Level 3

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

Synthetic Aperture Radar Imagery (NRCS)

jebidiah.jeffery — Thu, 17 Mar 2022 15:30:36 +0000

Synthetic Aperture Radar Imagery (NRCS)

Spaceborne Synthetic Aperture Radar (SAR) imagery maps the surface microwave radar reflectivity at resolutions from a sub-meter to 100 m depending on the particular SAR satellite and mode. Since a radar provides its own illumination, imagery is independent of the time of day. At typical radar frequencies, SARs can image through clouds, so SARs are considered "all-weather" instruments. Several geophysical parameters can be derived from SAR including sea surface wind speed.

jebidiah.jeffery Thu, 03/17/2022 - 11:30

Spaceborne Synthetic Aperture Radar (SAR) imagery maps the surface microwave radar reflectivity at resolutions from a sub-meter to 100 m depending on the particular SAR satellite and mode. Since a radar provides its own illumination, imagery is independent of the time of day. At typical radar frequencies, SARs can image through clouds, so SARs are considered "all-weather" instruments. Several gephysical parameters can be derived from SAR including sea surface wind speed.

For side-scanning SARs, the normalized radar cross section (NRCS) of the surface is proportional to the surface roughness on the scale of the radar wave length (from 3 to 30 cm). Ocean surface roughness on this scale is dependent on the local wind speed and direction. The higher the wind speed the larger the NRCS of the surface. The highest NRCS occurs when the radar look direction and wind direction are aligned and smallest when the radar looks cross wind.

The relationship between wind speed and direction and radar cross section is called the "geophysical model function." These functions are generally empirically determined.

Although we can predict an expected NRCS value for a given wind speed and direction, the reverse is not true. A specific NRCS value can be associated with many pairs of wind speed and direction. If we know the wind direction a priori, we can infer the wind speed.

For the operational SAR-derived wind speed products, we use the model directions from NOAA’s Global Forecast System (GFS) model on a 0.5 degree, latitude-longitude grid to initiate the retrievals. The wind speed retrievals are provided at 500 m resolution. These retrievals agree with independent wind speed estimates to better than 2 m/s.

Current users of these data include the US National Ice Center, the Alaska Weather Service, and the Naval Oceanographic Office.

A good review of SAR operational wind speed retrieval can be found at: http://tos.org/oceanography/assets/docs/26-2_monaldo.pdf

Product Families

Sea Surface Roughness

Monaldo, F.M., C.R. Jackson, and W.G. Pichel. 2013. Seasat to RADARSAT-2: Research to operations. Oceanography 26(2):34–45, http://dx.doi.org/10.5670/oceanog.2013.29
SAR Marine User Manual (2004) Christopher Jackson and John Apel, Editors

Measurements

Normalized Radar Cross-Section

Sea Surface Wind Speed

Processing Levels

Level 1

Level 2

Level 3

Spatial Coverage

Selective Acquisitions

Latency Groups

0 Hours <= 24 Hours (NRT)

Spatial Resolution Groups

0 m <= 100 m

100m < 2km

Spatial Resolution Details

NRCS: Variable, 0-10 m
Winds: 500 m

Platforms

Instruments

Processing Algorithms

SAROPS

Data Providers

Copernicus

CSA

NOAA

NESDIS

OSPO

STAR

Data Tool Links

CoastWatch Tools

CoastWatch Data Portal (select Spatial Search)

Data files: https://coastwatch.noaa.gov/data/pub0022/coastwatch/

Northern & Southern Hemisphere Products

NRCS Search Tool

XPOL Search Tool

Other Search Tools

RadarSat-2 + RCM SAR Winds Search Tool (MECB)

Sentinel-1 SAR Winds Search Tool (MECB)

RadarSat-2 + Sentinel-1 SAR Winds Search Tool (OSPO) [Formally Operational]

Sample Filenames

Filenames begin with satellite, data source, year, month, day, hour, minute, second, Julian seconds, center latitude, center longitude, polarization, delimited with an underscore NRCS: S1A_ESA_2016_12_07_18_18_57_0534449937_178.66E_51.85N_VV_C5_GFS05 CDF_nrcs_level2.png
Wind: S1A_ESA_2016_12_07_18_18_57_0534449937_178.66E_51.85N_VV_C5_GFS05 CDF_wind_level3.png

