Meteosat

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 Geo-Polar Blended Global Sea Surface Temperature Analysis (Level 4)

jebidiah.jeffery — Thu, 17 Mar 2022 15:24:13 +0000

NOAA Geo-Polar Blended Global Sea Surface Temperature Analysis (Level 4)

The NOAA geo-polar blended SST is a daily 0.05° (~5km) global high resolution satellite-based sea surface temperature (SST) Level-4 analyses generated on an operational basis. This analysis combines SST data from US, Japanese and European geostationary infrared imagers, and low-earth orbiting infrared (U.S. and European) SST data, into a single high-resolution 5-km product. The three flavors of blended SST products are night only; day/night, and diurnal warming.

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

The National Oceanic and Atmospheric Administration's (NOAA) office of National Environmental Satellite Data and Services (NESDIS) generates a daily 0.05° (~5km) global high resolution satellite-based sea surface temperature (SST) analyses on an operational basis. This analysis combines SST data from U.S, Japanese and European geostationary infrared imagers, and low-Earth orbiting infrared (U.S. and European) SST data, into a single high-resolution 5-km product - this grid spacing was chosen to allow the resolution to approach the Nyquist sampling criterion for the mid-latitude Rossby radius (~20 km), in order to preserve mesoscale oceanographic features such as eddies and frontal meanders. The input SST data themselves are also processed in-house via the Geo-SST Bayesian and physical retrieval approach (GOES-E/W, Meteosat-10), and, for polar-orbiting and Himawari-8, the Advanced Clear Sky Processor for Oceans (ACSPO).

Algorithms

The analysis employs a rigorous multi-scale optimal interpolation (OI) methodology that approximates the Kalman filter, together with a data-adaptive correlation length scale, to ensure a good balance between detail preservation and noise reduction. The multi-scale OI employs a quad-tree approach avoids the concomitant computation times that result from pursuing a straight OI methodology, while running the analysis at three different correlation scales (coarse, medium, fine) preserves mathematical rigor. The algorithm is fully described in Khellah et al. (2005).

Validation

The product accuracy verified against globally distributed buoys is ~0.02 K, with a robust standard deviation of ~0.25 K. The new analysis has proven a significant success even when compared to other products that purport to have a similar resolution. This analysis forms the basis for other operational environmental products such as coral reef bleaching risk and ocean heat content for tropical cyclone prediction.

Products

Three blended SST analysis products are generated:

Day/night which combines both day and night day;
Night time only which uses only nighttime data;
Diurnally Corrected Analysis which corrects for diurnal fluctuations on the Day/ night Analysis.

The near real-time products are generated operationally at NOAA/OSPO and re-served by CoastWatch for the convenience of a broader number of users. The night only product has been reprocessed (see below) and is available from Sept. 2002 to 2016.

Reprocessing

The analysis has been run for the period 1 Sept. 2002 through 31 Dec. 2016, incorporating reprocessed geostationary and polar-orbiting SSTs. This has been done primarily to furnish a consistent reference baseline for anomaly-based products such as those generated by Coral Reef Watch.

Future enhancements

Forthcoming enhancements include the incorporation of microwave SST products from low-earth orbiting platforms (e.g. GCOM-W1) in order to improve resolution of SST features in areas of persistent cloud and correct for diurnal effects via a turbulence model of upper ocean heating.

