In 2024, a total of 1,374 earthquakes with magnitude of five or more were recorded worldwide as of December that year. The Ring of Fire Large earthquakes generally result in higher death tolls in developing countries or countries where building codes are less stringent. China has suffered from a number of strong earthquakes that have resulted in extremely high death tolls. While earthquakes occur around the globe along the various tectonic plate boundaries, a significant proportion occur around the basin of the Pacific Ocean, in what is referred to as the Ring of Fire due to the high degree of tectonic activity. Many of the countries in the Ring of Fire, including Japan, Chile, the United States and New Zealand, led the way in earthquake policy and science as a result. The impacts of earthquakes The tragic loss of life is not the only major negative effect of earthquakes, a number of earthquakes have caused billions of dollars worth of damage to infrastructure and private property. The high cost of damage in the 2011 Fukushima and Christchurch earthquakes in Japan and New Zealand respectively demonstrates that even wealthy, developed countries who are experienced in dealing with earthquakes are ill-equipped when the large earthquakes hit.

The US Geological Survey maintains official records of earthquake activity from around the globe. This layer displays all earthquakes since 1900 with a magnitude 4.0 or greater, and updates once a day. For more recent activity, please see the Recent Earthquakes layer that updates every 5 minutes. Data source: original data is accessed here and updated using the OverwriteFS tool in ArcGIS Online. The full documentation for all of the fields can be found on the USGS ComCat site. For more information, please see the USGS PAGER program.RevisionsJune 13, 2022: Updated service with Z Coordinates set to 0 due to limitation on the negative Z value range for online services. This change allows users to support analytics and export for local client consumption. Depth can be leveraged by using the elevation field. Anticipating online enhancements, we set the custom projection to support the change in the Z value range. Refined the schema to improve efficiency. Layer has been Time-Enabled to allow for Time Series display, but disabled by default. Data download has now been enabled.May 5, 2023: Updated service with four duplicated fields to match the Recent Earthquake Service's data schema. Depth, Event Time, Event Type, TZ are the duplicated fields. This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to USGS source for official guidance.

Short-term seismicity forecasting models are increasingly developed and deployed for Operational Earthquake Forecasting (OEF) by government agencies and research institutions worldwide. To ensure their reliability, these forecasts must be rigorously tested against future observations in a fully prospective manner, allowing researchers to quantify model performance and build confidence in their predictive capabilities. The Collaboratory for the Study of Earthquake Predictability (CSEP) operated twenty-five fully automated M $\geq$ 3.95 seismicity models developed by nine research groups from Italy, California, New Zealand, the United Kingdom, and Japan. Between August 2007 and August 2018, these models produced over 50,000 daily forecasts for California, each specifying expected earthquake rates on a predefined space-magnitude grid over 24-hour periods. In this article, we describe the forecast database, summarize the underlying models, and demonstrate how to access and evaluate the forecasts using the open-source pyCSEP Python toolkit. The forecast data are publicly available through Zenodo, and the pyCSEP software is openly available on GitHub. This unprecedented dataset of fully prospective earthquake forecasts provides a critical benchmark for developing and testing next-generation OEF models, fostering advancements in earthquake predictability research.

