MetOp

Experimental Eddy Products

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

Experimental Eddy Products

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

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

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

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

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

Multiparameter Eddy Significance Index (MESI)

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

Algorithm

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

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

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

Data Used

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

Contents of file MESI_v1_multi_global_daily_s20240501_e20240501.nc

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

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

MUltiparameter NRT System for Tracking Eddies Retroactively (MUNSTER)

Algorithm

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

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

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

Data Used

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

Products

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

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

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

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

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

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

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

        num_anticyclones = 2416 ;
        num_cyclones = 2446 ;

Variables (only cyclonic shown here) include:

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

Temporal Start Date

May 5, 2020

Temporal Coverage

Varied

Product Families

Ocean Currents

Sea Surface Height

Sea Surface Salinity

Sea Surface Temperature

Multiparameter Eddy Significance Index (MESI)

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

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

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

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

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

MUltiparameter NRT System for Tracking Eddies Retroactively (MUNSTER)

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

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

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

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

Measurements

Geostrophic Currents

Processing Levels

Level 3

Level 4

Spatial Coverage

Platforms

Instruments

Data Providers

ESA

EUMETSAT

NASA

JPL

NOAA

NESDIS

STAR

CoastWatch

LSA

SST Team

Data Tool Links

Multiparameter Eddy Significance Index (MESI)

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

MUltiparameter NRT System for Tracking Eddies Retroactively (MUNSTER)

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

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

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

Sample Filenames

Multiparameter Eddy Significance Index (MESI)

MESI_v1_multi_global_daily_s20240501_e20240501.nc

MUltiparameter NRT System for Tracking Eddies Retroactively (MUNSTER)

Daily: MUNSTER_v1_eddyident_multi_global_daily_s20240508_e20240508.nc

Weekly: MUNSTER_v1_eddytraject_multi_global_7days_s20240508_e20240514.nc

Monthly: pending

HTTPS

Multiparameter Eddy Significance Index (MESI)

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

MUltiparameter NRT System for Tracking Eddies Retroactively (MUNSTER)

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

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

Monthly Eddy Tracks: product pending

Data license:

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

THREDDS

Multiparameter Eddy Significance Index (MESI)

link pending

MUltiparameter NRT System for Tracking Eddies Retroactively (MUNSTER)

link pending

ERDDAP

Multiparameter Eddy Significance Index (MESI)

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

MUltiparameter NRT System for Tracking Eddies Retroactively (MUNSTER)

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

NOAA NCEI Blended Seawinds (NBS v2)

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

NOAA NCEI Blended Seawinds (NBS v2)

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

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

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

Temporal Start Date

September 7, 1987

Temporal Coverage

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

Product Families

Sea Surface Winds

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

Measurements

Sea Surface Vector Winds

Processing Levels

Level 4

Spatial Coverage

Global

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

Latency Groups

0 Hours <= 24 Hours (NRT)

24+ hours (Delayed)

Latency Details

24 hrs (for NRT)

Spatial Resolution Groups

2km+

Spatial Resolution Details

0.25°

Platforms

Instruments

Data Providers

NOAA

NESDIS

NCEI

STAR

Product DOI

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

Data Tool Links

Near Real-time: Coming Soon

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

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

Science:

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

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

Sample Filenames

Retrospective:

NBSv02_wind_6hourly_YYYYMMDD.nc

NBSv02_wind_stress_6hourly_YYYYMMDD.nc,

NBSv02_wind_daily_YYYYMMDD.nc,

NBSv02_wind_stress_daily_YYYYMMDD.nc,

NBSv02_wind_monthly_YYYYMM.nc,

NBSv02_wind_stress_monthly_YYYYMM.nc

NRT:

NBSv02_wind_6hourly_YYYYMMDD_nrt.nc

NBSv02_wind_stress_6hourly_YYYYMMDD_nrt.nc

NBSv02_wind_daily_YYYYMMDD_nrt.nc

NBSv02_wind_stress_daily_YYYYMMDD_nrt.nc

HTTPS

Near Real-time:

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

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

Science:

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

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

THREDDS

NRT and Science:

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

ERDDAP

NRT and Science:

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

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

Vector Winds ASCAT Metop-A/B/C

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

Vector Winds ASCAT Metop-A/B/C

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

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

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

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

Temporal Start Date

March 5, 2020

Temporal Coverage

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

Product Families

Sea Surface Winds

Measurements

Sea Surface Vector Winds

Processing Levels

Level 3

Spatial Coverage

Global

Latency Groups

0 Hours <= 24 Hours (NRT)

Latency Details

Less than 24 hours

Spatial Resolution Groups

2km+

Spatial Resolution Details

25 km

Platforms

MetOp

Instruments

ASCAT

Data Providers

NOAA

NESDIS

OSPO

Data Tool Links

CoastWatch Data Portal

CoastWatch Near Real-Term Search

Sample Filenames

AS201630004Bas_WW.hdf

HTTPS

Products:

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

Metop:

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

THREDDS

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

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

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

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

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

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

Short Names

AVHRR_L2

AVHRR_L3

Temporal Coverage

Near real-time + 3 days

Product Families

Sea Surface Temperature

Measurements

Sea Surface Temperature - Polar-orbiting

Processing Levels

Level 2

Level 3

Spatial Coverage

CoastWatch US Regions

Latency Groups

0 Hours <= 24 Hours (NRT)

24+ hours (Delayed)