Winds from Special Sensor Microwave Imager (SSM/I)

jebidiah.jeffery — Thu, 17 Mar 2022 15:29:52 +0000

Winds from Special Sensor Microwave Imager (SSM/I)

Defense Meteorological Satellite Program (DMSP) / Special Sensor Microwave Imager (SSM/I) Wind Speed

jebidiah.jeffery Thu, 03/17/2022 - 11:29

Temporal Coverage

Near real-time + 3 days

Product Families

Sea Surface Winds

Measurements

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

0.33° (37 km)

Platforms

DMSP

Instruments

SSMIS

Data Providers

NOAA

NESDIS

OSPO

Data Tool Links

CoastWatch Near Real-Term Search

Sample Filenames

SSMI1630004as_WW.hdf

HTTPS

https://coastwatch.noaa.gov/pub/socd1/coastwatch/products/ssmi/

Vector Winds OSCAT-2 SCATSAT-1

jebidiah.jeffery — Thu, 17 Mar 2022 15:29:22 +0000

Vector Winds OSCAT-2 SCATSAT-1

Ocean Surface Vector Winds (OSVW; magnitude and direction) from the Ocean Scatterometer (OSCAT) on SCATSAT-1.

jebidiah.jeffery Thu, 03/17/2022 - 11:29

OSCAT-2, aboard the SCATSAT-1 mission of the Indian Space Research Organization (ISRO), is an active microwave sensor used to determine ocean surface level wind vectors through estimation of radar backscatter. The ISRO sensor data are processed by the NOAA OSVW team to estimate wind speed and direction in near real time with global coverage, every 4 hours with a spatial resolution of about 12.5 km. NOAA CoastWatch maps and distributes the OSVW products. See the "information" and "data access" tabs for more.

Temporal Coverage

Near real-time + 3 days

Product Families

Sea Surface Winds

Measurements

Sea Surface Vector Winds

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

12.5 km

Platforms

ScatSat-1

Instruments

OSCAT

Data Providers

NOAA

NESDIS

OSPO

Data Tool Links

CoastWatch Data Portal

CoastWatch Near Real-Term Search

Sample Filenames

SS201630004as_WW.hdf

HTTPS

https://coastwatch.noaa.gov/pub/socd7/coastwatch/scatsat/netcdf/

Vector Winds ASCAT Metop-A/B/C

jebidiah.jeffery — Thu, 17 Mar 2022 15:27:54 +0000

Vector Winds ASCAT Metop-A/B/C

Ocean Surface Vector Winds (OSVW; magnitude and direction) from the Advanced Scatterometer (ASCAT) on Metop-A, Metop-B, and Metop-C. Metop-A was decommissioned November 15, 2021.

jebidiah.jeffery Thu, 03/17/2022 - 11:27

Ocean Surface Vector Winds (OSVW; magnitude and direction) from the Advanced Scatterometer (ASCAT) on Metop-A, Metop-B and Metop-C. Metop-A was decommissioned November 15, 2021.

ASCAT instruments aboard the Meteorological Operational satellite program (MetOp) from EUMETSAT are a series of active microwave sensors used to determine ocean surface level wind vectors through estimation of radar backscatter. The ASCAT sensor data are processed operationally by NOAA OSPO to estimate wind speed and direction in near real with global coverage; products are generated every 4 hours with a spatial resolution of about 25 km. NOAA CoastWatch maps and distributes the OSVW products. See the "information" and "data access" tabs for more.

Temporal Start Date

March 5, 2020

Temporal Coverage

Near real-time + 3 days
Currently stored in and available from our CoastWatch collection:
Metop A from 25 March 2020 to 15 November 2021;
Metop B from 26 March 2020 to present;
Metop C from 3 September 2021 to present

