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188 results for “GRACE”
Results of "Storm Time Data Assimilation in the Thermosphere Ionosphere with TIDA" CHAMP, GRACE-A, and GRACE-B neutral density data assimilation into CTIPe for 2003 Halloween Storms
<p># README</p> <p>Results for the article "Storm Time Neutral Density Assimilation in the Thermosphere Ionosphere with<br> TIDA".</p> <p>There are three storms presented here:</p> <p>1. 2003 storm: October 26-30, 2003<br> 2. 2004 storm: July 26-30, 2004<br> 3. 2002 storm: September 27 - October 2, 2002.</p> <p>For each of these three storms, there are four runs. For each storm, we've done a run<br> assimilating all satellites, and then three more assimilating each satellite individually and<br> comparing against the others.</p> <p>Each directory name before underscore identifies the date the run was<br> started. After the underscore identifies the date assimilated.</p> <p>This readme uses the notation that in curly brackets the satellites assimilated are given.</p> <p>- a stands for GRACE-A<br> - b stands for GRACE-B<br> - c stands for CHAMP</p> <p>The runs are summarized below:</p> <p>1. 2003 storm: {a, b, c}: 2021-12-29T1259_...<br> 3. 2003 storm: {a}: 2022-01-01T1654_...<br> 4. 2003 storm: {b}: 2022-01-02T1423_...<br> 2. 2003 storm: {c}: 2021-12-29T2342_...</p> <p>5. 2004 storm: {a, b, c}: 2022-01-03T1614_...<br> 6. 2004 storm: {a}: 2022-01-04T1027_...<br> 7. 2004 storm: {b}: 2022-01-04T2144_...<br> 8. 2004 storm: {c}: 2022-01-05T0502_...</p> <p>9. 2002 storm: {a, b, c}: 2022-01-02T2025_...<br> 10. 2002 storm: {a}: 2022-01-05T1056_...<br> 11. 2002 storm: {b}: 2022-01-05T1854_...<br> 12. 2002 storm: {c}: 2022-01-06T1017_...</p> <p>## Example result directory</p> <p>2021-12-29T1259_d2003-10-27<br> ├── density_champ_density.csv<br> ├── density_grace-a_density.csv<br> ├── density_grace-b_density.csv<br> └── inputs<br> ├── reference_2003-10-27_input.txt<br> └── special_2003-10-27_input.txt<br> <br> 1 directory, 5 files</p> <p>## Zenodo doesn't support directories</p> <p>So, the file structure has been flattened in the following way:</p> <p>Before: ./aaa/bbb/ccc.png</p> <p>After: ./aaa-bbb-ccc.png</p> <p>https://unix.stackexchange.com/~/45659</p> <p> </p>
Seasonal Terrestrial Water Load Modulation of Seismicity at the Southeastern Margin of the Tibetan Plateau Constrained by GNSS and GRACE Data
<p>Data Set S1. The earthquake catalog is used to decluster aftershocks and background events, and the time range is from July 2004 to July 2021. This data set includes 672585 events in the study area.</p> <p>Data Set S2. Focal mechanism solutions of M ≥ 4 earthquakes at the southeastern margin of the Tibetan Plateau. The data set includes 634 solutions of earthquakes M ≥ 4, and the time range is from 2009 to 2017.</p>
Terrestrial water storage changes across the contiguous United States from GPS and GRACE, 2007–2017
<p>In this dataset, we provide terrestrial water storage anomalies (TWSA) from 2007-2017 at weekly time scales derived using Global Positioning System (GPS) displacements, further constrained by lower-resolution TWSA observations from the Gravity Recovery and Climate Experiment (GRACE).</p> <p>There are six fields in the HDF5 product provided here:</p> <ol> <li>'/cmwe', which provides terrestrial water storage in units of cm. of water equivalent.</li> <li>'/latitude', latitude at the center of each 0.5 degree grid cell</li> <li>/longitude', longitude at the center of each 0.5 degree grid cell</li> <li>'/time', time in days since January 1st, 2007. The resolution of our time series is weekly, and the first day in our record is January 3rd, 2007.</li> <li>'/signal_to_noise_ratio', the variance of the signal divided by variance of noise for each grid cell in the dataset. Please read the supplementary information document in the paper below for more details.</li> <li>'/uncertainty', 95% confidence interval for each grid cell in the dataset. Please read the supplementary information document in the paper below for more details.</li> </ol> <p>As a condition of using these data, we request that you acknowledge the authors of this data set by citing the following peer-reviewed publication. </p> <p>Adusumilli, S., Borsa, A. A., Fish, M. A., McMillan, H. K., & Silverii, F. (2019). A decade of water storage changes across the contiguous United States from GPS and Satellite Gravity. <em>Geophysical Research Letters</em>, 46, 13006-13015. <a href="https://doi.org/10.1029/2019GL085370">https://doi.org/10.1029/2019GL085370</a></p>