This dataset is composed of GPS stations (1 file) and seismometers (1 file) multivariate time series data associated with three types of events (normal activity / medium earthquakes / large earthquakes). Files Format: plain textFiles Creation Date: 02/09/2019Data Type: multivariate time seriesNumber of Dimensions: 3 (east-west, north-south and up-down)Time Series Length: 60 (one data point per second)Period: 2001-2018Geographic Location: -62 ≤ latitude ≤ 73, -179 ≤ longitude ≤ 25Data Collection - Large Earthquakes: GPS stations and seismometers data are obtained from the archive [1]. This archive includes 29 large eathquakes. In order to be able to adopt a homogeneous labeling method, dataset is limited to the data available from the American Incorporated Research Institutions for Seismology - IRIS (14 large earthquakes remaining over 29). > GPS stations (14 events): High Rate Global Navigation Satellite System (HR-GNSS) displacement data (1-5Hz). Raw observations have been processed with a precise point positioning algorithm [2] to obtain displacement time series in geodetic coordinates. Undifferenced GNSS ambiguities were fixed to integers to improve accuracy, especially over the low frequency band of tens of seconds [3]. Then, coordinates have been rotated to a local east-west, north-south and up-down system. > Seismometers (14 events): seismometers strong motion data (1-10Hz). Channel files are specifying the units, sample rates, and gains of each channel. - Normal Activity / Medium Earthquakes: > GPS stations (255 events: 255 normal activity): High Rate Global Navigation Satellite System (HR-GNSS) normal activity displacement data (1Hz). GPS data outside of large earthquake periods can be considered as normal activity (noise). Data is downloaded from [4], an archive maintained by the University of Oregon which stores a representative extract of GPS noise. It is an archive of real-time three component positions for 240 stations in the western U.S. from California to Alaska and spanning from October 2018 to the present day. The raw GPS data (observations of phase and range to visible satellites) are processed with an algorithm called FastLane [5] and converted to 1 Hz sampled positions. Normal activity MTS are randomly sampled from the archive to match the number of seismometers events and to keep a ratio above 30% between the number of large earthquakes MTS and normal activity in order not to encounter a class imbalance issue.> Seismometers (255 events: 170 normal activity, 85 medium earthquakes): seismometers strong motion data (1-10Hz). Time series data collected from the international Federation of Digital Seismograph Networks (FDSN) client available in Python package ObsPy [6]. Channel information is specifying the units, sample rates, and gains of each channel. The number of medium earthquakes is calculated by the ratio of medium over large earthquakes during the past 10 years in the region. A ratio above 30% is kept between the number of 60 seconds MTS corresponding to earthquakes (medium + large) and total (earthquakes + normal activity) number of MTS to prevent a class imbalance issue. The number of GPS stations and seismometers for each event varies (tens to thousands). Preprocessing:- Conversion (seismometers): data are available as digital signal, which is specific for each sensor. Therefore, each instrument digital signal is converted to its physical signal (acceleration) to obtain comparable seismometers data- Aggregation (GPS stations and seismometers): data aggregation by second (mean)Variables:- event_id: unique ID of an event. Dataset is composed of 269 events.- event_time: timestamp of the event occurence - event_magnitude: magnitude of the earthquake (Richter scale)- event_latitude: latitude of the event recorded (degrees)- event_longitude: longitude of the event recorded (degrees)- event_depth: distance below Earth's surface where earthquake happened (km)- mts_id: unique multivariate time series ID. Dataset is composed of 2,072 MTS from GPS stations and 13,265 MTS from seismometers.- station: sensor name (GPS station or seismometer)- station_latitude: sensor (GPS station or seismometer) latitude (degrees)- station_longitude: sensor (GPS station or seismometer) longitude (degrees)- timestamp: timestamp of the multivariate time series- dimension_E: East-West component of the sensor (GPS station or seismometer) signal (cm/s/s)- dimension_N: North-South component of the sensor (GPS station or seismometer) signal (cm/s/s)- dimension_Z: Up-Down component of the sensor (GPS station or seismometer) signal (cm/s/s)- label: label associated with the event. There are 3 labels: normal activity (GPS stations: 255 events, seismometers: 170 events) / medium earthquake (GPS stations: 0 event, seismometers: 85 events) / large earthquake (GPS stations: 14 events, seismometers: 14 events). EEW relies on the detection of the primary wave (P-wave) before the secondary wave (damaging wave) arrive. P-waves follow a propagation model (IASP91 [7]). Therefore, each MTS is labeled based on the P-wave arrival time on each sensor (seismometers, GPS stations) calculated with the propagation model.[1] Ruhl, C. J., Melgar, D., Chung, A. I., Grapenthin, R. and Allen, R. M. 2019. Quantifying the value of real‐time geodetic constraints for earthquake early warning using a global seismic and geodetic data set. Journal of Geophysical Research: Solid Earth 124:3819-3837.[2] Geng, J., Bock, Y., Melgar, D, Crowell, B. W., and Haase, J. S. 2013. A new seismogeodetic approach applied to GPS and accelerometer observations of the 2012 Brawley seismic swarm: Implications for earthquake early warning. Geochemistry, Geophysics, Geosystems 14:2124-2142.[3] Geng, J., Jiang, P., and Liu, J. 2017. Integrating GPS with GLONASS for high‐rate seismogeodesy. Geophysical Research Letters 44:3139-3146.[4] http://tunguska.uoregon.edu/rtgnss/data/cwu/mseed/[5] Melgar, D., Melbourne, T., Crowell, B., Geng, J, Szeliga, W., Scrivner, C., Santillan, M. and Goldberg, D. 2019. Real-Time High-Rate GNSS Displacements: Performance Demonstration During the 2019 Ridgecrest, CA Earthquakes (Version 1.0) [Data set]. Zenodo.[6] https://docs.obspy.org/packages/obspy.clients.fdsn.html[7] Kennet, B. L. N. 1991. Iaspei 1991 Seismological Tables. Terra Nova 3:122–122.

