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1,321 results for “Navigator”
ARCTAS DC-8 Aircraft In-situ Meteorological and Navigational Data
ARCTAS_MetNav_AircraftInSitu_DC8_Data is the in-situ meteorological and navigational data for the DC-8 aircraft collected during the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites sub-orbital campaign. Also featured in this product is water vapor data from the DLH. Data collection for this product is complete.The Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA’s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.ARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.ARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.During ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.The ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth’s environment and climate.
Tropical Ozone Transport Experiment – Vortex Ozone Transport Experiment (TOTE-VOTE) In Situ DC-8 Meteorology and Navigation Data
TOTE-VOTE_MetNav_AircraftInSitu_DC8_Data_1 features the in situ meteorology and navigation data collected onboard the DC-8 aircraft during the Tropical Ozone Transport Experiment – Vortex Ozone Transport Experiment (TOTE-VOTE) Campaign. Instruments included in this dataset include the Microwave Temperature Profiler (MTP), DC-8 Data Acquisition and Distribution System (DADS) and Diode Laser Hygrometer (DLH). Data collection is complete.The Tropical Ozone Transport Experiment – Vortex Ozone Transport Experiment (TOTE-VOTE) campaign was conducted by NASA from December 1995 to February 1996. TOTE-VOTE took place in the Pacific region with the goal of gaining a better understanding of background transport processes from tropical/polar regions to midlatitudes. Nineteen flights were conducted using the NASA DC-8 aircraft and balloon sondes with the purpose of measuring the transport of filaments of air moved in or out of the arctic polar vortex and the tropical stratospheric reservoir. TOTE-VOTE also utilized ground-based instruments along with aircrafts.Various instrumentation was used during TOTE-VOTE in order to achieve the mission objectives. The DC-8 aircraft was equipped with the NCAR NOxyO3 instrument, the NASA Langley Airborne Differential Absorption Lidar (DIAL) system, the Forward Scattering Spectrometer Probe (FSSP), the Microwave Temperature Profiler (MTP), the Multiple-Angle Aerosol Spectrometer Probe (MASP), and the diode laser spectrometer system, historically known as the Differential Absorption Carbon monOxide Measurement (DACOM). The NCAR NOxyO3 is a type of 4-channel chemiluminescence instrument that was used to quantify NOx (NO and NO2), NOy (total reactive nitrogen), and ozone (O3) in the air. The DIAL system used four lasers to make DIAL O3 profiles, along with collecting data on aerosol backscattering, aerosol depolarization ratio, aerosol extinction, and aerosol optical depth. The FSSP is an optical particle counter that measured particle size distribution. The MTP is a passive microwave radiometer that measured natural thermal emissions and was used during TOTE-VOTE to record temperature. The MASP spectrometer collected in-situ measurements of particle concentration, particle size distribution, and particle extinction. Along with the MASP’s in-situ measurements, the DACOM spectrometer utilized three diode lasers at different wavelengths to take in-situ measurements of N2O, CO, CH4, and CO2 for TOTE-VOTE. Ground-based instruments collected lidar data while balloon sondes gathered information on wind direction, wind speed, atmospheric pressure, and air temperature.
