NASA modis-terra Project

Description

Mission Objectives: The Surface Water and Ocean Topography (SWOT) mission aims to provide valuable data and information about the world's oceans and its terrestrial surface water such as lakes, rivers, and wetlands. SWOT is being developed jointly by NASA and Centre National D'Etudes Spatiales (CNES), with contributions from the Canadian Space Agency (CSA) and United Kingdom Space Agency (UKSA).

MITgcm_LLC4320_Pre-SWOT_JPL_L4_BassStrait_v1.0

This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Bass Strait region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.

MITgcm_LLC4320_Pre-SWOT_JPL_L4_Boknis_v1.0

This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Baltic Sea region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.

NSIDC-0630

The Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature ESDR, Version 2 data set is a multi-sensor Level 3 Earth Science Data Record (ESDR) with improvements upon Version 1 in cross-sensor calibration and quality checking, modern file formats, better quality control, improved projection grids, and local time-of-day (LTOD) processing. These data are gridded to three EASE-Grid 2.0 projections (North Azimuthal, South Azimuthal, and Cylindrical) and include enhanced-resolution imagery, as well as coarse-resolution, averaged imagery. Inputs include brightness temperature data from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave/Imager (SSM/I), Special Sensor Microwave Imager/Sounder (SSMIS), Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2).

MITgcm_LLC4320_Pre-SWOT_JPL_L4_CapeBasin_v1.0

This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Cape Basin region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.

NSIDC-0796

This data set provides spatial distributions of fast ice and glacial ice in eight fjords spanning the Southeast Greenland coast: Nansen, Kangerlusruaq, Ikertivaq, Skjoldungen, Tingmiarmiut, Napasorsvaq, Anoritup, and Kangerlluluk. Temporal coverage is discontinuous, depending on the availability and quality of images. Fjord data were sourced from USGS EarthExplorer, Copernicus Open Access Hub, and the NSIDC. Landsat-8 and MODIS imagery for ice identification were collected from NASA Worldview and USGS EarthExplorer. Fjord, fast ice, and glacial ice boundaries were manually delineated using ArcGIS. Glacial ice was further categorized as dense glacial melange (Type 3), substantial glacial ice with large icebergs (Type 2), low-density glacial ice with large icebergs (Type 1), consistent small ice surface without large icebergs (Type 0), or glacier surface (Type 99).

HMA2_GGP

This data set comprises results from a hybrid glacier evolution model that uses the mass balance module of the Python Glacier Evolution Model (PyGEM) and the glacier dynamics module of the Open Global Glacier Model (OGGM). Output parameters include projections of glacier mass change, fixed runoff, and various mass balance components at regionally aggregated and glacier scales.

MITgcm_LLC4320_Pre-SWOT_JPL_L4_GotlandBasin_v1.0

This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Gotland Basin region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.

AERDB_D3_AHI_H08

The H08 Deep Blue Level 3 daily aerosol data, 1x1 degree grid product, short-name AERDB_D3_AHI_H08, derived from the L2 (AERDB_L2_AHI_H08) input data, each D3 AHI/Himawari-8 product is produced daily at 1 x 1-degree horizontal resolution. In general, in this daily L3 (identified in the short-name as D3) aggregated product, each data field represents the arithmetic mean of all cells whose latitude and longitude places them within the bounds of each grid element. Another statistic like standard deviation is also provided in some cases. The final retrievals used in the aggregation process are Quality Assurance (QA)-filtered best-estimate values for cells that are measured on the day of interest. Further, at least three such retrievals are required to render the validity of a grid cell on any given day. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-3 (L3) Advanced Himawari Imager (AHI) Himawari-8 Deep Blue Daily Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments. The AERDB_D3_AHI_H08 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_D3_AHI_H08 Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue

AERDB_M3_AHI_H08

The H08 Deep Blue Level 3 Monthly aerosol data, 1x1 degree grid product, short-name AERDB_M3_AHI_H08, derived by aggregating the L3 daily (AERDB_D3_AHI_H08) input data, each M3 AHI/ Himawari-8 product is produced monthly at 1 x 1-degree horizontal resolution. This monthly L3 (identified in the shortname as M3) product’s statistics that include mean and standard deviation of the daily means are derived from the arithmetic mean values of the L3 daily product. As a mechanism to filter out poorly sampled grid elements, at least three valid days of data in the month are required to populate the monthly grid element. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-3 (L3) Advanced Himawari Imager (AHI) Himawari-8 Deep Blue Aerosol Deep Blue Monthly Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments. The AERDB_M3_AHI_H08 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_M3_AHI_H08 Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue

BAROCLINIC_HRET14

This dataset of Harmonic Constants for Baroclinic Tide Prediction was produced by Edward Zaron (Oregon State University) and Shane Elipot (University of Miami). It provides sea surface height and ocean surface currents associated with the predictable astronomical tide at the M2, S2, N2, K1, and O1 frequencies. The tidal harmonic constants, in-phase and quadrature with respect to the equilibrium potential, are provided on a latitude/longitude at 1/20-deg resolution. Using the software available at the Github repository, the dataset can be used to predict baroclinic tidal sea surface height and surface ocean currents at arbitrary time and location throughout the world oceans.
The harmonic constants were estimated within the time period from 1993 to 2021 and incorporate roughly 30 years of multi-satellite altimeter data and 20 years of data from drifting buoys. The observations were combined with a kinematic wave model and the internal wave polarization relations to prepare uniformly gridded estimates from the sparse and irregular data sampling. These files may be used by the altimeter community to compute corrections intended to remove baroclinic tidal variability from sea level anomaly observations. Researchers with an interest in ocean surface currents may also use these data to predict baroclinic tidal surface currents. Such information may be used to plan observational campaigns or optimize the design of future surface current mapping satellite missions.
This dataset is funded by NASA SWOT Science Team award #80NSSC21K0346 and NSF Physical Oceanography Program award #1850961. The software to make baroclinic tidal calculations using this dataset is regularly updated at the provided Github link, and an archived snapshot of the software is also provided in the documentation. The harmonic constants and prediction software may be updated every few years as additional data for mapping the tides becomes available.

HMA2_GFTP

This data set consists of 1 km resolution monthly land surface temperatures (MLSTs); mean annual ground temperatures (MAGTs); and estimates of permafrost extent (PE) in the High Mountain Asia region from 1 Jan 2003 – 31 Dec 2016. The data were generated by gap-filling daily MODIS Terra/Aqua Land surface temperatures (LSTs) with downscaled Atmospheric Infra-Red Sounder (AIRS) skin surface temperatures.

