PO.DAAC provides several ways to discover and access physical oceanography data, from the PO.DAAC Web Portal to FTP access to front-end user interfaces (see http://podaac.jpl.nasa.gov). That same data can also be discovered and accessed through PO.DAAC Web Services, enabling efficient machine-to-machine communication and data transfers. Dataset Search service searches PO.DAAC's dataset catalog.

PO.DAAC provides several ways to discover and access physical oceanography data, from the PO.DAAC Web Portal to FTP access to front-end user interfaces (see http://podaac.jpl.nasa.gov). That same data can also be discovered and accessed through PO.DAAC Web Services, enabling efficient machine-to-machine communication and data transfers.

PO.DAAC provides several ways to discover and access physical oceanography data, from the PO.DAAC Web Portal to FTP access to front-end user interfaces (see http://podaac.jpl.nasa.gov). That same data can also be discovered and accessed through PO.DAAC Web Services, enabling efficient machine-to-machine communication and data transfers. Extract service subsets a granule in PO.DAAC catalog and produces either netcdf3 or hdf4 files.

The NASA-SSH Simple Gridded Sea Surface Height from Standardized Reference Missions Only Version 1 dataset produced by NASA provides 2-D maps of sea surface height, or sea level, anomaly once every 7 days. The grids are based on observations of sea surface height from the radar altimeter satellites in the reference mission orbits, including TOPEX/Poseidon, the Jason series, and Sentinel-6. The data begin in Oct 1992 and continue through the present. They are created using the NASA-SSH Along-Track Sea Surface Height from Standardized Reference Missions Version 1 dataset. The grids consist of 10-days worth of observations, which covers approximately 1 complete repeat cycle of observations from the reference missions. The grids are produced on a 0.5-degree latitude and longitude grid, by taking a simple gaussian weighted spatial average with a width of 100 km. The grids are produced every 7 days to allow for easy interpolation in time. However, since they are created using 10-days of data, there is some overlap of information between adjacent time steps. The grids are also created using the basin flags to avoid mixing data from distinct ocean basins (for example, to avoid mixing observations from the Caribbean Sea with observations from the Pacific across the Isthmus of Panama). Connected basins are allowed to share data, however. This is accomplished by using a table of connections between basins. The basin connection table is available (https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/nasa-ssh/basin_connection_table.txt). The basin definitions can be downloaded as a shape file from https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/nasa-ssh/basin_polygon_files.tar.gz, or as a kml file https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/nasa-ssh/NASA-SSH_Basins.kmz. A new grid will be released approximately once per week, with a latency of a few weeks.

This dataset contains absolute dynamic topography (similar to sea level but with respect to the geoid) binned and averaged monthly on 1 degree grids. The coverage is from October 1992 to December 2010. These data were provided by AVISO (French space agency data provider) to support the CMIP5 (Coupled Model Intercomparison Project Phase 5) under the World Climate Research Program (WCRP) and was first made available via the JPL Earth System Grid. The dynamic topography are derived from sea surface height measured by several satellites including Envisat, TOPEX/Poseidon, Jason-1 and OSTM/Jason-2, and referenced to the geoid. Along with this dataset, two additional ancillary data files are included in the same directory which contain the number of observations and standard error co-located on the same 1 degree grids.

This dataset contains the Version 2.1 CYGNSS Level 3 Science Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 6 day latency. This version supersedes Version 2.0. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 2.1 release represents the second science-quality release. Here is a summary of improvements that reflect the quality of the Version 2.1 data release: 1) first time availability of wind speeds using the Geophysical Model Function (GMF) calibrated for Young Seas with Limited Fetch (YSLF) conditions; 2) inherits all other improvements made to the version 2.1 Level 2 data intended to improve the quality of the wind speed retrievals and uncertainty estimates. For a full list of improvements to the version 2.1 Level 2 data, please refer to the following dataset information page: https://podaac.jpl.nasa.gov/dataset/CYGNSS_L2_V2.1

