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15 results for “geostationary satellite”

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zenodo40/100

Composite geostationary weather satellite images (second time derivative of water vapor channel) for visualizing Lamb waves

Second time derivative of water vapor channel (6.2 micrometer) brightness temperature from geostationary weather satellites (Units: K s-2) Himawari-8 (original data obtained from NICT Science Cloud) GOES-16/17 (original data obtained from Amazon AWS) Meteosat-8/9/10/11 (original data obtained from EUMETSAT)   Time interval of the files: 5 minutes   Time interval of each satellite, dt for time derivative: Himaawri-8, GOES-16/17: 10 minutes, 10 minutes Meteosat-8/9/11: 15 minutes, 15 minutes Meteosat-10: 5 minutes, 10 minutes   Each file contains the latest images from those satellites at that time. The time stamp for each satellite represents the beginning of each full-disk scan.   Bias correction: Himawari-8: bias removal for each swath GOES-16/17, Meteosat-11: bias removal for each east-west line Meteosat-8/9/10: bias removal for each east-west line (note: satellite attitude was not stable)   Smoothing: Band-pass filter for each full-disk image separately: 2-40 degrees on lat-lon coordinate Stronger smoothing at latitudes higher than 60 degrees north/south   Down-sampling: Full-disk images were mapped to a 0.04-degree lat-lon coordinate. Then, composite images were produced at the 0.2-degree resolution.   Version 2: Improved interpolation algorithm Himawari-8: improved geolocation Meteosat-8/9: improved treatment of noise near the edge of full disk images

opencc-by-4.0May 2022View details →

zenodo40/100

Structure and dynamics of plasma irregularities over the equatorial ionospheric region: A study using spaced receiver technique employing geostationary satellites' radio signals-Data set

The study investigates the characteristic features of the ionospheric irregularities using spaced receiver technique. In the spaced receiver technique, we have used a trio of receivers separated by 40 and 100 m from each other. These receivers monitor scintillations patterns of the L1 signals transmitted by the geostationary satellites. The cross-correlation of the signals and the power spectral analysis yields the measure of characteristic features of the irregularities.   The data folder contains the S4 index, drift velocity of the irregularities, powerspectral slopes and size of the irregularities observed on four days. The folder also contains the gnuscript used for plotting.

opencc-by-4.0Jul 2024View details →

zenodo40/100

Los Alamos National Laboratory (LANL) and Geostationary Operational Environmental Satellite (GOES)–R Geosynchronous Particle Data for Ferradas, C. P., et al. (2023)

This repository contains data from a set of charged particle analyzers onboard the Los Alamos National Laboratory (LANL) and the Geostationary Operational Environmental Satellite (GOES)–R geosynchronous orbit satellites. The data are used in the following open access publication submitted to Frontiers in Astronomy and Space Sciences. Ferradas, C. P., M.-C. Fok, N. Maruyama, M. G. Henderson, S. Califf, S. A. Thaller, and B. T. Kress (2022), The effects of particle injections on the ring current development during the 7-8 September 2017 geomagnetic storm, Frontiers in Astronomy and Space Sciences, submitted.

opencc-by-4.0Nov 2022View details →

zenodo36/100

Supplement of "Algorithm for continual monitoring of fog life cycles based on geostationary satellite imagery as a basis for solar energy forecasting"

The file uploaded here is an animation that visually illustrates the outputs of the a newly developed machine learning based FLS (Fog and Low Stratus) detection algorithm for the SEVIRI (Spinning Enhanced Visible and InfraRed Imager) instrument onboard the MSG (Meteosat Second Generation) geo-stationary satellites over the 24hr cycle of the day for the day of 02/March/2021 and compares them with the corresponding raw channel values observed by SEVIRI. The proposed algorithm classifies each SEVIRI pixel as "clear-sky", "FLS", or "non-FLS-cloud" (identified with Khaki, Red, and Blue in the animation) based on the SEVIRI pixel values of BT12.0, BT8.7 - BT12.0, BT10.8 - BT12.0, and BT12.0 - BT13.4 plus the standard deviation of each of these variables in a spatial window sized 3x3 pixels with the central pixel being the target pixel.  In this animation, the left-hand panel shows a false-color RGB image constructed based on the SEVIRI raw channel data with the red, green, and blue channels being BT12.0- BT13.4, BT8.7 - BT12.0, and BT10.8 - BT12.0, respectively. In this panel, the green color represents the high clouds, and the light and dark red colors represent the clear-sky and FLS, respectively. The right-hand panel of this animation also shows the outputs of the ML FLS detection algorithm developed in the present study.