HMA2_MATCHA

This data set contains a 12 km resolution, simulated reanalysis of aerosol transport, chemistry, and deposition over the High Mountain Asia (HMA) region for 1 January 2003 through 31 August 2019. Two-dimensional surface data are provided at one hour intervals. Three-dimensional atmospheric data are provided at three-hour intervals for 35 sigma levels extending from the surface to 50 hPa. Also known as the Model for Atmospheric Transport and Chemistry in Asia (MATCHA), the data comprise a wide range of variables intended to help assess the impacts of aerosols on the cryosphere in the HMA region, including: concentrations of black/brown carbon and other light absorbing particles (LAPs), broken out by source region; longwave/shortwave heating rates due to LAPs; wet/dry deposition of LAPs; precipitation and hydrological data; and meteorological state variables. The simulation was generated using a fully coupled, regional chemistry-climate model (WRF-Chem-CLM-SNICAR), constrained by aerosol optical depth (AOD) and carbon monoxide (CO) satellite observations acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Measurements Of Pollution In The Troposphere (MOPITT) instruments, respectively.

HMA2_DCG_SMB

This High Mountain Asia data set contains 2 m resolution digital elevation models (DEMs), surface velocities, surface mass balance (SMB) rates, and SMB uncertainties for six debris-covered glaciers in Nepal. SMB rate is estimated by applying a Lagrangian specification to DEMs derived from very-high-resolution optical stereo imagery acquired by Maxar Technologies satellites WorldView-1, WorldView-2, WorldView-3, and GeoEye-1. This data set was granted permission for public release on 1 March 2024 under the National Reconnaissance Office (NRO) Electro-Optical Commercial Layer (EOCL) program.

HMA2_FGP

This data set contains Flood Geomorphic Potential (FGP) at 30 m resolution for the High Mountain Asia region and 8 m resolution over Nepal. FGP is a digital elevation model-derived index that provides high-resolution flood mapping based on bankfull elevations, defined in terms of river widths, and elevation differences between points under examination and the closest bankfull elevations in the river network.

HMA2_DDSMET

This High Mountain Asia (HMA) data set contains simulated meteorological data for the Indus Basin from 2000 through 2015, at three horizontal resolutions – 36 km, 12 km, and 4 km – and 9 pressure levels spanning 1000 hPa – 200 hPa. The data were produced by using the Advanced Research Weather Research & Forecasting (ARW-WRF) model to dynamically downscale Climate Forecast System Reanalysis (CFSR) data into three nested domains with increasing horizontal resolution.

HMA2_HFD

This High Mountain Asia (HMA) data set contains hydrological flow directions at 5 arc-minute resolution for the headwaters of the Amu Darya and Indus River basins. The domain spans parts of Afghanistan, Tajikistan, Kyrgyzstan, and Pakistan. Flow directions are reported in deterministic eight (D8) format. The data were developed to support the University of New Hampshire Water Balance Model and the "High Mountain Asia CMIP6 Monthly and Yearly Water Balance Projections, 2016-2099 for Parts of Afghanistan, Tajikistan, Kyrgyzstan, and Pakistan, Version 1" data set.

HMA_DEM8m_MOS

This data set contains 8-meter Digital Elevation Model (DEM) mosaics of high mountain Asia glacier and snow regions generated from very-high-resolution (VHR) commercial satellite imagery.

HMA_DEM8m_AT

This data set contains 8-meter Digital Elevation Models (DEMs) of high mountain Asia glacier and snow regions generated from very-high-resolution commercial stereoscopic satellite imagery.

HMA_DEM8m_CT

This data set contains 8-meter Digital Elevation Model (DEM) mosaics of high mountain Asia glacier and snow regions generated from from very-high-resolution commercial stereo satellite imagery.

HMA_GlacierAvg_dH

This data set contains average thickness changes for approximately 650 Himalayan glaciers between 1975 and 2000, and 1040 Himalayan glaciers between 2000 and 2016. The data were derived from HEXAGON KH-9 and ASTER digital elevation models (DEMs), by fitting robust linear trends to time series of elevation pixels over the glacier surfaces.

HMA2_WBP

This High Mountain Asia (HMA) data set comprises a suite of monthly and yearly water balance model (WBM) projections for the years 2016 – 2099, covering parts of Afghanistan, Tajikistan, Kyrgyzstan, and Pakistan (primarily the headwaters of the Amu Darya and Indus River basins). Projections are available for 12 Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models and two Shared Socioeconomic Pathways (SSP 2-4.5 and SSP 5-8.5). The data were generated using the University of New Hampshire WBM. A historical run is also available for the years 1980 through 2018, using as input ERA5 reanalysis temperature data and ensemble precipitation estimates.

HMA_RCMO_6H

This data product contains either 6-hourly accumulated or 6-hourly snapshots of modeled data in the High Mountain Asia region, generated by the Coupled-Ocean-Atmosphere-Waves-Sediment Transport (COAWST) modeling system (operated as a regional climate model). These modeled data span 15 years and have been used by the NASA High Mountain Asia Team (HiMAT) to research water resource use.

HMA_RCMO_D

This data product contains either daily averaged or daily accumulated modeled data in the High Mountain Asia region, generated by the Coupled-Ocean-Atmosphere-Waves-Sediment Transport (COAWST) modeling system (operated as a regional climate model). These modeled data span 15 years and have been used by the NASA High Mountain Asia Team (HiMAT) to research water resource use.

HMA_RCMO_1H

This data product contains either hourly accumulated or hourly snapshots of modeled data in the High Mountain Asia region, generated by the Coupled-Ocean-Atmosphere-Waves-Sediment Transport (COAWST) modeling system (operated as a regional climate model). These modeled data span 15 years and have been used by the NASA High Mountain Asia Team (HiMAT) to research water resource use.

HMA_RCMO_M

This data product contains either monthly averaged or monthly accumulated modeled data in the High Mountain Asia region, generated by the Coupled-Ocean-Atmosphere-Waves-Sediment Transport (COAWST) modeling system (operated as a regional climate model). These modeled data span 15 years and have been used by the NASA High Mountain Asia Team (HiMAT) to research water resource use.

HMA2_NLSMR

This data set consists of a water budget reanalysis for the High Mountain Asia (HMA) region spanning the years 2003 through 2020. Estimates are provided for more than 30 parameters, including storages; fluxes; snow depth, extent, and snow water equivalent; temperature (land surface, soil, snow, and ice); surface albedo; soil moisture; evapotranspiration; and streamflow. The data were generated using the Noah Multi-Parameterization (Noah-MP) land surface model (Version 4.0.1), driven by precipitation estimates and hydrological inputs developed specifically for HMA.