Short Names

GEO-POLAR

Temporal Start Date

September 1, 2022

Temporal Coverage

2002 - Present

Product Families

Sea Surface Temperature

Maturi, E., A. Harris, J. Mittaz, J. Sapper, G. Wick, X. Zhu, P. Dash, and P. Koner, 2017: A New High-Resolution Sea Surface Temperature Blended Analysis. Bull. Amer. Meteor. Soc., 98, 1015–1026, https://doi.org/10.1175/BAMS-D-15-00002.1
Ignatov, Alexander, Xinjia Zhou, Boris Petrenko, Xingming Liang, Yury Kihai, Prasanjit Dash, John Stroup, John Sapper, and Paul DiGiacomo. "AVHRR GAC SST Reanalysis Version 1 (RAN1)." Remote Sensing 8, no. 4 (April 9, 2016): 315. doi:10.3390/rs8040315.
Liu, Gang, Scott F. Heron, C. Mark Eakin, Frank E. Muller-Karger, Maria Vega-Rodriguez, Liane S. Guild, Jacqueline L. De La Cour, et al. "Reef-Scale Thermal Stress Monitoring of Coral Ecosystems: New 5-Km Global Products from NOAA Coral Reef Watch." Remote Sensing 6, no. 11 (November 20, 2014): 11579-606. doi:10.3390/rs61111579.
Donlon, C.J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and Wimmer, W., The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system, Remote Sens. Environ., 116, 140-158, doi:10.1016/j.rse.2010.10.017, 2012.
Reynolds, R.W., and Chelton, D.B., Comparisons of daily sea surface temperature analyses for 2007-08, J. Climate, 23, 3545-3562, 2010.
Khellah, F., P.W. Fieguth, M.J. Murray and M.R. Allen, Statistical Processing of Large Image Sequences, IEEE Trans. Geosci. Rem. Sens., 14, 80-93, 2005
Liu, Gang, Alan E. Strong, and William Skirving. "Remote Sensing of Sea Surface Temperatures during 2002 Barrier Reef Coral Bleaching." Eos, Transactions American Geophysical Union 84, no. 15 (April 15, 2003): 137-41. doi:10.1029/2003EO150001.
Harris, A., and Maturi, E., Assimilation of Satellite Sea Surface Temperature Retrievals. Bull. Amer. Meteor. Soc., 84, 1575-1580, 2003. doi:10.1175/BAMS-84-11-1575 Thiébaux, J., Rogers, E., Wang, W., and Katz, B., A New High-Resolution Blended Real-Time Global Sea Surface Temperature Analysis. Bull. Amer. Meteor. Soc., 84, 645-656, 2003.

Measurements

Sea Surface Temperature - Geostationary

Sea Surface Temperature - Polar-orbiting

Processing Levels

Level 4

Spatial Coverage

Global

Latency Groups

0 Hours <= 24 Hours (NRT)

Latency Details

Less than 24 hours

Spatial Resolution Groups

2km+

Spatial Resolution Details

5km

Platforms

Instruments

Data Providers

NOAA

NESDIS

OSPO

Data Tool Links

CoastWatch Data Portal

CoastWatch Near Real-Time Search

Sample Filenames

GPBCW_B2016298_WW00_sst_5km_night.hdf

HTTPS

Top Level:

https://coastwatch.noaa.gov/pub/socd2/coastwatch/sst_blended/sst5km/

Night (includes access to OSPO near real time and STAR reprocessed Sept. 2002 to 2016)

https://coastwatch.noaa.gov/pub/socd2/coastwatch/sst_blended/sst5km/night/

Day + Night:

https://coastwatch.noaa.gov/pub/socd2/coastwatch/sst_blended/sst5km/daynight/

Diurnal:

https://coastwatch.noaa.gov/pub/socd2/coastwatch/sst_blended/sst5km/diurnal/

THREDDS

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

ERDDAP

https://coastwatch.noaa.gov/erddap/search/index.html?page=1&itemsPerPage=1000&searchFor=blended+SST+Geo-polar

Seviri (MSG) - Geostationary - Level 3

jebidiah.jeffery — Thu, 21 Oct 2021 16:09:05 +0000

Seviri (MSG) - Geostationary - Level 3

jebidiah.jeffery Thu, 10/21/2021 - 12:09

The National Oceanic and Atmospheric Administration's Office of Satellite Data Processing and Distribution are generating operational sea surface temperature (SST) retrievals from the Geostationary Operational Environmental Satellites GOES-East and West. The generation of SSTs began with GOES-8 in 2000 and has continued to be generated through GOES-15. They are situated at longitude 135oW and 75oW, respectively, thus allowing the acquisition of high-temporal-resolution SST retrievals. The algorithm calculates SST by utilizing a fully physical retrieval scheme based on modified total least squares (MTLS, Koner et al., 2015) and a probabilistic (Bayesian) approach for cloud masking (Merchant et al., 2005). A more detailed description can be described in the Algorithms and Bayesian Cloud Mask section below.

