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403 results for “Satellite data”
Probabilistic linear inversion of satellite gravity gradient data applied to the northeast Atlantic
<p>% MATLAB scripts to calculate and plot figures as in manuscript by<br> %<br> % Minakov, A., & Gaina, C. (2021).<br> % Probabilistic linear inversion of satellite gravity gradient data applied<br> % to the northeast Atlantic. Journal of Geophysical Research: Solid Earth,<br> % 126, e2021JB021854. https://doi.org/10.1029/2021JB021854<br> % <br> % Last modified by alexamin@uio.no, 26/11/2021<br> %<br> % version v1.1<br> % </p> <p>% Contents of arhcive<br> % /data contains requiried and generated datasets <br> % /fig folder for output figures <br> % /plot scripts to produce figures <br> % /tools additional matlab tools and routines</p> <p>% Dataset in ..data/GOCE_NEATLANTIC is structure containing the full model<br> % <br> % Cm: [6670×6670 double] posterior model covariance matrix<br> % m: [29×23×10 double] mean denstity perturbation model<br> % Cd: [667×667 double] data covariance matrix<br> % d: [29×23 double] data vector (Trr)<br> % r: [1×10 double] distance<br> % lat: [29×1 double] latitute<br> % lon: [23×1 double] longitude<br> %<br> % Run /plot/fig_results.m to produce all figures <br> %<br> % Some scripts require GMT (Wessel et al. 2019) and SHBUNDLE (Sneeuw et al. 2018) software to be installed</p> <p>% and corresponding folders must be added to the matlab search path.</p> <p>% Also ScientificColorMaps7 by F. Crameri (2021) maybe required and have been included in the archive.</p>
Environmental data at the sampling event level collected with Inline instruments, almanach, models and satellites during the Tara Pacific Expedition 2016-2018
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples. The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis. Here we provide at the sampling event level, the environmental data originating from all instruments acquiring continuously during the full course of the campaign. This dataset is augmented with the addition of variables originating from almanach (local sun/moon set/rise, local zenith), from operational models obtained from Copernicus Marine Services, but also <strong>f</strong>rom satellite imagery (MODIS-AQUA satellite - Level 3 mapped product, 8 day average, 4km resolution) at <a href="https://oceandata.sci.gsfc.nasa.gov">https://oceandata.sci.gsfc.nasa.gov</a>. The zone corresponding to the station position and date was recovered either by taking a two pixel buffer around the given location (total zone being a 5 by 5 pixels square of 20 km side) and in order to propose an alternative measure in the inevitable case where clouds were present an alternative 12 pixels buffer was taken (total zone being a 25 by 25 pixels square of 100 km side). All data were provided as mean, standard deviation (sd) together with 0.05, 0.25, 0.5, 0.75 and 0.95 quartiles</p>
Data from: Hidden in plain sight: migration routes of the elusive Anadyr bar-tailed godwit revealed by satellite tracking
<p><strong>Abstract</strong></p> <p>Satellite and GPS tracking technology continues to reveal new migration patterns of birds which enables comparative studies of migration strategies and distributional information useful in conservation. Bar-tailed godwits in the East Asian–Australasian Flyway <em>Limosa lapponica baueri </em>and <em>L. l. menzbieri</em> are known for their long non-stop flights, however these populations are in steep decline. A third subspecies in this flyway, <em>L. l. anadyrensis</em>, breeds in the Anadyr River basin, Chukotka, Russia, and is morphologically distinct from <em>menzbieri</em> and <em>baueri</em> based on comparison of museum specimens collected from breeding areas. However, the non-breeding distribution, migration route and population size of <em>anadyrensis </em>are entirely unknown. Among 24 female bar-tailed godwits tracked in 2015–2018 from northwest Australia, the main non-breeding area for <em>menzbieri</em>, two birds migrated further east than the rest to breed in the Anadyr River basin, i.e. they belonged to the <em>anadyrensis </em>subspecies. During pre-breeding migration, all birds staged in the Yellow Sea and then flew to the breeding grounds in the eastern Russian Arctic. After breeding, these two birds migrated southwestward to stage in Russia on the Kamchatka Peninsula and on Sakhalin Island en route to the Yellow Sea. This contrasts with the other 22 tracked godwits that followed the previously described route of <em>menzbieri</em>, i.e. they all migrated northwards to stage in the New Siberian Islands before turning south towards the Yellow Sea, and onwards to northwest Australia. Since the Kamchatka Peninsula was not used by any of the tracked <em>menzbieri</em> birds, the 4 500 godwits counted in the Khairusova–Belogolovaya estuary in western Kamchatka may well be <em>anadyrensis</em>. Comparing migration patterns across the three bar-tailed godwits subspecies, the migration strategy of <em>anadyrensis </em>lies between that of <em>menzbieri </em>and <em>baueri</em>. Future investigations combining migration tracks with genomic data could reveal how differences in migration routines are evolved and maintained.