This product contains a time series of clock biases for healthy satellites in the Galileo constellation that are accumulated every minute throughout the day. In addition, formal 1-sigma uncertainties for the corrections are provided. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.

We have explored the temporal variability of the seismicity at global scale over the last 124 years, as well as its potential drivers. To achieve this, we constructed and analyzed an averaged global seismicity curve for earthquakes of magnitude equal or greater than 6.0 since 1900. Using Singular Spectrum Analysis, we decomposed this curve and compared the extracted pseudo-cycles with two global geophysical parameters associated with Earth’s tides: length-of-day variations and sea-level changes. Our results reveal that these three geophysical signal curves can be reconstructed up to ∼90% by the sum of up to seven periodic components ranging from 1 to ∼60 years, largely aligned with planetary ephemerides. We discuss these results in the framework of Laplace’s theory, with a particular focus on the phase relationships between seismicity, length-of-day variations, and sea-level changes to further elucidate the underlying physical mechanisms. Finally, integrating observations from seismogenic regions, we propose a possible trigger mechanism based on solid Earth–hydrosphere interactions, emphasizing the key role of water-rock interactions in modulating earthquake occurrence.

The Cape Egmont Fault Zone in the southern Taranaki Basin, New Zealand, is a complex series of synthetic and antithetic dip-slip normal faults accommodating present-day extension. The fault zone comprises new and reactivated faults developed over multiple phases of plate boundary deformation during the last 100 Myrs. The fault zone is well imaged on petroleum industry seismic reflection data, with a number of faults exposed and studied onshore.

The Cape Egmont Fault Zone is seismically active, with damaging historic earthquakes of up to Mw 5.4. Most earthquakes occur beneath the Late Cretaceous to Holocene sedimentary sequence at depths greater than 5–8 km. The maximum depth of fault rupture is c. 20 km, above which 90% of recorded earthquakes occur. Focal mechanisms from these earthquakes generally indicate strike-slip to oblique-normal faulting, which contrasts with the predominantly dip-slip faulting observed in the overlying sedimentary sequence and surface fault traces. Data from regional earthquake studies and petroleum well deformation show faults imaged in the sedimentary sequence to be preferentially oriented for slip in the present-day stress field.

The greatest earthquake risk is on major basement-penetrating crustal-scale faults greater than 20 km in length. Fault lengths and maximum vertical offsets of the sedimentary sequence, determined from a three-dimensional structural model, are consistent with global displacement-length scaling relationships. This validation permits fault lengths to be used to determine potential future earthquake magnitudes using global fault length-magnitude relationships. Fault lengths of post-Pliocene normal faults are typically ≤21 km, resulting in maximum predicted magnitudes Mw 6.3. The most likely earthquake magnitude from the fault population sampled is Mw 5.4 ± 0.5. The largest and most mature fault – the Cape Egmont Fault – is at least 53 km long and, depending on the number of segments ruptured during a future event, is capable of generating an earthquake between Mw 7 and 7.3.

