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111 results for “Satellite Observations”
Conjugate observation data between DMSP satellites and all-sky imager of Chinese Yellow River station at Ny-Ålesund, Svalbard from January 2005 to December 2009
<p>Chinese Arctic Yellow River Station (YRS) locates at Ny-Ålesund, Svalbard with the geographic coordinates (78.92°N, 11.93°E) and the corrected geomagnetic latitude 76.24°. The relation between local time and universal time is MLT≈UT+3hr. In November 2003, a set of monochromatic auroral observation system was installed at YRS, which is consisted of three identical all-sky imageries (ASIs) with the filters at 427.8nm, 557.7nm and 630.0nm, respectively. DMSP (Defense Meteorological Satellite Program) satellites are a collection of polar-orbit weather satellites launched by the U.S. department of defense. This series of satellites is sun synchronous satellite, which takes about 101 minutes to orbit the earth, has an altitude of about 835-850km, an inclination of about 96°, and crosses the equatorial plane daily from south to north (ascending segment) and from north to south (descending segment) at a fixed time (DMSP F13 is at 05:45LT and 17:45LT, and F15 is at 09:30LT and 21:30LT). DMSP satellite is equipped with Special Sensor for Particle Flux (SSJ/4), which can measure the fluxes of downgoing electrons and ions with energies between from 30eV to 30keV in 19 energy steps (34, 49, 71, 101, 150, 218, 320, 460, 670, 960 eV, and 1.4, 2.1, 3.0, 4.4, 6.5, 9.5, 14.0, 20.5, 29.5 keV), with a time resolution of 1 second. According to the orbit of the DMSP satellites and the observation of the ASIs of YRS, we obtained the conjugate observation periods of the satellite flying over the ASI.</p> <p>During the period from January 2005 to December 2009, a total of 136 conjugate observation events were obtained. Moreover, according to the morphological characteristics of discrete aurora in the all-sky image, 136 events are classified according to four typical forms of dayside discrete auroras, namely 27 events of drapery dayside corona (DDC), 24 events of radial dayside corona (RDC), 37 events of hot-spot aurora (HSA), and 48 events of arc aurora (ARC). The event list of 136 events is recorded in the “asi&dmsp@YRS03-09-v2.xlsx” file. The EPS file gives the trajectory of the DMSP satellite crossing the ASI in each conjugate observation event, while the JPG files are the corresponding all-sky images.</p>
The Global Navigation Satellite System (GNSS) data came from the Crustal Movement Observation Network of China
<p>The dataset reports the estimated vertical Total Electron Content (TEC) from 52 GPS receivers came from the Crustal Movement Observation Network of China on 4 March 2014. Every receiver's data is saved in a TXT file, whose time resolution is thirty seconds.</p>
Daily satellite and gauge observed rainfall dataset (1980-2019) for Oman at 1km spatial resolution in GeoTIFFs
<p>This is a re-gridded daily TRMM datasets. It has been resampled to 1km spatial resolution. The datasets have also been projected to UTM 40N</p>
Replication of manuscript entitled "Multi-decadal climate variability and satellite biases have amplified model-observation discrepancies in tropical troposphere warming estimates"
<p>Replication of manuscript entitled "Multi-decadal climate variability and satellite biases have amplified model-observation discrepancies in tropical troposphere warming estimates".</p> <p>This page contains datasets used to replicate figures in a manuscript entitled "Multi-decadal climate variability and satellite biases have amplified model-observation discrepancies in tropical troposphere warming estimates". These ascii or netcdf files can be easily read by NCL, Fortran and others.</p> <p> </p>
Observations of low-latitude traveling ionospheric disturbances by a 630-nm airglow imager and the CHAMP satellite over Indonesia