Product Families

Sea Surface Winds

Measurements

Sea Surface Vector Winds

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

25 km

Platforms

MetOp

Instruments

ASCAT

Data Providers

NOAA

NESDIS

OSPO

Data Tool Links

CoastWatch Data Portal

CoastWatch Near Real-Term Search

Sample Filenames

AS201630004Bas_WW.hdf

HTTPS

Products:

https://coastwatch.noaa.gov/pub/socd1/coastwatch/products/ascat/

Metop:

https://coastwatch.noaa.gov/pub/socd7/coastwatch/metop/ascat/

THREDDS

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

Synthetic Aperture Radar (Surface Roughness) Winds

jebidiah.jeffery — Thu, 17 Mar 2022 15:27:13 +0000

Synthetic Aperture Radar (Surface Roughness) Winds

jebidiah.jeffery Thu, 03/17/2022 - 11:27

Spaceborne Synthetic Aperture Radar (SAR) imagery maps the surface microwave radar reflectivity at resolutions from a sub-meter to 100 m depending on the particular SAR satellite and mode. Since a radar provides its own illumination, imagery is independent of the time of day. At typical radar frequencies, SARs can image through clouds, so SARs are considered "all-weather" instruments. Several gephysical parameters can be derived from SAR including sea surface wind speed.

The relationship between wind speed and direction and radar cross section is called the "geophysical model function." These functions are generally empirically determined.

Current users of these data are the US National Ice Center, the Alaska Weather Service, and the Naval Oceanographic Office.

A good review of SAR operational wind speed retrieval can be found at: http://tos.org/oceanography/assets/docs/26-2_monaldo.pdf

Product Families

Sea Surface Roughness

Sea Surface Winds

Monaldo, F.M., C.R. Jackson, and W.G. Pichel. 2013. Seasat to RADARSAT-2: Research to operations. Oceanography 26(2):34–45, http://dx.doi.org/10.5670/oceanog.2013.29
SAR Marine User Manual (2004) Christopher Jackson and John Apel, Editors

Measurements

Normalized Radar Cross-Section

Sea Surface Wind Speed

Processing Levels

Level 1

Level 2

Level 3

Spatial Coverage

Selective Acquisitions

Latency Groups

0 Hours <= 24 Hours (NRT)

Latency Details

Less than 24 hours

Spatial Resolution Groups

0 m <= 100 m

100m < 2km

Spatial Resolution Details

NRCS: Variable, 0-10 m
Winds: 500 m

Platforms

Instruments

Data Providers

NOAA

NESDIS

OSPO

Data Tool Links

CoastWatch Tools

CoastWatch Near Real-Time Search

Other Search Tools

Radarsat Constellation Mission / Radarsat-2 (MECB)

Sentinel-1 SAR Winds Search Tool (MECB)

Radarsat Constellation Mission + RadarSat-2 + Sentinel-1 SAR Winds Search Tool (OSPO) [Formally Operational]

Sample Filenames

Filenames begin with satellite, data source, year, month, day, hour, minute, second, Julian seconds, center latitude, center longitude, polarization, delimited with an underscore

NRCS: S1A_ESA_2016_12_07_18_18_57_0534449937_178.66E_51.85N_VV_C5_GFS05 CDF_nrcs_level2.png
Wind: S1A_ESA_2016_12_07_18_18_57_0534449937_178.66E_51.85N_VV_C5_GFS05 CDF_wind_level3.png

THREDDS

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

NCEI Archive

SAR Winds:

https://www.ncei.noaa.gov/products/sar-wind-data-quality-monitoring

VIIRS (SNPP) Level 3

jebidiah.jeffery — Thu, 17 Mar 2022 15:25:59 +0000

VIIRS (SNPP) Level 3

NOAA CoastWatch / OceanWatch / PolarWatch produces Level 3 sea surface temperature datasets using the VIIRS sensor aboard the SNPP satellite.

jebidiah.jeffery Thu, 03/17/2022 - 11:25

Short Names

VIIRS_L3

Temporal Coverage

Near real-time + 3 days

Product Families

Sea Surface Temperature

Measurements

Sea Surface Temperature - Polar-orbiting

Processing Levels

Level 3

Spatial Coverage

CoastWatch US Regions

Latency Groups

0 Hours <= 24 Hours (NRT)

Latency Details

Less than 24 hours

Spatial Resolution Groups

100m < 2km

Spatial Resolution Details

750m @ Nadir; ~1.7km at swath edge

Platforms

SNPP

Instruments

VIIRS

Data Providers

NOAA

NESDIS

STAR

Data Tool Links

CoastWatch Near Real-Time Search

Sample Filenames

2016_300_0530_npp_er.hdf [YYYY_JJJ_HHMM_PPP_RR.[png, tif, hdf]

