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111 results for “Satellite Observations”
EVEX rocket experiment: sounding rocket and C/NOFS satellite observations in equatorial regions of the ionosphere at sunset
<p>These data files support the JGR: Space Physics article by Pfaff, R. et al. (2022) entitled "Dual sounding rocket and C/NOFS satellite observations of DC electric fields and plasma density in the equatorial E and F region ionosphere at sunset".</p>
Seasonal sediment plumes in the Krishna-Godavari basin using satellite observations - Dataset
<p>Seasonal plume patterns of diffuse attenuation coefficient at the wavelength of 490 nm, K<sub>d</sub>(490), in the coastal waters of Krishna-Godavari, southeast coast of India basin, are examined through remote sensing data collected from July 2002 to October 2021 by the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua platform. This dataset provide point values extracted for K<sub>d</sub>(490) and SST at 16.32°N, 82.603°E in a 3x3 grid for turbidity region for the analysis of inter-annual variability in (a) spring, (b) summer, (c) autumn, and (d) winter during July 2002 to October 2021.</p>
Supplementary data for "How adequately are elevated moist layers represented in reanalysis and satellite observations?"
<p>The NetCDF files are the collocation datasets over Manus Island between GRUAN radiosondes, ERA5, the CLIMCAPS Aqua Level 2 retrieval dataset and the IASI L2 Climate Data Record (CDR). The collocation criteria are 30 minutes and 50 km. Additional filter criteria for the individual datasets and processing steps are described in the manuscript.</p> <p>The datasets are created using the collocation toolkit included in the python package "typhon". Variables in each dataset are split into two groups that represent the two collocated datasets. Each group contains a selection of the original dataset's variables, which are used in the manuscript such as H2O VMR, temperature, cloud fraction, etc. The variables are organized along the dimension "collocation" and along dataset and variable specific additional dimensions. Further documentation about the collocation toolkit and the structure of the resulting datasets can be found at https://github.com/atmtools/typhon.</p> <p> </p> <p> </p>
Sensitivity of fire weather indices, fuel sticks and satellite observations to fuel moisture content in Central European forests - Data
<p>This data repository contains datasets for destructively measured fuels of different types (FMC_insitu), meteorological data including 10-hour fuel stick measurements (FWS_30min) and calculated fire weather index components (FWS_FWI_24h) for four different sites in the Tharandt forest and Saxon Switzerland National Park in the Free State of Saxony (Germany) during the years 2022 (only DE-Tha) and 2023 (all four sites).</p> <p>The provided folders contain .csv files for each study site. Meteorological data in 30min for DE-Tha can be derived from the ICOS data portal (https://data.icos-cp.eu/portal/). For the remaining three sites (DE-BLB, DE-BWB, DE-SHW), past and recent data can be viewed via EMS Brno (e.g., http://www.emsbrno.cz/p.axd/en/Beech__Landberg.TU__DRESDEN.html). Upon request, the authors can share the data. </p> <p><strong>FMC_insitu</strong>: Destructively sampled fuel moisture content of different fuel types.</p> <p><strong>FWS_30min</strong>: Original measurements from the fire weather stations in 30 min time steps</p> <p><strong>FWS_FWI_24h</strong>: Measurements from fire weather stations in 24h time steps and the calculated fire weather index and its components. As requested for calculation of the FWI, meteorological variables are used at 13:00 (UTC), while PREC and PBC are the 24h sum prior to 13:00. </p> <p><strong>readme.txt</strong>: Description of repository content and the variables provided within the .csv files.</p> <p><strong>stations.csv</strong>: Contains the coordinates and a short description of the study sites. </p>
Actors and Satellites in the African Earth Observations Sector: Insights from the 2021 Radiant Earth ML for EO Market Map and the Union of Concerned Scientists Database
<p>The database of organizational actors, "Actors and Satellites in the African Earth Observations Sector: Insights from the 2021 Radiant Earth ML for EO Market Map and the Union of Concerned Scientists Database" analyzed in "<span>Whose Priorities? Examining Inequities in Earth </span><span>Observation Advancements Across Africa" </span>this study, is available on Zenodo, an open-access repository developed under the European OpenAIRE program. The dataset comprises information on 310 space-centric earth observation organizations, including headquarters locations. For the 31 organizations in our sample, we provide additional details including the African countries where their projects are active, the type of initiative or program, other focus areas, organizational classification (commercial, government, or nongovernmental), funding source (public or private), organizational type (research, startup, or established industry), capabilities (data analysis, data storage, image labeling, competition platforms), involvement in early warning systems, data accessibility, availability of global products, and whether they build commercial satellites.</p> <p>This open sharing of the compiled organizational data aims to promote transparency, reproducibility, and additional investigations into the evolving landscape of earth observation activities globally and across Africa. Analyses of this dataset's relationships, funding flows, and priorities can provide further insights to guide equitable advancement of earth observation capabilities.</p>