HMA2_DSPAT

This data set consists of daily, 5 km resolution precipitation and mean, near-surface air temperature projections from 2015 through 2100 for the High Mountain Asia (HMA) region. The data were generated by statistically downscaling 0.5° resolution model data from the Geophysical Fluid Dynamic Laboratory (GFDL) Seamless System for Prediction and EArth System Research (SPEAR) 30-member ensemble climate model. Projections are provided for two Shared Socioeconomic Pathways (SSPs): SSP2-4.5 and SSP5 8.5. A historical model run from 1990 through 2014 is also available.

HMA2_LHI

This data set projects the daily hazard of rainfall-triggered landslides in the High Mountain Asia region from 2015 through 2100, at 5 km resolution. Projections are provided for two Shared Socioeconomic Pathways (SSPs)—SSP2-4.5 and SSP5 8.5—based on downscaled temperature and precipitation projections from a 30-member ensemble climate model. Landslide hazard is represented by a landslide hazard indicator (LHI), computed with a machine learning (ML) model trained on historical temperatures and precipitation from 1990 through 2019 and a catalog of documented landslides. Two historical LHI data sets are also available: the ML model LHIs generated for 1990 through 2019; and retrodicted LHIs computed by inputting downscaled temperatures and precipitation for 1990 through 2014 to the ensemble climate model.

HMA2_RSRD

This data set reports daily, reach-scale river discharge for 114,147 river reaches in the High Mountain Asia region from 1 January 2004 through 31 December 2019. The data were generated by combining ensemble hydrologic modeling with data assimilation of remotely sensed discharge from Landsat and PlanetScope satellite imagery.

HMA_EAPrecip_FLOR

This data set includes three climate simulations of daily precipitation over the Himalayan region for summer and winter, covering different time periods: two 30-member ensemble simulations spanning 40-year time periods in the 20th century (1961-2000) and 21st century (2061-2100), and a present-day climate simulation from 1982 to 2017 nudged to reanalysis winds. These precipitation estimates were simulated by the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory (GFDL) Forecast-oriented Low Ocean Resolution version of the CM2.5 model (GFDL FLOR).

HMA_Precip_FLOR

This data set features seven standard annual mean extreme precipitation indices: Rx1day, Rx5day, CWD, R10mm, R20mm, R95pTOT, and R99pTOT. They were selected on the basis of potential relevance to landslide activity from the 27 indices established by the joint CCl/CLIVAR/JCOMM Expert Team (ET) on Climate Change Detection and Indices (ETCCDI). The seven indices were simulated by the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory (GFDL) Forecast-oriented Low Ocean Resolution version of CM2.5 (GFDL FLOR).

HMA_Glacier_dH_Mosaics

This data set contains thickness change mosaics that include approximately 650 Himalayan glaciers between 1975 and 2000, and 1040 Himalayan glaciers between 2000 and 2016. The data were derived from HEXAGON KH-9 and ASTER digital elevation models (DEMs), by fitting robust linear trends to time series of elevation pixels over the glacier surfaces.

HMA_Glacier_dH

This data set contains gridded thickness changes for approximately 650 Himalayan glaciers between 1975 and 2000, and 1040 Himalayan glaciers between 2000 and 2016. The data were derived from KH-9 HEXAGON and ASTER digital elevation models (DEMs), by fitting robust linear trends to time series of elevation pixels over the glacier surfaces.

HMA_AWS

This data set contains meteorological data, such as air temperature, pressure, rainfall intensity, relative humidity, and wind direction/speed measured by the International Centre for Integrated Mountain Development (ICIMOD).

HMA_SDI

This data set contains thermal-dome-corrected downward shortwave irradiance at the bottom of atmosphere, measured by the Shared Mobile Atmospheric Research and Teaching Radar (SMART-R) and collected by the International Centre for Integrated Mountain Development (ICIMOD).

HMA_SBRF

This data set contains snow bidirectional reflectance factor (BRF) between 350 and 2500 nm collected on the Yala Glacier on 23 April and 24 April 2018 by the International Centre for Integrated Mountain Development (ICIMOD).

HMA_Snowfield

This data set contains measurements of several different snow properties, including reflectance at 1310 nm, specific surface area, and optical mean radius, collected on the Yala Glacier, Nepal. These data were collected on 23 April and 24 April 2018 by the International Centre for Integrated Mountain Development (ICIMOD) using the IceCube instrument.

HMA_STParams

This data set provides daily-averaged NASA Land Information System (LIS) output at a spatial resolution of 1 km. LIS was driven by uncorrected Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) data, using the Noah Multiparameterization Land Surface Model (Noah-MP). Modeled parameters include snow water equivalent (SWE), snow depth, surface temperature, and soil temperature profile.

HMA_LIS_LandSurfaceHydro

The data provided in this data set are simulated using the Noah-Multiparameterization Land Surface Model (Noah-MP LSM) Version 3.6 within the NASA Land Information System (LIS) Version 7.2. The data files contain estimates of water, energy fluxes, and land surface states for the High Mountain Asia (HMA) region.

HMA_MAR3_5

This data set provides modeled surface and atmospheric fields from the Modèle Atmosphérique Régionale (MAR) regional climate model (version 3.5) over the Himalayan region at 10 km spatial resolution. Modeled parameters include surface mass and energy balance components, near-surface atmospheric properties, and snowpack properties.

HMA_OptDepth

This data set contains monthly mean MODIS Level 3 data from aboard the Aqua and Terra satellites. The parameters provided in this data set are aerosol optical depth (AOD) and Angstrom exponent (AE) at a spatial resolution of 1º by 1º.

HMA_GL_RCP

This data set comprises results from the Python Glacier Evolution Model (PyGEM) that include projections of glacier mass change, glacier runoff, and the various components associated with changes in mass and runoff.

HMA_GL_RCPR

This data set comprises a rasterized (gridded) version of the of glacier point data from the Python Glacier Evolution Model (PyGEM) that include projections of glacier mass change, glacier runoff, and the various components associated with changes in mass and runoff.

AERDB_L2_AHI_H08

The Himawari-08 AHI Deep Blue Aerosol L2 Full Disk product, short-name AERDB_L2_AHI_H08 is produced every 30 minutes and contains full-disk observation data. The L2 data products comprise 10 x 10 native GEO pixels. Each spectral band with 0.5 km or 2 km resolution is downscaled or upscaled to a nominal ~1 km horizontal pixel size in the production process. To distinguish them from native instrument pixels, these 10 x 10 aggregated pixels are also called retrieval pixels. Therefore, the L2 products’ image dimensions are roughly 10 km x 10 km at the sub-satellite point and are larger away from that point because of the combined effects of the sensor’s scanning geometry and Earth’s curvature. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-2 (L2) Advanced Himawari Imager (AHI) Himawari-8 Deep Blue Aerosol Full-Disk dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments. The AERDB_L2_AHI_H08 product, in netCDF4 format, contains 51 Science Data Set (SDS) layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_L2_AHI_H08 Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue

MITgcm_LLC4320_Pre-SWOT_JPL_L4_LabradorSea_v1.0

This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Labrador Sea region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.