HTTPS

https://www.star.nesdis.noaa.gov/data/socd3/coastwatch/products/legacy_sst/viirs/

AVHRR (MetOp-1/2) Level 2/3

jebidiah.jeffery — Thu, 17 Mar 2022 15:24:52 +0000

AVHRR (MetOp-1/2) Level 2/3

NOAA CoastWatch / OceanWatch / PolarWatch produces Level 2/3 sea surface temperature datasets using AVHRR sensors aboard the Metop 1/2 satellites.

jebidiah.jeffery Thu, 03/17/2022 - 11:24

Short Names

AVHRR_L2

AVHRR_L3

Temporal Coverage

Near real-time + 3 days

Product Families

Sea Surface Temperature

Measurements

Sea Surface Temperature - Polar-orbiting

Processing Levels

Level 2

Level 3

Spatial Coverage

CoastWatch US Regions

Latency Groups

0 Hours <= 24 Hours (NRT)

24+ hours (Delayed)

Latency Details

Less than 24 hours

Spatial Resolution Groups

2km+

Spatial Resolution Details

4km @Nadir; ~30km @swath edge

Platforms

MetOp

Instruments

AVHRR

Data Providers

ESA

EUMETSAT

Data Tool Links

CoastWatch Near Real-Time Search

Sample Filenames

2016_300_1446_n19_ax.hdf [YYYY_JJJ_HHMM_PPP_RR.[png, tif, hdf]

HTTPS

Swath + Mapped Regions:

https://www.star.nesdis.noaa.gov/data/socd3/coastwatch/products/legacy_sst/avhrr/

ACSPO Global SST from VIIRS

jebidiah.jeffery — Thu, 17 Mar 2022 15:23:21 +0000

ACSPO Global SST from VIIRS

These VIIRS SST data are produced using the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) SST system, from the afternoon NPP, N20 and N21 satellites, in two formats: L2P and L3U (uncollated). The data are reported in 10 min granule files in NetCDF4 format, compliant with the GHRSST Data Specifications v2 (GDS2). For each satellite, there are 144 granules per 24 hr interval, with a total data volume of ~7.1 GB/day for L2P, and ~0.4 GB/day for L3U.

jebidiah.jeffery Thu, 03/17/2022 - 11:23

These VIIRS SST data are produced using the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) SST system, from the afternoon NPP and N20 satellites, in two formats: L2P and L3U (uncollated). The data are reported in 10 min granule files in NetCDF4 format, compliant with the GHRSST Data Specifications v2 (GDS2). There are 144 granules per 24 hour interval, with a total data volume of ~7 GB/day for L2P, and ~0.5 GB/day for L3U (per-sensor) .

The L2P/L3U SST data is produced at NOAA STAR using the official enterprise ACSPO algorithms V2.80. A 2-week rotated buffer of the NRT data is provided here on Coast Watch, whereas the full data set is available from PO.DAAC and NOAA NCEI (see links under "Data Access"). Note that the NCEI archive has not been backfilled with ACSPO V2.80 and therefore contains SST data from earlier ACSPO versions, while PO.DAAC contains the full V2.80 archive. A complete archive of ACSPO V2.80 data is available on the Coast Watch archive for L3U data only due to large L2P data volume.

ACSPO retrievals are made in full VIIRS swath (~3,000 km). For data assimilation applications (such as production of L4 analyses, especially those that blend satellite and in situ data), correction for the Sensor-Specific Error Statistics (SSES; reported in ACSPO files; Petrenko et al., 2016) biases is strongly recommended. The NOAA SST team worked with the NOAA calibration team and used an up-to-date converter from RDR (L0) to SDR (L1b), to address quarterly spikes in day-time SST bias which coincide with quarterly warm-up-cool-down calibration exercises onboard NPP/N20. The algorithms used in VIIRS reanalysis version 3 (RAN3) are documented in Jonasson et al., 2022.