GRACE-FO LRI1B SYSU Dataset
<p>The GRACE (Gravity Recovery and Climate Experiment) and its successor, GRACE-FO (GRACE Follow-On), provide crucial data for global static and time-variable gravity field recovery. In particular, the Laser Ranging Interferometer(LRI) instrument on GRACE-FO provide the first laser interferometric range measurements between remote satellites, with the noise level reaching 300pm/√Hz@1Hz, creating new opportunities for high-precision geophysical applications.</p> <p>The Geodesy and Navigation Team at Sun Yat-sen University (SYSU), in collaboration with Innovation Academy for Precision Measurement Science and Technology(AMP), and other partners developed an independently produced GRACE-FO LRI1B data product (SYSU LRI1B S10). We applied meticulous processing in time-tags correction, phase anomaly processing, and scale factor estimation to minimize the impact of outliers such as time-tags anomalies and phase anomalies on the LRI1B data. The precision of LRI1B S10 reaches 1nm/s/√Hz@0.1Hz, which is superior to Jet Propulsion Laboratory's (JPL) LRI1B RL04 product (~5nm/s/√Hz@0.1Hz), and comparable to similar products developed by Albert Einstein Institute (AEI). Gravity field recovery results based on LRI1B S10 show that its degree variance beyond degree 30 is better than LRI1B RL04, with better consistency with AEI's results. This data product provides a crucial basis for future GRACE-like gravity satellite missions (e.g., TianQin 2), supporting advancements in data processing, gravity field recovery, and surface mass migration research.</p> <p> This dataset covers the entire period of LRI measurements on the GRACE-FO mission from June 2018 to July 2023. It can be directly used for gravity field recovery, mascon solutions, and other scientific applications. Future processed LRI1B data from GRACE-FO will continue to be released.</p> <p>YIN H, et al. 2024. Raw data processing of laser ranging interferometer based on GRACE-FO gravity satellite[J]. Chinese Journal of Geophysics. doi: 10.6038/cjg2024R0814</p>
Filling the data gap between GRACE and GRACE-FO based on a two-step reconstruction method
<p><span>GRACE, </span><span>GRACE-FO</span><span>, Data gap, long short-term memory Bayesian convolutional neural network (LSTM-BCNN)</span></p>
Flood Detection Using GRACE Terrestrial Water Storage and Extreme Precipitation
<p>The flood day products were derived from GRACE Terrestrial Water Storage and Extreme Precipitation. We used GRACE terrestrial water storage and precipitation data combined with high-frequency filtering, anomaly detection and flood potential index methods to successfully extract historical flood days globally between Apr. 1st, 2002, and Aug. 31st, 2016, and further compared and validated the results with Dartmouth Flood Observatory (DFO) data, Global Runoff Data Centre (GRDC) discharge data, news reports and social media data. The results showed that GRACE-based flood days could cover 81% of the flood events in the DFO database, 87% of flood events extracted by MODIS and supplement many additional flood events not recorded by the DFO. Moreover, the probability of detection greater than or equal to 0.5 reached 62% among 261 river basins compared to flood events derived from the GRDC discharge data. These detection capabilities and detection results are both good. We finally provided flood day products with 1° spatial resolution covering the range of 60°S—60°N from Apr. 1st, 2002, to Aug. 31st, 2016. This research provides a data foundation for the mechanistic analysis and attribution of global flood events.</p>
