SMAP

Experimental Eddy Products

michael.soracco — Fri, 09 Aug 2024 18:34:42 +0000

Experimental Eddy Products

The sea surface height team in NOAA’s Laboratory for Satellite Altimetry produces two experimental mesoscale eddy products:

Multiparameter Eddy Significance Index (MESI)
MUltiparameter NRT System for Tracking Eddies Retroactively (MUNSTER)

michael.soracco Fri, 08/09/2024 - 14:34

The sea surface height team in NOAA’s Laboratory for Satellite Altimetry produces two experimental mesoscale eddy products:

Multiparameter Eddy Significance Index (MESI): daily 0.25° x 0.25° global fields of the Multiparameter Eddy Significance Index (MESI), which incorporates Level-3 sea level anomalies (SLA) from NOAA’s LSA, along with Geo-polar sea surface temperatures (SSTs) from NOAA CoastWatch, gap-filled ocean color chlorophyll-a (Chl-a) from NOAA CoastWatch, and sea surface salinity (SSS) from NASA’s Soil Moisture Active Passive (SMAP) mission.
MUltiparameter NRT System for Tracking Eddies Retroactively (MUNSTER): daily mesoscale eddy properties, contours, and trajectories from version 1.0 of the MUltiparameter NRT System for Tracking Eddies Retroactively (MUNSTER). The MUNSTER product suite is based on the NRT sea level anomaly (SLA) fields produced by NOAA’s LSA, which are also available through NOAA CoastWatch. This product includes the MESI data as a gridded variable. Additionally, the suite will include files for:
- Daily identification of mesoscale anticyclonic and cyclonic eddies, including properties and contours for each eddy. This file is organized by eddy.
- Daily tracking of eddy trajectories over a 7-day period, including properties and contours for each eddy included in the trajectory. This file is organized by trajectory.

Multiparameter Eddy Significance Index (MESI)

MESI serves as a first look indicator of the potential impact a given mesoscale eddy may have on upper ocean dynamics, with potential implications for nutrient cycling, mixed layer dynamics, and fisheries. Further information about the applications and functions of MESI can be found in Roman-Stork et al., (2023).

Algorithm

MESI combines longitudinally normalized values of SLA, eddy kinetic energy derived from satellite altimetry, SST, SSS, and ocean color chlorophyll-a (Equation 1 below). SST and Chl-a data were regridded down to 0.25° x 0.25° from their original, higher resolutions in order to match the gridding of the coarser datasets. The absolute values of SST, SSS, Chl-a, and the log₁₀ value of EKE were combined with the normalized value of SLA such that the polarity or sign of SLA is maintained. This format allows for all positive values of MESI to correspond to anticyclonic eddies, or positive SLA values, and all negative values of MESI to correspond to cyclonic eddies, or negative SLA values.

MESI = SLA_norm * abs(log10(EKE_norm)) * abs(SST_norm) * abs(SSS_norm) * abs(Chla_norm)

MESI values greater than +/- 1 are considered to be “high”, and correspond to a high likelihood of a mesoscale eddy having a strong impact on the upper ocean’s circulation and nutrient cycling. MESI values less than +/- 1 are considered to be “low”, and are considered to have a low likelihood of having a mesoscale eddy significantly impact the upper ocean’s circulation.

Data Used

The SLA and EKE used for MESI were taken from the near real time (NRT) 0.25° x 0.25° Level-3 global fields produced by NOAA’s LSA and available from NOAA CoastWatch (found here; Scharoo et al., 2013). EKE was calculated from the geostrophic currents included with the SLA product, and a logarithmic value of this calculated EKE was used in the calculation of MESI. SST values used in MESI were taken from the NOAA CoastWatch Geo-Polar night SST product (found here; Maturi et al., 2017), and this high resolution dataset was regridded onto the 0.25° x 0.25° grid from the SLA and EKE values. Ocean color Chl-a values were obtained from the science quality MSL12 DINEOF gap-filled analysis available from NOAA CoastWatch (found here; Liu et al., 2019). Like SST, the Chl-a fields used in their calculation were of higher resolution and were regridded to 0.25° x 0.25° for this analysis. The SSS values used in MESI calculations were taken from NASA’s SMAP SSS from NASA’s Jet Propulsion Lab (JPL) V5.0 product that used their Combined Active Passive (CAP) algorithm (original data obtained from PO.DAAC; Fore et al., 2016). SMAP SSS data were not regridded.

