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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

Oceanic Heat Content, Mixed Layer Depth and Depths of 20°C and 26°C Isotherms

jebidiah.jeffery — Thu, 17 Mar 2022 15:31:14 +0000

Oceanic Heat Content, Mixed Layer Depth and Depths of 20°C and 26°C Isotherms

Oceanic Heat Content (OHC) is the measure of the integrated vertical temperature from the sea surface to the depth of the 26°C isotherm and computed from the altimeter-derived isotherm depths in the upper ocean relative to 20°C. Global 0.25 degree grids are generated daily for OHC, mixed layer depth and depths of 20°C and 26°C isotherms for 3 ocean basins: North Atlantic, North Pacific and South Pacific

jebidiah.jeffery Thu, 03/17/2022 - 11:31

Oceanic Heat Content (OHC) is the measure of the integrated vertical temperature from the sea surface to the depth of the 26°C isotherm and computed from the altimeter-derived isotherm depths in the upper ocean relative to 20°C. Satellite data inputs for sea surface temperature (SST) are from the NOAA GeoPolar Blended sea surface temperature and for the sea surface height anomaly (SSHA) are from at least 2 satellite altimetry missions. Global 0.25 degree grids are generated daily for OHC, mixed layer depth and depths of 20°C and 26°C isotherms for 3 ocean basins: North Atlantic, North Pacific and South Pacific.

BACKGROUND

More than 90% of the warming on the Earth over the past 50 years occurred in the Ocean (Shay L.K. 2019).The heat content of the ocean is the amount of heat energy (in joules) stored within a pre-defined volume of the upper ocean. To determine the heat content value in the ocean, NOAA/NESDIS produces a daily operational suite of satellite-derived Oceanic Heat Content (OHC) products for the North Atlantic, and the North and South Pacific basins. A suite of OHC products for the Indian Ocean are planned which will contribute significantly toward global coverage. OHC is an important climate change indicator and provides a high quality climatic data record. These suites of satellite-derived OHC products are validated against over one million in-situ measurements from multiple platforms to assess biases and uncertainties. The NOAA OHC product is shown to be the best product available for Hurricane Intensity Forecasting (Meyers et. al. 2015). Daily display of these OHC products provides valuable data to address key science questions related to climate such as: 1) the extent of warming (or cooling) in the warm pools of the Atlantic and Pacific Ocean basins; 2) thermodynamic processes in the equatorial wave guides associated with eastward propagating Kelvin Waves (ENSO); and linkages to the Madden-Julian Oscillation) across the tropics. Benefits for climate studies are a new understanding of the upper ocean thermodynamics, dynamics and air-sea processes relevant to tropical cyclone intensity forecasting, climatic variability (e.g., OHC anomalies over various time scales), fisheries, and coral reef bleaching. Figure 1 are the input fields (Altimetry and SST) used to generate the OHC Products.

DEFINITION

Oceanic Heat Content (OHC) is defined as the measure of the integrated vertical temperature from the sea surface to the depth of the 26°C isotherm and computed from the altimeter-derived isotherm depths in the upper ocean relative to 20°C.

METHODOLOGY

Algorithm

OHC values are estimated using four points: 1) the sea surface temperature obtained from NOAA/NESDIS Geo Polar SST Analysis; 2) the altimeter-estimates of the 20°C isotherm within a two-layer reduced gravity scheme; 3) the depth of the 26°C isotherm from a climatological relationship between the depths of the 20°C and 26°C isotherm; and 4) flexibility in estimating other isotherm depths (e.g., 25°C) then integrate. The full algorithm description is in the Algorithm Theoretical Basis Document (ATBD) (see documentation link).

Validation

Validation is accomplished by 1) calculating OHC from various in situ data sources (Argo, XBT, PIRATA/TAO, and Air-deployed probes); 2) then compared to the satellite-derived OHC grid point that is closest to that source in time and space; 3) regression analysis and root-mean-square deviation (RMSD) statistics are used to determine an agreement between the satellite and in situ data; and the accuracy has to be within 10 percent of the in situ data (kJ cm^-2).

The addition of new satellite data requires new validation of the product.

Visual images of the OHC product suite are created on a daily basis.