</p> <p> </p> <p><strong>Data set</strong></p> <p>Stopping sites and migration timing of satellite-tracked bar-tailed godwits in the East Asian-Australasian Flyway</p> <p>file name: Chan et al. 2022 BARG_Stops_Timing.xlsx</p> <p>The sheet 'stopping_sites' contains stopping sites of bar-tailed godwits tracked with solar Argos satellite transmitters, and their respective arrival and departure times at each site. The sheet 'timing' contains departure and arrival times at the non-breeding and breeding sites in 2017. The transmitters were deployed in Roebuck Bay and Eighty Mile Beach, Australia, and were operating on an 8 h on and 25 h off duty cycle. </p> <p> </p> <p>Measurements of satellite-tracked bar-tailed godwits in the East Asian-Australasian Flyway</p> <p>file name: Chan et al. 2022 BARG_measurements.csv</p> <p>The datafile contains bill, wing and tarsus lengths and sex of bar-tailed godwits tracked with solar Argos satellite transmitters. The birds were captured in Roebuck Bay and Eighty Mile Beach, Australia. </p> <p> </p> <p><strong>Journal Article</strong></p> <p>Chan, Y.-C., Tibbitts, T. L., Dorofeev, D., Hassell, C. J. and Piersma T. (2022) Hidden in plain sight: migration routes of the elusive Anadyr bar-tailed godwit revealed by satellite tracking. J Avian Biol e02988. <a href="https://doi.org/10.1111/jav.02920">https://doi.org/10.1111/jav.02988</a></p>
SDUST2020 MSS: A global 1′×1′ mean sea surface model determined from multi-satellite altimetry data
<p>SDUST2020 MSS (Shandong University of Science and Technology 2020 mean sea surface) model with a grid of 1′×1′ is established with 19-year moving average method from multi-satellite altimetry data over 27-year (from January 1993 to December 2019). Its spatial coverage is 80°S-84°N. The missions data of Topex/Poseidon, Jason-1, Jason-2, Jason-3, ERS-1, ERS-2, GFO, Envisat, SARAL, HY-2A, Sentinel-3A and Cryosat-2 are ingested in the SDUST2020 MSS model.</p>
TOMCAT model data & IASI satellite data of O3, CO, H2O, CH4 and OH/derived OH for 2010 and 2017
<p>Monthly mean data of ozone (O3), carbon monoxide (CO), water vapour (H2O), methane (CH4) and the hydroxyl radical (OH) for 2010 and 2017.</p> <p>Model data is from the 3D chemical transport model TOMCAT (Chipperfield, 2006).</p> <p>Satellite observations are from the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A satellite and retrieved using schemes developed by the Rutherford Appleton Laboratory (RAL). The Ch4 is from RAL's CH4 retrieval scheme (Siddans et al. 2020) and the O3, CO and H2O retrievals are from the extended version of RAL’s Infrared and Microwave Sounding (IMS-extended) scheme (Pope et al. 2021). </p> <p>Full description of the data can be found in Pimlott et al. (2022) (preprint: https://doi.org/10.5194/acp-2022-79) which has now been accepted for publication in ACP. </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>
Tables and data for "Downward Trend in Methane Detected in a Northern Colorado Oil and Gas Production Region Using AIRS Satellite Data"
<p>These are tables and data files for the paper "Downward Trend in Methane Detected in a Northern Colorado Oil and Gas Production Region Using AIRS Satellite Data" submitted to the Journal of Atmospheric Research: Atmospheres</p>
Replication data for measurement report: Evolution and distribution of NH3 over Mexico City from ground-based and satellite infrared spectroscopic measurements