In each valid water pixel (defined as ocean, sea, lake or river, up to 5km inland; note that in "invalid" pixels, defined as those with >5km inland, fill values are reported), the following layers are reported in both L2P and L3U, among others: SSTs derived using multi-channel SST (MCSST; night) and Non-Linear SST (NLSST; day) algorithms (Petrenko et al., 2014); ACSPO clear-sky mask (ACSM; provided in each pixel as part of l2p_flags; Petrenko et al., 2010); SSES bias and standard deviation (Petrenko et al., 2016); GFS/MERRA wind speed; and ACSPO SST minus reference (Canadian Met Centre L4 SST).

Only ACSM "confidently clear" pixels (equivalent to GDS2 "quality level"=5; QLs are also reported for each pixel) should be used. The ACSM also provides day/night, land, ice, twilight, and glint flags. Note that users of ACSPO data have the flexibility to ignore the ACSM, derive their own clear-sky mask, and use SSTs in those pixels.

Both L2P and L3U SSTs are monitored and validated against in situ data (Xu and Ignatov, 2014) in SQUAM (Dash et al., 2010) and ARMS systems, and brightness temperatures are monitored in MICROS (Liang and Ignatov, 2011).

Short Names

L2P: ACSPO_VIIRS_L2P

L3U: ACSPO_VIIRS_L3U

Temporal Start Date

February 1, 2012

Temporal Coverage

NRT NPP L2P/L3U: 1 Feb 2012 to present
NRT N20 L2P/L3U: 5 Jan 2018 to present
NRT N21 L2P/L3U: 19 March 2023 to present
RAN NPP L2P/L3U: 1 Feb 2012 to present
RAN N20 L2P/L3U: 5 Jan 2018 to present
RAN N21 L2P/L3U: 19 March 2023 to present

Product Families

Sea Surface Temperature

Jonasson, O.; Ignatov, A.; Pryamitsyn, V.; Petrenko, B.; Kihai, Y. JPSS VIIRS SST Reanalysis Version 3. Remote Sens. 2022, 14, 3476. doi:10.3390/rs14143476
Dash, P., A. Ignatov, Y. Kihai & J. Sapper, 2010: The SST Quality Monitor (SQUAM). JTech, 27, 1899-1917, doi:10.1175/2010JTECHO756.1, www.star.nesdis.noaa.gov/sod/sst/squam/
Ding, Y., A. Ignatov, I. Gladkova, M. Crosberg, and C. Chu, 2017: ACSPO Regional Monitor for SST (ARMS). BoM - NOAA SST Workshop, 18-21 April 2017, Melbourne, Australia (presentation), www.star.nesdis.noaa.gov/sod/sst/arms/
Liang, X. & A. Ignatov, 2011: Monitoring of IR Clear-sky Radiances over Oceans for SST (MICROS). JTech, 28, 1228-1242, doi:10.1175/JTECH-D-10-05023.1, www.star.nesdis.noaa.gov/sod/sst/micros/
Xu, F. & A. Ignatov, 2014: In situ SST Quality Monitor (iQuam). JTech, 31, 164-180, doi:10.1175/JTECH-D-13-00121.1, www.star.nesdis.noaa.gov/sod/sst/iquam/
Petrenko, B., A. Ignatov, Y. Kihai, P. Dash, 2016: Sensor-Specific Error Statistics for SST in the Advanced Clear-Sky Processor for Oceans. JTech, 33, 345-359, doi:10.1175/JTECH-D-15-0166.1
Petrenko, B., A. Ignatov, Y. Kihai, J. Stroup, P. Dash, 2014: Evaluation and Selection of SST Regression Algorithms for JPSS VIIRS. JGR, 119, 4580-4599, doi:10.1002/2013JD020637
Petrenko, B., A. Ignatov, Y. Kihai, and A. Heidinger, 2010: Clear-Sky Mask for ACSPO. JTech, 27, 1609-1623, doi:10.1175/2010JTECHA1413.1

Measurements

Sea Surface Temperature - Polar-orbiting

Processing Levels

Level 2

Level 3

Spatial Coverage

Global

The ACSPO NPP/N20/N21 VIIRS RAN3 data set is provided by NOAA STAR. We strongly recommend contacting NOAA SST team led by A. Ignatov before the data are used for any publication or presentation.