The NASA-SSH Along-Track Sea Surface Height from Standardized Reference Missions Version 1 dataset produced by NASA provide observations of sea surface height, or sea level, anomaly measured using radar altimeter satellites in the reference mission orbit. These include TOPEX/Poseidon, the Jason series, and Sentinel-6. The data begin in Oct 1992, with data from TOPEX/Poseidon, and continues to the present. In this data set all missions have been referenced to a common baseline, additional quality control has been performed, and errors with wavelengths around one orbital cycle have been reduced. The data consist of along-track observations of sea surface height, collected approximately once per second (1 Hz), and are parsed into files containing one day’s worth of data per file. A flag variable is included to allow users to easily select only valid observations, and a variable containing sea surface height with the flag applied and a small amount along track smoothing (~20 km), is suggested for most users. Additionally, a “basin” flag variable is provided, along with a table defining it. This allows users to easily select all observations from a specific body of water. The basin flag assigns a number to each point corresponding to a specific ocean basin or lake. A table is included with a text description of each basin number. A text version of that table is available (https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/nasa-ssh/basin_name_table.txt). The basin definitions can be downloaded as a shape file from https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/nasa-ssh/basin_polygon_files.tar.gz, or as a kml file https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/nasa-ssh/NASA-SSH_Basins.kmz. New data will be released approximately once per week, with a latency of a few weeks.

This is the PI-produced SMAP sea water salinity, level 2 v1.0 orbital/swath product from the NASA Soil Moisture Active Passive (SMAP) observatory. It is based on the Parameterized Rain Impact Model (PRIM) developed at the University of Central Florida (UCF) Central Florida Remote Sensing Lab (CFRSL), Orlando, FL; University of Washington (UW) Applied Physics Lab (APL), Seattle, WA.

The PRIM product range extended from March 31, 2015 to September 30, 2021. It includes data for a range of parameters: derived SMAP sea water salinity at surface, 1m depth and 5m depth, and probability of salinity stratification (PSS), rainfall rate and wind speed data. Each data file covers one 98-minute orbit (15 files per day), and corresponds to a JPL SMAP Level 2B CAP Sea Surface Salinity V5.0 file which corresponds to a single orbit on a given day.

The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board Instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Observations are global in extent and provided at 25km swath grid with an approximate spatial resolution of 60 km.

Campaign: Advanced Very High Resolution Radiometer Oceans Pathfinder Sea Surface Temperature Data Sets (AVHRR Pathfinder SST v5) The 4 km AVHRR Pathfinder Version 5 SST Project (Pathfinder V5) is a new reanalysis of the AVHRR data stream developed by the University of Miami's Rosenstiel School of Marine and Atmospheric Science (RSMAS) and the NOAA National Oceanographic Data Center (NODC). In partnership with NODC and RSMAS is NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC), which has years of experience serving and developing earlier versions of the Pathfinder dataset. Currently in the third year of a three-year demonstration effort, it is hoped that this system can be implemented as an ongoing effort as part of a broader SST climate data record system. Provenance: local copy of the 4 km AVHRR Pathfinder Version 5 SST Monthly Means Data Set (daytime measurements) downloaded via FTP from: ftp://podaac.jpl.nasa.gov/pub/sea_surface_temperature/avhrr/pathfinder/data_v5/monthly/day/04km/. Files are located under WELLE.ZMAW.DE:/scratch/local3/u290022/DATA/SATELLITE/AVHRR/pathfinder/data_v5/monthly/day/04km/. The 4 km AVHRR Pathfinder Version 5 SST Project (Pathfinder V5) is a new reanalysis of the AVHRR data stream developed by the University of Miami's Rosenstiel School of Marine and Atmospheric Science (RSMAS) and the NOAA National Oceanographic Data Center (NODC). In partnership with NODC and RSMAS is NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC). Methods: this reprocessing uses an improved version of the Pathfinder algorithm and processing steps to produce twice-daily global SST and related parameters back to 1981, at a resolution of approximately 4 km, the highest possible for a global AVHRR data set. Temporal averages for 5-day, 7-day, 8-day, Monthly, and Yearly periods are also produced. Current key improvements over the original 9 km Pathfinder SST data set include a more accurate, consistent land mask, higher spatial resolution, and inclusion of sea ice information. Additional improvements including better flagging of aerosol-contaminated retrievals and the provision of wind and aerosol ancillary data will be implemented in a future Version 6 reprocessing. Additionally the parameters in version 5.0 are contained in separate files which are in the HDF-SDS (scientific data set) format, unlike version 4.1 which was in HDF-RASTER. The data can be accessed via the NODC, see http://www.nodc.noaa.gov/SatelliteData/pathfinder4km for more information regarding user guide, tools, available data, quality, etc. The data can also be accessed via NASA's PO.DAAC website, see: http://podaac.jpl.nasa.gov/PRODUCTS/p216.html