opencc-by-4.0Nov 2023View details →

zenodo36/100

Insights into Internal Solitary Waves East of Dongsha Atoll from Integrating Geostationary Satellite and Mooring Observations

The dataset used to produce the figures in the manuscript. All the data are in MATLAB file format.

opencc-by-4.0Mar 2024View details →

zenodo36/100

Data and models for "Center-fixing of tropical cyclones using uncertainty-aware deep learning applied to high-temporal-resolution geostationary satellite imagery" by Lagerquist et al.

The file geocenter_models.tar contains all models comprising the GeoCenter ensemble: 3 convolutional neural networks (CNN), 3 isotonic-regression files (one for correcting each CNN’s mean estimate), and 3 more isotonic-regression files (one for correcting each CNN’s ensemble spread). Every model is found in a subdirectory whose names indicate which infrared (IR) wavelengths are used as input to the CNN. For example: <ul> <li> wavelengths-microns=3.900-7.340-13.300/model.weights.h5: An HDF5 file containing the trained CNN that uses data from bands 7, 10, 16 (corresponding to 3.9, 7.34, and 13.3 microns on the GOES ABI imager). The trained CNN can always be read by neural_net_utils.read_model() in the ml4tccf library (https://doi.org/10.5281/zenodo.15116854). </li> <li> wavelengths-microns=3.900-7.340-13.300/model_metadata.p: A Pickle file containing metadata for the trained CNN. This file is needed to read the CNN itself with neural_net_utils.read_model(). Otherwise, you will probably never need to access this metafile directly. </li> <li> wavelengths-microns=3.900-7.340-13.300/isotonic_regression/isotonic_regression.dill: A Dill file containing isotonic-regression models used to bias-correct the ensemble mean from the same CNN. The trained isotonic-regression models can always be read by scalar_isotonic_regression.read_file() in the ml4tccf library. Note that there are technically two isotonic-regression models for every CNN’s ensemble mean: one that bias-corrects the x-coordinate of the TC-center, another that bias-corrects the y-coordinate. </li> <li> wavelengths-microns=3.900-7.340-13.300/uncertainty_calibration/uncertainty_calibration.dill: A Dill file containing isotonic-regression models used to bias-correct the ensemble spread from the same CNN. In the ml4tccf code, I make a distinction between “isotonic_regression” (correcting the ensemble mean) and “uncertainty_calibration” (correcting the ensemble spread), but note that both models are isotonic regression and use the sklearn.isotonic.IsotonicRegression class. The trained uncertainty-calibration models can always be read by scalar_uncertainty_calibration.read_file() in the ml4tccf library. Again, note that there are technically two uncertainty-calibration models per CNN: one for spread in the x-coordinate, one for spread in the y-coordinate. </li> </ul>   As mentioned above, every trained CNN can be read by neural_net_utils.read_model(). Also, every trained CNN can be applied to new data (inference mode) by neural_net_utils.apply_model(). The input argument model_object should be the object returned by neural_net_utils.read_model(), and I suggest setting num_examples_per_batch = 10 to avoid out-of-memory errors. The only other input argument is predictor_matrices, which is a list of two numpy arrays. The first numpy array contains IR imagery centered at the first-guess TC center, and the second numpy array contains ATCF scalars. The first numpy array should have dimensions S (number of TC samples) x 300 (grid rows) x 300 (grid columns) x 9 (lag times) x 3 (wavelengths). Lag times should be in the following order: 240, 210, 180, 150, 120, 90, 60, 30, 0 min ago.  Wavelengths should be in the order indicated by the subdirectory name.  The numpy array itself should contain normalized brightness temperatures at the given lag times and wavelengths, following the grid specifications laid out in the journal paper (a plate carrée grid with 2-km spacing). The original IR data (brightness temperatures) must be normalized to z-scores using the same normalization parameters as in the journal paper, i.e., those based on the training data. See details below. The second numpy array in predictor_matrices should have dimensions S (number of TC samples) x 9 (variables). The variables must in the order: absolute latitude, cosine of longitude, sine of longitude, TC intensity, minimum central pressure, tropical flag, subtropical flag, extratropical flag, disturbance flag. The journal paper contains details on all these variables in one table. These variables must come from A-deck files at the second-most recent synoptic time. Like the IR data, these ATCF scalars must be normalized to z-scores using the same normalization parameters as in the journal paper. See details below.   Once you have predictions (estimated TC-center locations) from a CNN, you can bias-correct these predictions. To read the isotonic-regression model for the given CNN’s ensemble mean, use scalar_isotonic_regression.read_file() in the ml4tccf library. To apply the same model, use scalar_isotonic_regression.apply_models(). For the CNN’s ensemble spread, use scalar_uncertainty_calibration.read_file() and scalar_uncertainty_calibration.apply_models().   To normalize the IR data, you will need the file ir_satellite_normalization_params.tar included with this dataset. Within the tar file is a single zarr file. You can read the zarr file with normalization.read_file() in the ml4tccf library; then you can normalize new data with normalization.normalize_data().   To normalize the ATCF data, you will need the file a_deck_normalization_params.nc included with this dataset. This is a NetCDF file, containing the full set of training values for all 5 ATCF variables that are normalized (the binary storm-type flags are not normalized). You can read this file using any of the standard Python methods for reading NetCDF files, such as xarray.open_dataset(). To normalize new ATCF data, you can use the method normalization._normalize_one_variable(), where the argument actual_values_training is the list of training values from a_deck_normalization_params.nc for the given variable, while actual_values_new is the list of values to be normalized (currently in physical units, to be converted to z-score units).