Contents of file MESI_v1_multi_global_daily_s20240501_e20240501.nc

Global information:
  Data source:         Satellite data
  Date:                2024/05/01 JD 122
  Time:                00:00:00 UTC
  Scene time:          day/night
  Projection type:     mapped
  Transform ident:     noaa.coastwatch.util.trans.GeographicProjection
  Map projection:      Geographic
  Map affine:          0 0.25 0.25 0 -179.88 -89.88
  Spheroid:            WGS 84
  Origin:              NOAA/NESDIS Center for Satellite Applications and Research (STAR)
  Format:              Java-NetCDF interface (netCDF-4 ucar.nc2.dataset.conv.CF1Convention)
  Reader ident:        noaa.coastwatch.io.CommonDataModelNCReader

Variable information:
  Variable    Type    Dimensions   Units                                Scale   Offset
  ssh         float   720x1440     m                                    -       -
  sst         float   720x1440     K                                    -       -
  sss         float   720x1440     psu                                  -       -
  chlora      float   720x1440     mg m-3                               -       -
  eke         float   720x1440     m2 s-2                               -       -
  mesi        float   720x1440     -                                    -       -
  time        int     1            seconds since 1970-01-01T00:00:00Z   -       -
  latitude    float   720          degrees_north                        -       -
  longitude   float   1440         degrees_east                         -       -

MUltiparameter NRT System for Tracking Eddies Retroactively (MUNSTER)

Algorithm

MUltiparameter NRT System for Tracking Eddies Retroactively (MUNSTER) Algorithm MUNSTER uses an algorithm adapted from Chaigneau et al., (2008, 2009) and Peglaisco et al., (2015). It employs a closed contour method applied to the daily gridded NRT SLA fields produced by NOAA’s LSA. The closed contour method locally identifies maxima and minima within the filtered SLA fields with a contour interval of 0.1 cm, and the eddy edge is defined as the outermost closed contour around the identified eddy center such that at least 4 grid points are included within the contour. From this analysis, eddy amplitude, radius, area, and location are identified, and then multiple additional properties are calculated. Observations from SST, ocean color Chlorophyll-a, SSS, and MESIv1 are also collocated with the identified eddy.

Eddy trajectories are calculated using SLA fields such that eddy contours at time n and time n+1 are found to overlap. When no overlap is found, the eddy is considered to have dissipated. If multiple successive contours overlap with a given contour, a cost function is employed that uses amplitude, radius, and EKE to calculate splitting and merging events. The cost function seeks to minimize the result, and the contour with the smaller result is chosen (Pegliasco et al., 2015).

Eddy properties, or characteristics, are calculated as mean values of tracked variables across an eddy on a given day. The location of the eddy center and eddy centroid are provided along with a variety of properties, including eddy amplitude, radius, area, EKE, the Okubo-Weiss parameter, divergence, mean geostrophic currents, SST, SSS, Chl-a, and the Multiparameter Eddy Significance Index (MESI). More information about MESI can be found in Roman-Stork et al., (2023) or on this product page (above).

Data Used

The SLA and EKE used for MUNSTER were taken from the near real time (NRT) 0.25° x 0.25° Level-3 global fields produced by NOAA’s LSA and available from NOAA CoastWatch (found here; Scharoo et al., 2013). EKE was calculated from the geostrophic currents included with the SLA product, and a logarithmic value of this calculated EKE was used in the calculation of MESI. SST values used in MUNSTER were taken from the NOAA CoastWatch Geo-Polar night SST product (found here; Maturi et al., 2017), and this high resolution dataset was regridded onto the 0.25° x 0.25° grid from the SLA and EKE values. Ocean color Chl-a values were obtained from the science quality MSL12 DINEOF gap-filled analysis available from NOAA CoastWatch (found here; Liu et al., 2019). Like SST, the Chl-a fields used in their calculation were of higher resolution and were regridded to 0.25° x 0.25° for this analysis. The SSS values used in MUNSTER calculations were taken from NASA’s SMAP SSS from NASA’s Jet Propulsion Lab (JPL) V5.0 product that used their Combined Active Passive (CAP) algorithm (original data obtained from PO.DAAC; Fore et al., 2016). SMAP SSS data were not regridded.