A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the UK Met Office using optimal interpolation (OI) on a global 0.054 degree grid. The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) analysis uses satellite data from sensors that include the Advanced Very High Resolution Radiometer (AVHRR), the Advanced Along Track Scanning Radiometer (AATSR), the Spinning Enhanced Visible and Infrared Imager (SEVIRI), the Advanced Microwave Scanning Radiometer-EOS (AMSRE), the Tropical Rainfall Measuring Mission Microwave Imager (TMI), and in situ data from drifting and moored buoys. This analysis has a highly smoothed SST field and was specifically produced to support SST data assimilation into Numerical Weather Prediction (NWP) models.

OSCAR (Ocean Surface Current Analysis Real-time) contains near-surface ocean current estimates, derived using quasi-linear and steady flow momentum equations. The horizontal velocity is directly estimated from sea surface height, surface vector wind and sea surface temperature. These data were collected from the various satellites and in situ instruments. The model formulation combines geostrophic, Ekman and Stommel shear dynamics, and a complementary term from the surface buoyancy gradient. Data are on a 1/3 degree grid with a 5 day resolution. OSCAR is generated by Earth Space Research (ESR) https://www.esr.org/research/oscar/oscar-surface-currents/. This collection contains data in 5-day files. For yearly files, see https://doi.org/10.5067/OSCAR-03D1Y

This dataset contains 50m horizontal resolution gridded digital elevation models (DEMs) of Greenland Ice Sheet outlet glaciers collected during the NASA Oceans Melting Greenland mission. Between 2016 and 2019 the GLacier and Land Ice Surface Topography Interferometer airborne (GLISTIN-A) radar measured surface elevations around the periphery of the Greenland Ice Sheet using Ka-Band (8.4 mm wavelength) single-pass interferometry. Level 2 (L2) GLISTIN-A elevation data, available on the JPL UAVSAR website (uavsar.jpl.nasa.gov), were collected each year in 81 swaths of varying lengths and 10-12km widths and then mapped to 3m horizontal grids. This Level 3 (L3) dataset was created to facilitate analysis of the year-to-year glacier surface elevation changes. Improvements over the L2 dataset include: a consistent swath numbering scheme (1 to 81) corresponding to repeated flight lines; common regular equal-area grids for each swath; filtering and flagging of outliers; an ancillary geoid layer; and UTM map projections corresponding to swath location. The interested user may generate their own L3 DEMs at different horizontal resolutions and projections using the Python 3 resample_GLISTIN_DEMs package available which will be available from https://github.com/NASA/resample_GLISTIN_DEMs