Monthly mass-term sea level variations used for "A Novel Slepian Approach for Determining Mass-term Sea Level from GRACE over the South China Sea"
<p>The dataset contains the monthly mass-term sea level variations in the South China Sea from 2005 to 2015. Five datasets were used to calculate the mass-term sea level variations: the GRACE Spherical Harmonics (noted SHC), the GRACE Mascon solutions (noted Mascon), the GRACE Slepian Function solutions (noted EST), ECCO Ocean botton pressure model (noted ECCO), and steric-corrected satellite altimetry dataset (noted AsS). Besides, the signal (with trend, seasonal and semi-seasonal information) and the residual mass-term sea level was given separately. </p> <p>The boundary of the South China Sea and the longtidue and latitude of the study area were also given. Please noted that the longitude and latitude of the steric-corrected satellite altimetry dataset was different from the other four.</p> <p>For further information about the Slepian function method, please refer to our released code:</p> <p>https://github.com/KMartin0013/Slepian_ocean_add-on.git</p> <p>If you use this dataset, please cite our work properly:</p> <p>Zhongtian, Ma, Hok Sum Fok, Robert Tenzer, Jianli Chen. A Novel Slepian Approach for Determining Mass-term Sea Level from GRACE over the South China Sea. <em>Int. J. Appl. Earth. Obs. Geoinf.</em>, 132, 104065, 2024. doi:<a title="https://doi.org/10.1016/j.jag.2024.104065" href="https://doi.org/10.1016/j.jag.2024.104065" rel="nofollow">https://doi.org/10.1016/j.jag.2024.104065</a></p>
Accelerometer-Derived Atmospheric Density from the CHAMP and GRACE Satellites
<p>Archive of the thermospheric density dataset (including estimated uncertainties) for CHAMP and GRACE satellite accelerometer missions, first released in February of 2011, with an extension of GRACE density data in 2012. </p>
Changing intensity of hydroclimatic extreme events revealed by GRACE and GRACE-FO Data sets
<p>Updated 2 February 2023.<br> This archive contains the key data files (including figure data) associated with:</p> <p>Rodell, M., and B. Li, 2023: Changing intensity of hydroclimatic extreme events revealed by GRACE and GRACE-FO, Nature Water, accepted.</p> <p>Figure1_time_series_data.xlsx – Data used to create the 14 inset time series plots in Figure 1.</p> <p>fig1_extents.zip – Contains spatial data used to create the "Top wet events" and "Top dry events" maps in Figure 1, in both text and NetCDF formats.</p> <p>Figure2_data.xlsx – Time series data used to create Figure 2.</p> <p>Figure3a_Koeppen-Geiger-ASCII.zip - Contains a text data file of spatial data (latitude, longitude, class) used to create the climate class map in Figure 1. Note that only the main climates (first letter of class code) are used: A = Tropical, B = Dry, C = Temperate, D = Continental, E = Polar (no data). See http://koeppen-geiger.vu-wien.ac.at/present.htm for details.</p> <p>Figure3bc_data.xlsx – Time series data used to create Figures 3b and 3c.</p> <p>Figure4_data.xlsx – Location, year, and intensity data used to create the maps in Figure 4.</p> <p>all_event_intensity.xlsx - Contains the centroid location (longitude and latitude), year, and intensity (km3mo) of all 505 wet extreme events and 551 dry extreme events identified and analyzed in this study.</p> <p>Source data and code used in this study are available as follows.</p> <p>Data Availability<br> The GRACE/FO products (CSR GRACE/GRACE-FO RL06 Mascon Solutions, version 02) used in our analyses are available from the University of Texas Center for Space Research (https://www2.csr.utexas.edu/grace/RL06_mascons.html). The output from a global GRACE/FO data assimilating instance of the Catchment land surface model (GRACEDADM_CLSM025GL_7D 3.0) used to fill the 11-month gap between the GRACE and GRACE-FO missions and 18 additional missing months is available from the Goddard Earth Sciences Data and Information Services Center (https://disc.gsfc.nasa.gov/datasets/GRACEDADM_CLSM025GL_7D_3.0/). The climate oscillation indicator data can be downloaded from the NOAA Physical Sciences Laboratory (https://psl.noaa.gov/data/climateindices/list/ and https://psl.noaa.gov/gcos_wgsp/Timeseries/DMI/). The global mean temperature data are available from the NASA Goddard Institute for Space Studies (https://data.giss.nasa.gov/gistemp/). Köppen-Geiger climate map data are available for download from http://koeppen-geiger.vu-wien.ac.at/present.htm.