SARAL

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

Sea level Anomaly and Geostrophic Currents, multi-mission, global, optimal interpolation, gridded

jebidiah.jeffery — Thu, 17 Mar 2022 15:11:17 +0000

Sea level Anomaly and Geostrophic Currents, multi-mission, global, optimal interpolation, gridded

The NOAA Laboratory for Satellite Altimetry's (LSA) sea surface height team produces 0.25-degree longitude/latitude Level-3 sea level anomaly (SLA) daily datasets by applying optimal interpolation to along-track satellite observations over the global ocean from a constellation of radar altimeter missions. Theses grids are produced with near-real time (3-5 hour latency) data. Geostrophic Currents are produced from the SLA and are included in the dataset.

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

The NOAA Laboratory for Satellite Altimetry (LSA) sea surface height team produces 0.25-degree longitude/latitude Level-3 sea level anomaly (SLA) daily datasets by applying optimal interpolation to along-track satellite observations over the global ocean from a constellation of radar altimeter missions. Geostrophic currents are derived from the SLA and are included in the dataset. Theses grids are produced with near-real time (NRT; 3-5 hour latency) data and a delayed-time (DT), improved accuracy, product is available for the time period 2012 to 2018.

Sea Level Anomaly Product (pictured above)

The NOAA/NESDIS Laboratory for Satellite Altimetry provides near-real time (NRT) and delayed-time (DT) gridded Level-3 sea level anomaly (SLA) products. The data are produced by applying optimal interpolation to along-track satellite observations from a constellation of radar altimeter missions. The along-track altimetry data for each of the missions are from the Radar Altimetry Database System (RADS). This product is a global ocean product with a spatial resolution of 0.25-degree longitude/latitude. The near-real time gridded SLA product is available from February 2017 through the present. Currently, Jason-3, AltiKa, Cryosat-2, Sentinel-3A, and Sentinel-3B data are used in the processing of the NRT dataset. Jason-2 was included through 1 October 2019. The NRT dataset is also part of NOAA’s OceanWatch Monitor. The delayed time SLA data are available for years 2012-2018. The DT data are of different quality than the NRT gridded SLA data. For each day, along-track data from all available missions are used and only final Geophysical Data Records (GDR)/Non-Time Critical (NTC) data are used in the processing of the DT dataset, making this product to be more accurate.

Geostrophic Currents Product

The NOAA/NESDIS Laboratory for Satellite Altimetry provides near-real time and delayed-time, zonal and meridional components of the geostrophic current. The currents are produced using the NOAA/NESDIS Laboratory for Satellite Altimetry's daily sea level anomaly grids and the conventional f-plane geostrophic current equations. At the equator, the beta-plane approximation is used. This product is a global ocean product with a spatial resolution of 0.25-degree longitude/latitude. The near-real time geostrophic currents are available from March 2019 through the present. The delayed time geostrophic currents are available for years 2012-2018. The geostrophic currents are variables within the NRT and DT SLA data files.

Figure: NOAA LSA NRT Gridded Geostrophic Currents in m s^-1 for 9 May 2019. Left is zonal component (ugos), right is meridional component (vgos). Black is land. Gray is ice or missing data.

NOTE: Jason-2 will permanently cease acquisition of scientific data at 06:48 UTC on 1 October 2019 due to aging-related issues onboard the spacecraft.

The gridded multi-mission, Sea level Anomaly and Geostrophic Currents Near-real time products will continue to include Jason-2 until October 11.
When produced, the gridded multi-mission, Sea level Anomaly and Geostrophic Currents delayed-mode products will include Jason-2 up to October 11.

Radar Altimeter Database System

The Radar Altimeter Database System (RADS) is an effort of the Department of Earth Observation and Space Systems (DEOS) at TU Delft and the NOAA Laboratory for Satellite Altimetry to establish a harmonized, validated, and cross-calibrated sea level data base from satellite altimeter.