<p>This dataset of atmospheric ammonia (NH3) has been generated from solar absorption spectra measured in central Mexico using ground-based Fourier-Transform Infrared (FTIR) spectrometers. The FTIR experiments have been operated by the “Spectroscopy and Remote Sensing” Research Group of the ICAyCC-UNAM (Instituto de Ciencias de la Atmósfera y Cambio Climático of the Universidad Nacional Autónoma de México, http://www.epr.atmosfera.unam.mx/)</p> <p>Related Publication:<br> Herrera, B., Bezanilla, A., Blumenstock, T., Dammers, E., Hase, F., Clarisse, L., Magaldi, A., Rivera, C., Stremme, W., Strong, K., Viatte, C., Van Damme, M., and Grutter, M.: Measurement report: Evolution and distribution of NH3 over Mexico City from ground-based and satellite infrared spectroscopic measurements, Atmos. Chem. Phys. https://doi.org/10.5194/acp-2022-217, Accepted, 2022.</p> <p>Abstract:<br> Ammonia (NH3) is the most abundant alkaline compound in the atmosphere, with consequences for the environment, human health, and radiative forcing. In urban environments, it is known to play a key role in the formation of secondary aerosols through its reactions with nitric and sulphuric acids. However, there are only a few studies about NH3 in Mexico City. In this work, atmospheric NH3 was measured over Mexico City between 2012 and 2020 by means of ground-based solar absorption spectroscopy using Fourier transform infrared (FTIR) spectrometers at two sites (urban and remote). Total columns of NH3 were retrieved from the FTIR spectra and compared with data obtained from the Infrared Atmospheric Sounding Interferometer (IASI) satellite instrument. The diurnal variability of NH3 differs between the two FTIR stations and is strongly influenced by the urban sources. Most of the NH3 measured at the urban station is from local sources, while the NH3 observed at the remote site is most likely transported from the city and surrounding areas. The evolution of the boundary layer and the temperature play a significant role in the recorded seasonal and diurnal patterns of NH3. Although the vertical columns of NH3 are much larger at the urban station, the observed annual cycles are similar for both stations, with the largest values in the warm months, such as April and May. The IASI measurements underestimate the FTIR NH3 total columns by an average of 32.2 ± 27.5 % but exhibit similar temporal variability. The NH3 spatial distribution from IASI shows the largest columns in the northeast part of the city. In general, NH3 total columns over Mexico City exhibited an average annual increase of 92 ± 3.9 x 1013 molecules/cm2 yr (urban) and 8.4 ± 1.4 x 1013 molecules/cm2 yr (remote) was observed in Mexico City at both FTIR stations and a decadal increase of 62 % with IASI data.</p> <p>Description UNAM_FTIRdata.csv:<br> Atmospheric composition measurements made at the Universidad Nacional Autónoma de Mexico Observatory on the rooftop of the Instituto de Ciencias de la Atmósfera y Cambio Climático (UNAM, 19.33°N, 99.18°W, 2280 m.a.s.l.) located at the south of Mexico City. <br> These are retrieved from Fourier Transfor InfraRed (FTIR) solar absorption spectra recorded with a Vertex 80 spectrometer from April 2012 to October 2019. <br> The dataset contains the local time (YYYY-MM-DD hh:mm:ss AM/PM), the total columns (molecules/cm2), total error (molecules/cm2), systematic error (molecules/cm2), random error (molecules/cm2), and Degrees of Freddom (DOF).</p>
Supplementary data for: Comparison of optical flow derivation techniques for retrieving tropospheric winds from satellite image sequences
<p>This study introduces a validation technique for quantitative comparison of algorithms which retrieve winds from passive detection of cloud- and water vapor-drift motions, also known as Atmospheric Motion Vectors (AMVs). The technique leverages airborne wind-profiling lidar data collected in tandem with 1-min refresh rate geostationary satellite imagery. AMVs derived with different approaches are used with accompanying numerical weather prediction model data to estimate the full profiles of lidar-sampled winds which enables ranking of feature tracking, quality control, and height-assignment accuracy and encourages meso-scale, multi-layer, multi-band wind retrieval solutions. The technique is used to compare the performance of two brightness motion, or "optical flow," retrieval algorithms used within AMVs, 1) Patch Matching (PM; used within operational AMVs) and 2) an advanced Variational Optical Flow (VOF) method enabled for most atmospheric motions by new-generation imagers. The VOF AMVs produce more accurate wind retrievals than the PM method within the benchmark in all imager bands explored. It is further shown that image regions with low texture and multi-layer-cloud scenes in visible and infrared bands are tracked significantly better with the VOF approach, implying VOF produces representative AMVs where PM typically breaks down. It is also demonstrated that VOF AMVs have reduced accuracy where the brightness texture does not advect with the mean wind (e.g. gravity waves), where the image temporal noise exceeds the natural variability, and when the height-assignment is poor. Finally, it is found that VOF AMVs have improved performance when using fine-temporal refresh rate imagery, such as 1-min versus 10-min data.</p>