Short Names

Satellite derived Ocean Heat Content

Temporal Coverage

Daily

Product Families

Ocean Heat Content

ATBD: Shay, L. K., J. K. Brewster, E. Maturi, P. C.,Meyers and C. McCaskill, Algorithm Theoretical Basis Document for Satellite-dervied Oceanic Heat Content Product, Version 3.1, June 2015

Shay, L.K., G.J. Goni and P.G. Black.(2000) Effects of a warm oceanic feature on Hurricane Opal., Monthly Weather Review, 125(5), 1366-1383.
De Maria, M. , M. Mainelli , L.K. Shay, J.A. Knaff, and J. Kaplan. (2005) Further improvements to the statistical hurricane intensity prediction scheme (SHIPS). Weather and Forecasting, 20(4), 531-543.
Ali, M. M., P.S.V. Jagadeesh and Sarika Jain (2007a). Effects of Eddies on Bay of Bengal Cyclone Intensity, EOS, Vol. 88, p 93, 95.
Halliwell GR, Shay LK, Jacob SD, Smedstad OM, Uhlhorn EW (2008) Improving Ocean Model Initialization for Coupled Tropical Cyclone Forecast Models Using GODAE Nowcasts. Monthly Weather Review, 136(7): 2576-2591.
Mainelli M, De Maria M, Shay LK, Goni G (2008) Application of Oceanic Heat Content Estimation to Operational Forecasting of Recent Atlantic Category 5 Hurricanes. Weather and Forecasting 23(1): 3-16.
Shay LK and Uhlhorn EW (2008) Loop Current Response to Hurricanes Isidore and Lili. Monthly Weather Review 136(9): 3248
Jaimes, B. and L. K. Shay. (2009) Mixed layer cooling in mesoscale eddies during Katrina and Rita. Monthly Weather Review. 137(12), 4188-4207.
Hallliwell, G., L. K. Shay, J. Brewster, and W. Teague, (2011) Evaluation and sensitivity analysis of an ocean model to hurricane Ivan in the northern Gulf of Mexico. Monthly Weather Review. 139(3), 921-945.
Shay, L. K., and J. Brewster (2010). Eastern Pacific oceanic heat content estimation for hurricane forecasting. Monthly Weather Review . 138, 2110-2131.
Shay, L. K., P. C. Meyers and J. K. Brewster, (2012) Development and analysis of the Systematically Merged Atlantic Region Temperature and Salinity (SMARTS) climatology for ocean heat content estimates. J. Atmos and Oceanogr. Tech. (In Preparation)
Meyers, P. C., L. K. Shay, and J. K. Brewster, (2013) Development and analysis of the Systematically Merged Atlantic Region Temperature and Salinity (SMARTS) climatology for ocean heat content estimates. J. Atmos and Oceanogr. Tech. (Submitted)

Measurements

Depth of 20° and 26° Isotherms

Ocean Heat Content

Ocean Mixed-Layer Depth

Sea Surface Height Anomalies

Sea Surface Temperature - Geostationary

Sea Surface Temperature - Polar-orbiting

Processing Levels

Level 4

Spatial Coverage

Global

Latency Groups

24+ hours (Delayed)

Latency Details

~36 h

Spatial Resolution Groups

2km+

Spatial Resolution Details

0.25 degree gridded

Platforms

Instruments

Data Providers

NOAA

NESDIS

OSPO

Data Tool Links

CoastWatch Tools

CoastWatch Data Portal

OSPO Tools

Satellite Ocean Heat Content Suite

Sample Filenames

ohc_naQG3_2012_215.nc, ohc_npQG3_2012_215.nc, ohc_spQG3_2012_215.nc

HTTPS

https://coastwatch.noaa.gov/pub/socd2/coastwatch/ocean_heat

THREDDS

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

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

ACSPO Global SST from ABI

SST.Team — Thu, 17 Mar 2022 15:19:53 +0000

ACSPO Global SST from ABI

The ABI SST data are produced from GOES-East (at present GOES-19, before 2025/04/06 GOES-16) and GOES-West (at present GOES-18, before 2023/01/10 GOES-17) satellite using the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) v.3.0 and v290 enterprise system. Currently, near-real time (NRT) data are produced at STAR, with a ~2-6 hour latency. A Reanalysis (RAN) dataset for GOES-16 is also available. The data are available in NetCDF4 format, compliant with the GHRSST Data Specifications v2 (GDS2). Currently, the data are archived on PO.DAAC and available at this Coast Watch page as a 2week rotated buffer.

