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JPL GRACE and GRACE-FO Mascon Ocean, Ice, and Hydrology Equivalent Water Height Coastal Resolution Improvement (CRI) Filtered Release 06.3 Version 04
This dataset contains gridded monthly global water storage/height anomalies relative to a time-mean, derived from GRACE and GRACE-FO and processed at JPL using the Mascon approach (RL06.3Mv04). A Coastal Resolution Improvement (CRI) filter has been applied to this data set to reduce signal leakage errors across coastlines. For most land hydrology, oceanographic as well as land-ice applications this is the recommend data set for the analysis of surface mass changes. The data are provided in a single data file in netCDF format, with water storage/height anomalies in equivalent water thickness units (cm). The data are derived from solving for monthly gravity field variations on geolocated spherical cap mass concentration functions, rather than global spherical harmonic coefficients. Additionally, realistic geophysical information is introduced during the computation to intrinsically remove correlated errors. Thus, these Mascon grids do not need to be de-correlated or smoothed, like traditional spherical harmonic gravity solutions. The complete Mascon solution consists of 4,551 independent estimates of surface mass change that have been derived using an equal-area 3-degree grid of individual mascons. A subset of these individual mascons span coastlines, and contain mixed land and ocean mass change signals. In a post-processing step, the CRI filter is applied to those mixed land/ocean Mascons to separate land and ocean mass. The land mask used to perform this separation is provided in the same directory as this dataset, as are uncertainty values, and the gridded mascon-ID number to enable further analysis. Since the individual mascons act as an inherent smoother on the gravity field, a set of optional gain factors (for continental hydrology applications) that can be applied to the solution to study mass change signals at sub-mascon resolution is also provided within the same data directory as the Mascon data. For use-case examples and further background on the gain factors, please see Wiese, Landerer & Watkins, 2016, https://doi.org/10.1002/2016WR019344. This RL06.3Mv04 is an updated version of the previous Tellus JPL Mascon RL06.1Mv03 (DOI, 10.5067/TEMSC-3JC63). For a detailed description on the Mascon solution, including the mathematical derivation, implementation of geophysical constraints, and solution validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. For a detailed description of the CRI filter implementation, please see Wiese et al., 2016, doi:10.1002/2016WR019344.
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JPL GRACE and GRACE-FO Mascon Ocean, Ice, and Hydrology Equivalent Water Height JPL Release 06.3 Version 04
공공데이터포털
This dataset contains gridded monthly global water storage/height anomalies relative to a time-mean, derived from GRACE and GRACE-FO and processed at JPL using the Mascon approach (RL06.3Mv04). These data are provided in a single data file in netCDF format, and can be used for analysis for ocean, ice, and hydrology phenomena. The data are provided in a single data file in netCDF format, with water storage/height anomalies in equivalent water thickness units (cm). The data are derived from solving for monthly gravity field variations on geolocated spherical cap mass concentration functions, rather than global spherical harmonic coefficients. Additionally, realistic geophysical information is introduced during the computation to intrinsically remove correlated errors. Thus, these Mascon grids do not need to be de-correlated or smoothed, like traditional spherical harmonic gravity solutions. The complete Mascon solution consists of 4,551 independent estimates of surface mass change that have been derived using an equal-area 3-degree grid of individual mascons. Please note that this dataset does not correct for leakage errors across coastlines; it is therefore recommended only for users who want to apply their own algorithm to separate between land and ocean mass very near coastlines. This RL06.3Mv04 is an updated version of the previous Tellus JPL Mascon RL06.1Mv03. For more information, please visit https://grace.jpl.nasa.gov/data/get-data/jpl_global_mascons/. For a detailed description on the Mascon processing, including the mathematical derivation, implementation of geophysical constraints, and validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. This product is intended for expert use only; other users are encouraged to use the CRI-filtered Mascon dataset, which is available here: https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_GRID_RL06.3_V4.
Glacier Bay oceanography 2021 data quality evaluations (OC M)
공공데이터포털
At the end of each survey year, a review is done to determine if post-season calibration data (OC_G) indicate that sensor drift over the cruise year is significant. The original log sheet images (OC_H) are also reviewed for annotations regarding data exceptions. The raw and processed vertical profile data plots are reviewed both separately and jointly for irregular instrument readings. The Project Leader flags questionable and unusable casts for the year using the data quality report spreadsheets. Inoperable individual sensors encountered during a cast are also noted on the sheets. The Data Manager, after validating the spreadsheet forms, publishes the forms to the Data Store and then updates appropriate columns in the cumulative database (OC_D) to reflect quality levels of all observations.
Glacier Bay oceanography 2021 data quality evaluations (OC M)
공공데이터포털
At the end of each survey year, a review is done to determine if post-season calibration data (OC_G) indicate that sensor drift over the cruise year is significant. The original log sheet images (OC_H) are also reviewed for annotations regarding data exceptions. The raw and processed vertical profile data plots are reviewed both separately and jointly for irregular instrument readings. The Project Leader flags questionable and unusable casts for the year using the data quality report spreadsheets. Inoperable individual sensors encountered during a cast are also noted on the sheets. The Data Manager, after validating the spreadsheet forms, publishes the forms to the Data Store and then updates appropriate columns in the cumulative database (OC_D) to reflect quality levels of all observations.