<p>We report the first comparison of ground and satellite measurements of low-latitude traveling ionospheric disturbances (TIDs). Three TID events were simultaneously observed by a 630-nm airglow imager and the CHAMP satellite on April 30, 2006 (event 1), September 28, 2006 (event 2), and April 12, 2004 (event 3) at Kototabang, Indonesia (geographic coordinates: 0.2$^\circ$S, 100.3$^\circ$E, geomagnetic latitude: 10.6$^\circ$S). In 630-nm airglow images of all three events, there are clear southward-moving structures. Events 1 and 2 are a single pulse with horizontal scales of $\sim$500--1000 km. Event 3 shows five wave fronts with a horizontal scale size of 500--1000 km. All three TIDs are medium-scale TIDs. Horizontal wavelengths of both airglow intensity at an average emission altitude of 250 km and CHAMP neutral density variations measured at 400 km are estimated by fitting a sinusoidal function to the observed data. For events 1 and 3, estimated horizontal wavelengths are nearly equal, indicating both instruments are observing the same wave. For event 1, the CHAMP electron density mapped along the geomagnetic field line onto the airglow altitude does not show wave structure similar<br> to the airglow variation. For events 2 and 3, the relationship of electron density and airglow intensity is unclear. These results suggest that the cause of the observed TID is not caused by ionospheric plasma instability but by gravity waves in the thermosphere. According to these results, we conclude that the observed TIDs in all three cases are caused by gravity waves in the thermosphere.</p>
The prediction data analyzed in "Seasonal Arctic sea ice prediction using a newly developed fully coupled regional model with the assimilation of satellite sea ice observations"
<p>The outputs of seasonal predictions with the new modeling system analyzed in the article including:</p> <p>Sea ice concentration (SIC)</p> <p>Sea ice thickness (SIT)</p> <p>Sea surface temperature (SST)</p> <p>Near surface air temperature (T2) </p>
Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations: Preprocessed Satellite and In-situ observation datasets
<p>This record includes all of the prepared data used in the manuscript, "Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations" (citation information forthcoming). As a part of this manuscript, we analyzed the ability for machine learning models to extract sea surface information (from salinity, temperature, sea height anomaly) to predict mixed layer depth. In this manuscript there are two experimental datasets: (1) info derived from CESM POP2 ocean model dataset (1989-1998), and (2) info derived from a combination of satellite sources and MLD from Argo profiles. More details below. </p> <p>All of these data files are preprocessed and organized to be used with the ml-ocean-bl github code found at https://github.com/NCAR/ml-ocean-bl/mloceanbl/.</p> <ul> <li><strong>CESM POP2 Ocean model dataset</strong></li> </ul> <p>Preprocessed sea surface salinity (SSS), temperature (SST), sea surface height anomalies (SSH), and ocean mixed layer depth (MLD, or HMXL) derived from the CESM POP2 Ocean model. Specifically, CESM POP2 model in a hindcast forced by JRA55do atmospheric reanalysis from 1958 to present and initialized with an oceanic climatology as in e.g. <a href="https://journals.ametsoc.org/view/journals/phoc/aop/JPO-D-20-0217.1/JPO-D-20-0217.1.xml">Deppenmeier et al. (2021)</a>. The model outputs include the ocean mixed layer depth (MLD), sea surface salinity (SSS), sea surface temperature (SST), and sea height anomaly (SSH) at a temporal frequency of 5-days and an approximate latitude and longitude resolution of 0.1 degrees.</p> <p>Relevant files:</p> <ol> <li>full_EPO.nc, full_SIO.nc <ul> <li>NetCDF4 containing SSS, SST, SSH, MLD for the equatorial Pacific Ocean (EPO) and southern Indian Ocean (SIO) (see manuscript for details). Data is regridded onto a 1/2 degree lat/lon 5 day grid to correspond with data used for Argo datasets (see below).</li> </ul> </li> <li>clim_EPO.nc, clim_SIO.nc, clim_std_EPO.nc, std_clim_EPO.nc, std_clim_SIO.nc <ul> <li>NetCDF4 containing mean and standard deviation climatologies of SSS, SST, SSH, and MLD for EPO and SIO.