New sub-surface radiometric ages constraining the age of the ring plain created by Taranaki Maunga and its predecessors support 3 Myrs of continuous volcanism in the region.

Hornblende from 14 igneous samples from onshore petroleum exploration wells have yielded 40Ar/39Ar plateau ages ranging from c. 2.9 Ma to 0.15 Ma. Seven 40Ar/39Ar samples from near the base of the Taranaki Maunga ring plain (previously dated at <0.2 Ma) ranged from 0.515 Ma in the Kapuni-15 well on the south side of Taranaki to 0.39 Ma in Rahotu-1 on the western side. These data suggest that surficial volcanism, including Taranaki Maunga, initiated between 0.5 to 0.4 Ma.

DOI: https://doi.org/10.21420/ED9K-EP20

Cite data as: Seebeck H, Thrasher GP, Viskovic GPD, Macklin C, Bull S, Wang X, Nicol A, Holden C, Kaneko Y, Mouslopoulou V, Begg JG. 2021. Geologic, earthquake and tsunami modelling of the active Cape Egmont Fault Zone. Lower Hutt (NZ): GNS Science. 370 p. (GNS Science report; 2021/06). doi:10.21420/100K-VW73. (with data available at DOI: https://doi.org/10.21420/ED9K-EP20)

Thrasher GP, Viskovic GPD, Sagar M, Seebeck H. 2024. Subsurface igneous rocks of the Taranaki Peninsula. Lower Hutt (NZ): GNS Science. 77 p. (GNS Science report; 2023/47). https://doi.org/10.21420/8BEH-6P24. (with data available at DOI: https://doi.org/10.21420/ED9K-EP20)

This data set contains receiver functions calculated from three-component waveform data available in IRISDMC from 2000 to 2019. The waveform data are for earthquakes greater than magnitude 6.0, depth smaller than 100 km, and epicentral distances between 30 and 95 degrees. The raw waveform data are first converted to displacement and high pass filtered with a corner frequency of 0.02 Hz. Then the waveforms are windowed 20 s before and 400 s after the P arrival for analysis. The vertical-, north- and east-components are rotated to produce the L-, Q-and T-components. Subsequently, the [-20 s, 60 s] portion around the P arrival on the L-component is cut out as a parent waveform and deconvolved from the full L- and Q-components to produce the L- and Q-receiver functions. The data set is organized by year. The receiver functions in each year are compressed into a zip file named by the year when the waveforms were recorded. The receiver functions are in SAC format (https://seiscode.iris.washington.edu/projects/sac), which can be read using the Seismic Analysis Code (https://ds.iris.edu/ds/nodes/dmc/forms/sac/) or the Obspy package (https://github.com/obspy/obspy). The filename contains information of the origin time of the earthquake, the name of seismic network, the name of the seismic station, and component. For example, in the file name ‘TA.Z59A..BHL.M.2013.059.1405.SACD.01’, ‘TA’ stands for the network name, ‘Z59A’ is the station name, ‘BHL’ means the L component, ‘2013.059.1405’ means the earthquake occurred at 14:05 on the 59th day of the year 2013.