Data and code for "Air pollution impacts from warehousing in the United States uncovered with satellite data"
<div> <div> <p>This repository (ver 2024.06.11) contains data and code supporting the analyses and visualizations in the manuscript "Air pollution impacts from warehousing in the United States uncovered with satellite data" in the journal <em>Nature </em><em>Communications</em> (DOI forthcoming). A description of the data files and code can be found in the README. </p> </div> </div>
Structure and dynamics of plasma irregularities over the equatorial ionospheric region: A study using spaced receiver technique employing geostationary satellites' radio signals-Data set
<p>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. </p> <p> </p> <p> </p>
Figure 6 in Main Pattern of the Caspian Sea Surface Oil Pollution Revealed by Satellite Data
Figure 6. Examples of the manifestations of oil slicks from natural seeps on the seafloor in the Sefid Rud Cap area in satellite images. Parts of SAR-C Sentinel-1A images: a) February 16, 2015, 14:36 UTC; b) August 9, 2016, 14:36 UTC; c) October 27, 2017, 14:36 UTC. Parts of MSI Sentinel-2A images (color composites of 4, 3 and 2 spectral channels): d) September 14, 2015, 07:39 UTC; e) May 28, 2016, 07:29 UTC; f) June 25, 2019, 07:38 UTC.
Figure 3 in Validation of Wind Speed Calculated on Satellite Altimetry Data by Measurements on Weather Stations Located Along the White Sea Coast
Figure 3. The dependence of the speed wind at a height Figure 4. In-situ MS data breakdown scheme for a
Figure 4 in Interannual Variability of Water Exchange Anomalies Between the Northern, Middle and Southern Caspian Based on Satellite Altimetry Data
Figure 4. Temporal variability of anomalies of surface geostrophic velocities (m/s) directed normal to 133 (a) and 209 (b) tracks. Positive values correspond to the southeast direction of currents, negative values correspond to the northwest direction.
Figure 1 in Interannual Variability of Water Exchange Anomalies Between the Northern, Middle and Southern Caspian Based on Satellite Altimetry Data
Figure 1. The Caspian Sea. Main parts of the Caspian Sea: (1) – the Northern Caspian (2) - the Middle Caspian; (3) – the Southern Caspian; (4) – the Kara-Bogaz-Gol Bay. Isobaths are shown in meters. The coastline corresponds to year 1934, when the sea level was -26.46 m relative to the World Ocean level (Lebedev, 2018).
Figure 7 in Main Pattern of the Caspian Sea Surface Oil Pollution Revealed by Satellite Data
Figure 7. Consolidated maps of oil slicks due to natural seepages on the seafloor revealed from SAR imagery: a) in the Cheleken area; b) in the Sefid Rud Cape area.
Figure 5 in Validation of Wind Speed Calculated on Satellite Altimetry Data by Measurements on Weather Stations Located Along the White Sea Coast
Figure 5. The dependence of the correlation coefficient between in-situ wind speed at the WS and remote sensing data on the orientation angle of the main quadrants (a) and their position relative to the White Sea coastline (b).
Figure 4 in Main Pattern of the Caspian Sea Surface Oil Pollution Revealed by Satellite Data
Figure 4. Statistics on the frequency of detection of oil patches in the "Oil Rocks" oil producing area in SAR images in dependence on near-surface winds.
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>
MAX-DOAS tropospheric NO2 column measurements in Islamabad, Pakistan (33°N, 73°E) from 2015 to 2019 and comparisons with OMI and TROPOMI satellite data
<p>This data presents an intercomparison of NO<sub>2</sub> retreival settings using Differential Optical Absorption Spectroscopy (DOAS) and those based on literature published over last 20 years. Moreover, it presents comparison of NO<sub>2</sub> Vertical Column Densities(VCD) obtained from ground based MAX-DOAS in Islamabad, Pakistan with satellite data from 2015-2019. MAX-DOAS has retrieved data at seven elevation angles i.e., 2, 4, 5, 10, 15, 30, 45. On the other hand, VCDs are in molecules per cm<sup>2</sup>. However, in order to collect NO2 dataset, DOASIS was used was used to obtain data from MAX-DOAS and further analyzed using QDOAS. Then geometric approximation was applied to obtain VCDs that are presented in this data set.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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