Products

Daily mesoscale anticyclonic and cyclonic eddy mean properties and eddy contours, organized by eddy
Daily mesoscale anticyclonic and cyclonic eddy trajectories over a 7-day period, organized by trajectory

MUNSTER is a threshold-free product, and thus does not exclude low amplitude, or short-lived eddies from its analysis. This product suite is designed to be user-friendly and not require the download of multiple data products, such that numerous quantities used in mesoscale eddy analysis are already contained within the MUNSTER product suite and tracked along with each eddy, including amplitude, radius, are, eddy kinetic energy (EKE), sea surface temperature (SST), and sea surface salinity (SSS), and ocean color chlorophyll-a.

Products distributed by NOAA CoastWatch are divided into MUNSTER I and MUNSTER II datasets where:

MUNSTER I: NetCDF datasets of daily gridded (2D) and 1D data. These datasets include daily MESI data, eddy locations, and eddy properties and are described within this webpage.
MUNSTER II: NetCDF datasets of multi-day eddy trajectories and inter-relationships. These include eddy locations and properties over the course of a 7-day period, indicating the path of each eddy.

Variables stored within the datasets may be single or multi-dimensional. Where appropriate, gridded datasets are defined with Climate-Forecast (CF) Metadata conventions. Gridded data are interoperable with the CoastWatch Utilities:

Contents of file MUNSTER_v1_eddyident_multi_global_daily_s20240530_e20240530.nc
Global information:
  Data source:         Satellite data
  Date:                2024/05/30 JD 151
  Time:                00:00:00 UTC
  Scene time:          day/night
  Projection type:     mapped
  Transform ident:     noaa.coastwatch.util.trans.GeographicProjection
  Map projection:      Geographic
  Map affine:          0 0.25 0.25 0 -179.88 -59.88
  Spheroid:            WGS 84
  Origin:              NOAA/NESDIS Center for Satellite Applications and Research (STAR)
  Format:              Java-NetCDF interface (netCDF-4 ucar.nc2.dataset.conv.CF1Convention)
  Reader ident:        noaa.coastwatch.io.CommonDataModelNCReader
Variable information:
  Variable       Type    Dimensions  Units                              Scale     Offset
  mesi           float   480x1440    -                                  -         -
  Uinside_anti   float   480x1440    m s-1                              -         -
  Vinside_anti   float   480x1440    m s-1                              -         -
  Label_anti     int     480x1440    -                                  -         -
  Uinside_cyclo  float   480x1440    m s-1                              -         -
  Vinside_cyclo  float   480x1440    m s-1                              -         -
  Label_cyclo    int     480x1440    -                                  -         -
  time           int     1           seconds since 1970-01-01T00:00:00Z -         -
  latitude       float   480         degrees_north                      -         -
  longitude      float   1440        degrees_east                       -         -

Additional variables exist within the dataset pertaining to identified eddies. These eddies are defined as cyclonic or anticyclonic. Eddy attributes are assigned to the 1-D dimension:

        num_anticyclones = 2416 ;
        num_cyclones = 2446 ;