opencc-by-4.0Sep 2024View details →

zenodo36/100

Thunderstorm activity over the Qinghai–Tibet Plateau indicated by the combined data of the FY-2E geostationary satellite and WWLLN

This dataset is for the article titled "Thunderstorm activity over the Qinghai–Tibet Plateau indicated by the combined data of the FY-2E geostationary satellite and WWLLN" which is being submitted to the Remote Sensing for review.  Abstract: Thunderstorm activity over the Qinghai–Tibet Plateau (QTP) has important climatic effects and disaster impacts. Using the thunderstorm feature dataset (TFD) established based on the black body temperature (TBB) and cloud classification (CLC) products of the Fengyun-2E (FY-2E) geostationary satellite, as well as the lightning data of the World Wide Lightning Location Network (WWLLN), the temporal and spatial distributions and some cloud properties of the thunderstorms over the QTP were analyzed. Approximately 93.9% and 82.7% of thunderstorms over the QTP occur from May to September and from 12 to 21 o'clock local time, and the corresponding peaks are in August and at 14:00, respectively. There are three centers featuring frequent thunderstorms in the southeast, south-central, and southwest regions of the QTP. The average thunderstorm cloud area (the region with TBB ≤ −32℃) is 1.8 × 104 km2. Approximately 32.9% of thunderstorms have strong convective cells (SCCs) composed of areas with TBB ≤ −52℃.The average number and area ratio of SCCs are 3.6 and 25.4%, respectively, and their spatial distribution is given. The average cloud area and the number and area ratio of SCCs of extreme-lightning thunderstorms (thunderstorms with the top 10% of lightning numbers) are approximately 30.0, 3.9, and 1.5 times those of normal thunderstorms. The spatial distribution of the thunderstorm activity is quite different from that of lightning activity given by the Lightning Imaging Sensor (LIS) and Optical Transient Detector (OTD) over the northeastern and southwestern QTP, which may mean that the convection intensity, cloud structure, and charge structure of the thunderstorms over the QTP are different between different regions and seasons.

opencc-by-4.0Apr 2022View details →

zenodo36/100

A synchronized estimation of hourly ground-level concentrations of six criteria air pollutants in China using data from the first geostationary air-quality monitoring satellite

This dataset provides the ground-level concentrations of six criteria air pollutants estimated from the first geostationary air quality monitoring satellite GEMS with a multi-output random forest model.

opencc-by-4.0Aug 2022View details →

zenodo32/100

Dataset for "Public Health Benefits from Improved Identification of Severe Air Pollution Events with Geostationary Satellite Data"

Dataset for "Public Health Benefits from Improved Identification of Severe Air Pollution Events with Geostationary Satellite Data" to be published in GeoHealth doi: 10.1029/2023GH000890

opencc-by-4.0Oct 2023View details →

zenodo32/100

Improvement of the aerosol forecast and analysis over East Asia with joint assimilation of two geostationary satellite observations