</p> <p>Code Availability<br> The python code for the ST-DBSCAN clustering algorithm was obtained from the Github repository, https://github.com/gitAtila/ST-DBSCAN. Statistical analyses were performed and figures were generated using NCL software.</p>
Spatial database of the repertory of all the roads in Spain in the year of grace of 1543 by Juan Villuga [shapefile]
<p>This file provides a digital version of the roads documented by Juan Villuga in 1543. The file consists of 139 geographic entities (linear features), each entity represents a road documented by Juan Villuga with its respective name. The ETRS89 30N projection has been used. The vectorization has been made from two sources: 1) Villuga, Juan. 1543. Repertorio de todos los caminos de España en el año de gracia de 1543. Barcelona: Institut Cartografic i Geològic de Catalunya, R: RL 3419. (Accessed 14/05/2014). http://cartotecadigital.icgc.cat/cdm/singleitem/collection/espanya/id/2618/rec/1; and 2) Villuga, Juan. 1950 [1543]. Repertorio de todos los caminos de España. Madrid: Reimpresiones Bibliográficas.</p> <p>Este archivo proporciona una versión digital de los caminos por Juan Villuga en 1543. El archivo consta de 139 entidades geográficas de tipo línea, cada entidad representa un camino documentado por Juan Villuga con su respectivo nombre. Se ha utilizado la proyección ETRS89 30N. La vectorización ha sido realizada a partir de dos fuentes: 1) Villuga, Juan. 1543. Repertorio de todos los caminos de España en el año de gracia de 1543. Barcelona: Institut Cartografic i Geològic de Catalunya, R: RL 3419. (Consulta 14/05/2014). http://cartotecadigital.icgc.cat/cdm/singleitem/collection/espanya/id/2618/rec/1; y 2) Villuga, Juan. 1950 [1543]. Repertorio de todos los caminos de España. Madrid: Reimpresiones Bibliográficas. </p>
Global state outputs from GRACE-CLSM data assimilation product from Girotto et al. 2016
<p>Mean and standard deviation of ensemble average state variables and terrestrial water storage (TWS) for GRACE-CLSM data assimilation product from Girotto et al. 2016. The dataset contains both the data assimilation (DA) output and the open-loop (OL), pre-assimilation states used in Scheliga et al. <em>In review.</em></p> <p># GENERAL INFORMATION</p> <p>Title of Dataset: Global state outputs from GRACE-CLSM data assimilation product from Girotto et al. 2016</p> <p><br>## Principal Investigator Information<br>* Name: Manuela Girotto<br>* ORCID: 0000-0003-2795-2063<br>* Institution: University of California, Berkeley<br>* Email: mgirotto@berkeley.edu</p> <p>## Co-Author Information<br>* Name: Ann Scheliga<br>* ORCID: 0000-0002-0217-6451<br>* Institution: University of California, Berkeley<br>* Email: ann_scheliga@berkeley.edu</p> <p>Date of data generation: approximately 2021-09-09</p> <p>Funding sources that supported the collection of the data: NASA Sea Level Change Team (Grant 80NSSC21K0321).</p> <p><br># SHARING/ACCESS INFORMATION</p> <p>* Licenses/restrictions placed on the data: CC-BY-NC<br>* Links to publications that describes the data: Scheliga, A., Girotto, M., Gerlein-Safdi C. Anthropogenic processes and sea-level rise contributions with data assimilation of GRACE and GRACE-FO terrestrial water storage. In review.<br>* Recommended citation for this dataset: Girotto, M. et al. Assimilation of gridded terrestrial water storage observations from GRACE into a land surface model. WRR. https://doi.org/10.1002/2015WR018417</p> <p> </p>
Thermospheric density form GRACE-FO satellite
Open the record for dataset details and reuse information.