STRAT ER-2 Meteorological and Navigational Data
STRAT_MetNav_AircraftInSitu_ER2_Data is the in-situ meteorological and navigational data collected during the Stratospheric Tracers of Atmospheric Transport (STRAT) campaign. Data from the Meteorological Measurement System (MMS), ER-2 Nav Recorder (NavRec), Microwave Temperature Profiler (MTP), and the Composition and Photo-Dissociative Flux Measurement (CPFM) are featured in this collection. Data collection for this product is complete.The STRAT campaign was a field campaign conducted by NASA from May 1995 to February 1996. The primary goal of STRAT was to collect measurements of the change of long-lived tracers and functions of altitude, latitude, and season. These measurements were taken to aid with determining rates for global-scale transport and future distributions of high-speed civil transport (HSCT) exhaust that was emitted into the lower atmosphere. STRAT had four main objectives: defining the rate of transport of trace gases from the stratosphere and troposphere (i.e., HSCT exhaust emissions), improving the understanding of dynamical coupling rates for transport of trace gases between tropical regions and higher latitudes and lower altitudes (between tropical regions, higher latitudes, and lower altitudes are where most ozone resides), improving understanding of chemistry in the upper troposphere and lower stratosphere, and finally, providing data sets for testing two-dimensional and three-dimensional models used in assessments of impacts from stratospheric aviation. To accomplish these objectives, the STRAT Science Team conducted various surface-based remote sensing and in-situ measurements. NASA flew the ER-2 aircraft along with balloons such as ozonesondes and radiosondes just below the tropopause in the Northern Hemisphere to collect data. Along with the ER-2 and balloons, NASA also utilized satellite imagery, theoretical models, and ground sites. The ER-2 collected data on HOx, NOy, CO2, ozone, water vapor, and temperature. The ER-2 also collected in-situ stratospheric measurements of N2O, CH4, CO, HCL, and NO using the Aircraft Laser Infrared Absorption Spectrometer (ALIAS). Ozonesondes and radiosondes were also deployed to collect data on CO2, NO/NOy, air temperature, pressure, and 3D wind. These balloons also took in-situ measurements of N2O, CFC-11, CH4, CO, HCL, and NO2 using the ALIAS. Ground stations were responsible for taking measurements of O3, ozone mixing ratio, pressure, and temperature. Satellites took infrared images of the atmosphere with the goal of aiding in completing STRAT objectives. Pressure and temperature models were created to help plan the mission.
GPM Ground Validation Navigation Data DC-8 OLYMPEX
The GPM Ground Validation NASA DC-8 Navigation Data OLYMPEX dataset supplies navigation data collected by the NASA DC-8 aircraft for flights that occurred during November 5, 2015 through December 19, 2015 for the Olympic Mountains Experiment (OLYMPEX) GPM Ground Validation field campaign. This navigation dataset consists of multiple altitude, pressure, temperature, airspeed, and ground speed measurements in ASCII-IWG1 and XML data formats.
Ground-Based Global Navigation Satellite System (GNSS) Observation Data (1-second sampling, sub-hourly files) from NASA CDDIS
This dataset consists of ground-based Global Navigation Satellite System (GNSS) Observation Data (1-second sampling, sub-hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe’s Galileo, China’s Beidou, Japan’s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The sub-hourly GNSS observation files (un-compacted) contain 15 minutes of GPS or multi-GNSS observation (1-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per 15 minutes per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/high-rate_data.html.
Ground-Based Global Navigation Satellite System (GNSS) IGS Differential Code Bias Product from NASA CDDIS
This dataset consists of differential code biases (DCBs) from a network of ground-based Global Navigation Satellite System (GNSS) station and available from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe’s Galileo, China’s Beidou, Japan’s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. DCBs are the systematic errors, or biases, between two GNSS code observations at the same or different frequencies. DCBs are required for code-based positioning of GNSS receivers, extracting ionosphere total electron content (TEC), and other applications. Proper knowledge of DCBs is crucial to many navigation applications but also non-navigation applications such as ionospheric analysis and time transfer. With all of the new signals offered by modernized and new GNSS constellations, analysts now require a comprehensive multi-GNSS DCB product. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/gnss_differential_code_bias_product.html.
DISCOVER-AQ Colorado Deployment P-3B Aircraft In Situ Meteorological and Navigational Data
DISCOVERAQ_Colorado_MetNav_AircraftInSitu_P3B_Data contains in situ meteorological and navigational data collected onboard NASA's P-3B aircraft during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This product features navigational data for the P-3B aircraft, along with data from the DLH. This data product contains data for only the Maryland deployment and data collection is complete.Understanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.DISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).The first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.
Global Navigation Satellite System (GNSS) IGS Ionosphere Vertical Total Electron Content (VTEC) Final Product from NASA CDDIS
This derived product set consists of Global Navigation Satellite System Final Ionosphere Vertical Total Electron Content (VTEC) product (daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). The VTEC product files also include Delay Code Bias (DCB) values for GNSS satellites and ground receivers derived during the analysis. GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe’s Galileo, China’s Beidou, Japan’s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. GNSS observations from a global network can be utilized for atmospheric measurements. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce independently computed VTEC maps. The IGS Ionosphere Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS VTEC maps. The final VTEC maps are computed with a resolution of 2 hours in UT, 5 degrees in longitude and 2.5 degrees in latitude; they have an availability with a latency of 11 days.