This dataset contains sea surface temperature data obtained from daily 1-km horizontal resolution SST estimates acquired from the Jet Propulsion Laboratory\u2019s Multi-Scale High Resolution SST (JPL MUR SST) records via the Physical Oceanography Distributed Active Archive Center (PO.DAAC) at the NASA JPL, Pasadena, CA (https://podaac.jpl.nasa.gov).\r \r NOTE: Data from 2012 are given twice at each site; only the first set, along with the 2003-2011 data, was used in the original analysis in Baumann et al (2016). The 2012-2015 data were only available following revision in the peer review process. It became useful for making comparisons between the in-situ data and satellite data.\r \r These data were used in a coral study in: Baumann JH, Townsend JE, Courtney TA, Aichelman HE, Davies SW, Lima FP, et al. (2016) Temperature Regimes Impact Coral Assemblages along Environmental Gradients on Lagoonal Reefs in Belize. PLoS ONE 11(9): e0162098. https://doi.org/10.1371/journal.pone.0162098. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv acquisition_description=Conventional 1-km resolution satellite-derived SST measurements (infrared, IR) are contaminated by clouds, creating data-void areas. Microwave (MW) data sets can penetrate clouds to gain better temporal coverage, but with a much coarser spatial resolution (25 km) [36]. MUR combines these two datasets to present a more comprehensive and complete SST product. It employs multi-resolution variational analysis (MRBA) as an interpolation method to combine high resolution datasets with more conventional datasets, generating a product that contains no cloud contamination [36]. MUR reports estimates of foundation SST, or SST at the base of the diurnal thermocline (~5-10m depth). Comparison of in-situ temperature (recorded by HOBO\u00ae v2 data loggers), MUR, and other SST products revealed that MUR outperforms other products in estimating in- situ temperature, although it also underestimates the temperature corals experience at depth (S1 Fig). However, due to its temporal coverage and temporal resolution, high spatial resolution, lack of cloud contamination, and smaller method error compared to similar products such as Group for High Resolution SST (GHRSST), MUR was determined to be the ideal SST product for use in the current study. awards_0_award_nid=635862 awards_0_award_number=OCE-1459522 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1459522 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=Michael E. Sieracki awards_0_program_manager_nid=50446 cdm_data_type=Other comment=Sea surface temperature time-series Belize Mesoamerican Barrier Reef System (MBRS), 2003-2015 Daily 1-km horizontal resolution SST estimates from
the Jet Propulsion Laboratory's Multi-Scale High Resolution SST (JPL MUR SST) (https://podaac.jpl.nasa.gov) PI's: K. Castillo, J. Baumann version: 2018-04-16 Published in Baumann et al, PLoS ONE (2016) 11(9) DOI: 10.1371/journal.pone.0162098 NOTE: data from 2012 are given twice at each site; only the first set,
along with the 2003-2011 data, was used in the original analysis in Baumann et al (2016) Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.734406.1 Easternmost_Easting=-88.002 geospatial_lon_max=-88.002 geospatial_lon_min=-88.629 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/734406 institution=BCO-DMO instruments_0_acronym=AVHRR instruments_0_dataset_instrument_description=One of several instruments used by NASA to produce sea surface temperature data products. instruments_0_dataset_instrument_nid=734436 instruments_0_description="The AVHRR instrument consists of an array of small sensors that record (as digital numbers) the amount of visible and infrared radiation reflected and (or) emitted from the Earth's surface" (more information). instruments_0_instrument_external_identifier=https://vocab.nerc.ac.uk/collection/L05/current/122/ instruments_0_instrument_name=Advanced Very High Resolution Radiometer instruments_0_instrument_nid=455 metadata_source=https://www.bco-dmo.org/api/dataset/734406 param_mapping={'734406': {'long': 'flag - longitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/734406/parameters people_0_affiliation=University of North Carolina at Chapel Hill people_0_affiliation_acronym=UNC-Chapel Hill people_0_person_name=Karl D. Castillo people_0_person_nid=51711 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=University of North Carolina at Chapel Hill people_1_affiliation_acronym=UNC-Chapel Hill people_1_person_name=Justin Baumann people_1_person_nid=733684 people_1_role=Student people_1_role_type=related people_2_affiliation=University of North Carolina at Chapel Hill people_2_affiliation_acronym=UNC-Chapel Hill people_2_person_name=Justin Baumann people_2_person_nid=733684 people_2_role=Contact people_2_role_type=related people_3_affiliation=Woods Hole Oceanographic Institution people_3_affiliation_acronym=WHOI BCO-DMO people_3_person_name=Nancy Copley people_3_person_nid=50396 people_3_role=BCO-DMO Data Manager people_3_role_type=related project=Thermal History and Coral Growth projects_0_acronym=Thermal History and Coral Growth projects_0_description=Description from NSF award abstract: Rising global ocean surface temperatures have reduced coral growth rates, thereby negatively impacting the health of coral reef ecosystems worldwide. Recent studies on tropical reef building corals reveal that corals' growth in response to ocean warming may be influenced by their previous seawater temperature exposure - their thermal history. Although these recent findings highlight significant variability in coral growth in response to climate change, uncertainty remains as to the spatial scale at which corals' thermal history influences how they have responded to ocean warming and how they will likely respond to predicted future increases in ocean temperature. This study investigates the influence of thermal history on coral growth in response to recent and predicted seawater temperatures increases across four ecologically relevant spatial scales ranging from reef ecosystems, to reef communities, to reef populations, to an individual coral colony. By understanding how corals have responded in the past across a range of ecological scales, the Principal Investigator will be able to improve the ability to predict their susceptibility and resilience, which could then be applied to coral reef conservation in the face of climate change. This research project will broaden the participation of undergraduates from underrepresented groups and educate public radio listeners using minority voices and narratives. The scientist will leverage current and new partnerships to recruit and train minority undergraduates, thus allowing them to engage high school students near field sites in Florida, Belize, and Panama. Through peer advising, undergraduates will document this research on a digital news site for dissemination to the public. The voice of the undergraduates and scientist will ground the production of a public radio feature exploring the topic of acclimatization and resilience - a capacity for stress tolerance within coral reef ecosystems. This project will provide a postdoctoral researcher and several graduate students with opportunities for field and laboratory research training, teaching and mentoring, and professional development. The results will allow policy makers from Florida, the Mesoamerican Barrier Reef System countries, and several Central American countries to benefit from Caribbean-scale inferences that incorporate corals' physiological abilities, thereby improving coral reef management for the region. Coral reefs are at significant risk due to a variety of local and global scale anthropogenic stressors. Although various stressors contribute to the observed decline in coral reef health, recent studies highlight rising seawater temperatures due to increasing atmospheric carbon dioxide concentration as one of the most significant stressors influencing coral growth rates. However, there is increasing recognition of problems of scale since a coral's growth response to an environmental stressor may be conditional on the scale of description. This research will investigate the following research questions: (1) How has seawater temperature on reef ecosystems (Florida Keys Reef Tract, USA; Belize Barrier Reef System, Belize; and Bocas Del Toro Reef Complex, Panama), reef communities (inshore and offshore reefs), reef populations (individual reefs), and near reef colonies (individual colonies), varied in the past? (2) How has seawater temperature influenced rates of coral growth and how does the seawater temperature-coral growth relationship vary across these four ecological spatial scales? (3) Does the seawater temperature-coral growth relationship forecast rates of coral growth under predicted end-of-century ocean warming at the four ecological spatial scales? Long term sea surface temperature records and small-scale high-resolution in situ seawater temperature measurements will be compared with growth chronologies for the reef building corals Siderastrea siderea and Orbicella faveolata, two keystone species ubiquitously distributed throughout the Caribbean Sea. Nutrients and irradiance will be quantified via satellite-derived observations, in situ measurements, and established colorimetric protocols. Field and laboratory experiments will be combined to examine seawater temperature-coral growth relationships under recent and predicted end-of-century ocean warming at four ecologically relevant spatial scales. The findings of this study will help us bridge the temperature-coral growth response gap across