Flood Detection Using GRACE Terrestrial Water Storage and Extreme Precipitation
<p>The supplementary figures contain 2380 precipitation-type flood events recorded by the DFO compared with results derived based on GRACE and MODIS. "DFO-GRACE-MODIS" folder means comparison among flood derived from DFO, GRACE and MODIS while "DFO-GRACE" folder means comparison between DFO and GRACE due to lack of MODIS-derived flood inundation. As for detailed information, please refer to the corresponding paper. </p>
The Three Graces
The Three Graces, Antonio Canova (?), 1813-14, Ny Carlsberg Glyptotek. Made with Memento Beta Source: Objaverse 1.0 / Sketchfab
Dataset used in Kittel et al., 2017 (https://doi.org/10.5194/hess-2017-549), including model files and processed remote sensing observations (CryoSat-2 radar altimetry and GRACE total water storage)
<p>Dataset used in</p> <p>Kittel, C. M. M., Nielsen, K., Tøttrup, C., Bauer-Gottwein, P., 2017.Informing a hydrological model of the Ogooué with multi-mission remote sensing data. Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-549</p> <p>The dataset contains</p> <p>Model files:</p> <ul> <li>River delineation of the Ogooué river based on the SRTM 3 arc-second DEM </li> <li>Climate input data for the Ogooué model subbasins (TRMM and FEWS-RFE precipitation and ECMWF temperature)</li> <li>Parameter files</li> </ul> <p>Processed remote sensing data:</p> <ul> <li>CryoSat-2 satellite altimetry data over the Ogooué River from July 2010 to February 2015</li> <li><strong> </strong>Water mask derived from Sentinel-1 SAR, used to filter CryoSat-2 data</li> <li>GRACE TWS time series for the Ogooué basin</li> </ul> <p>The data is provided in a .zip file with a README.txt file providing additional information and details on the data, including where to obtain similar/original datasets.</p> <p>(c) Author(s) and Technical University of Denmark (DTU) 2018</p>
LUH GRACE(-FO) K-band range-rate post-fit residuals
<p>This dataset contains GRACE (Gravity Recovery and Climate Experiment) and GRACE Follow-On K-band range-rate post-fit residuals obtained as part of the gravity field recovery at LUH (Leibniz University Hannover). Data covers the period: 2002/04 - 2023/09.</p>
Amazing Grace
<p>This famous tune is dedicated to my wife Maria (Masha) Ovechkina as a token of my love to her and as a memory of our honeymoon in Scotland. It was played for Masha by Grant MacLeod (The Munro Bagpiper) on the banks of Loch Lomond, Scotland, on 17 March 2023. -Mike Van der Brug</p>
Spatial database of the towns of the repertoire of all the roads of Spain in the year of grace of 1543 by Juan Villuga [shapefile]
<p>Este archivo proporciona una versión digital de los pueblos categorizados por Juan Villuga en 1543. El archivo consta de 1070 entidades geográficas de tipo punto, cada entidad representa un pueblo documentado por Juan Villuga con su respectivo nombre. Se ha utilizado la proyección ETRS89 30N. La vectorización ha sido realizada a partir de dos fuentes: 1) Villuga, Juan. 1543. Repertorio de todos los caminos de España en el año de gracia de 1543. Barcelona: Institut Cartografic i Geològic de Catalunya, R: RL 3419. (Consulta 14/05/2014). <a href="http://cartotecadigital.icgc.cat/cdm/singleitem/collection/espanya/id/2618/rec/1">http://cartotecadigital.icgc.cat/cdm/singleitem/collection/espanya/id/2618/rec/1</a>; y 2) Villuga, Juan. 1950 [1543]. Repertorio de todos los caminos de España. Madrid: Reimpresiones Bibliográficas.