Latency Groups

0 Hours <= 24 Hours (NRT)

24+ hours (Delayed)

Latency Details

NRT: <6 hours
RAN: 8 weeks

Spatial Resolution Groups

100m < 2km

2km+

Spatial Resolution Details

L2P: 0.75km @ Nadir; ~1.5km @ swath edge
L3U: 0.02°

Platforms

NOAA

SNPP

Instruments

VIIRS

Processing Algorithms

ACSPO

Data Providers

NOAA

NESDIS

STAR

Data Tool Links

Near Real-Time (NRT) [14 Day Rotated Buffer]

NPP + N20 + N21 L2P:

CoastWatch Data Search

Sample Filenames

L2P: 20120301001000-STAR-L2P_GHRSST-SSTsubskin-VIIRS_NPP-ACSPO_V2.80-v02.0-fv01.0.nc
L3U: 20120301001000-STAR-L3U_GHRSST-SSTsubskin-VIIRS_NPP-ACSPO_V2.80-v02.0-fv01.0.nc

HTTPS

Near Real-Time (NRT) [14 Day Rotated Buffer]

NPP L2P:

https://coastwatch.noaa.gov/data/pub0012/coastwatch/sst/nrt/viirs/npp/l2p

N20 L2P:

https://coastwatch.noaa.gov/data/pub0012/coastwatch/sst/nrt/viirs/n20/l2p

N21 L2P:

https://coastwatch.noaa.gov/data/pub0012/coastwatch/sst/nrt/viirs/n21/l2p

NPP L3U:

https://coastwatch.noaa.gov/data/pub0012/coastwatch/sst/nrt/viirs/npp/l3u

N20 L3U:

https://coastwatch.noaa.gov/data/pub0012/coastwatch/sst/nrt/viirs/n20/l3u

N21 L3U:

https://coastwatch.noaa.gov/data/pub0012/coastwatch/sst/nrt/viirs/n21/l3u

Reanalysis Version 3 (RAN3) [Scientific Quality. New data Is added in Delayed Mode (DM)]

NPP L3U:

https://coastwatch.noaa.gov/data/pub0012/coastwatch/sst/ran/viirs/npp/l3u

N20 L3U:

https://coastwatch.noaa.gov/data/pub0012/coastwatch/sst/ran/viirs/n20/l3u

N20 L3U:

https://coastwatch.noaa.gov/data/pub0012/coastwatch/sst/ran/viirs/n21/l3u

*NRT - Near-Real Time data (1-3 hour latency).
*DM - Delayed mode "scientific quality" SST. Produced on a 8-week delay.
*RAN2 - Reanalysis-2 of VIIRS SST (NPP: Feb'2012-Apr'2019; N20: Jan'2018-Apr'2019). Produced using ACSPO V2.61.
*RAN3 - Reanalysis-3 of VIIRS SST (NPP: Feb'2012-Dec'2021; N20: Jan'2018-Dec'2021). Produced using ACSPO V2.80.
NPP - Suomi NPP satellite platform
N20 - NOAA-20 satellite platform
N21 - NOAA-21 satellite platform

THREDDS

Near Real-Time (NRT) [14 Day Rotated Buffer]

NPP L2P:

https://coastwatch.noaa.gov/thredds/catalog/swathNPPVIIRSNRTL2PWW00/catalog.html

N20 L2P:

https://coastwatch.noaa.gov/thredds/catalog/swathN20VIIRSNRTL2PWW00/catalog.html

N21 L2P:

https://coastwatch.noaa.gov/thredds/catalog/swathN21VIIRSNRTL2PWW00/catalog.html

NPP L3U:

https://coastwatch.noaa.gov/thredds/catalog/gridNPPVIIRSNRTL3UWW00/catalog.html

N20 L3U:

https://coastwatch.noaa.gov/thredds/catalog/gridN20VIIRSNRTL3UWW00/catalog.html

N21 L3U:

https://coastwatch.noaa.gov/thredds/catalog/gridN21VIIRSNRTL3UWW00/catalog.html

Reanalysis Version 3 (RAN3) [Scientific Quality. New data is added in Delayed Mode (DM)]

NPP L3U:

https://coastwatch.noaa.gov/thredds/catalog/gridNPPVIIRSSCIENCEL3UWW00/catalog.html