A global Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-B (MetOp-B) platform (launched 17 Sep 2012). The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real time from Metop/AVHRR. Global AVHRR level 1b data are acquired at Meteo-France/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved from the AVHRR infrared channels (3.7, 10.8 and 12.0 micrometer) using a multispectral algorithm. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, together with a radiatiave transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. This global L3C product is derived from full resolution AVHRR l1b data that are re-mapped onto a 0.05 degree grid twice daily. The product format is compliant with the GHRSST Data Specification (GDS) version 2.

The monthly land mass grids contain water mass anomalies given as equivalent water thickness derived from GRACE & GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. The Equivalent water thickness 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. Version 03 (v03) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13).Data grids are provided in ASCII/netCDF/GeoTIFF formats. For the RL06 version, all GRACE products in the ASCII format have adopted the YAML encoding header, which is in full compliance with the PO.DAAC metadata best practices.

1. Summary:

The upcoming Surface Water and Ocean Topography (SWOT) satellite mission, planned to launch in 2022, will vastly expand observations of river water surface elevation (WSE), width, and slope. In order to facilitate a wide range of new analyses with flexibility, the SWOT mission will provide a range of relevant data products. One product the SWOT mission will provide are river vector products stored in shapefile format for each SWOT overpass (JPL Internal Document, 2020b). The SWOT vector data products will be most broadly useful if they allow multitemporal analysis of river nodes and reaches covering the same river areas. Doing so requires defining SWOT reaches and nodes a priori, so that SWOT data can be assigned to them. The SWOt River Database (SWORD) combines multiple global river- and satellite-related datasets to define the nodes and reaches that will constitute SWOT river vector data products. SWORD provides high-resolution river nodes (200 m) and reaches (~10 km) in shapefile and netCDF formats with attached hydrologic variables (WSE, width, slope, etc.) as well as a consistent topological system for global rivers 30 m wide and greater.