SST.Team Thu, 03/17/2022 - 11:19

The ABI SST data are produced from GOES-EAST, now GOES-19 (GOES-16 with ACSPO v2.70 before 2025/04/06) satellite using the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) v3.00 and GOES-WEST, nowGOES-18 satellite using the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) v2.90 enterprise system, GOES-17 (GOES-WEST) used ACSPO v2.71. Currently, for GOES-19 and GOES-18 near-real time (NRT) data are produced at STAR, with a latency of 2 to 6 hours (typically closer to 2 hours). For GOES-16 a reanalysis and reanalysis ver. 2 (ACSPO v2.90) starting 2017-Dec-15 exist. The data are available in NetCDF4 format, compliant with the GHRSST Data Specifications v2 (GDS2). The data is archived with PODAAC with start date 2019-Feb-01 and end date 2023-Jan-10 (G17W) , 2022-Jun-7 (G18W) and 2017-Dec-15 (G16E) respectively. The NRT data is also available at this Coast Watch page as a 2-week rotated buffer. There is a plan to reprocess the GOES-17 and -18 ABI data and to repeat the reprocessing of the GOES-16 data.

The data are reported hourly, in ABI Full Disk (FD), for view zenith angle not exceeding 68° in GHRSST Data Specifications v2 (GDS2) format, in swath projection (L2P) and 0.02° gridded L3C, 24 FDs per day, with a total data volume of 6.3GB/day for L2P and 0.5GB/day for L3C, respectively.

It should be noted that, due to an issue with its Loop Heat pipe, the L1b data for G17 was compromised for certain hours of the day and cannot be used to generate sensible SSTs. Therefore, SSTs from G17 was not reported for the times between 09:00 UTC and 19:00 UTC. Since this is a hardware issue it is unlikely to be resolved in the near future. (see Pennybacker et al., 2019). The new GOES-West (G18) is free from such problems.

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 L3C: 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); NCEP wind speed; and ACSPO SST minus reference (Canadian Met Centre L4 SST). For L2P, brightness temperatures (BTs) in 3.9, 8.6, 10, 11, and 12 µm bands are also reported, for those users interested in direct "radiance assimilation" (e.g., NOAA NCEP, NASA GMAO).

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. They may also ignore ACSPO SST and derive their own SSTs from the original BTs.

Both L2P and L3C SSTs are 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

ABI_G16-STAR-L2P-v2.70

ABI_G16-STAR-L3C-v2.70

ABI_G17-STAR-L2P-v2.71

ABI_G17-STAR-L3C-v2.71

G18-ABI-L2P-ACSPO-v2.90

G18-ABI-L3C-ACSPO-v2.90

Temporal Start Date

December 15, 2017

Temporal Coverage

G16/EAST RAN+NRT: 2017-Dec-15 to 2019-May-07 (RAN) to present (NRT) (PODAAC)

G17/WEST NRT: 2019-Oct-16 to present (PODAAC)

G18/WEST RAN+NRT: 2022-Jun-7 to present(RAN 2 months delayed) (NRT) (PODAAC)

Product Families

Sea Surface Temperature

Algorithms:

Petrenko, B., A. Ignatov, Y. Kihai, M. Pennybacker, 2019: Optimization of sensitivity of GOES-16 ABI sea surface temperature by matching satellite observations with L4 analysis. Remote Sens, 11, 206, doi: 10.3390/rs11020206, https://www.mdpi.com/2072-4292/11/2/206/htm
Pennybacker, M., A. Ignatov, O. Jonasson, I. Gladkova, B. Petrenko, Y. Kihai, 2019, Mitigation of the GOES-17 ABI performance issues in the NOAA ACSPO SST products, Proc. SPIE 11014, Ocean Sensing and Monitoring XI, 110140Q (30 May 2019); doi:10.1117/12.2521051, ftp://ftp.star.nesdis.noaa.gov/pub/sod/osb/aignatov/SPIE/2019/Papers/PennybackerEtAl_Mitigation_G17_SST_110140Q.pdf
Ignatov, A., I. Gladkova, Y. Ding, F. Shahriar, Y. Kihai, X. Zhou, JPSS VIIRS level 3 uncollated SST Product at NOAA”. J. Appl. Remote Sens., 11(3), 032405, doi:10.1117/1.JRS.11.032405 (2017), ftp://ftp.star.nesdis.noaa.gov/pub/sod/osb/aignatov/Irina/L3U/JARS_11_3_032405.pdf