Global Navigation Satellite System (GNSS) IGS Ultra-Rapid Combined Orbit Solution Product (sub-daily files, generated 4 times/day) from NASA CDDIS
This derived product set consists of Global Navigation Satellite System Ultra-Rapid Orbit Product (daily files, generated daily) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe’s Galileo, China’s Beidou, Japan’s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS ultra-rapid combined orbit products. The ultra-rapid orbit and clock combination is a sub-daily solution, released four times per day, at 03:00, 09:00, 15:00, and 21:00 UTC (prior to GPS week 1267 they were released twice daily). In this way the average age of the predictions is reduced to 6 hours (compared to 36 hours for the old IGS predicted products and 9 hours for the twice-daily ultra-rapid solutions). IGS ultra-rapid orbit files contain 48 hours of tabulated orbital ephemerides, and the start/stop epochs continuously shift by 6 hours with each update. The first 24 hours of each IGS ultra-rapid orbit are based on the most recent GNSS observational data from the IGS hourly tracking network. At the time of release, the observed orbits have an initial latency of 3 hours. The next 24 hours of each file are predicted orbits, extrapolated from the observed orbits. The orbits within each ultra-rapid product file are, however, continuous at the boundary between the observed and predicted parts. Normally, the predicted orbits between 3 and 9 hours into the second half of each ultra-rapid orbit file are most relevant for true real time applications. All orbit solution files utilize the extended standard product-3 (SP3c) format. The reduced latency on availability of these products allows for significantly improved orbit predictions and reduced errors for user applications.
Ground-Based Global Navigation Satellite System (GNSS) Data (1-second sampling, real-time streams) from NASA CDDIS
Global Navigation Satellite System (GNSS) real-time 1 to multi-second sampled data available from the Crustal Dynamics Data Information System (CDDIS). Global Navigation Satellite System (GNSS) provide autonomous geo-spatial positioning with global coverage. GNSS real-time data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Other GNSS (Europe’s Galileo, China’s Beidou, Japan’s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs) are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure; CDDIS began streaming real-time data from these systems in 2015. The real-time observation data from a global permanent network of ground-based receivers are transmitted from the CDDIS in 1 to multi-second intervals in raw receiver or RTCM (Radio Technical Commission for Maritime Services) format.
Ground-Based Global Navigation Satellite System (GNSS) GLONASS (GLObal NAvigation Satellite System) Observation Data (30-second sampling, daily files) from NASA CDDIS
This dataset consists of ground-based Global Navigation Satellite System GLONASS Observation Data (30-second sampling, daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. The GLONASS data sets from ground receivers at the CDDIS consist of observations from the Russian GLObal NAvigation Satellite System (GLONASS); Russia's GLONASS is similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily GLONASS GNSS observation files (un-compacted) contain one day of GLONASS observation (30-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/daily_30second_data.html.
TCSP ER-2 Navigation Data V1
The TCSP ER-2 Navigation Data contains information recorded by the on-board navigation and data collection systems of the NASA ER-2 high-altitude research aircraft. In addition to typical navigation data (e.g., date, time, latitude/longitude, and altitude) it contains outside meteorological parameters such as wind speed, wind direction, and temperature. These data were collected during the Tropical Cloud Systems and Processes (TCSP) field campaign in July 2005, with flights based out of Juan Santamaria Airport in San Jose, Costa Rica. The main goal of the campaign was to gain further insight into the structure and lifecycle of tropical weather systems. These navigation dataset files are available from July 2 through July 27, 2005 in ASCII and PDF formats.
Ground-Based Global Navigation Satellite System (GNSS) Satellite-Based Augmentation System (SBAS) Broadcast Ephemeris Data (daily files) from NASA CDDIS
This dataset consists of ground-based Global Navigation Satellite System (GNSS) Satellite-Based Augmentation System (SBAS) Broadcast Ephemeris Data (daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLONASS. Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe’s Galileo, China’s Beidou, Japan’s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily SBAS broadcast ephemeris files contain one day of SBAS broadcast navigation data in RINEX format from a global permanent network of ground-based receivers, one file per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/daily_30second_data.html.