MITgcm_LLC4320_Pre-SWOT_JPL_L4_MarmaraSea_v1.0

This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Marmara Sea region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.

NSIDC-0756

This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, contains a bed topography and bathymetry map of Antarctica. Bed topography is deduced by subtracting ice thickness from the surface elevation; using Ice Flow Perturbation Analysis (IFPA); and by other methods. Surface elevations are obtained from the Reference Elevation Model of Antarctica (REMA) and high-resolution satellite maps.

NSIDC-0724

This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of monthly image mosaics of the Greenland coastline and ice sheet periphery constructed from composited MODIS imagery. See Greenland Ice Mapping Project (GIMP) for related data.

NSIDC-0792

This ITS_LIVE data set, part of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, includes quarterly estimates of Antarctic ice shelf surface elevation, thickness, basal melt rate, surface mass balance, firn air content, and associated errors, from 17 March 1992 through 16 December 2017 at 1920 m resolution. The data were generated from four European Space Agency (ESA) satellite radar altimetry missions—ERS-1, ERS-2, Envisat, and CryoSat-2—using a novel data fusion approach and the Glacier Energy and Mass Balance model (GEMB).

NSIDC-0547

This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of two digital Greenland image maps each for the 2005, 2010, and 2015 measurement periods: the MOG Surface Morphology Image Map and the MOG Grain Size Image Map. The image maps are constructed from MODIS imagery acquired during 2005, 2010, and 2015 and provide nearly cloud-free views of all land areas and islands larger than a few hundred meters, including the ice caps on Baffin Island, Devon Island, Axel Heiberg Island, and Ellesmere Island.

NSIDC-0530

This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, offers users 25 km Northern Hemisphere snow cover extent represented by four different variables. Three of the snow cover variables are derived from the Interactive Multisensor Snow and Ice Mapping System, MODIS Cloud Gap Filled Snow Cover, and passive microwave brightness temperatures, respectively. The fourth variable merges the three source products into a single representation of snow cover.

MITgcm_LLC4320_Pre-SWOT_JPL_L4_WesternMed_v1.0

This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the western Mediterranean Sea region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.

MOD10A1F

This global Level-3 data set (MOD10A1F) provides daily cloud-free snow cover derived from the MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid data set (MOD10A1). Grid cells in MOD10A1 which are obscured by cloud cover are filled by retaining clear-sky views of the surface from previous days. A separate parameter is provided which tracks the number of days in each cell since the last clear-sky observation. Each data granule contains a 10° x 10° tile projected to the 500 m sinusoidal grid. The terms "Version 61" and "Collection 6.1" are used interchangeably in reference to this release of MODIS data.

NSIDC-0791

This data set presents new global snow cover classification regimes derived from the MODIS Terra cloud gap-filled NDSI data (MOD10A1F), elevation, and temperature climatology inputs. The six data granules are available as NetCDF (.nc) files, with each containing a unique snow cover classification spanning 2001 to 2023. The six classifications included in this data set are: (1) snow class climatology (SSC), (2) core snow season length (CSS), (3) snow cover duration (SCD), (4) full snow season length (FSS), (5) snow persistence (SP), and (6) snow season persistence (SSP).

MOD29

This global Level-2 (L2) product provides daily sea ice extent and ice surface temperature. The data are derived from Level-1B radiances acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite. Each data granule contains 5 minutes of swath data observed at a resolution of 1000 m. The terms "Version 61" and "Collection 6.1" are used interchangeably in reference to this release of MODIS data.

MOD29E1D

This global Level-3 (L3) data set provides Northern and Southern Hemisphere maps of sea ice extent and ice surface temperature. The maps are generated by compositing 1 km observations from the 'MODIS/Terra Sea Ice Extent Daily L3 Global 1km EASE-Grid Day’ (https://doi.org/10.5067/MODIS/MOD29P1D.061) product. These data are provided daily in the EASE-Grid polar projection at a resolution of approximately 4 km. The terms "Version 61" and "Collection 6.1" are used interchangeably in reference to this release of MODIS data.

MOD29P1D

This global Level-3 (L3) data set provides daily daytime sea ice extent and ice surface temperature derived from the 'MODIS/Terra Sea Ice Extent 5-Min L2 Swath 1km' (https://doi.org/10.5067/MODIS/MOD29.061) product. Each data granule is a tile consisting of 10 x 10 degrees of data gridded to the Lambert Azimuthal Equal Area Scalable Earth Grid (EASE-Grid). The terms "Version 61" and "Collection 6.1" are used interchangeably in reference to this release of MODIS data.

MOD29P1N

This global Level-3 (L3) data set provides daily nighttime ice surface temperature derived from the 'MODIS/Terra Sea Ice Extent 5-Min L2 Swath 1km' (https://doi.org/10.5067/MODIS/MOD29.061) product. Each data granule is a tile consisting of 10 x 10 degrees of data gridded to the Lambert Azimuthal Equal Area Scalable Earth Grid (EASE-Grid). The terms "Version 61" and "Collection 6.1" are used interchangeably in reference to this release of MODIS data.

MOD10_L2

This global Level-2 (L2) data set provides daily snow cover detected using the Normalized Difference Snow Index (NDSI) and a series of screens designed to alleviate errors and flag uncertain snow cover detections. The NDSI is derived from radiance data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite: DOI:10.5067/MODIS/MOD02HKM.061 and DOI:10.5067/MODIS/MOD021KM.061. Each data granule contains 5 minutes of swath data observed at a resolution of 500 m. The terms "Version 61" and "Collection 6.1" are used interchangeably in reference to this release of MODIS data.

MOD10C2

This global level-3 (L3) data set provides the maximum percentage of snow-covered land and persistent cloud-covered land observed over eight-days, within 0.05° (approx. 5 km) MODIS climate modeling grid (CMG) cells. Percentages are computed from snow cover observations in the 'MODIS/Terra Snow Cover 8-Day L3 Global 500m SIN Grid' data set (DOI:10.5067/MODIS/MOD10A2.061). The terms "Version 61" and "Collection 6.1" are used interchangeably in reference to this release of MODIS data.