Glacier Bay oceanography 2023 data quality evaluations (OC M)
공공데이터포털
At the end of each survey year, a review is done to determine if post-season calibration data (OC_G) indicate that sensor drift over the cruise year is significant. The original log sheet images (OC_H) are also reviewed for annotations regarding data exceptions. The raw and processed vertical profile data plots are reviewed both separately and jointly for irregular instrument readings. The Project Leader flags questionable and unusable casts for the year using the data quality report spreadsheets. Inoperable individual sensors encountered during a cast are also noted on the sheets. The Data Manager, after validating the spreadsheet forms, publishes the forms to the Data Store and then updates appropriate columns in the cumulative database (OC_D) to reflect quality levels of all observations.
CSR TELLUS GRACE-FO Level-3 Monthly Land Water-Equivalent-Thickness Surface Mass Anomaly Release 6.3 version 04
공공데이터포털
This data set is produced by the Center for Space Research (CSR) GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) program and derives the terrestrial water storage anomaly given as equivalent water thickness. These monthly grids are derived from GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. This quantity represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Data grids are provided in ASCII/netCDF/GeoTIFF formats. GRACE-FO was launched on 22 May 2018, and extends the original GRACE mission (2002 – 2017) and expands its legacy of scientific achievements in tracking earth surface mass changes. Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Additionally, this release 06.3 is an updated version of the Level 3 products in coordination with the release of the analogous Level 2 products used to generate them. It differs from RL06.1 only in the Level-1B accelerometer transplant data that is used for the GF2 (GRACE-FO 2) satellite; see respective L-2 data descriptions. RL06.3 uses the ACX2-L1B data products. All GRACE-FO RL06.3 Level-3 fields are fully compatible with the GRACE RL06 data.
Glacier Bay oceanography 2020 data quality evaluations (OC M)
공공데이터포털
At the end of each survey year, a review is done to determine if post-season calibration data (OC_G) indicate that sensor drift over the cruise year is significant. The original log sheet images (OC_H) are also reviewed for annotations regarding data exceptions. The raw and processed vertical profile data plots are reviewed both separately and jointly for irregular instrument readings. The Project Leader flags questionable and unusable casts for the year using the data quality report spreadsheets. Inoperable individual sensors encountered during a cast are also noted on the sheets. The Data Manager, after validating the spreadsheet forms, publishes the forms to the Data Store and then updates appropriate columns in the cumulative database (OC_D) to reflect quality levels of all observations.
Glacier Bay oceanography 2020 data quality evaluations (OC M)
공공데이터포털
At the end of each survey year, a review is done to determine if post-season calibration data (OC_G) indicate that sensor drift over the cruise year is significant. The original log sheet images (OC_H) are also reviewed for annotations regarding data exceptions. The raw and processed vertical profile data plots are reviewed both separately and jointly for irregular instrument readings. The Project Leader flags questionable and unusable casts for the year using the data quality report spreadsheets. Inoperable individual sensors encountered during a cast are also noted on the sheets. The Data Manager, after validating the spreadsheet forms, publishes the forms to the Data Store and then updates appropriate columns in the cumulative database (OC_D) to reflect quality levels of all observations.
Glacier Bay oceanography 2022 data quality evaluations (OC M)
공공데이터포털
At the end of each survey year, a review is done to determine if post-season calibration data (OC_G) indicate that sensor drift over the cruise year is significant. The original log sheet images (OC_H) are also reviewed for annotations regarding data exceptions. The raw and processed vertical profile data plots are reviewed both separately and jointly for irregular instrument readings. The Project Leader flags questionable and unusable casts for the year using the data quality report spreadsheets. Inoperable individual sensors encountered during a cast are also noted on the sheets. The Data Manager, after validating the spreadsheet forms, publishes the forms to the Data Store and then updates appropriate columns in the cumulative database (OC_D) to reflect quality levels of all observations.
ECCO Sea-Ice and Snow Concentration and Thickness - Snapshot llc90 Grid (Version 4 Release 4)
공공데이터포털
This dataset provides instantaneous sea-ice and snow concentration, thickness, and pressure loading on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
Glacier Bay oceanography 2024 data quality evaluations (OC M)
공공데이터포털
At the end of each survey year, a review is done to determine if post-season calibration data (OC_G) indicate that sensor drift over the cruise year is significant. The original log sheet images (OC_H) are also reviewed for annotations regarding data exceptions. The raw and processed vertical profile data plots are reviewed both separately and jointly for irregular instrument readings. The Project Leader flags questionable and unusable casts for the year using the data quality report spreadsheets. Inoperable individual sensors encountered during a cast are also noted on the sheets. The Data Manager, after validating the spreadsheet forms, publishes the forms to the Data Store and then updates appropriate columns in the cumulative database (OC_D) to reflect quality levels of all observations.
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