These are FY-4A satellite AOD products and SONET AOD datasets used in the manuscript titled "Improvement of the aerosol forecast and analysis over East Asia with joint assimilation of two geostationary satellite observations"  to Geophysical Research Letters.

opencc-by-4.0Oct 2022View details →

zenodo32/100

Geostationary Satellite Surface Gaseous Pollutants Dataset - CO

High spatiotemporal resolution CO dataset of China's near-surface based on the Geostationary satellite Himawari-8 (2015.09 - 2021.08)

opencc-by-4.0Aug 2022View details →

zenodo32/100

Synergistic Observation of Geostationary Satellites FY-4A and FY-4B to Estimate Near-surface Pollutant Concentration dataset - CO

High spatiotemporal resolution CO dataset of China's near-surface  for summer and autumn 2022 based on the Geostationary satellite FY-4A and FY-4B

opencc-by-4.0Aug 2023View details →

nasa28/100

GHRSST Level 2P Western Atlantic Regional Skin Sea Surface Temperature from the Geostationary Operational Environmental Satellites (GOES) Imager on the GOES-13 satellite (GDS version 2)

The Geostationary Operational Environmental Satellites (GOES) operated by the United States National Oceanic and Atmospheric Administration (NOAA) support weather forecasting, severe storm tracking, meteorology and oceanography research. Generally there are several GOES satellites in geosynchronous orbit at any one time viewing different earth locations including the GOES-13 launched 24 May 2006. The radiometer aboard the satellite, The GOES N-P Imager, is a five channel (one visible, four infrared) imaging radiometer designed to sense radiant and solar reflected energy from sampled areas of the earth. The multi-element spectral channels simultaneously sweep east-west and west-east along a north-to-south path by means of a two-axis mirror scan system retuning telemetry in 10-bit precision. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the far IR channels of GOES-13 at full resolution on a half hourly basis. In native satellite projection, vertically adjacent pixels are averaged and read out at every pixel. L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 2.0. The full disk image is subsetted into granules representing distinct northern and southern regions.

restrictednotspecifiedApr 2025View details →

nasa28/100

GHRSST Level 3C sub-skin Sea Surface Temperature from the Geostationary Operational Environmental Satellites (GOES 13) Imager in East position (GDS V2) produced by OSI SAF

A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset for the America Region (AMERICAS) based on retrievals from the GOES-13 Imager on board GOES-13 satellite. The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT),Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near realtime from GOES 13 in East position. GOES 13 imager level 1 data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system.SST is retrieved from the GOES 13 infrared channels (3.9 and 10.8 micrometer) using a multispectralalgorithm. Due to the lack of 12 micrometer channel in the GOES 13 imager, SST retrieval is not possiblein daytime conditions. Atmospheric profiles of water vapor and temperature from a numericalweather prediction model, together with a radiatiave transfer model, are used to correct themultispectral algorithm for regional and seasonal biases due to changing atmospheric conditions.Every 30 minutes slot is processed at full satellite resolution. The operational products are thenproduced by remapping over a 0.05 degree regular grid (60S-60N and 135W-15W) SST fieldsobtained by aggregating 30 minute SST data available in one hour time, and the priority beinggiven to the value the closest in time to the product nominal hour. The product format is compliantwith the GHRSST Data Specification (GDS) version 2.

restrictednotspecifiedApr 2025View details →

nasa28/100

GHRSST Level 2P Central Pacific Regional Skin Sea Surface Temperature from the Geostationary Operational Environmental Satellites (GOES) Imager on the GOES-15 satellite (GDS version 2)

The Geostationary Operational Environmental Satellites (GOES) operated by the United States National Oceanic and Atmospheric Administration (NOAA) support weather forecasting, severe storm tracking, meteorology and oceanography research. Generally there are several GOES satellites in geosynchronous orbit at any one time viewing different earth locations including the GOES-15 launched 4 March 2010. The radiometer aboard the satellite, The GOES N-P Imager, is a five channel (one visible, four infrared) imaging radiometer designed to sense radiant and solar reflected energy from sampled areas of the earth. The multi-element spectral channels simultaneously sweep east-west and west-east along a north-to-south path by means of a two-axis mirror scan system retuning telemetry in 10-bit precision. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the far IR channels of GOES-15 at full resolution on a half hourly basis. In native satellite projection, vertically adjacent pixels are averaged and read out at every pixel. L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 2.0. The full disk image is subsetted into granules representing distinct northern and southern regions.

restrictednotspecifiedApr 2025View details →

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record