N20 L3U:

https://coastwatch.noaa.gov/thredds/catalog/gridN20VIIRSSCIENCEL3UWW00/catalog.html

N21 L3U:

https://coastwatch.noaa.gov/thredds/catalog/gridN21VIIRSSCIENCEL3UWW00/catalog.html

PO.DAAC Archive

Reanalysis / Reprocessed (RAN3)* [New data is added in NRT* and overwritten by DM* data with a 8 week delay]

NPP L2P: 10.5067/GHVRS-2PO28

N20 L2P: 10.5067/GHV20-2PO28

N21 L2P: 10.5067/GHN21-2P280

NPP L3U: 10.5067/GHVRS-3UO28

N20 L3U: 10.5067/GHV20-3UO28

N21 L3U: 10.5067/GHN21-3U280

NCEI Archive

Reanalysis / Reprocessed (RAN2)* [New data is added in NRT]

NPP L2P: 10.7289/v5pr7sx5

N20 L2P: 10.25921/sfs7-9688

N21 L2P: 10.25921/5mbf-ma56

NPP L3U: 10.7289/v5kk98s8

N20 L3U: 10.25921/7c1m-rw73

N21 L3U: 10.25921/xkhb-5e96

ACSPO Global SST from MODIS

jebidiah.jeffery — Thu, 17 Mar 2022 15:23:08 +0000

ACSPO Global SST from MODIS

The ACSPO MODIS SST data are produced from Terra and Aqua satellites using the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) enterprise system (Ignatov et al., 2016). Complete archive of ACSPO MODIS SST data is available for both the Terra (24 Feb 2000 - present) and Aqua (4 July 2002 - present) missions in 0.02° L3C (level 3 collated) format. New data is added in delayed mode (DM) with a 2-month latency. The last orbital maintenance maneuvers for Aqua and Terra were performed on 27 Feb 2020 (Terra) and 18 Mar 2021 (Aqua) and their orbit has been drifting ever since. We do not recommend usage of ACSPO MODIS SST dated a year or later after each satellites' last orbital maintenance maneuver. For more recent SST data from low earth orbit satellites, we recommend the ACSPO VIIRS and AVHRR FRAC datasets.

jebidiah.jeffery Thu, 03/17/2022 - 11:23

The MODIS SST data are produced from Terra and Aqua satellites using the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) enterprise system (Ignatov et al., 2016). Complete archive of ACSPO MODIS SST data is available for the entire Terra (24 Feb 2000-present) and Aqua (4 July 2002-present) missions in L3C (level 3 collated; 0.02°) format. New data is added in delayed mode (DM) with a 2-month latency. The last orbital maintenance maneuvers for Aqua and Terra were performed on 27 Feb 2020 (Terra) and 18 Mar 2021 (Aqua) and their orbit has been drifting ever since. We do not recommend usage of ACSPO MODIS SST dated a year or later after each satellites' last orbital maintenance maneuver. For more recent SST data fromlow earth orbit satellites, we recommend the ACSPO VIIRS and AVHRR FRAC datasets.

The data are reported daily, separated into daytime and nighttime viewing conditions (two files per 24hr) in GHRSST Data Specifications v2 (GDS2) format, in 0.02° gridded L3C (C=collated), 2 files per day, with a total data volume of 0.4 GB/day.

ACSPO retrievals are made in full MODIS swath (~2,300 km). For data assimilation applications (such as production of L4 analyses, especially those that blend satellite and in situ data), correction for the Sensor-Specific Error Statistics (SSES; reported in ACSPO files; Petrenko et al., 2016) biases is strongly recommended.

The following layers are reported in L3C files: SSTs derived using the Non-Linear SST (NLSST) algorithm (Petrenko et al., 2014); ACSPO clear-sky mask (ACSM; provided in each pixel as part of l2p_flags; Petrenko et al., 2010); SSES bias and standard deviation (Petrenko et al., 2016); MERRA2 wind speed; and ACSPO SST minus reference (Canadian Met Centre L4 SST). Only ACSM "confidently clear" pixels (equivalent to GDS2 "quality level"=5; also reported for each pixel) should be used. The ACSM also provides day/night, land, ice, twilight, and glint flags.