Supplementary dataset for "Global Climatology of the Daytime Surface Cooling of Urban Parks Using Satellite Observations"
<p>This dataset supplements the paper "Global Climatology of the Daytime Surface Cooling of Urban Parks Using Satellite Observations" in Geophysical Research Letters by Agathangeldis et al.</p> <ul> <li>The file "parks.gpkg" contains the boundaries of the parks used in the study, along with the Surface Park Cool Island (SPCI) intensity and additional metadata as attributes.</li> <li>The file "parks.csv" includes the same information as "parks.gpkg" but without the geospatial information.</li> <li>The file "seasonal_values" provides the SPCI intensity for each park, broken down by season.</li> </ul>
The Impact of Satellite Trails on Hubble Space Telescope Observations: satellite classifications
<p>This repository contains the Hubble Space Telescope (HST) observations with satellite classifications, released in the paper "<a href="https://www.nature.com/articles/s41550-023-01903-3"><em>The Impact of Satellite Trails on Hubble Space Telescope Observations</em></a>" (DOI: 10.1038/s41550-023-01903-3<em>).</em></p> <p>This table contains 114 607 individual HST images taken in the last 19 years and publicly released in the <a href="https://hst.esac.esa.int/">eHST</a> archive by 3 October 2021, with satellite trail classifications made with machine learning and citizen science.</p> <p>We processed the individual images by adding the two ACS/WFC (WFC3/UVIS, respectively) apertures side-by-side, without correcting for geometric distortions and without the gap between the two detectors (hence why the satellite trails can appear discontinuous in the images). Note that these are not original images from the eHST archive, and therefore are not meant for other scientific analysis. The dataset contains 3072 HST images with satellites (2.7% of the dataset) and 3228 satellite trails in total. The classifications were visually inspected and vetted by the authors (images are flagged with the 'satellite' flag). </p> <p>The table contains the following columns:</p> <ul> <li>observation IDs: both individual exposures (simple_id) and composite images, multiple individual exposures processed and stacked (composite_id);</li> <li>instrument: ACS/WFC or WFC3/UVIS;</li> <li>start time and end time of exposure;</li> <li>exposure duration; </li> <li>right ascension (ra) and declination (dec);</li> <li>'satellite' flag [empty otherwise];</li> <li>no_sat: number of satellites in the image;</li> <li>image URL - URL for the individual HST observations used for satellite classification;</li> <li>additional metadata columns, as available in the <a href="https://hst.esac.esa.int/">eHST</a> archive.</li> </ul> <p>The satellites were classified by volunteers on the <a href="http://www.asteroidhunter.org">Hubble Asteroid Hunter</a> citizen science project and with a machine learning classifier. Please cite the paper (<a href="https://www.nature.com/articles/s41550-023-01903-3">Kruk et al.</a>, <a href="https://www.nature.com/articles/s41550-023-01903-3">https://www.nature.com/articles/s41550-023-01903-3</a>) when using the data in this repository.</p>
Satellite_Observed Changes in Forest Cover over Northern China from 1996_2020
<p>This dataset is associated with a research article entitled "Satellite_Observed Changes in Forest Cover over Northern China from 1996_2020".</p> <p>As an important part of the land surface, forest is an important factor affecting global carbon, water cycle and climate change; Fractional Forest Cover (FFC) represents the proportion of forest canopy cover area to the whole pixel observed from the vertical direction. It is an important parameter for monitoring forest resources and is related to forest structure or attributes, such as forest area or forest distribution density. It is a variable representing forest cover on a continuous scale.</p> <p>Based on the remote sensing images of Gaofen-2 and Landsat-8, a model for estimating the FFC in the three-north regions of China is constructed based on the ensemble machine learning method. From 1996 to 2020, the annual FFC products with a resolution of 30 meters covering the three northern regions of China with long time series have been generated. The data values contained in the FFC range from 0 to 100, the "Nodata" value is set to -99, and the data file is provided in Geo-Tiff format.</p>
Potential impact of assimilating visible and infrared satellite observations compared to radar reflectivity for convective‐scale NWP
<p>Contains</p> <ul> <li>raw_data <ul> <li>nature run initial conditions for the cases "random" and "warm-bubble"</li> <li>ensemble initial condition sounding profiles</li> </ul> </li> <li>evaluation_metrics <ul> <li>csv files of FSS, RMSE, MAE, ensemble spread, mean difference<br> to reproduce figures of FSS, RMSE and spread in the paper and more.<br> See the README.txt</li> </ul> </li> <li>figures <ul> <li>Map_VIS06 ... Simulated satellite images of visible reflectance for ensemble members and truth</li> <li>Map_VIS06_probability ... Map of ensemble probability for visible reflectance > 0.6 and the nature in red contours</li> </ul> </li> </ul> <p> </p>
Data set for manuscript 'Quantifying geomorphically effective floods using satellite observations of river mobility'
<p>Data underlying the plots / used in the modelling work for the paper 'Quantifying geomorphically effective floods using satellite observations of river mobility', submitted to <em>Geophysical Review Letters.</em></p>
Data from: Leveraging satellite observations to reveal ecological drivers of pest densities across landscapes
Open the record for dataset details and reuse information.