MOD10A2

This global Level-3 (L3) data set provides the maximum snow cover extent observed over an eight-day period within 10° x 10° MODIS sinusoidal grid tiles. Tiles are generated by compositing 500 m observations from the 'MODIS/Terra Snow Cover Daily L3 Global 500m Grid' data set (DOI:10.5067/MODIS/MOD10A1.061). A bit flag index is used to track the eight-day snow/no-snow chronology for each 500 m cell. The terms "Version 61" and "Collection 6.1" are used interchangeably in reference to this release of MODIS data.

MOD10C1

This global Level-3 (L3) data set provides the percentage of snow-covered land and cloud-covered land observed daily, within 0.05° (approx. 5 km) MODIS Climate Modeling Grid (CMG) cells. Percentages are computed from snow cover observations in the 'MODIS/Terra Snow Cover Daily L3 Global 500m Grid' data set (DOI:10.5067/MODIS/MOD10A1.061). The terms "Version 61" and "Collection 6.1" are used interchangeably in reference to this release of MODIS data.

MOD10A1

This global Level-3 (L3) data set provides a daily composite of snow cover and albedo derived from the 'MODIS/Terra Snow Cover 5-Min L2 Swath 500m' data set (DOI:10.5067/MODIS/MOD10_L2.061). Each data granule is a 10°x10° tile projected to a 500 m sinusoidal grid. The terms "Version 61" and "Collection 6.1" are used interchangeably in reference to this release of MODIS data.

MOD10CM

This global Level-3 (L3) data set provides monthly mean snow cover extent within 0.05° (approx. 5 km) MODIS Climate Modeling Grid (CMG) cells. This data set is derived from snow cover observations in the 'MODIS/Terra Snow Cover Daily L3 Global 0.05Deg CMG’ data set (DOI:10.5067/MODIS/MOD10C1.061). The terms "Version 61" and "Collection 6.1" are used interchangeably in reference to this release of MODIS data.

MODGRNLD

This multilayer data set includes standard MODIS Collection 6.1 ice surface temperature (IST) and derived melt map, as well as MODIS Collection 6.0 albedo and water vapor for Greenland, at a spatial resolution of 0.78 km. These fields enable the relationship between IST and surface melt to be evaluated by researchers studying surface changes on the Greenland ice sheet. Water vapor is included to assist with evaluating the accuracy of the IST data and the model output. Also included is an ice mask and a basins mask for delineating drainage basins in Greenland. Surface temperature is a fundamental input for dynamical ice sheet models because it is a component of the ice sheet radiation budget and mass balance. Surface temperature also influences ice sheet processes, such as surface melt. This data set may be used as a resource for model-validation studies such as comparing MERRA-2 surface temperature with MODIS IST, and for comparing MODIS IST, albedo and water vapor with products from sensors on other satellites such as VIIRS and AIRS The temporal coverage for this data set spans 1 March 2000 through 31 December 2019, with the exception of the IST data, which has been extended through 31 Aug 2021.

MSLSP30NA

MSLSP V1 data was decommissioned on December 14, 2021. Users are encouraged to use the improved MSLSP V1.1 data product. NASA’s Multi-Source Land Imaging (MuSLI) Land Surface Phenology (LSP) Yearly North America 30 meter (m) Version 1 product (MSLSP) provides a Land Surface Phenology product for North America derived from Harmonized Landsat Sentinel-2 (HLS) data. Data from the combined Landsat 8 Operational Land Imager (OLI) and Sentinel 2A and 2B Multispectral Instrument (MSI) provide the user community with dates of phenophase transitions, including the timing of greenup, maturity, senescence, and dormancy. MSLSP30NA is aligned with the Military Grid Reference System (MGRS) at 30 m spatial resolution. These datasets are useful for a wide range of applications, including ecosystem and agro-ecosystem modeling, monitoring the response of terrestrial ecosystems to climate variability and extreme events, crop-type discrimination, land cover, land use, and land cover change mapping. Provided in the MSLSP product are variables for percent greenness, onset greenness dates, Enhanced Vegetative Index (EVI2) amplitude, maximum EVI2, and data quality information for up to two phenological cycles per year. For areas where the data values are missing due to cloud cover or other reasons, the data gaps are filled with good quality values from the year directly preceding or following the product year. A low-resolution browse image representing maximum EVI is also available for each MSLSP30NA granule. Known Issues: Data are sparse in 2016 and early 2017, as Sentinel-2B was not yet launched, and Sentinel-2A was not fully operational, leading to poorer quality retrievals of phenology in 2016 and 2017. However, poor quality pixels can be masked with Quality Assurance (QA) flags. * Disturbance has not been explicitly accounted for or mapped, which can lead to premature detections of senescence and dormancy when sharp spectral changes occur. * Pixels with more than two growth cycles per year (e.g., alfalfa fields) may not be accurately characterized, especially if they occur in rapid succession.

MSLSP30NA

The Multi-Source Land Surface Phenology (LSP) Yearly North America 30 meter (m) Version 1.1 product (MSLSP) provides a Land Surface Phenology product for North America derived from Harmonized Landsat Sentinel-2 (HLS) data. Data from the combined Landsat 8 Operational Land Imager (OLI) and Sentinel-2A and 2B Multispectral Instrument (MSI) provides the user community with dates of phenophase transitions, including the timing of greenup, maturity, senescence, and dormancy at 30m spatial resolution. These data sets are useful for a wide range of applications, including ecosystem and agro-ecosystem modeling, monitoring the response of terrestrial ecosystems to climate variability and extreme events, crop-type discrimination, and land cover, land use, and land cover change mapping. Provided in the MSLSP product are layers for percent greenness, onset greenness dates, Enhanced Vegetative Index (EVI2) amplitude, and maximum EVI2, and data quality information for up to two phenological cycles per year. For areas where the data values are missing due to cloud cover or other reasons, the data gaps are filled with good quality values from the year directly preceding or following the product year. A low resolution browse image representing maximum EVI is also available for each MSLSP30NA granule. Known Issues: Data are sparse in 2016 and early 2017, as Sentinel-2B was not yet launched, and Sentinel-2A was not fully operational, leading to poorer quality retrievals of phenology in 2016 and 2017. However, poor quality pixels can be masked with Quality Assurance (QA) flags. * Disturbance has not been explicitly accounted for or mapped, which can lead to premature detections of senescence and dormancy when sharp spectral changes occur. * Pixels with more than two growth cycles per year (e.g., alfalfa fields) may not be accurately characterized, especially if they occur in rapid succession.

MITgcm_LLC4320_Pre-SWOT_JPL_L4_NewCaledonia_v1.0

This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the New Caledonia region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.

MITgcm_LLC4320_Pre-SWOT_JPL_L4_ROAM_MIZ_v1.0

This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Northeast Weddell Sea region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.

MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWAustralia_v1.0

This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Northwest Australian Shelf region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.