</li> </ul> </li> <li>std_anomalies_EPO.nc, std_anomalies_SIO.nc <ul> <li>NetCDF4 containing SSS, SST, SSH, and MLD standardized anomalies for EPO and SIO. This is the dataset directly used for training in aforementioned manuscript. Use with ml-ocean-bl/ml-ocean-test/data. </li> </ul> </li> </ol> <ul> <li><strong>Satellite and Argo datasets</strong></li> </ul> <p>Preprocessed satellite sea surface salinity (SSS), temperature (SST), and sea surface height anomalies (SSH) and Argo-based mixed layer depth (MLD) profiles. Original data can be found at:</p> <p>(SST): Remote Sensing Systems. 2017. MW optimum interpolated SST data set. Ver. 5.0. PO.DAAC, CA, USA. Further information available at at <a href="https://doi.org/10.5067/GHMWO-4FR05">https://doi.org/10.5067/GHMWO-4FR05</a>. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/REMSS/mw_OI/v5.0/.</p> <p>(SSS): Oleg Melnichenko. 2018. Aquarius L4 Optimally Interpolated Sea Surface Salinity. Ver. 5.0. PO.DAAC, CA, USA. Further information at <a href="https://doi.org/10.5067/AQR50-4U7CS">https://doi.org/10.5067/AQR50-4U7CS</a>. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/SalinityDensity/aquarius/L4/IPRC/v5/7day. </p> <p>(SSH): Zlotnicki, Victor; Qu, Zheng; Willis, Joshua. 2019. SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1609. Ver. 1812. PO.DAAC, CA, USA. Information available at <a href="https://doi.org/10.5067/SLREF-CDRV2">https://doi.org/10.5067/SLREF-CDRV2</a>. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/SeaSurfaceTopography/merged_alt/L4/cdr_grid</p> <p>(MLD) Argo-based ocean surface mixed layer depths using the buoyancy gradient definition of Whitt Nicholson and Carranza (2019) processed dataset available at https://doi.org/10.5281/zenodo.4291175.</p> <p>Relevant files:</p> <ol> <li>https://github.com/NCAR/ml-ocean-bl/mloceanbl/preprocess_mld.py and .../preprocess_sss_sst_ssh.py. <ul> <li>Preprocessing code</li> </ul> </li> <li>sss_sst_ssh_anomalies.nc. <ul> <li>Regridded and resampled SSS, SST, SSH onto a 1/2 degree lat/lon 7day grid. Contains preprocessed seasonal data along with anomalies.</li> </ul> </li> <li> mldb_climatology_climatologystd_binned.nc <ul> <li>Smoothed argo-based mixed layer depths are used to calculate climatologies and standardized climatologies. 4 degree lat/lon gridded climatologies.</li> </ul> </li> <li>mldb_full_anomalies_stdanomalies_climatology_stdclimatology.nc <ul> <li>Contains the Argo profile-derived MLD, anomalies, standard anomalies, climatologies, and standardized climatologies with corresponding argo locations, times, and corresponding weeks. </li> </ul> </li> <li>equatorial_pacific_model_oi_re.nc, southern_indian_model_oi_re.nc <ul> <li>Model outputs for the Equatorial Pacific Ocean and Southern Indian Ocean. These gridded files contain the model outputs (vlcnn, vlcnn variance, OI, OI variance, reanalysis, and reanalysis variance - see manuscript for nomenclature details) at each of the 200 weeks available. It should be noted that, in the equatorial Pacific Ocean, the lat/lon location of (-138.75, -9.75) is masked during the training and filled with a NaN in the .nc files. </li> </ul> </li> </ol> <p> </p> <p> </p> <p>Contact D. Foster with any questions.</p> <p> </p>
Supporting data for Assessing clouds using satellite observations through three generations of global atmosphere models
<p>Monthly data from CAM4, CAM5, and CAM6 that are needed to reproduce the analysis and figures in the manuscript entitled: Assessing clouds using satellite observations through three generations of global atmosphere models by Brian Medeiros, Jonah Shaw, Jennifer Kay, and Isaac Davis.</p>
Observed properties of Starlink satellites
<p>Tabulated measurements of Starlink satellites observed by the Multi-site All-Sky CAmeRA (MASCARA) instrument. This was created by Peter Breslin during his master thesis at Leiden University. Thesis title: 'Mega-constellation satellites: Assessing their interference on ground-based astronomy'.</p>
Data from: Predicting photosynthesis-irradiance relationships from satellite remote-sensing observations
Open the record for dataset details and reuse information.
Determining a lower limit of luminosity for the first satellite observation of a reverse beam terrestrial gamma-ray flash associated with a cloud to ground lightning leader
Open the record for dataset details and reuse information.