1. Intensive observations

In this study, we collected and processed GPS data from three sources to obtain a crustal horizontal velocity field. The dataset from the first source was raw GPS observations primarily from Phase I of the Crustal Movement Observation Network of China (CMONOC), which was resurveyed every 2 or 3 years from 1999 to 2007, and Phase II of the CMONOC, which involved campaign surveys every year from 2009 to 2020 and continuous surveys from 2010. The dataset from the second source was obtained from the National Key Research and Development Program of China. This dataset contained data from 31 continuous-measurement sites located close to the Anninghe–Zemuhe–Daliangshan fault zone, which were operated from August 2019 to August 2021, and 38 campaign sites from the National GPS Geodetic Control Network of China (NGGCNC), which were measured in 2014 and 2019. All of the campaign surveys used dual-frequency GPS receivers and choke ring antennas, with an operation of 3–4 consecutive days. The dataset from the third source consisted of published GPS velocities from existing studies of the Daliang Shan and its adjacent areas.In this study, we collected and processed GPS data from three sources to obtain a crustal horizontal velocity field. The dataset from the first source was raw GPS observations primarily from Phase I of the Crustal Movement Observation Network of China (CMONOC), which was resurveyed every 2 or 3 years from 1999 to 2007, and Phase II of the CMONOC, which involved campaign surveys every year from 2009 to 2020 and continuous surveys from 2010. The dataset from the second source was obtained from the National Key Research and Development Program of China. This dataset contained data from 31 continuous-measurement sites located close to the Anninghe–Zemuhe–Daliangshan fault zone, which were operated from August 2019 to August 2021, and 38 campaign sites from the National GPS Geodetic Control Network of China (NGGCNC), which were measured in 2014 and 2019. All of the campaign surveys used dual-frequency GPS receivers and choke ring antennas, with an operation of 3–4 consecutive days. The dataset from the third source consisted of published GPS velocities from existing studies of the Daliang Shan and its adjacent areas.

2. Data processing

We employed the GAMIT and GLOBK software (Herring et al., 2015a, 2015b) to process the raw GPS data and derived the GPS positioning time series with respect to the international terrestrial reference frame for 2014 (ITRF2014) (Altamimi et al., 2017). We utilized the GAMIT software to process the double-differenced carrier-phase observations and acquired regional daily loosely constrained solutions for the site coordinates and satellite orbits. The geophysical models used have been described by Hao et al. (2021). In addition, we employed the same strategy to process ~70 evenly distributed ITRF core GPS sites to acquire global daily loosely constrained solutions. Then, we employed the GLOBK software to combine the same regional and global daily solutions to obtain a GPS time series.

Three large earthquakes occurred in the study area: the 2004 M 9.1 Sumatra earthquake, the 2008 M 8.0 Sichuan Wenchuan earthquake, and the 2013 M 7.0 Sichuan Lushan earthquake. For the GPS time series for the campaign sites, we utilized the coseismic slip model of the 2004 Sumatra earthquake (Chlieh et al., 2007). We interpolated the coseismic displacements of the 2008 Wenchuan earthquake (Shen et al., 2009) to correct the coseismic offsets. We only used the data observed before 2008 for those GPS sites contaminated by significant postseismic deformation related to the 2008 Wenchuan earthquake (Wang & Shen, 2020). For the GPS sites affected by the coseismic deformation caused by the 2013 Lushan earthquake (Jiang et al., 2014), we also used data observed before the mainshock to mitigate the coseismic and postseismic deformation. After removing the transient deformation caused by the earthquakes, we used the weighted least-squares adjustment method to estimate linear trends of the velocities. We used the linear trend, seasonal variations, coseismic offset, and color noise model for the continuous GPS sites to fit the time series. We utilized the maximum likelihood estimation (MLE) technique and the CATS software (Williams et al., 2004; Williams., 2008) to estimate the characteristics of the noise in the residuals of the GPS time series after removing the linear trend and seasonal variations (Hao et al., 2016). Then, we obtained the GPS velocities with respect to the ITRF2014 and applied Euler rotation to transfer it to the Eurasia-fixed frame (Altamimi et al., 2017).

The reference frames of the GPS velocities reported in previous studies are different from ours. Therefore, to transfer the latter to our selected frame, we employed the Helmert transformation with four parameters through common sites for our velocities and the published velocities. We only chose spatially uniformly distributed common sites with post-fit residuals of less than 1.0 mm/yr in the north-ward and east-ward components. Finally, we derived the geodetically consistent GPS crustal movement in the Daliang Shan and its adjacent areas with respect to the stable Eurasian Plate. Additionally, in order to reduce the residual rigid motion caused by the far-field reference of the Eurasian Plate, we chose the stable South China block as the near-field reference frame. Subsequently, our derived GPS velocities were translated into the South China block reference frame using the published Euler rotation vectors (Hao et al., 2019).