The co-occurrence of MHW characteristics and satellite-observed Chlorophyll concentration in the California Current System,1996~2020
The characteristics of each MHW event and the most negative anomalies of satellite-observed Chlorophyll concentration in each MHW event in the Southern California Current System (30-35.5°N, 117-125 °W) were calculated.
Supplementary Material for "Time-domain modelling of 3-D Earth's and planetary electromagnetic induction effect in ground and satellite observations"
<p>1. Magnetic field residuals from Observatory and Swarm data. Details about data origin and pre-processing are given in the main paper.</p> <p>2. Time series of external Spherical Harmonic coefficients estimated from observatory and satellite data as described in the main paper.</p>
Converging findings of climate models and satellite observations on the positive impact of European forests on cloud cover
<p>Overview:<br>This repository hosts a comprehensive dataset resulting from a Space for Time (S4T) analysis (<em>Duveiller et al. 2018</em>). The dataset spans monthly data from 2004 to 2014, providing detailed insights into cloud cover dynamics and land cover characteristics. Leveraging observations from the Cloud CCI MODIS-Aqua dataset (<em>Stengel et al. 2017</em>) and RegCM5 (<em>Giorgi et al. 2023</em>) model outputs at 0.05 degrees resolution, it offers valuable resources for researchers studying atmospheric and terrestrial interactions.</p> <p>Contents:</p> <p>s4t_ESACCI.zip:<br>Output of the space-for-time algorithm applied to the Global MODIS-Aqua cloud cover data for low, medium, and high clouds.<br>s4t_RegCM5.zip:<br>Output of the space for time algorithm applied to the European RegCM5 cloud data for low, medium, and high clouds.<br>Variables:</p> <p>Cloud Area Fractions:<br>Includes low (cll), medium (clm), and high (clh) cloud area fractions, expressed as percentages.<br>Cloud layers are categorized based on cloud top pressure (CTP), following the convention of the International Satellite Cloud Climatology Project.</p>
Insights into Internal Solitary Waves East of Dongsha Atoll from Integrating Geostationary Satellite and Mooring Observations
<p>The dataset used to produce the figures in the manuscript. All the data are in MATLAB file format. </p>
Satellite Precipitation Products data performance evaluation with observed datasets
<p>Datasets used for evaluation of Satellite Precipitation Products (SPPs). We used Three open-source datasets from different sources for our evaluation of the SPPs applicability in real-time flood forecasting.</p>
Dataset for the research article titled "Evaluation of Reanalysis and Satellite Products against Ground-based Observations in a Desert Environment "
Open the record for dataset details and reuse information.
Observational dataset for "Using Satellite and ARM Observations to Evaluate Cold Air Outbreak Cloud Transitions in E3SM Global Storm-Resolving Simulations"
<p>This observational dataset include the DOE ARM ground based observations and satellite for the paper titled “Using Satellite and ARM Observations to Evaluate Cold Air Outbreak Cloud Transitions in E3SM Global Storm-Resolving Simulations” on GRL.</p> <p>For the original data source, all ARM observational data sets used in this study are publicly available from the ARM data archive site (https://adc.arm.gov/discovery/#/results/iopShortName::amf2019comble/datastream::anxarmbeatmM1.c1/datastream::anxarmbecldradM1.c1/datastream::anxarsclkazr1kolliasM1.c0). MODIS MOD06 L2 cloud product are publicly available from (https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MOD06 L2, DOI:10.5067/MODIS/MOD06 L2.061).</p> <p>CloudSat products can be ordered from the CloudSat Data Processing center (https://www.cloudsat.cira.colostate.edu/order/). To download CloudSat data, a new user must first create an account by filling out the signup form (https://www.cloudsat.cira.colostate.edu/accounts/signup/).</p>
Global anthropogenic NOx emissions from 2019 to 2022 based on satellite NO2 observations and GEOS-Chem model
<p><a href="../api/records/10947114/draft/files/emissions.nc/content" target="_blank" rel="noopener noreferrer">emissions.nc</a><a href="10052904"> includes the monthly data of global anthropogenic nox emissions from 2019 to 2022, using the GEOS-Chem model combined with TROPOMI satellite observation data</a></p>
Reconstructing Ocean Subsurface Temperature and Salinity with Satellite Observations
<p> Ocean data are crucial for ocean science and climate change research. While moored buoys and Argo floats can provide data on ocean temperature and salinity from the surface to the deep ocean, their spatial and temporal distribution is sparse and discontinuous. In recent years, satellite-based ocean observations have been widely used. These observations offer high spatial resolution and temporal continuity but are often limited to surface ocean quantities. In this study, we develop a novel algorithm to infer ocean subsurface temperature and salinity using satellite observations of ocean surface properties. Different from current prevalent machine learning methods, the algorithm proposed is efficient and interpretable. The resultant dataset has a global coverage with a high spatial resolution (0.25°x0.25°) and has been validated against in-situ observations with satisfactory accuracy.</p>
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.