MITgcm_LLC4320_Pre-SWOT_JPL_L4_NWPacific_v1.0

This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Northwest Pacific Ocean region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.

EN1_MDSI_MER_RR__1P

The Medium Resolution Imaging Spectrometer (MERIS) is one of 10 sensors deployed in March of 2002 on board the polar-orbiting Envisat-1 environmental research satellite by the European Space Agency (ESA). The MERIS instrument is a moderate-resolution wide field-of-view push-broom imaging spectroradiometer capable of sensing in the 390 nm to 1040 nm spectral range. Being a programmable instrument, it had the unique capability of selectively adjusting the width and location of its 15 bands through ground command. The instrument has a 68.5-degree field of view and a swath width of 1150 meters, providing a global coverage every 3 days at 300 m resolution. Communication with the Envisat-1 satellite was lost suddenly on the 8th of April, 2012, just weeks after celebrating its 10th year in orbit. All attempts to re-establish contact were unsuccessful, and the end of the mission was declared on May 9th, 2012. The 4th reprocessing cycle, in 2020, has produced both the full-resolution and reduced-resolution L1 and L2 MERIS products. EN1_MDSI_MER_RR__1P is the short-name for the MERIS Level-1 reduced resolution, geolocated and calibrated top-of-atmosphere (TOA) radiance product. This product contains the TOA upwelling spectral radiance measurements at reduced resolution. The in-band reference irradiances for the 15 MERIS bands are computed by averaging the in-band solar irradiance for each pixel. Each pixel’s in-band solar irradiance is computed by integrating the reference solar spectrum with the band-pass of each pixel. The Level-1 product contains 22 data files: 15 files contain radiances for each band (one band per file) along with associated error estimates, and 7 annotation data files. It also includes a Manifest file that provides metadata information describing the product.

MITgcm_LLC4320_Pre-SWOT_JPL_L4_RockallTrough_v1.0

This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Rockall Trough region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.

WAF_DEALIASED_SASS_L2

Contains Seasat-A Scatterometer (SASS) wind vector measurements for the entire Seasat mission, from July 1978 until October 1978. The data are global and presented chronologically in by swath. Each record contains data binned in 100 km cells. No wind vectors are computed for the cells along the left and right edges of the swath. Wind direction ambiguities are resolved using a global weather prediction model. This complete dataset is the result of the reprocessing efforts on behalf of Frank Wentz, Robert Atlas, and Michael Freilich.

CHELTON_SEASAT_SASS_L3

Contains monthly averaged ocean surface wind stress derived from Seasat-A Scatterometer (SASS) wind retrievals, from July 1978 until October 1978, gridded on a 2.5-degree by 2.5 degree global grid. The vector average wind stress is stored in units of dynes per centimeter squared (dyn/cm^2). Data is provided in formatted ASCII text. The primary data set used to construct these wind stress fields consists of 96 days of SASS vector winds supplied by Robert Atlas at GSFC. The directional ambiguities in the raw SASS data had been objectively removed using the GSFC Laboratory for Atmospheric Sciences atmospheric general circulation model.

SPL4SMAU

SMAP Level-4 (L4) surface and root zone soil moisture (L4_SM) data are provided in three products. The SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/T5RUATAQREF8) product is a series of 3-hourly time average geophysical land surface fields that are output by the L4_SM algorithm. It is likely of primary interest to most users. The SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/02LGW4DGJYRX) product provides diagnostics from the land surface analysis updates. It consists of a series of 3-hourly instantaneous (or snapshot) files that contain the assimilated SMAP observations, the corresponding land model predictions and analysis estimates, and additional data assimilation diagnostics. Lastly, the SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/PXQIBL2ALDZD) product provides static (time-invariant) land surface model constants that will be needed by some users for further interpretation of the geophysical land surface fields. This product consists of only one granule (file) per L4_SM data product version (as defined by a distinct Science Version ID). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.

SPL4CMDL

The Level-4 (L4) carbon product (SPL4CMDL) provides global gridded daily estimates of net ecosystem carbon (CO2) exchange derived using a satellite data based terrestrial carbon flux model informed by the following: Soil Moisture Active Passive (SMAP) L-band microwave observations, land cover and vegetation inputs from the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and the Goddard Earth Observing System Model, Version 5 (GEOS-5) land model assimilation system. Parameters are computed using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.

NSIDC-0779

This data product contains global daily 1 km resolution surface soil moisture derived from the SMAP L-band radiometer. Specifically, MODIS land surface temperature data is used with the SMAP Enhanced L2radiometer Half-Orbit 9 km EASE-Grid Soil Moisture product in a downscaling algorithm to estimate soil moisture. The data set is validated by in situ soil moisture measurements from dense soil moisture networks representing different global land cover types.

NSIDC-0797

This data set is derived by downscaling Soil Moisture Active Passive (SMAP) enhanced Level-3 9 km brightness temperatures (TB) using Cyclone Global Navigation Satellite System (CYGNSS) reflectivity data and employing a slightly modified version of the SMAP baseline active-passive TB algorithm. The SMAP Single Channel Algorithm – Vertical polarization (SCA-V) is then used to derive soil moisture from SMAP/CYGNSS Tb data. The main parameter of this data set is surface soil moisture presented on the Global EASE-Grid 2.0 projection, with each data point representing the top 5 cm of the soil column. For SMAP-derived data, see SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture, Version 5; for CYGNSS-derived data, see CYGNSS Level 1, Version 2.1.

SV19MA_DEM

These digital elevation model (DEM) data consist of ground surface elevations derived from source lidar measurements collected in April and August 2022 in the vicinity of Petersham, MA during the SMAPVEX19-22 campaign. This location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The two acquisition periods occurred to characterize differences during "leaf-off” and "leaf-on" conditions.

SV19MA_DSM

These digital surface model (DSM) data consist of surface elevations derived from source lidar measurements collected in August 2022 in the vicinity of Petersham, MA during the SMAPVEX19-22 campaign. The location was selected due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The August collection period was selected to characterize ‘leaf-on’ conditions. DSM data represents the highest elevation of features on the Earth’s surface, which may include bare-earth, vegetation, and human-made objects.

SV19MB_DEM

These digital elevation model (DEM) data consist of ground surface elevations derived from source lidar measurements collected in April and August 2022 in the vicinity of Millbrook, NY during the SMAPVEX19-22 campaign. This location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The two acquisition periods occurred to characterize differences during "leaf-off" and "leaf-on" conditions.

SV19MB_DSM

These digital surface model (DSM) data consist of surface elevations derived from source lidar measurements collected in August 2022 in the vicinity of Millbrook, NY during the SMAPVEX19-22 campaign. The location was selected due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The August collection period was selected to characterize ‘leaf-on’ conditions. DSM data represents the highest elevation of features on the Earth’s surface, which may include bare-earth, vegetation, and human-made objects.