Monitoring:

SQUAM (https://www.star.nesdis.noaa.gov/sod/sst/squam/): Dash, P., A. Ignatov, Y. Kihai & J. Sapper, 2010: The SST Quality Monitor (SQUAM). JTech, 27, 1899-1917,doi:10.1175/2010JTECHO756.1
iQUAM (https://www.star.nesdis.noaa.gov/sod/sst/iquam/): Xu, F. & A. Ignatov, 2014: In situ SST Quality Monitor (iQuam). JTech, 31, 164-180, doi:10.1175/JTECH-D-13-00121.1
ARMS (https://www.star.nesdis.noaa.gov/sod/sst/arms/): Ding, Y., A. Ignatov, K. He, I. Gladkova, 2018: ACSPO Regional Monitor for SST (ARMS). GHRSTT XIX: Science Team Meeting, June 2018, Darmstadt, Germany, ftp://ftp.star.nesdis.noaa.gov/pub/sod/osb/aignatov/GHRSST/G19/Presentations/G19_Oral_ARMS_Ignatov_v02.pptx
MICROS (https://www.star.nesdis.noaa.gov/sod/sst/micros/):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

Measurements

Sea Surface Temperature - Geostationary

Processing Levels

Level 2

Level 3

Spatial Coverage

Western Hemisphere

The ACSPO ABI 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

0 Hours <= 24 Hours (NRT)

Latency Details

NRT: 2-6 hours (in most cases closer to 2 hours)

Spatial Resolution Groups

2km+

Spatial Resolution Details

L2P: 2km at Nadir to ~12km at VZA=68°
L3C: 0.02°

Platforms

GOES-East

GOES-West

Instruments

ABI

Processing Algorithms

ACSPO

Data Providers

NOAA

NESDIS

STAR

Product DOI

https://doi.org/10.5067/GHG16-2PO27

https://doi.org/10.5067/GHG16-3UO27

https://doi.org/10.5067/GHG17-2PO71

https://doi.org/10.5067/GHG17-3UO71

https://doi.org/10.5067/GHG18-2PO29

https://doi.org/10.5067/GHG18-3CO29

Data Tool Links

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

G18 + G19 L3C:

CoastWatch Data Portal

Sample Filenames

L2P: 20191016000000-STAR-L2P_GHRSST-SSTsubskin-ABI_G16-ACSPO_V2.70-v02.0-fv01.0.nc
L3C: 20191016000000-STAR-L3C_GHRSST-SSTsubskin-ABI_G16-ACSPO_V2.70-v02.0-fv01.0.nc

HTTPS

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

G18 L2P/L3C:

https://coastwatch.noaa.gov/data/pub0014/coastwatch/sst/nrt/abi/g18/

G19 L2P/L3C:

https://coastwatch.noaa.gov/data/pub0014/coastwatch/sst/nrt/abi/g19/

Reanalysis ver. 2 (RAN2) [2 months delayed]

G16 L2P/L3C:

https://coastwatch.noaa.gov/data/pub0014/coastwatch/sst/ran/abi/g16/

THREDDS

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

G18 L2P:

https://coastwatch.noaa.gov/thredds/catalog/swathG18ABINRTL2CWW00/catalog.html

G18 L3C:

https://coastwatch.noaa.gov/thredds/catalog/gridG18ABINRTL3CWW00/catalog.html

G19 L2P:

https://coastwatch.noaa.gov/thredds/catalog/swathG19ABINRTL2CWW00/catalog.html

G19 L3C:

https://coastwatch.noaa.gov/thredds/catalog/gridG19ABINRTL3CWW00/catalog.html

Reanalysis ver. 2 (RAN2)

G16 L2P/L3C:

https://coastwatch.noaa.gov/thredds/catalog/socd/coastwatch/acspo/abi/catalog_g16_ran.html

PO.DAAC Archive

G16 L2P:

G16 L3C:

G17 L2P:

G17 L3C:

G18 L2P:

G18 L3C:

NCEI Archive

NCEI archive contains an incomplete copy of PO.DAAC archive

GHRSST-ABI_G16-STAR-L2P

10.25921/ayf6-c438

GHRSST-ABI_G16-STAR-L3C

10.25921/rtf0-q898

GHRSST-ABI_G17-STAR-L2P
10.25921/6yga-qb89

GHRSST-ABI_G17-STAR-L3C
10.25921/4cs0-ym62

GHRSST-ABI_G18-STAR-L2P
10.25921/sr4z-pj22

GHRSST-ABI_G18-STAR-L3C
10.25921/jaxf-9b11

Seascape Pelagic Habitat Classification

jebidiah.jeffery — Thu, 23 Dec 2021 17:21:24 +0000

Seascape Pelagic Habitat Classification

MBON Seascapes identifies spatially explicit water masses with particular biogeochemical features using a model and satellite-derived measurements. The seascape product is generated as monthly and 8-day composites at 5 km spatial resolution

jebidiah.jeffery Thu, 12/23/2021 - 12:21

The US and global Marine Biodiversity Observation Network (MBON) partnered with the US Integrated Ocean Observation System (IOOS), NOAA/OAR/AOML and NOAA/NESDIS/STAR to develop and routinely generate this Seascape Pelagic Habitat Classification product (MBON Seascapes) and make it available on CoastWatch. Derived from dynamic fields of satellite and modelled data, MBON Seascapes are classified and used as a biogeographical framework to describe dynamic, changing ocean habitats for MBON and other applications. MBON Seascapes provide information about the quality and extent of different oceanographic habitats or features and can be used to assess and predict the different planktonic and fisheries communities that reside within seascapes. Current MBON Seascapes products include monthly and 8-day time steps at 5 km resolution. High resolution (1 km) case studies are planned on a case by case basis through cooperation with US and global MBON partners.

Table of Seascape ID number definitions. SST, sea surface temperature; SSS sea surface salinity; ADT, absolute dynamic topography; ICE, Ice; CDOM, chromophoric dissolved organic material; CHLA, chlorophyll-a; NFLH, normalized fluorescent line height; NaN, not a number.

Table of Seascape ID number definitions

Short Names

seascapes

Temporal Coverage

Monthly and 8-Day

Product Families

Seascape Classification

Montes Enrique, Djurhuus Anni, Muller-Karger Frank E., Otis Daniel, Kelble Christopher R., Kavanaugh Maria T., 2020. Dynamic Satellite Seascapes as a Biogeographic Framework for Understanding Phytoplankton Assemblages in the Florida Keys National Marine Sanctuary, United States. Frontiers in Marine Science, 7, https://doi.org/10.3389/fmars.2020.00575
Kavanaugh, M.T., Church, M.J., Davis, C.O., Karl, D.M., Letelier, R.M. and Doney, S.C., 2018. ALOHA from the Edge: Reconciling three decades of in situ Eulerian observations and geographic variability in the North Pacific Subtropical Gyre. Frontiers in Marine Science, 5, p.130.
Muller-Karger, F., Digna Rueda-Roa, Francisco Chavez, Maria Kavanaugh, and Mitchell A. Roffer. 2017. Megaregions Among the Large Marine Ecosystems of the Americas. Environmental Development. http://dx.doi.org/10.1016/j.envdev.2017.01.005.
Kavanaugh, M.T., Oliver, M J., Chavez, F. P., Letelier, R.M., Muller-Karger, F.E., Doney, S.C. 2016. Seascapes as a new vernacular for ocean monitoring, management and conservation. ICES Journal of Marine Science. doi:10.1093/icesjms/fsw086.
Kavanaugh, M.T., Hales, B., Saraceno, M., Spitz, Y.H., White, A.E. and Letelier, R.M., 2014. Hierarchical and dynamic seascapes: A quantitative framework for scaling pelagic biogeochemistry and ecology. Progress in Oceanography, 120, pp.291-304.
Muller-Karger, F.E., M.T. Kavanaugh, E. Montes, W.M. Balch, M. Breitbart, F.P. Chavez, S.C. Doney, E.M. Johns, R.M. Letelier, M.W. Lomas, H.M. Sosik, and A.E. White. 2014. A framework for a marine biodiversity observing network within changing continental shelf seascapes. Oceanography 27(2):18–23, http://dx.doi.org/10.5670/oceanog.2014.56.