Global Dataset of Ecohydrological Parameters Inferred from Satellite Observations
<p>This dataset contains global maps of</p> <p>(1) ecohydrological parameters for a theoretical model of the probability distribution of soil saturation;<br> (2) convergence, uncertainty and goodness-of-fit diagnostics; and<br> (3) soil water stress and uptake indexes,</p> <p>associated with analysis in: Bassiouni, M., S.P. Good, C.J. Still, and C.W. Higgins (2020), Plant water uptake thresholds inferred from satellite soil moisture. Geophysical Research Letters. <a href="https://doi.org/10.1029/2020GL087077">https://doi.org/10.1029/2020GL087077 </a></p> <p>All variable descriptions and units are included in the .nc metadata.</p> <p>Code associated with this dataset are publicly available:<br> Probabilistic Inference of Ecohydrological Parameters (PIEP): <a href="http://doi.org/10.5281/zenodo.1257718">http://doi.org/10.5281/zenodo.1257718</a>.<br> Data Management for Global PIEP: <a href="http://doi.org/10.5281/zenodo.3235820">http://doi.org/10.5281/zenodo.3235820</a></p> <p><strong>Abstract</strong><br> Empirical functions are widely used in hydrological, agricultural, and earth system models to parameterize plant water uptake. We infer soil water potentials at which uptake is downregulated from its maximum rate and at which uptake is zero, in biomes with < 60% woody vegetation at 36-km grid resolution. We estimate thresholds through Bayesian inference using a stochastic water balance framework to construct theoretical soil moisture probability distributions consistent with satellite surface soil moisture. The global median Nash–Sutcliffe efficiency between empirical soil moisture distributions derived from satellite soil moisture observations and best-fit theoretical distributions using inferred parameters is 0.8. Spatially variable thresholds capture location-specific vegetation and climate characteristics and can be connected to biome-level water uptake strategies.</p>
Data for 'Impact of Satellite Observations on Forecasting Sudden Stratospheric Warmings'
<p>These data were used for making plots in the manuscript entitled 'Impact of Satellite Observations on Forecasting Sudden Stratospheric Warmings'. DOI: 10.1029/2019GL086233. Data format is NetCDF.</p>
Satellite observations reveal inequalities in the progress and effectiveness of recent electrification in sub-Saharan Africa
<p>Replication code and data for the paper "Satellite observations reveal inequalities in the progress and effectiveness of recent electrification in sub-Saharan Africa" published in One Earth (DOI: 10.1016/j.oneear.2020.03.007)</p> <p>________________________________</p> <p>This repository hosts:</p> <ul> <li> <p>A JavaScript file to process remotely-sensed data into Google Earth Engine (step 1)</p> </li> <li> <p>A R script, to be run after the successful completion of the Google Earth Engine processing (step 2)</p> </li> <li> <p>Supporting files to run the analysis (e.g. a shapefile of provinces, validation data, etc.)</p> </li> <li> <p>Create a Google account, if you do not have one, and require access to Earth Engine <a href="https://signup.earthengine.google.com/">https://signup.earthengine.google.com</a>.</p> </li> <li> <p>Make sure your Google Drive has enough cloud storage space available.</p> </li> <li> <p>Clone the repository.</p> </li> <li> <p>Run the JavaScript file in Google Earth Engine and wait that the data processing is complete <strong>(can take >24 hours)</strong></p> </li> <li> <p>Run the R script, which will reproduce the analysis and the figures contained in the paper.</p> </li> <li> <p>Open the QGIS project files to replicate maps with the appropriate layout.</p> </li> </ul> <p>Source code-related issues should be opened directly on GitHub. Broader questions of the methods should be addressed to <a href="mailto:giacomo.falchetta@feem.it">giacomo.falchetta@feem.it</a></p>
Datensatz zur Bestimmung des ILRS-Referenzpunktes am Satellite Observing System Wettzell
<p>Die Kombination von geodätischen Raumtechniken ist essentiell für die Bestimmung eines globalen geodätischen Referenzrahmens sowie von Erdrotationsparametern. Eine direkte Verknüpfung der unterschiedlichen Raumtechniken ist aufgrund der geringen physischen Verknüpfungen nicht ohne Zusatzinformationen sinnvoll möglich. Eine Schlüsselrolle spielen hierbei lokale Verbindungsvektoren (Local-Ties), die zwischen den geometrischen Referenzpunkten der Raumtechniken definiert sind. Diese Verbindungsvektoren lassen sich an Forschungseinrichtungen wie dem Geodätischen Observatorium Wettzell durch präzise terrestrische Vermessung bestimmen. Eine besondere Herausforderung stellen hierbei die Referenzpunkte von VLBI-Radio- und SLR-Laserteleskopen dar, da diese nicht materialisiert und direkt taktil bestimmt werden können.<br> In diesem Beitrag wird eine indirekte Methode zur Bestimmung des geometrischen Referenzpunktes eines VLBI-Radio- bzw. SLR-Laserteleskopes vorgestellt. Das entwickelte Modell erlaubt eine automatisierte und prozessbegleitende messtechnische Erfassung aller relevanten Größen. Der neue Modellansatz erfordert darüber hinaus keine Synchronisation zwischen dem Messinstrument und dem Teleskop, sodass Messunsicherheiten minimiert werden. Eine erfolgreiche Validierung erfolgte 2018 am Satellite Observing System Wettzell, bei der die Datenerhebung vollständig automatisiert mit dem Lasertracker AT401 durchgeführt wurde, und der Referenzpunkt mit einer Unsicherheit von 50 μm bestimmt werden konnte.</p>