Variables (only cyclonic shown here) include:

         float cyclonic_center_lon(num_cyclones) ;
                cyclonic_center_lon:long_name = "longitude of point inside eddy with lowest sla" ;
                cyclonic_center_lon:units = "degrees_east" ;
                cyclonic_center_lon:valid_min = -180. ;
                cyclonic_center_lon:valid_max = 180. ;
        float cyclonic_center_lat(num_cyclones) ;
                cyclonic_center_lat:long_name = "latitude of point inside eddy with lowest sla" ;
                cyclonic_center_lat:units = "degrees_north" ;
                cyclonic_center_lat:valid_min = -90. ;
                cyclonic_center_lat:valid_max = 90. ;
        float cyclonic_centroid_lon(num_cyclones) ;
                cyclonic_centroid_lon:long_name = "longitude of geometric center of eddy" ;
                cyclonic_centroid_lon:units = "degrees_east" ;
                cyclonic_centroid_lon:valid_min = -180. ;
                cyclonic_centroid_lon:valid_max = 180. ;
        float cyclonic_centroid_lat(num_cyclones) ;
                cyclonic_centroid_lat:long_name = "latitude of geometric center of eddy" ;
                cyclonic_centroid_lat:units = "degrees_north" ;
                cyclonic_centroid_lat:valid_min = -90. ;
                cyclonic_centroid_lat:valid_max = 90. ;
        float cyclonic_contour_lon(num_cyclones, max_contour_cyclones) ;
                cyclonic_contour_lon:long_name = "longitude of eddy contour points" ;
                cyclonic_contour_lon:units = "degrees_east" ;
                cyclonic_contour_lon:valid_min = -180. ;
                cyclonic_contour_lon:valid_max = 180. ;
        float cyclonic_contour_lat(num_cyclones, max_contour_cyclones) ;
                cyclonic_contour_lat:long_name = "latitude of eddy contour points" ;
                cyclonic_contour_lat:units = "degrees_north" ;
                cyclonic_contour_lat:valid_min = -90. ;
                cyclonic_contour_lat:valid_max = 90. ;
        float cyclonic_radius(num_cyclones) ;
                cyclonic_radius:long_name = "equivalent radius of cyclonic eddies" ;
                cyclonic_radius:units = "m" ;
        float cyclonic_area(num_cyclones) ;
                cyclonic_area:long_name = "area of cyclonic eddies" ;
                cyclonic_area:units = "m2" ;
        float cyclonic_amplitude(num_cyclones) ;
                cyclonic_amplitude:long_name = "amplitude of cyclonic eddies" ;
                cyclonic_amplitude:units = "m" ;
        float cyclonic_mean_eke(num_cyclones) ;
                cyclonic_mean_eke:long_name = "mean eddy kinetic energy" ;
                cyclonic_mean_eke:units = "m2 s-2" ;
        float cyclonic_mean_speed(num_cyclones) ;
                cyclonic_mean_speed:standard_name = "sea_water_speed" ;
                cyclonic_mean_speed:long_name = "mean eddy speed" ;
                cyclonic_mean_speed:units = "m s-1" ;
        float cyclonic_mean_vorticity(num_cyclones) ;
                cyclonic_mean_vorticity:standard_name = "ocean_relative_vorticity" ;
                cyclonic_mean_vorticity:long_name = "mean eddy vorticity" ;
                cyclonic_mean_vorticity:units = "s-1" ;
        float cyclonic_center_vorticity(num_cyclones) ;
                cyclonic_center_vorticity:standard_name = "ocean_relative_vorticity" ;
                cyclonic_center_vorticity:long_name = "vorticity at eddy center" ;
                cyclonic_center_vorticity:units = "s-1" ;
        float cyclonic_normalized_center_vorticity(num_cyclones) ;
                cyclonic_normalized_center_vorticity:standard_name = "ocean_relative_vorticity" ;
                cyclonic_normalized_center_vorticity:long_name = "normalized vorticity at eddy center" ;
                cyclonic_normalized_center_vorticity:units = "s-1" ;
        float cyclonic_mean_strain_rate(num_cyclones) ;
                cyclonic_mean_strain_rate:long_name = "mean eddy straining deformation rate" ;
                cyclonic_mean_strain_rate:units = "s-1" ;
        float cyclonic_mean_shear_rate(num_cyclones) ;
                cyclonic_mean_shear_rate:long_name = "mean eddy shearing deformation rate" ;
                cyclonic_mean_shear_rate:units = "s-1" ;
        float cyclonic_mean_ow(num_cyclones) ;
                cyclonic_mean_ow:long_name = "mean eddy Okubo-Weiss parameter" ;
                cyclonic_mean_ow:units = "s-1" ;
        float cyclonic_mean_sst(num_cyclones) ;
                cyclonic_mean_sst:standard_name = "sea_surface_temperature" ;
                cyclonic_mean_sst:long_name = "mean eddy sea surface temperature" ;
                cyclonic_mean_sst:units = "K" ;
        float cyclonic_mean_sss(num_cyclones) ;
                cyclonic_mean_sss:standard_name = "sea_surface_salinity" ;
                cyclonic_mean_sss:long_name = "mean eddy sea surface salinity" ;
                cyclonic_mean_sss:units = "psu" ;
        float cyclonic_mean_chla(num_cyclones) ;
                cyclonic_mean_chla:standard_name = "mass_concentration_of_chlorophyll_a_in_sea_water" ;
                cyclonic_mean_chla:long_name = "mean eddy chlorophyll-a concentration" ;
                cyclonic_mean_chla:units = "mg m-3" ;
        float cyclonic_mean_mesi(num_cyclones) ;
                cyclonic_mean_mesi:long_name = "mean mesoscale eddy significance index" ;