References

Altamimi, Z., Métivier, L, Rebischung, P., Rouby, H., Collilieux, X., 2017. ITRF2014 plate motion model. Geophys. J. Int. 209:1906–1912

Chlieh, M., Avouac, J. P. , Hjorleifsdottir, V. , Song, T. , Ji, C. , Sieh, K., Sladen, A., Hebert, H., Prawirodirdjo, L., Bock, Y., Galetzka, J., 2007. Coseismic slip and afterslip of the great Mw 9.15 Sumatra-Andaman earthquake of 2004. Bulletin of the Seismological Society of America, 97(1A), 152–173.

Hao, M., Freymueller, J. T., Wang, Q. L., Cui, D. X., Qin, S. L. 2016. Vertical crustal movement around the southeastern Tibetan Plateau constrained by GPS and GRACE data. Earth and Planetary Science Letters, 437, 1-8. http://dx.doi.org/10.1016/j.epsl.2015.12.038.

Hao, M., Li, Y., Zhuang, W., 2019. Crustal movement and strain distribution in east Asia revealed by GPS observations. Scientific Reports, https://doi.org/10.1038/s41598-019-53306-y, 16797.

Hao, M., Wang, Q., Zhang, P., Li, Z., Li, Y., Zhuang, W., 2021. “Frame wobbling” causing crustal deformation around the Ordos block. Geophysical Research Letters 48, e2020GL091008. https://doi.org/10.1029/2020GL091008.

Herring, T.A., King, R.W., McClusky, S.C., 2015a. GAMIT reference manual, GPS analysis at MIT, Release 10.6. Massachusetts Institute of Technology, Cambridge.

Herring, T.A., King, R.W., McClusky, S.C., 2015b. GAMIT reference manual, global Kalman filter VLBI and GPS analysis program, Release 10.6. Massachusetts Institute of Technology, Cambridge.

Jiang, Z., Wang, M., Wang, Y., Wu, Y., Che, S., Shen, Z.K., Bürgmann, R., Sun, J., Yang, Y., Liao, H., Li, Q., 2014. GPS constrained coseismic source and slip distribution of the 2013 Mw6.6 Lushan, China, earthquake and its tectonic implications. Geophysical Research Letters 41, 407–413, doi:10.1002/2013GL058812.

Shen, Z.K., Sun, J., Zhang, P., Wan, Y., Wang, M., Bürgmann, R., Zeng, Y.H., Gan, W.J., Wang, Q.L., 2009. Slip maxima at fault junctions and rupturing of barriers during the 2008 Wenchuan earthquake. Nat Geosci 2:718–724.

Wang, M., Shen, Z.K., 2020. Present-day crustal deformation of continental China derived from GPS and its tectonic implications. J. Geophys. Res. 125 (2) https://doi. org/10.1029/2019JB018774.

Williams, S.D.P., 2008. CATS: GPS coordinate time series analysis software. GPS Solutions, 12, 147–153. http://dx.doi.org/10.1007/s10291-007-0086-4.

Williams, S.D.P., Bock, Y., Fang, P., Jamason, P., Nikolaidis, R.M., Prawirodirdjo, L., Miller, M., Johnson, D.J. 2004. Error analysis of continuous GPS position time series. J. Geophys. Res. 109 (B03412). http://dx.doi.org/10.1029/2003JB002741.