This dataset is public for a manuscript under review in Water Resources Research (WRR).

2. Data Formats:

The SWORD database is provided in netCDF and shapefile formats. All files start with a two-digit continent identifier (“af” – Africa, “as” – Asia / Siberia, “eu” – Europe / Middle East, “na” – North America, “oc” – Oceania, “sa” – South America). File syntax denotes the regional information for each file and varies slightly between netCDF and shapefile formats.

NetCDF files are structured in 3 groups: centerlines, nodes, and reaches. The centerline group contains location information and associated reach and node ids along the original GRWL 30 m centerlines (Allen and Pavelsky, 2018). Node and reach groups contain hydrologic attributes at the ~200 m node and ~10 km reach locations (see description of attributes below). NetCDFs are distributed at continental scales with a filename convention as follows: [continent]_sword_v1.nc (i.e. na_sword_v1.nc).

SWORD shapefiles consist of four main files (.dbf, .prj, .shp, .shx). There are separate shapefiles for nodes and reaches, where nodes are represented as ~200 m spaced points and reaches are represented as polylines. All shapefiles are in geographic (latitude/longitude) projection, referenced to datum WGS84. Shapefiles are split into HydroBASINS (Lehner and Grill, 2013) Pfafstetter level 2 basins (hbXX) for each continent with a naming convention as follows: [continent]_sword_[nodes/reaches]_hb[XX]_v1.shp (i.e. na_sword_nodes_hb74_v1.shp; na_sword_reaches_hb74_v1.shp).

3. Attribute Description:

This list contains the primary attributes contained in the SWORD netCDFs and shapefiles.

• x: Longitude of the node or reach ranging from 180°E to 180°W (units: decimal degrees).
• y: Latitude of the node or reach ranging from 90°S to 90°N (units: decimal degrees).
• node_id: ID of each node. The format of the id is as follows: CBBBBBRRRRNNNT where C = Continent (the first number of the Pfafstetter basin code), B = Remaining Pfafstetter basin code up to level 6, R = Reach number (assigned sequentially within a level 6 basin starting at the downstream end working upstream), N = Node number (assigned sequentially within a reach starting at the downstream end working upstream), T = Type (1 – river, 2 – lake off river, 3 – lake on river, 4 – dam or waterfall, 5 – unreliable topology, 6 – ghost node).
• node_length (node files only): Node length measured along the GRWL centerline points (units: meters).
• reach_id: ID of each reach. The format of the id is as follows: CBBBBBRRRRT where C = Continent (the first number of the Pfafstetter basin code), B = Remaining Pfafstetter basin codes up to level 6, R = Reach number (assigned sequentially within a level 6 basin starting at the downstream end working upstream, T = Type (1 – river, 2 – lake off river, 3 – lake on river, 4 – dam or waterfall, 5 – unreliable topology, 6 – ghost reach).
• reach_length (reach files only): Reach length measured along the GRWL centerline points (units: meters).
• wse: Average water surface elevation (WSE) value for a node or reach. WSEs are extracted from the MERIT Hydro dataset (Yamazaki et al., 2019) and referenced to the EGM96 geoid (units: meters).
• wse_var: WSE variance along the GRWL centerline points used to calculate the average WSE for each node or reach (units: square meters).
• width: Average width for a node or reach (units: meters).
• width_var: Width variance along the GRWL centerline points used to calculate the average width for each node or reach (units: square meters).
• facc: Maximum flow accumulation value for a node or reach. Flow accumulation values are extracted from the MERIT Hydro dataset (Yamazaki et al., 2019) (units: square kilometers).
• n_chan_max: Maximum number of channels for each node or reach.
• n_chan_mod: Mode of the number of channels for each node or reach.
• obstr_type: Type of obstruction for each node or reach based on the Globale Obstruction Database (GROD, Whittemore et al., 2020) and HydroFALLS data (http://wp.geog.mcgill.ca/hydrolab/hydrofalls). Obstr_type values: 0 - No Dam, 1 - Dam, 2 - Channel Dam, 3 - Lock, 4 - Low Permeable Dam, 5 - Waterfall.
• grod_id: The unique GROD ID for each node or reach with obstr_type values 1-4.
• hfalls_id: The unique HydroFALLS ID for each node or reach with obstr_type value 5.
• dist_out: Distance from the river outlet for each node or reach (units: meters).
• type: Type identifier for a node or reach: 1 – river, 2 – lake off river, 3 – lake on river, 4 – dam or waterfall, 5 – unreliable topology, 6 – ghost reach/node.
• lakeflag (reach files only): GRWL water body identifier for each reach: 0 – river, 1 – lake/reservoir, 2 – tidally influenced river, 3 – canal.
• slope (reach files only): Reach average slope calculated along the GRWL centerline points. Slopes are calculated using a linear regression (units: meters/kilometer).
• n_nodes (reach files only): Number of nodes associated with each reach.
• n_rch_up (reach files only): Number of upstream reaches for each reach.
• n_rch_down (reach files only): Number of downstream reaches for each reach.
• rch_id_up (reach files only): Reach IDs of the upstream neighboring reaches.
• rch_id_dn (reach files only): Reach IDs of the downstream neighboring reaches.
• swot_obs (reach files only): The maximum number of SWOT passes to intersect each reach during the 21 day orbit cycle.
• swot_orbits (reach files only): A list of the SWOT orbit tracks that intersect each reach during the 21 day orbit cycle.

