the period 2018–2019, which is determined by the measurements from the triangular mesonet of radar wind profile in Beijing. Each data file is stored in CSV format, containing the triangle area averaged divergence, vertical velocity and vorticity at 400、450、500、550, 600, 650, 700, 750, 775, 800, 825, 850, 875 and 900 hPa levels. The name for each data is formatted as RWP_YYYY_NNN hPa_Lead-MM min.csv, where YYYY refers to 2018 and 2019, NNN refers to 400、450、500、550, 600, 650, 700, 750, 775, 800, 825, 850, 875 and 900 hPa, and MM refers to 12, 18, 24, 30, 36 and 42 minutes prior to rainfall onset. </p>
TROPICS03 L2B Atmospheric Vertical Temperature and Moisture Profiles (AVTP, AVMP) V1.0
The "Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload. Each SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.This dataset is from the TROPICS03 satellite, as the Validated Stage-1 release of the Level 2B geophysical retrieval of atmospheric vertical temperature (kelvins) at the larger unified F-band resolution, retrieval of vertical moisture (g/kg) at the finer G-band spatial resolution, and total Precipitable Water (mm) at the finer G-band spatial resolution. Each TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data.
Advanced Vertical Atmospheric Profiling System Dropsondes (AVAPS) IMPACTS
The Advanced Vertical Atmospheric Profiling System (AVAPS) IMPACTS dataset consists of vertical atmospheric profile measurements collected by the Advanced Vertical Atmospheric Profiling System (AVAPS) dropsondes released from the NASA P-3 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. AVAPS uses a Global Positioning System (GPS) dropsonde to measure atmospheric state parameters (temperature, humidity, wind speed/direction, pressure) and location in 3-dimensional space during the dropsonde’s descent. The AVAPS dataset files are available from January 12, 2020, through February 28, 2023, in ASCII-ict format.
TROPICS05 L2B Neural-network Atmospheric Vertical Temperature & Moisture Profiles V0.2
The "Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload.Each SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.This dataset is the Level-2B Neural Network Atmospheric Vertical Profiles (NNAVP) – Neural Network vertical profile retrieval approach for temperature profiles in Kelvin (K) and water vapor mixing ratio profiles in (kg/kg). Retrievals are done in all non and precipitating conditions, over both land and ocean. Temperature profiles go from surface to 20 km and water profiles from surface to 10-km. The geophysical retrieval of atmospheric vertical temperature is at the larger unified F-band spatial resolution while the retrieval of vertical moisture is at the finer G-band spatial resolution. Each TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data.
TROPICS03 L2B Neural-network Atmospheric Vertical Temperature & Moisture Profiles V1.0
The "Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload.Each SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.This dataset is the Level-2B Neural Network Atmospheric Vertical Profiles (NNAVP) – Neural Network vertical profile retrieval approach for temperature profiles in Kelvin (K) and water vapor mixing ratio profiles in (kg/kg). Retrievals are done in all non and precipitating conditions, over both land and ocean. Temperature profiles go from surface to 20 km and water profiles from surface to 10-km. The geophysical retrieval of atmospheric vertical temperature is at the larger unified F-band spatial resolution while the retrieval of vertical moisture is at the finer G-band spatial resolution. Each TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data.
TROPICS01 L2B Neural-network Atmospheric Vertical Temperature & Moisture Profiles V1.0
The "Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats" (TROPICS) mission has a goal of providing nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. The mission comprises a constellation of five identical Space Vehicles (SVs) conforming to the 3U form factor and hosting a passive microwave spectrometer payload.Each SV hosts an identical high-performance spectrometer named the TROPICS Millimeter-wave Sounder (TMS) that will provide temperature profiles using seven channels near the 118.75-GHz oxygen absorption line, water vapor profiles using three channels near the 183-GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel near 205 GHz that is more sensitive to cloud-sized ice particles.This dataset is the Level-2B Neural Network Atmospheric Vertical Profiles (NNAVP) – Neural Network vertical profile retrieval approach for temperature profiles in Kelvin (K) and water vapor mixing ratio profiles in (kg/kg). Retrievals are done in all non and precipitating conditions, over both land and ocean. Temperature profiles go from surface to 20 km and water profiles from surface to 10-km. The geophysical retrieval of atmospheric vertical temperature is at the larger unified F-band spatial resolution while the retrieval of vertical moisture is at the finer G-band spatial resolution. Each TROPICS netCDF file contains a granule of data with 81 spots and approximately 2880 scans, where a granule is defined as an orbit's worth of data.
Atmospheric Profiles: TOVS - NOAA (FIFE)
The TOVS data were acquired from NOAA/NESDIS to monitor atmospheric conditions that occurred over the FIFE study area during 1987. The TOVS data were obtained from NESDIS in the standard TOVS sounding product format containing atmospheric sounding data for NOAA-9 and NOAA-10 satellites over the FIFE study area. The TOVS sounding products information is derived from three sensors which measure the intensity of upwelling radiation in the various spectral intervals that occur at maxima over broad layers and depths of the atmosphere. These radiance measurements are processed into Earth-located, calibrated radiance values, "clear" radiances (radiances corrected for cloud effects and angle-of-view), estimates of water vapor in three atmospheric layers (converted to precipitable water in these layers), mean temperatures for selected atmospheric layers, tropopause height and temperature estimates, and geopotential thickness of selected atmospheric layers.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.