This dataset includes an estimate of tsunami origins and runup for the period January1970 - June 2015. First dataset contains tsunami origins. This product was designed by National Geophysical Data Center (NGDC), Tsunami database, NOAA and modified by UNEP/GRID-Europe (doubtfull events removed (VAL < 2)). Credit: National Geophysical Data Center (NGDC), Tsunami database, NOAA. Attributes descriptions: EV_ID: Event GRID ID, ISO3YEAR: Country and year, ISO3: Country ISO3, ID_NAT: Event GRID ID and ISO3, ID_CAT: Event NGDC ID, YEAR: Year, START_DATE: Year, Month and Day (YYYYMMDD), TIME_GMT: Hours, Minutes and seconds (_HHMMSS), MAG: Earthquake magnitude, FOCALDEPTH: Earthquake depth (kilometer), COUNTRY: Country given per NGDC, MAXWATERHE: Maximum water height above sea level (meter), DEATH: number of killed people DAMAGE: Damage cost (Million dollars), LAT: Latitude (decimal degrees) LONG: Longitude (decimal degrees). Missing values replaced by -9999. Second dataset contains tsunami runup. This product was designed by National Geophysical Data Center (NGDC), Tsunami database, NOAA and modified by UNEP/GRID-Europe (doubtfull events removed). Credit: National Geophysical Data Center (NGDC), Tsunami database, NOAA. Attributes descriptions: EV_ID: Event GRID ID, ISO3YEAR: Country and year, ISO3: Country ISO3, ID_NAT: Event GRID ID and ISO3, ID_CAT: Event NGDC ID,ID_TE: Event GRID ID of the source event (km), WATERH_: Water height (meter), TRAVTIME_H: Travel time from source (hours), TRAVTIME_M: Travel time from source minutes), YEAR: Year, START_DATE: Year, Month and Day (YYYYMMDD),DISTSOURCE: distance from the source event (km) , COUNTRY: Country given per NGDC, LOCATION: Location given per NGDC, LAT: Latitude (decimal degrees) LONG: Longitude (decimal degrees). Missing values replaced by -9999.

This dataset consists of ground-based Global Navigation Satellite System (GNSS) Mixed 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 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 daily GNSS broadcast ephemeris files contain one day of mixed multi-GNSS navigation (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.

Visualization OverviewThis visualization represents a "false color" band combination (Red = DNB, Green = DNB, Blue = Inverted M15) of data collected by the VIIRS instrument on the joint NASA/NOAA Suomi-NPP satellite. The imagery is most useful for identifying nighttime lights from cities, fires, boats, and other phenomena. At its highest resolution, this visualization represents the underlying data scaled to a resolution of 500m per pixel at the equator.The algorithm to combine the VIIRS DNB and M15 bands into an RGB composite was originally designed by the Naval Research Lab and was subsequently incorporated into NASA research and applications efforts. As you will see, nighttime city lights appear in shades of yellow, while clouds appear in shades of blue to yellow/white as the illumination from the moon changes over the lunar month. Hence, this visualization is colloquially referred to as a "blue-yellow RGB."The following guidelines will aid in understanding this visualization.Interpretation of both the presence and relative brightness of the city lights will be affected by the lunar cycle. This composite offers a qualitative assessment of the light conditions and should not be used as the sole source of information concerning power outages. During bright moonlight conditions, moonlight reflected from cloud tops and the land surface may also provide a yellow hue to those features. Comparisons of cloud-free conditions before and after a period of significant change, such as new city growth, disasters, fires, or other factors, may exhibit a change in emitted light (yellows) from those features over time.Multi-Spectral BandsAt its highest resolution, this visualization represents the underlying data scaled from its native 750m per pixel resolution to 500m per pixel at the equator. The following table lists the VIIRS bands that are utilized to create this visualization. See here for a full description of all VIIRS bands.BandDescriptionWavelength (µm)Resolution (m)DNBVisible (reflective)0.5 - 0.9750DNBVisible (reflective)0.5 - 0.9750M15 (Inverted)Longwave IR10.26 - 11.26750Temporal CoverageBy default, this layer will display the imagery currently available for today’s date. This imagery is a "daily composite" that is assembled from hundreds of individual data files. When viewing imagery for “today,” you may notice that only a portion of the map has imagery. This is because the visualization is continually updated as the satellite collects more data. To view imagery over time, you can update the layer properties to enable time animation and configure time settings. Currently, this layer is available from present back to April 30th, 2021. In the coming months, this will be extended to the start of the mission (October 28th, 2011).Data AccessThis visualization is generated from hourly and daily Near-Real Time versions of the "VIIRS/NPP Daily Gridded Day Night Band 500m Linear Lat Lon Grid Night" (VNP46A1_NRT) data product distributed by the Land, Atmosphere Near real-time Capability for EOS (LANCE). A standard quality version of the data product (VNP46A1), which is distributed by the Level-1 and Atmosphere Archive & Distribution System Distributed Active Archive Center (LAADS DAAC), is also available within 1-2 days of acquisition. You may use the Earthdata Search client to search for near real-time and science quality data files and associated documentation and services. Additionally, you may use the Worldview Snapshots tool to download custom images in a GeoTIFF , JPEG, PNG, or KMZ format for offline use.NASA Global Imagery Browse Services (GIBS), NASA Worldview, & NASA LANCEThis visualization is provided through the NASA Global Imagery Browse Services (GIBS), which are a set of standard services to deliver global, full-resolution satellite imagery for hundreds of NASA Earth science datasets and science parameters. Through its services, and the NASA Worldview client, GIBS enables interactive exploration of NASA's Earth imagery for a broad range of users. The data and imagery are generated within 3 hours of acquisition through the NASA LANCE capability.Esri and NASA Collaborative ServicesThis visualization is made available through an ArcGIS image service hosted on Esri servers and facilitates access to a NASA GIBS service endpoint. For each image service request, the Esri server issues multiple requests to the GIBS service, processes and assembles the responses, and returns a proper mosaic image to the user. Processing occurs on-the-fly for each and every request to ensure that any update to the GIBS imagery is immediately available to the user. As such, availability of this visualization is dependent on both the Esri and the NASA GIBS services.