CAMEX-4 ER-2 NAVIGATION DATA V1
The CAMEX-4 ER-2 Navigation data files contain information recorded by on board navigation and data collection systems. In addition to typical navigation data (e.g. date, time, lat/lon and altitude) these files contain outside meteorological parameters such as wind speed and direction and temperature. These ascii text files was recorded every second for the length of the sortie. Additionally, graphical representations of these measured parameters are shown in .gif files.
DC-8 Navigation Data CPEX
The DC-8 Navigation Data CPEX dataset is a subset of airborne measurements that include GPS positioning and trajectory data, aircraft orientation, and atmospheric state measurements of temperature, pressure, water vapor, and horizontal winds. These measurements were taken from the NASA DC-8 aircraft during the Convective Processes Experiment (CPEX) field campaign. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of America-Caribbean Oceanic region during the early summer of 2017. These data files are available from May 25, 2017 through June 28, 2017 in ASCII format.
Global Navigation Satellite System (GNSS) IGS Rapid Combined Satellite and Receiver 5-Minute Clock Solution (30-second resolution, daily files, generated daily) Product from NASA CDDIS
This derived product set consists of Global Navigation Satellite System Rapid Satellite and Receiver Clock Product (30-second granularity, daily files, generated daily) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe’s Galileo, China’s Beidou, Japan’s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce GNSS satellite and ground receiver clock values. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS rapid combined satellite and receiver clock products. The rapid combination is a daily solution available approximately 17 hours after the end of the previous UTC day. All satellite and receiver clock solution files utilize the clock RINEX format and span 24 hours from 00:00 to 23:45 UTC. For most applications the user of IGS products will not notice any significant differences between results obtained using the IGS Final and the IGS Rapid products.
Global Navigation Satellite System (GNSS) IGS Final Combined Station Position Solution Summary Product from NASA CDDIS
This derived product set consists of Global Navigation Satellite System Final Combined Station Positions/Velocities Summary Product available from the Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe’s Galileo, China’s Beidou, Japan’s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce precise orbits identifying the position and velocity of the GNSS satellites as well as precise station positions and velocities for the network of GNSS receivers. The IGS Reference Frame Coordinator uses these individual AC solutions to generate the official IGS station position/velocity product. The final products are considered the most consistent and highest quality IGS solutions and consists of daily and weekly station position and velocity files in SINEX format, generated on a daily/weekly basis by combining solutions from individual IGS ACs, approximately 11-17 days after the end of the solution week.
Ground-Based Global Navigation Satellite System (GNSS) IRNSS Broadcast Ephemeris Data (sub-hourly files) from NASA CDDIS
This dataset consists of ground-based Global Navigation Satellite System (GNSS) Indian Regional Navigation Satellite System (IRNSS) Broadcast Ephemeris Data (sub-hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLONASS. Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe’s Galileo, China’s Beidou, Japan’s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The high-rate (1-second) IRNSS broadcast ephemeris files contain 15 minutes of IRNSS broadcast navigation data in RINEX format from a global permanent network of ground-based receivers, one file per 15 minutes per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/high-rate_data.html.
Ground-Based Global Navigation Satellite System (GNSS) High-Rate Meteorological Data (sub-hourly files) from Co-Located Receivers from NASA CDDIS
This dataset consists of ground-based Meteorological Data (sub-hourly files) from instruments co-located with Global Navigation Satellite System (GNSS) receivers from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe’s Galileo, China’s Beidou, Japan’s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The sub-hourly meteorological data files contain 15 minutes of meteorological data (temperature, pressure, humidity, etc.) in RINEX format from a global permanent network of ground-based receivers, one file per 15 minutes per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/high-rate_data.html.
SEAC4RS ER-2 Aircraft In-Situ Meteorological and Navigational Data
SEAC4RS_MetNav_AircraftInSitu_ER2_Data are in-situ meteorological and navigational data collected onboard the ER-2 aircraft during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. Data collection for this product is complete.Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for UT/LS chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions.The airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of the trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 was operating in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest.
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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.