4. References:

Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. Science, 361(6402), 585-588.

JPL Internal Document (2020b). Surface Water and Ocean Topography Mission Level 2 KaRIn high rate river single pass vector product, JPL D-56413, Rev. A, https://podaac-tools.jpl.nasa.gov/drive/files/misc/web/misc/swot_mission_docs/pdd/D-56413_SWOT_Product_Description_L2_HR_RiverSP_20200825a.pdf

Lehner, B., Grill G. (2013): Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrological Processes, 27(15): 2171–2186. Data is available at www.hydrosheds.org.

Whittemore, A., Ross, M. R., Dolan, W., Langhorst, T., Yang, X., Pawar, S., Jorissen, M., Lawton, E., Januchowski-Hartley, S., & Pavelsky, T. (2020). A Participatory Science Approach to Expanding Instream Infrastructure Inventories. Earth's Future, 8(11), e2020EF001558.

Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G., & Pavelsky, T. (2019). MERIT Hydro: A high-resolution global hydrography map based on latest topography datasets. Water Resources Research. https://doi.org/10.1029/2019WR024873.

HydroFALLS: http://wp.geog.mcgill.ca/hydrolab/hydrofalls/

This dataset contains the Global Mean Sea Level (GMSL) trend generated from the Integrated Multi-Mission Ocean Altimeter Data for Climate Research Version 5.1. The GMSL trend is a 1-dimensional time series of globally averaged Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 that covers September 1992 to present with a lag of up to 4 months. The data are reported as variations relative to a 20-year TOPEX/Jason collinear mean. Bias adjustments and cross-calibrations were applied to ensure SSHA data are consistent across the missions; Glacial Isostatic Adjustment (GIA) was also applied. The data are available as a table in ASCII format. Changes between the version 4.2 and version 5.x releases are described in detail in the user handbook.

The IPRC/SOEST Aquarius OI-SSS v4 product is a level 4, near-global, 0.5 degree spatial resolution, 7-day, optimally interpolated salinity dataset based on version 4.0 of the AQUARIUS/SAC-D level 2 mission data. This is a PI led dataset produced at the International Pacific Research Center (IPRC) at the University of Hawaii (Manoa) School of Ocean and Earth Science and Technology. The optimal interpolation (OI) mapping procedure used to create this product corrects for systematic spatial biases in Aquarius SSS data with respect to Argo near-surface salinity observations and takes into account available statistical information about the signal and noise, specific to the Aquarius instrument. Bias fields are constructed by differencing the Argo from Aquarius derived SSS fields obtained separately using ascending and descending satellite observations for each of the three Aquarius beams, and by removal of small-scale noise and low-pass filtering along-track using a two-dimensional Hanning window procedures prior to application of the OI algorithm. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath. The Aquarius polar orbit is sun synchronous at 657 km with a 6 pm, ascending node, and has a 7-Day repeat cycle.