Temporal Start Date

May 5, 2020

Temporal Coverage

Varied

Product Families

Ocean Currents

Sea Surface Height

Sea Surface Salinity

Sea Surface Temperature

Multiparameter Eddy Significance Index (MESI)

Fore, A.G., Yueh, S.H., Tang, W., Stiles, B.W., Hayashi, A.K., 2016. Combined Active/Passive Retrievals of Ocean Vector Wind and Sea Surface Salinity With SMAP. IEEE Transactions on Geoscience and Remote Sensing 54, 7396–7404. https://doi.org/10.1109/TGRS.2016.2601486

Liu, X., Wang, M., 2019. Filling the gaps of missing data in the merged VIIRS SNPP/NOAA-20 ocean color product using the DINEOF method. Remote Sensing 11. https://doi.org/10.3390/rs11020178

Maturi, E., Harris, A., Mittaz, J., Sapper, J., Wick, G., Zhu, X., Dash, P., Koner, P., 2017. A new high-resolution sea surface temperature blended analysis. Bulletin of the American Meteorological Society 98, 1015–1026. https://doi.org/10.1175/BAMS-D-15-00002.1

Roman‐Stork, H. L., Byrne, D. A., & Leuliette, E. W. (2023). MESI: a multiparameter eddy significance index. Earth and Space Science, 10(2), https://doi.org/10.1029/2022EA002583

Scharroo, R., Leuliette, E., Lillibridge, J., Byrne, D., Naeije, M., Mitchum, G., States, U., States, U., States, U., States, U., 2013. RADS : CONSISTENT MULTI-MISSION PRODUCTS 5–8.

MUltiparameter NRT System for Tracking Eddies Retroactively (MUNSTER)

Chaigneau, A., Eldin, G., Dewitte, B., 2009. Eddy activity in the four major upwelling systems from satellite altimetry (1992-2007). Progress in Oceanography 83, 117–123. https://doi.org/10.1016/j.pocean.2009.07.012

Chaigneau, A., Gizolme, A., Grados, C., 2008. Mesoscale eddies off Peru in altimeter records: Identification algorithms and eddy spatio-temporal patterns. Progress in Oceanography 79, 106–119. https://doi.org/10.1016/j.pocean.2008.10.013

Liu, X., Wang, M., 2019. Filling the gaps of missing data in the merged VIIRS SNPP/NOAA-20 ocean color product using the DINEOF method. Remote Sensing 11. https://doi.org/10.3390/rs11020178

Pegliasco, C., Chaigneau, A., Morrow, R., 2015. Main eddy vertical structures observed in the four major Eastern Boundary Upwelling Systems. Journal of Geophysical Research: Oceans 120, 6008–6033. https://doi.org/10.1002/2015JC010950

Measurements

Geostrophic Currents

Processing Levels

Level 3

Level 4

Spatial Coverage

Platforms

Instruments

Data Providers

ESA

EUMETSAT

NASA

JPL

NOAA

NESDIS

STAR

CoastWatch

LSA

SST Team

Data Tool Links

Multiparameter Eddy Significance Index (MESI)

https://coastwatch.noaa.gov/cw_html/cwViewer.html?lat=27.00&lon=-80.80&z=3&daysback=7&layer0=basemapWI&layer1=MESIb