MODIS SST is monitored and validated against in situ data (Xu and Ignatov, 2014) in SQUAM (Dash et al., 2010) and ARMS (Ding et al., 2017) systems, and BTs are monitored in MICROS (Liang and Ignatov, 2011).

Short Names

ACSPO_MODIS_RAN_L3C

Temporal Start Date

February 24, 2000

Temporal Coverage

24 Feb 2000-present (Terra)
4 July 2002-present (Aqua)

Product Families

Sea Surface Temperature

Dash, P., A. Ignatov, Y. Kihai & J. Sapper, 2010: The SST Quality Monitor (SQUAM). JTech, 27, 1899-1917, doi:10.1175/2010JTECHO756.1, www.star.nesdis.noaa.gov/sod/sst/squam/
Ding, Y., A. Ignatov, I. Gladkova, M. Crosberg, and C. Chu, 2017: ACSPO Regional Monitor for SST (ARMS). BoM - NOAA SST Workshop, 18-21 April 2017, Melbourne, Australia (presentation), www.star.nesdis.noaa.gov/sod/sst/arms/
Liang, X. & A. Ignatov, 2011: Monitoring of IR Clear-sky Radiances over Oceans for SST (MICROS). JTech, 28, 1228-1242, doi:10.1175/JTECH-D-10-05023.1, www.star.nesdis.noaa.gov/sod/sst/micros/
Xu, F. & A. Ignatov, 2014: In situ SST Quality Monitor (iQuam). JTech, 31, 164-180, doi:10.1175/JTECH-D-13-00121.1, www.star.nesdis.noaa.gov/sod/sst/iquam/
Petrenko, B., A. Ignatov, Y. Kihai, P. Dash, 2016: Sensor-Specific Error Statistics for SST in the Advanced Clear-Sky Processor for Oceans. JTech, 33, 345-359, doi:10.1175/JTECH-D-15-0166.1
Petrenko, B., A. Ignatov, Y. Kihai, J. Stroup, P. Dash, 2014: Evaluation and Selection of SST Regression Algorithms for JPSS VIIRS. JGR, 119, 4580-4599, doi:10.1002/2013JD020637
Petrenko, B., A. Ignatov, Y. Kihai, and A. Heidinger, 2010: Clear-Sky Mask for ACSPO. JTech, 27, 1609-1623, doi:10.1175/2010JTECHA1413.1

Measurements

Sea Surface Temperature - Polar-orbiting

Processing Levels

Level 3

Spatial Coverage

Global

The ACSPO MODIS SST data are provided by NOAA STAR. We strongly recommend contacting NOAA SST team led by A. Ignatov before the data are used for any publication or presentation.

Latency Groups

24+ hours (Delayed)

Latency Details

2 months

Spatial Resolution Groups

2km+

Spatial Resolution Details

L3C: 0.02°

Platforms

Aqua

Terra

Instruments

MODIS

Processing Algorithms

ACSPO

Data Providers

NOAA

NESDIS

STAR

Sample Filenames

Terra L3C (night): 20120721120000-STAR-L3C_GHRSST-SSTsubskin-MODIS_T_N-ACSPO_V2.80-v02.0-fv01.0.nc
Terra L3C (day): 20120721120000-STAR-L3C_GHRSST-SSTsubskin-MODIS_T_D-ACSPO_V2.80-v02.0-fv01.0.nc

Aqua L3C (night): 20120721120000-STAR-L3C_GHRSST-SSTsubskin-MODIS_A_N-ACSPO_V2.80-v02.0-fv01.0nc
Aqua L3C (day): 20120721120000-STAR-L3C_GHRSST-SSTsubskin-MODIS_A_D-ACSPO_V2.80-v02.0-fv01.0.nc

HTTPS

Reanalysis / Reprocessed (RAN) [New data is added in delayed mode (DM) with a 2 month delay]

L3C:

https://coastwatch.noaa.gov/pub/socd2/coastwatch/sst/ran/modis/

THREDDS

Reanalysis / Reprocessed (RAN) [New data is added in delayed mode (DM) with a 2 month delay]

L3C:

https://coastwatch.noaa.gov/thredds/socd/coastwatch/acspo/catalog_sst_acspo_modis_ran.html