This derived product set consists of Global Navigation Satellite System Final Satellite and Receiver Clock Product (30-second granularity, daily files, generated weekly) 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 final combined satellite and receiver clock products. The AC clock products consist of daily station and satellite clock solution files, generated on a weekly basis with a delay of approximately 10 days (from the last day of the week). All satellite and receiver clock solution files utilize the clock RINEX format and span 24 hours from 00:00 to 23:45 UTC.

This product contains a time series of position and velocity components for healthy satellites in the GLONASS constellation that are accumulated every minute throughout the day. In addition, formal 1-sigma uncertainties for the positions and velocities are provided. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.

This dataset consists of ground-based Meteorological Data (daily, 24 hour files) from instruments co-located with Global Navigation Satellite System (GNSS) GLONASS receivers 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 meteorological data files contain one day of meteorological data (temperature, pressure, humidity, etc.) 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.

This dataset consists of ground-based Global Navigation Satellite System (GNSS) Compact Observation Data (30-second sampling, daily, 24 hour 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 daily GNSS observation files (compact) contain one day of GPS or multi-GNSS observation (30-second sampling) data in compact 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.

This dataset consists of ground-based Global Navigation Satellite System (GNSS) Galileo 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 Galileo broadcast ephemeris files contain one day of Galileo 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.

This product contains a time series of position and velocity components for healthy satellites in the Galileo constellation that are accumulated every minute throughout the day. In addition, formal 1-sigma uncertainties for the positions and velocities are provided. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.

This product contains a time series of position and velocity components for healthy satellites in the GPS constellation that are accumulated every minute throughout the day. In addition, formal 1-sigma uncertainties for the positions and velocities are provided. The product is generated at JPL's Global Differential GPS Operations Centers in real-time. The data in this product can be concatenated with other daily products to provide larger coverage in time.

This dataset consists of ground-based Global Navigation Satellite System (GNSS) Quasi-Zenith Satellite System (QZSS) 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 QZSS broadcast ephemeris files contain one day of QZSS 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.