MUltiparameter NRT System for Tracking Eddies Retroactively (MUNSTER)

Anticyclonic: https://coastwatch.noaa.gov/cw_html/cwViewer.html?lat=27.00&lon=-80.80&z=3&daysback=7&layer0=basemapWI&layer1=acyclonicEddyb

Cyclonic: https://coastwatch.noaa.gov/cw_html/cwViewer.html?lat=27.00&lon=-80.80&z=3&daysback=7&layer0=basemapWI&layer1=cyclonicEddyb

KML: https://coastwatch.noaa.gov/data/pub0016/coastwatch/static/netcdf_test_cases/eddy/kml/

Sample Filenames

Multiparameter Eddy Significance Index (MESI)

MESI_v1_multi_global_daily_s20240501_e20240501.nc

MUltiparameter NRT System for Tracking Eddies Retroactively (MUNSTER)

Daily: MUNSTER_v1_eddyident_multi_global_daily_s20240508_e20240508.nc

Weekly: MUNSTER_v1_eddytraject_multi_global_7days_s20240508_e20240514.nc

Monthly: pending

HTTPS

Multiparameter Eddy Significance Index (MESI)

https://coastwatch.noaa.gov/data/pub0054/coastwatch/products/eddy_tracking/netcdf/mesi/

MUltiparameter NRT System for Tracking Eddies Retroactively (MUNSTER)

Daily Eddies: https://coastwatch.noaa.gov/data/pub0054/coastwatch/products/eddy_tracking/netcdf/munster/eddy_identification/

Weekly Eddy Tracks: https://coastwatch.noaa.gov/data/pub0054/coastwatch/products/eddy_tracking/netcdf/munster/eddy_tracks/

Monthly Eddy Tracks: product pending

Data license:

'Data courtesy of NOAA'
'Sentinel Data courtesy of Copernicus Program'
'Generated using AVISO+ Products'

THREDDS

Multiparameter Eddy Significance Index (MESI)

link pending

MUltiparameter NRT System for Tracking Eddies Retroactively (MUNSTER)

link pending

ERDDAP

Multiparameter Eddy Significance Index (MESI)

https://coastwatch.noaa.gov/erddap/griddap/noaacweddymesidaily.graph

MUltiparameter NRT System for Tracking Eddies Retroactively (MUNSTER)

https://coastwatch.noaa.gov/erddap/griddap/noaacweddymunsterdaily.graph

NOAA NCEI Blended Seawinds (NBS v2)

jebidiah.jeffery — Thu, 15 Sep 2022 16:54:41 +0000

NOAA NCEI Blended Seawinds (NBS v2)

jebidiah.jeffery Thu, 09/15/2022 - 12:54

The NOAA NCEI Blended Seawinds (NBS) version 2.0 dataset synthesizes observations from multiple satellites (up to seven satellites since June 2002) to create gridded wind speeds . This is an updated version of the existing NOAA NBS v1.0, which is a global gridded 0.25° and 6-hourly sea surface winds product that has wide applications in marine transportation, marine ecosystem and fisheries, offshore winds, weather and ocean forecasts, and other areas. Blending the data from multiple satellites fills in the temporal and spatial data gaps present in each source dataset, and reduces subsampling aliases and random errors. These satellites include scatterometers and microwave radiometers/imagers, which do not provide accurate observations of intensive high-speed hurricane winds because their signals saturate in very high winds or degrade in the presence of rain.
Recent advancements in satellite wind retrievals revealed that the L-band (1.42 GHz) instrument on the Soil Moisture Active Passive (SMAP) satellite and the AMSR2 All-Weather channel (~6.9 GHz) can provide accurate hurricane winds of up to 65 m/s (145 MPH) without being affected by rain; these data are incorporated in a new version of the Blended Sea Winds, NBS v2.0, using a multi-sensor data fusion technique based on random errors, enabling it to resolve very high winds, especially along the eyewalls of tropical cyclones and hurricanes. NBS v2.0 provides both a long-term record of >30 years retrospectively since July 1987 and a near-real-time (NRT) mode with 1-day latency. With the present seven satellites, there are very few data gaps over the global ocean in the 6-hourly interpolated fields, and those gaps are mostly in the higher latitudes (beyond 40°N or S). To produce a version of “gap-free” Level 4 wind data, the few remaining gaps are filled with the ERA-5 reanalysis winds in the delayed mode NBS (latency and cycle of a month) and with NOAA/NCEP/GFS forecast winds in the NRT version. The reanalysis or forecast data are also used to calculate the wind directions, which is essential to retrieve wind and wind stress components.