The GOES-13 and 15 imagers observe both northern and southern hemisphere sectors every half an hour. These 5-band (0.6, 3.9, 6.7, 10.7, 13.3 µm) images are processed to retrieve SST retrievals at 4-km resolution. The 3.9, 10.7 & 13.3 µm channels are used to determine the SST, while the 0.6, 3.9 and 10.7 µm channels are used to detect cloud contamination. Individual sectors are output as GHRSST Level-2 P SST product files, and retrievals are also remapped, averaged and composited hourly into single-byte per pixel "flat" binary files and posted to a server for user access. The retrievals are available approximately 90 minutes after the nominal epoch of the SST determinations. 3-hour and 24-hour composite files are also made available. CoastWatch Regional Imagery is generated every three hours by combining the 1-hourly SST images for these areas.

The same algorithm approach is used to generate SSTs from Meteosat-11 data (centered at 0¡ longitude), taking a sub-selection of channels from the SEVIRI instrument that corresponds with those of the GOES-Imager. The main differences for the end-user are that the Meteosat SST products are "full-disk", every 15 minutes, and have a resolution at the nadir point of ~3-km, at least for the GHRSST L2P data.

Algorithms

Prior to the implementation of the fully physical retrieval algorithm with GOES-13 and 15, the algorithm retrieval schemes were still based on Radiative Transfer Modeling (RTM), generating skin temperatures rather than bulk temperatures. The form of the prior GOES operational SST equation was:

Short Names

GOES_L3

Temporal Coverage

Near real-time + 3 days

Product Families

Sea Surface Temperature

Koner, P. K., A. Harris, and E. Maturi. "A Physical Deterministic Inverse Method for Operational Satellite Remote Sensing: An Application for Sea Surface Temperature Retrievals." IEEE Transactions on Geoscience and Remote Sensing 53, no. 11 (November 2015): 5872-88. doi:10.1109/TGRS.2015.2424219.
Kurihara, Yukio, Hiroshi Murakami, and Misako Kachi. "Sea Surface Temperature from the New Japanese Geostationary Meteorological Himawari-8 Satellite." Geophysical Research Letters 43, no. 3 (February 16, 2016): 2015GL067159. doi:10.1002/2015GL067159.
Maturi, Eileen, Andy Harris, Jon Mittaz, Chris Merchant, Bob Potash, Wen Meng, and John Sapper. "NOAA's Sea Surface Temperature Products From Operational Geostationary Satellites." Bulletin of the American Meteorological Society 89, no. 12 (December 1, 2008): 1877-88. doi:10.1175/2008BAMS2528.1.
Merchant, Christopher J., and Pierre Le Borgne. "Retrieval of Sea Surface Temperature from Space, Based on Modeling of Infrared Radiative Transfer: Capabilities and Limitations." Journal of Atmospheric and Oceanic Technology 21, no. 11 (November 1, 2004): 1734-46. doi:10.1175/JTECH1667.1.
Merchant, C. J., A. R. Harris, E. Maturi, and S. Maccallum. "Probabilistic Physically Based Cloud Screening of Satellite Infrared Imagery for Operational Sea Surface Temperature Retrieval." Quarterly Journal of the Royal Meteorological Society 131, no. 611 (October 1, 2005): 2735-55. doi:10.1256/qj.05.15.
Merchant, C. J., A. R. Harris, E. Maturi, O. Embury, S. N. MacCallum, J. Mittaz, and C. P. Old. "Sea Surface Temperature Estimation from the Geostationary Operational Environmental Satellite-12 (GOES-12)." Journal of Atmospheric and Oceanic Technology 26, no. 3 (March 1, 2009): 570-81. doi:10.1175/2008JTECHO596.1.
Wick, Gary A., John J. Bates, and Donna J. Scott. "Satellite and Skin-Layer Effects on the Accuracy of Sea Surface Temperature Measurements from the GOES Satellites." Journal of Atmospheric and Oceanic Technology 19, no. 11 (November 1, 2002): 1834-48. doi:10.1175/1520-0426(2002)019<1834%3ASASLEO>2.0.CO%3B2 (2002).
Wu, Xiangqian, W. Paul Menzel, and Gary S. Wade. "Estimation of Sea Surface Temperatures Using GOES-8/9Radiance Measurements." Bulletin of the American Meteorological Society 80, no. 6 (June 1999): 1127-38.