Data from: Using satellite AIS to improve our understanding of shipping and fill gaps in ocean observation data to support marine spatial planning
1. A key stage underpinning marine spatial planning (MSP) involves mapping the spatial distribution of ecological processes and biological features, as well the social and economic interests of different user groups. One sector, merchant shipping (vessels that transport cargo or passengers), however, is often poorly represented in MSP due to a perceived lack of fine-scale spatially explicit data to support decision making processes. 2. Here, using the Republic of Congo as an example, we show how publicly accessible satellite derived Automatic Identification System (S-AIS) data can address gaps in ocean observation data for shipping at a national scale. We also demonstrate how fine-scale (0.05 km2 resolution) spatial data layers derived from S-AIS (intensity, occupancy) can be used to generate maps of vessel pressure to provide an indication of patterns of impact on the marine environment and potential for conflict with other ocean-user groups. 3. We reveal that passenger vessels, offshore service vessels, bulk carrier and cargo vessels and tankers account for 93.7% of all vessels and vessel traffic annually, and that these sectors operate in a combined area equivalent to 92% of Congo's exclusive economic zone(EEZ) – far exceeding the areas allocated for other user-groups (conservation, fisheries and petrochemicals). We also show that the shallow coastal waters and habitats of the continental shelf are subject to more persistent pressure associated with shipping; and that the potential for conflict among user groups is likely to be greater with fisheries, whose zones are subject to the highest vessel pressure scores than with conservation or petrochemical sectors. 4. Synthesis and applications. Shipping dominates ocean use, and so excluding this sector from decision making could lead to increased conflict among user groups, poor compliance and negative environmental impacts. This study demonstrates how Satellite derived Automatic Identification System data can provide a comprehensive mechanism to fill gaps in ocean observation data and visualise patterns of vessel behaviour and potential threats to better support marine spatial planning at national scales.13-Feb-2018
Data supporting Satellite in-situ electron density observations of the mid-latitude storm enhanced density on the noon meridional plane in the F region during the 20 November 2003 magnetic storm
<p>This is the data for the submitted paper: Satellite in-situ electron density observations of the mid-latitude storm enhanced density on the noon meridional plane in the F region during the 20 November 2003 magnetic storm. The data contains five files. The file NE_TGWeimer_2003324 is the electron density (NE) data along the CHAMP orbit on Nov 20, 2003. The file TGSED4_Mlat30_2003324 is the NE, HMF2, WI_ExB, VI_ExB and VN at Mlat = 30 on the noon meridional plane (MLT = 12 hr) in the Northern hemisphere on Nov 20, 2003. The temporal resolution is 1-min. The file TGSED4_Mlat60_2003324 is the NE, HMF2, WI_ExB, VI_ExB and VN at Mlat = 60 on the noon meridional plane (MLT = 12 hr) in the Northern hemisphere on Nov 20, 2003. The temporal resolution is 1-min. The file TGSED3_Mlat30_2003324 is the NE at Mlat = 30 as a function of MLT and UT. The temporal resolution is 5-min. The file TGSED3_Mlat60_2003324 is the NE at Mlat = 60 as a function of MLT and UT. The temporal resolution is 5-min.</p>
Data and code for "Ambient Formaldehyde over the United States from Ground-Based (AQS) and Satellite (OMI) Observations"
<p>This file contains data and code in the study entitled "Ambient Formaldehyde over the United States from Ground-Based (AQS) and Satellite (OMI) Observations" in the journal <em>Remote Sensing</em>. </p>
Improvement of the aerosol forecast and analysis over East Asia with joint assimilation of two geostationary satellite observations
<p>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. </p>
Raw in situ observational data sets for manuscript entitled "Effect of Typhoon Kalmaegi (2014) in northern South China Sea explored using Muti-platform satellite and Buoy observations data"
<p> Raw in situ observational data sets for manuscript entitled "Effect of Typhoon Kalmaegi (2014) in northern South China Sea explored using Muti-platform satellite and Buoy observations data" , which has been submitted to Progress in Oceanography for reviewing. Only data sets collected by field observations were uploaded. Other data sets can be download from the relevant open access websites (see the descriptions in the manuscript).</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.