These products were developed in response to the increasing demand for higher resolution global datasets, and can be used to improve the accuracy of ocean and weather conditions forecasts. Please contact ncei.satellite.ocean.internal@noaa.gov for more information about these products.

Temporal Start Date

September 7, 1987

Temporal Coverage

Temporal: 6 hour, 1 day, 1 month; beginning July 1987

Product Families

Sea Surface Winds

Saha, K.; Huai-Min, Z. Hurricane and Typhoon Storm Wind Resolving NOAA NCEI Blended Sea Surface Wind (NBS) Product. Frontiers in Marine Sciences – Ocean Observation 2022, 9, 1–12. https://doi.org/10.3389/fmars.2022.935549.
Peng, G., H.-M. Zhang, H.P. Frank, J.-R. Bidlot, M. Higaki, S. Stevens, and W.R. Hankins, 2013: Evaluation of various surface wind products with OceanSITES buoy measurements. Weather and Forecasting, 28, 1281–1303, https://doi.org/10.1175/WAF-D-12-00086.1.
Zhang, H.-M., J. J. Bates, and R. W. Reynolds, 2006: Assessment of composite global sampling: Sea surface wind speed, Geophysical Research Letters, 33, L17714, https://doi.org/10.1029/2006GL027086.
Zhang, H.-M., R.W. Reynolds, and J.G. Bates, 2006: Blended and gridded high resolution global sea surface wind speed and climatology from multiple satellites: 1987–present. 14th Conference on Satellite Meteorology and Oceanography, Atlanta, GA, American Meteorological Society, Paper 100004. [https://ams.confex.com/ams/Annual2006/webprogram/Paper100004.html].

Measurements

Sea Surface Vector Winds

Processing Levels

Level 4

Spatial Coverage

Global

Saha, K.; Huai-Min, Z. Hurricane and Typhoon Storm Wind Resolving NOAA NCEI Blended Sea Surface Wind (NBS) Product. Frontiers in Marine Sciences – Ocean Observation 2022, 9, 1–12. https://doi.org/10.3389/fmars.2022.935549.

Latency Groups

0 Hours <= 24 Hours (NRT)

24+ hours (Delayed)

Latency Details

24 hrs (for NRT)

Spatial Resolution Groups

2km+

Spatial Resolution Details

0.25°

Platforms

Instruments

Data Providers

NOAA

NESDIS

NCEI

STAR

Product DOI

https://doi.org/10.3389/fmars.2022.935549

Data Tool Links

Near Real-time: Coming Soon

Wind (6 hr [vector] + Daily [raster]): portal

Wind Stress (taux,tauy): portal -- coming soon

Science:

Wind (6 hr [vector] + Daily [raster]): portal

Wind Stress (taux,tauy): portal -- coming soon

Sample Filenames

Retrospective:

NBSv02_wind_6hourly_YYYYMMDD.nc

NBSv02_wind_stress_6hourly_YYYYMMDD.nc,

NBSv02_wind_daily_YYYYMMDD.nc,

NBSv02_wind_stress_daily_YYYYMMDD.nc,

NBSv02_wind_monthly_YYYYMM.nc,

NBSv02_wind_stress_monthly_YYYYMM.nc

NRT:

NBSv02_wind_6hourly_YYYYMMDD_nrt.nc

NBSv02_wind_stress_6hourly_YYYYMMDD_nrt.nc

NBSv02_wind_daily_YYYYMMDD_nrt.nc

NBSv02_wind_stress_daily_YYYYMMDD_nrt.nc

HTTPS

Near Real-time:

Wind (magnitude+uv components): https://coastwatch.noaa.gov/data/pub0015/coastwatch/blended/wind/nrt/uvcomp/

Wind Stress (taux,tauy): https://coastwatch.noaa.gov/data/pub0015/coastwatch/blended/wind/nrt/stress/

Science:

Wind (magnitude+uv components): https://coastwatch.noaa.gov/data/pub0015/coastwatch/blended/wind/science/uvcomp/

Wind Stress (taux,tauy): http://coastwatch.noaa.gov/data/pub0015/coastwatch/blended/wind/science/stress/

THREDDS

NRT and Science:

https://coastwatch.noaa.gov/thredds/socd/coastwatch/catalog_ncei_global_winds.html

ERDDAP

NRT and Science:

https://coastwatch.noaa.gov//erddap/search/index.html?page=1&itemsPerPage=1000&searchFor=noaacwBlendedWind

Sea Surface Salinity - Near Real Time - SMAP

jebidiah.jeffery — Thu, 14 Oct 2021 14:30:59 +0000

Sea Surface Salinity - Near Real Time - SMAP

jebidiah.jeffery Thu, 10/14/2021 - 10:30

CoastWatch/OceanWatch produces 0.25-degree longitude/latitude Level-3 gridded sea-surface salinity (SSS) daily mean dataset by box averaging Soil Moisture Active Passive (SMAP) satellite observations over the global ocean. Daily files are available back to the beginning of 2015. An eight day mean composite (8 day running mean) is also generated daily. The 8 day composite is available from January 2017.

CoastWatch/OceanWatch Level-3 SSS products are generated directly from the NASA Jet Propulsion Laboratory (JPL) SMAP Level-2B SSS Near Real-time CAP Sea Surface Salinity V5.0 swath HDF5 files. These products improve the application of satellite SSS products in NOAA by minimizing the data latency to within 24 hours of Level-2 data availability.

Short Names

SMAP SSS

Temporal Start Date

April 1, 2015

Product Families

Sea Surface Salinity

SMAP Salinity and Wind Speed User's Guide, Version 5.0, JPL

Measurements

Sea Surface Salinity

Sea Surface Wind Speed

Processing Levels

Level 3

Spatial Coverage

Global

Latency Groups

0 Hours <= 24 Hours (NRT)

Latency Details

Less than 24 hours

Spatial Resolution Groups

2km+

Spatial Resolution Details

30 km nominal sensor resolution

CoastWatch Level-3 product is gridded to 0.25º (~25 km)

Platforms

SMAP

Instruments

SMAP

Data Providers

NASA

JPL

Data Tool Links

Data Portal:

Daily (1 day):
https://coastwatch.noaa.gov/cw_html/cwViewer.html?lat=20.63&lon=-71.57&z=2&layer0=basemapWI&layer1=SMAPd

HTTPS

https://www.star.nesdis.noaa.gov/data/socd1/coastwatch/products/smap/nc/

For direct download of daily composites, use this link and complete the pattern for with daily composite filename:

    https://www.star.nesdis.noaa.gov/data/socd1/coastwatch/products/miras/nc/SM_Dyyyyddd_Map_SATSSS_data_#day.nc

where yyyy = Year , ddd=day of year, # = 1 or 8. SMAP daily files are available from the beginning of 2015 to present. Composites (8 day) are available beginning January 2017.

For example:

https://www.star.nesdis.noaa.gov/data/socd1/coastwatch/products/smap/nc/SP_D2018100_Map_SATSSS_data_1day.nc

THREDDS

Top Level:

https://www.star.nesdis.noaa.gov/thredds/socd/coastwatch/catalog_smos_smap.html

SMAP Aggregated:

https://www.star.nesdis.noaa.gov/thredds/socd/coastwatch/catalog_smap_aggregated.html

SMAP Per-file:

https://www.star.nesdis.noaa.gov/thredds/catalog/SMAP/catalog.html