SNEX_Met_Raw

This dataset contains raw meteorological data collected as part of the ongoing the NASA SnowEx mission, from five meteorological stations installed between 2016-2017 in Grand Mesa, Colorado, to provide supporting data for SnowEx field campaigns and forcing data for modeling

SNEX_Met

This dataset contains meteorological data collected as part of the ongoing the NASA SnowEx mission, from five meteorological stations installed between 2016-2017 in Grand Mesa, Colorado, to provide supporting data for SnowEx field campaigns and forcing data for modeling. Each station collects a suite of meteorological data at a fixed geographic point, from varying heights above and below the surface elevation. Measured data include: air temperature, relative humidity, long and shortwave solar radiation, barometric pressure, soil moisture and temperature, and derived snow depth. The temporal data coverage varies between each station, but spans October 2016 to August 2022. The dataset(s) contain air temperature and relative humidity (10ft and 20ft levels), 4-component radiation (shortwave, longwave), barometric pressure, soil-moisture and temperature (three depths), and a snow-depth product. Data coverage varies for each met station, but spans the time period of October 2016 – August, 2022. The data frequency is hourly and times are in UTC. The data is monotonic (no duplicate or mis-orderd timestamps) and steps have been taken to remove erroneous data. Periods of missing data are filled with NaN values. The scripts used to process raw data into the current format are available on the github page (https://github.com/wrudisill/GrandMesaMetData/blob/main/process_data_initial.py).

SNEX17_UWScat

This data set consists of ground-based scatterometer data acquired during the SnowEx 2017 campaign at Grand Mesa, Colorado, USA, a snow-covered, forested study site about 40 miles east of the city of Grand Junction, CO. The data comprise operational parameters used during data acquisition, Mueller matrices for each acquisition, and the nearfield-corrected normalized radar cross section (NRCS) in VV, VH, HV, and HH polarizations. Range profile data are also provided for each scan which report the raw power returned as a function of range from the antenna.

SNEX17_SSD

This data set contains 15-min snow depth observations for two study sites on Grand Mesa, CO, USA, acquired as part of NASA's 2017 SnowEx campaign. The data were recorded using two arrays of Judd Communications Ultrasonic Depth Sensors, configured as a TLS K footprint on the west side of the mesa and a TLS N footprint in the east. The sensors were positioned to represent three primary vegetation conditions: open-canopy; canopy-edge; and closed-canopy. A total of 10 and 7 sensors recorded usable data at the west and east sites, respectively, from the beginning of the snow season in November 2016 through the end in June 2017. These data can be used for a variety of purposes, including: model forcing, calibration, and validation; evaluation of airborne and satellite remote sensing data; to analyze how vegetation affects snow accumulation and ablation.

SMAP_L1_L3_ANC_GEOS

This ancillary SMAP product contains three dynamic GMAO GEOS-5 modeled data sets. Each data set contains surface and atmospheric parameters pertinent to SMAP provided in 1) hourly, 2) 3-hour, and 3) averaged over 3-hour intervals.

SMAP_L1_L3_ANC_NOAA

This ancillary SMAP product contains six dynamic data sets originally produced by NOAA or NOAA-affiliated organizations. 1) NCEP Geophysical Forecast System modeled data provided in 6-hour time steps 2) Daily Reynolds Sea Surface Temperature data 3) Snow Cover data from NOAA Interactive Multisensor Snow and Ice Mapping System 4) NOAA Solar Radio Flux 5) GPS-derived total electron content used to compute the Faraday rotation correction for the SMAP radar 6) Instantaneous wave height measures

SMAP_L1_L3_ANC_STATIC

This ancillary SMAP product contains more than 50 data sets. These data sets contain the inputs necessary to create SMAP products from raw instrument counts, such as permanent masks (land, water, forest, urban, mountain, etc.), the grid cell average elevation and slope derived from a Digital Elevation Model (DEM), permanent open water fraction, soils information (primarily sand and clay fraction), vegetation parameters, and surface roughness parameters.

SMAP_L4_C_ANC_BPLUT

This ancillary SMAP product contains biophysical characteristics (biome parameters) used to estimate carbon fluxes and soil organic carbon in the SMAP L4 Carbon algorithm. Biophysical characteristics were established from previous studies and the parameters defined for the MODIS MOD17 operation GPP algorithm. This data set was refined through regional and global comparisons and calibration of prototype SMAP L4 Carbon calculations.

SMAP_L4_C_ANC_FPAR_CLIM

This ancillary SMAP product contains a static climatology data set. The climatology data is derived from MODIS Fractional Photosynthetically Active Radiation (FPAR) models and represents a global 8-day average.

SMAP_L4_C_ANC_MET_LOG

This ancillary SMAP product contains daily meteorological model log files, including model outputs. The meteorological model is derived from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) data set and used as an input in the SMAP L4 Carbon algorithm.

SMAP_L4_C_ANC_MET_RIP

This ancillary SMAP product contains meteorological model configurations, including model inputs. The meteorological model is derived from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) data set and used as an input in the SMAP L4 Carbon algorithm.

SMAP_L4_C_ANC_MDL_LOG

This ancillary SMAP product contains SMAP L4 Carbon model log files, including model outputs.

SMAP_L4_C_ANC_MDL_RIP

This ancillary SMAP product contains SMAP L4 Carbon model configurations, including model inputs.

SMAP_L4_C_ANC_MOD_LOG

This ancillary SMAP product contains MODIS Fractional Photosynthetically Active Radiation (FPAR) model log files, including model outputs.

SMAP_L4_C_ANC_MOD_RIP

This ancillary SMAP product contains MODIS Fractional Photosynthetically Active Radiation (FPAR) model configurations, including model inputs.

SMAP_L4_C_ANC_MET

This ancillary SMAP product contains dynamic surface meteorological forcing data. The meteorological model is derived from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) data set and used as an input in the SMAP L4 Carbon algorithm. The forcing data is processed from hourly GEOS-5 files into daily values. Daily files are processed every eight days.

SMAP_L4_SM_ANC_CAT_TILE

This ancillary SMAP product contains tile information for the NASA Land Data Assimilation System (LDAS) Catchment model, including center-of-mass latitude/longitude, minimum/maximum latitude/longitude, and the land area fraction of tiles.

SMAP_L4_SM_ANC_PARAM

This ancillary SMAP product contains three dynamic Land Data Assimilation Systems (LDAS) data sets. These data sets include Brightness Temperature (TB) scaling parameters; catchment model parameters such as topographic statistics, soil texture, and soil hydraulic parameters; and LDAS L-band microwave radiative transfer model parameters.