Short Names

RADS-NRT-SLA-Daily-L4

Temporal Start Date

January 1, 2017

Product Families

Ocean Currents

Sea Surface Height

CryoSat Ground Segment, Instrument Processing Facility L1B, Products Specification Format, ESA: CS-RS-ACS-GS-5106, Issue: 6.4, April 2015. https://earth.esa.int/documents/10174/125273/CryoSat_L1_Products_Format_Specification
CryoSat Product Handbook, April 2012, https://earth.esa.int/documents/10174/125272/CryoSat_Product_Handbook
Jason-3 Product Handbook, SALP-MU-M-OP-16118-CN, edition 1.2, Feb. 2016 https://www.nodc.noaa.gov/media/pdf/jason2/j3_user_handbook.pdf
Leuliette, E. W., and R. Scharroo (2010). Integrating Jason-2 into a Multiple-Altimeter Climate Data Record. Marine Geodesy, 33(1), 504–517. doi:10.1080/01490419.2010.487795
OSTM/Jason-2 Products Handbook, CNES: SALP-MU-M-OP-15815-CN EUMETSAT : EUM/OPS-JAS/MAN/08/0041 JPL : OSTM-29-1237 : NOAA/NESDIS : Polar Series/OSTM J400, Issue: 1 rev 10, October, 2016, https://www.nodc.noaa.gov/media/pdf/jason2/j2_user_handbook.pdf
SARAL/AltiKa Products handbook, SALP-MU-M-OP-15984-CN, edition 2.5, July 2016; http://www.aviso.altimetry.fr/fileadmin/documents/data/tools/SARAL_Altika_products_handbook_01.pdf
Scharroo, R., E. Leuliette, M. Naeije, C. Martin-Puig, and N. Pires (2016), RADS Version 4: An Efficient Way to Analyse the Multi-Mission Altimeter Database, Living Planet Symposium, Proceedings of the conference held 9-13 May 2016 in Prague, Czech Republic. Edited by L. Ouwehand. ESA-SP Volume 740, ISBN: 978-92- 9221-305- 3.
Sentinel-3 User Handbook, ESA: GMES-S3OP-EOPG-TN-13-0001, Issue 1, September 2013, https://sentinel.esa.int/documents/247904/685236/Sentinel-3_User_Handbook

Measurements

Geostrophic Currents

Sea Surface Height

Processing Levels

Level 4

Spatial Coverage

Global

LSA data are distributed at no cost to the users to promote scientific utilization of the data with the stipulation that the users of the data agree to follow the policy described below:

LSA data are produced on a best effort basis.
In publications, presentations, or on web pages based on LSA data the following acknowledgment should be included.
"Altimetry data are provided by the NOAA Laboratory for Satellite Altimetry."

Latency Groups

0 Hours <= 24 Hours (NRT)

Latency Details

12 hours

Spatial Resolution Groups

2km+

Spatial Resolution Details

0.25°

Platforms

Instruments

Processing Algorithms

RADS

Data Providers

ESA

EUMETSAT

NOAA

NESDIS

STAR

LSA

Data Tool Links

CoastWatch Data Portal

CoastWatch Near Real-Time Search

Sample Filenames

1-day: rads_global_nrt_sla_20170313_20170314_000.nc

HTTPS

https://coastwatch.noaa.gov/data/pub0015/coastwatch/rads/sla/

THREDDS

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

ERDDAP

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

Along-track significant wave height, wind speed and sea level anomaly from multiple altimeters

jebidiah.jeffery — Thu, 17 Mar 2022 15:09:40 +0000

Along-track significant wave height, wind speed and sea level anomaly from multiple altimeters

The NOAA Laboratory for Satellite Altimetry's (LSA) sea surface height team produces 0.25-degree longitude/latitude Level-3 significant wave height, wind speed, and sea level anomaly (SLA) daily datasets by applying optimal interpolation to along-track satellite observations over the global ocean from a constellation of radar altimeter missions. Theses grids are produced with near-real time (3-5 hour latency) data.

jebidiah.jeffery Thu, 03/17/2022 - 11:09

Temporal Start Date

September 1, 2016

Temporal Coverage

Along-track data products available 09-2016 to present for Jason-3; Sentinel-3A; CryoSat-2; SARAL.

Jason-2 ended 1 Oct 2019.

Product Families

Sea Surface Height

Sea Surface Winds

Measurements

Along-track Significant Wave Height

Sea Surface Height Anomalies