</p> <p>La tabla de atributos contiene información de la denominación de cada pueblo dada por Juan Villuga (1543), de la categorización dada por Villuga, de la denominación dada por Gonzalo Menéndez Pidal (1951), de la denominación actual y de la precisión de la digitalización.</p> <p>Cuanto a la metodología, en primer lugar, se transcribieron los núcleos y se relacionaron con la información de sus respectivas rutas y clasificación definida por Villuga (capital, ciudad importante, ciudad pequeña, venta) a través de una tabla en formato .xml. Cada núcleo, a su vez, se asoció también con la denominación actual y las establecidas en las cartografías de Villuga y Menéndez Pidal. De esta forma, se registraron los cambios en la toponimia entre las cartografías de 1546 y 1941. Otra característica adicional que decidimos considerar fue el atributo de "exactitud" que se refiere a la calidad de la información espacial (las coordenadas). Cuando los datos de un núcleo no eran exactos al georreferenciarlos, añadimos el atributo "inexacto", de modo que la calidad de los datos también quedara registrada en la propia tabla de atributos.</p>
GRACE High-Resolution Trend Mascons - Greenland Ice Sheet (2007-2015)
<p>High-resolution mascon trend solution, computed for the Greenland Ice Sheet over the time period from January 2007 and January 2015, where each mascon regression model (including the trend) has been directly estimated from the Gravity Recovery and Climate Experiment (GRACE) Level 1B data. The GAD product has not been restored, meaning the ocean mascons are consistent with the Level 2 GSM product information.</p><p>Description of columns in the dataset:</p><ol><li>Latitude center (deg)</li><li>Longitude center (deg)</li><li>Mass change trend (cm w.e. / yr)</li><li>Mass change trend uncertainty (cm w.e. / yr)</li><li>Latitude minimum (deg)</li><li>Latitude maximum (deg)</li><li>Longitude minimum (deg)</li><li>Longitude maximum (deg)</li><li>Area of mascon (sq. km)</li><li>Label of mascon</li></ol><p>When citing this dataset, please also include this citation:</p><p>Loomis, B. D., D. Felikson, T. J. Sabaka, and B. Medley (2021). High‐spatial‐resolution mass rates from GRACE and GRACE‐FO: Global and ice sheet analyses. <i> Journal of Geophysical Research: Solid Earth, </i><a href="https://doi.org/10.1029/2021JB023024">https://doi.org/10.1029/2021JB023024</a></p>
Mount Grace Priory Carthusian monastery
A charterhouse is a monastery of the Carthusians. The order was founded in the 11th century, the first houses in England being established in the 12th or 13th century. It is a settlement planned to provide a community of contemplative monks with facilities for worship, accommodation and, to some extent, subsistence. Carthusian life was centred on solitude and favoured meditation over communal meeting. In taking this approach to monastic life the Carthusians were unique amongst orders in the West. In contrast to other monastic establishments the components of the charterhouse were devoted to individual accommodation in preference to communal buildings. Most notable were the individual cells and gardens built for each monk, these being ranged around a great cloister. see: https://historicengland.org.uk/listing/the-list/list-entry/1013019 https://historicengland.org.uk/listing/the-list/list-entry/1013019 https://en.wikipedia.org/wiki/Carthusians#Charterhouse Source: Objaverse 1.0 / Sketchfab
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