SMAP_L4_SM_ANC_LOG

This ancillary SMAP product includes Land Data Assimilation Systems (LDAS) Catchment model log files, including model outputs.

SMAP_L4_SM_ANC_RST

This ancillary SMAP product contains static restart files for the Land Data Assimilation Systems (LDAS) Catchment model. This product includes prognostic variables for both the catchment model and perturbations model.

SMAP_L4_SM_ANC_RIP

This ancillary SMAP product contains Land Data Assimilation Systems (LDAS) model configurations, including model inputs.

MITgcm_LLC4320_Pre-SWOT_JPL_L4_ACC_SMST_v1.0

This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Southern Ocean region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.

SWOT_SIMULATED_L2_KARIN_SSH_ECCO_LLC4320_CALVAL_V1

This dataset provides simulated sea surface height (SSH) in a format similar to the future SWOT Level 2 (L2) SSH data from KaRIn. The simulated data were generated by the "ECCO LLC4320" global ocean simulation. ECCO, which means "Estimating the Circulation and Climate of the Ocean", is a data assimilation and model (and the international consortium of scientists who maintains it) based on the MIT general circulation model (MITgcm) that assimilates and constrains observational data from numerous sources to estimate the ocean state. The model operates on the Lat-Lon-Cap (LLC) grid with a nominal horizontal resolution of 1/48-degrees (when approximated over the entire model domain, corresponding to ~2-km cell size at the equator). SSH data produced by ECCO LLC4320 were rendered from the native output format into the format prescribed in the SWOT L2 SSH PDD to aid ongoing data product development and to benefit future users of data produced during operational phases of the SWOT mission.

SWOT_SIMULATED_L2_NADIR_SSH_ECCO_LLC4320_CALVAL_V1

This dataset provides simulated sea surface height (SSH) in a format similar to the future SWOT Level 2 (L2) altimetry data stream from the Poseidon 3C nadir altimeter. The simulated data were generated by the "ECCO LLC4320" global ocean simulation. ECCO, which means "Estimating the Circulation and Climate of the Ocean", is a data assimilation and model (and the international consortium of scientists who maintains it) based on the MIT general circulation model (MITgcm) that assimilates and constrains observational data from numerous sources to estimate the ocean state. The model operates on the Lat-Lon-Cap (LLC) grid with a nominal horizontal resolution of 1/48-degrees (when approximated over the entire model domain, corresponding to ~2-km cell size at the equator). SSH data produced by ECCO LLC4320 were rendered from the native output format into the format prescribed in the SWOT L2 SSH PDD to aid ongoing data product development and to benefit future users of data produced during operational phases of the SWOT mission.

VJ130

This data set reports sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System's first satellite (JPSS-1). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique.

VJ130P1D

This data set reports sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique. VIIRS flies on board the Joint Polar Satellite System 1 (JPSS-1), also known as NOAA-20.

VJ129

This data set reports the location of sea ice cover derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System's first satellite (JPSS-1). Following the approach used by MODIS, sea ice is detected using the Normalized Difference Snow Index.

VJ129P1D

This data set reports sea ice cover/extent derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes sea ice cover using Normalized Difference Snow Index (NDSI). VIIRS flies on board the Joint Polar Satellite System 1 (JPSS-1), also known as NOAA-20.

VJ110C1

This global Level-3 data set provides the percentage of snow-covered land and cloud-covered land observed daily, within 0.05° (approx. 5 km at the equator) MODIS/VIIRS Climate Modeling Grid (CMG) cells. Percentages are computed from snow cover observations in the 'VIIRS/JPSS1 Snow Cover Daily L3 Global 375m SIN Grid' data set (DOI:10.5067/UAJGR7WVWDDI).

VJ230

This data set reports sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System's second satellite (JPSS-2). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique.

VJ229

This data set reports the location of sea ice cover derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System's second satellite (JPSS-2). Following the approach used by MODIS, sea ice is detected using the Normalized Difference Snow Index.

VNP30

This data set reports sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (NPP) satellite. Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique.

VNP30P1D

This data set estimates of sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique. VIIRS flies on board the Suomi National Polar-orbiting Partnership (NPP) satellite.

VNP29

This data set reports the location of sea ice cover derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (NPP) satellite. Following the approach used by MODIS, Sea Ice is detected using the Normalized Difference Snow Index.

VNP10C1

This global Level-3 data set provides the percentage of snow-covered land and cloud-covered land observed daily, within 0.05° (approx. 5 km) MODIS/VIIRS Climate Modeling Grid (CMG) cells. Percentages are computed from snow cover observations in the 'VIIRS/NPP Snow Cover Daily L3 Global 375m SIN Grid' data set (DOI:10.5067/45VDCKJBXWEE).

MITgcm_LLC4320_Pre-SWOT_JPL_L4_WestAtlantic_v1.0

This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the West Atlantic region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.

MITgcm_LLC4320_Pre-SWOT_JPL_L4_Yongala_v1.0

This dataset provides a regional multivariate oceanographic state estimate from a global ocean numerical simulation with a focus on the Yongala region. The global ocean simulation is based on the MIT general circulation model (MITgcm) with Lat-Lon-Cap grid (LLC) layout and 1/48-degree (2km at equator) nominal horizontal resolution. This simulation is often referred to as LLC4320 in the community and existing publications. The simulation has 90 vertical levels, with about 1-m vertical resolution at the surface and 30 m down to 500 m, for optimized resolution of the upper-ocean processes. The model has zero parameterized horizontal diffusivity. In the vertical direction, the K-Profile Parameterization (KPP) is used for boundary layer turbulent mixing. It is spun up progressively from the lower resolution MITgcm simulation from the Estimating the Circulation & Climate of the Ocean (ECCO), and forced by the 6-hourly ERA-Interim atmosphere reanalysis ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim ). A synthetic surface pressure field consisting of the 16 most dominant tidal constituents is used to dynamically mimic the tidal forcing. The dataset provides hourly oceanographic variables at native grid. Three-dimensional variables include temperature, salinity, and velocity. Two-dimensional variables include sea level anomaly, ocean mixed layer thickness, bottom pressure anomaly, net freshwater flux, net heat flux, shortwave radiative flux, net salt flux, and ocean surface stress.

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Data Access

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Update Frequency

Varies by dataset

License

Creative Commons BY 4.0

Documentation

https://podaac.jpl.nasa.gov/SWOT

Managed By

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Contact

https://earthdata.nasa.gov/contact

How to Cite

NASA modis-terra Project was accessed on DATE from https://registry.opendata.aws/nasa-modis-terra.