Latency Details

Less than 24 hours

Spatial Resolution Groups

2km+

Spatial Resolution Details

4km @Nadir; ~30km @swath edge

Platforms

MetOp

Instruments

AVHRR

Data Providers

ESA

EUMETSAT

Data Tool Links

CoastWatch Near Real-Time Search

Sample Filenames

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

HTTPS

Swath + Mapped Regions:

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

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 AVHRR FRAC

jebidiah.jeffery — Thu, 17 Mar 2022 15:21:57 +0000

ACSPO Global SST from AVHRR FRAC

The AVHRR FRAC SST data are produced from AVHRR/3s onboard Metop-A, -B and -C satellites using the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) v2.80 enterprise system, described in (Ignatov et al., 2016). Currently, near-real time (NRT) L2P and 0.02° L3U (gridded uncollated) data for Metop-A, -B and -C is produced at STAR (with ~2 to 6hrs latency). The data are being archived at PO.DAAC, and also available at this CoastWatch page as a 2 weeks rotated buffer. Metop-A mission officially ended at 2021-11-15 and the production of the ACSPO SST for the Metop-A ended at 2021-11-14 due to data degradation on 2021-11-15.

jebidiah.jeffery Thu, 03/17/2022 - 11:21

he AVHRR FRAC SST data are produced from AVHRR/3s onboard Metop-A, -B and -C satellites using the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) v2.80 enterprise system, described in (Ignatov et al., 2016). Currently, near-real time (NRT) L2P and 0.02° L3U (gridded uncollated) data for Metop-A, -B and -C is produced at STAR (with ~2 to 6hrs latency). The data are being archived at PO.DAAC, and also available at this Coast watch page as a 2 weeks rotated buffer. Metop-A mission officially ended at 2021-11-15 and the production of the ACSPO SST for the Metop-A ended at 2021-11-14 due to data degradation on 2021-11-15.

The full-mission global AVHRR FRAC SST is produced from all three Metop First Generation satellites: A (2006-12-01 to 2021-11-14), B (2012-present) and C (2018-present), using the ACSPO enterprise system. Historical reprocessing (Reanalysis-1, RAN1) starts at the beginning of each mission and continues into near-real time (NRT). The L2P+L3U version of FRAC RAN1 is being archived in PO.DAAC.

The data are reported in 10min granule files in GHRSST Data Specifications v2 (GDS2) format, in swath projection (L2P) and 0.02° gridded L3U (U=uncollated), 144 granules per day, with a total data volume of 8GB/day for L2P and 0.4GB/day for L3U, respectively.

ACSPO retrievals are made in full AVHRR swath (~2,800 km). For data assimilation applications (such as production of L4 analyses, especially those that blend satellite and in situ data), correction for the Sensor-Specific Error Statistics (SSES; reported in ACSPO files; Petrenko et al., 2016) biases is strongly recommended.

In each valid water pixel (defined as ocean, sea, lake or river, up to 5km inland; note that in "invalid" pixels, defined as those with >5km inland, fill values are reported), the following layers are reported in both L2P and L3U: 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 for the NRT data and from MERRA-2 for RAN; and ACSPO SST minus reference (Canadian Met Centre L4 SST).

Only ACSM "confidently clear" pixels (equivalent to GDS2 "quality level"=5; also reported for each pixel) should be used. The ACSM also provides day/night, land, ice, twilight, and glint flags.

Both L2P and L3U SSTs are monitored and validated against in situ data iQuam (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

AVHRRF_MA-STAR-L2P-v2.80

AVHRRF_MA-STAR-L3U-v2.80

AVHRRF_MB-STAR-L2P-v2.80

AVHRRF_MB-STAR-L3U-v2.80

AVHRRF_MC-STAR-L2P-v2.80

AVHRRF_MC-STAR-L3U-v2.80

Temporal Start Date

December 1, 2006

Temporal Coverage

2-week rotated

Product Families

Sea Surface Temperature

Algorithms

Petrenko, B., A. Ignatov, Y. Kihai, P. Dash, 2016: Sensor-Specific Error Statistics for SST in the Advanced Clear-Sky Processor for Oceans. JTech, 33, 345-359, doi:10.1175/JTECH-D-15-0166.1

Petrenko, B., A. Ignatov, Y. Kihai, J. Stroup, P. Dash, 2014: Evaluation and Selection of SST Regression Algorithms for JPSS VIIRS. JGR, 119, 4580-4599, doi:10.1002/2013JD020637

Petrenko, B., A. Ignatov, Y. Kihai, and A. Heidinger, 2010: Clear-Sky Mask for ACSPO. JTech, 27, 1609-1623, doi:10.1175/2010JTECHA1413.1

Monitoring

Dash, P., A. Ignatov, Y. Kihai & J. Sapper, 2010: The SST Quality Monitor (SQUAM). JTech, 27, 1899-1917, doi:10.1175/2010JTECHO756.1, www.star.nesdis.noaa.gov/sod/sst/squam/

Ding, Y., A. Ignatov, I. Gladkova, M. Crosberg, and C. Chu, 2017: ACSPO Regional Monitor for SST (ARMS). BoM - NOAA SST Workshop, 18-21 April 2017, Melbourne, Australia (presentation), www.star.nesdis.noaa.gov/sod/sst/arms/

Liang, X. & A. Ignatov, 2011: Monitoring of IR Clear-sky Radiances over Oceans for SST (MICROS). JTech, 28, 1228-1242, doi:10.1175/JTECH-D-10-05023.1, www.star.nesdis.noaa.gov/sod/sst/micros/

Xu, F. & A. Ignatov, 2014: In situ SST Quality Monitor (iQuam). JTech, 31, 164-180, doi:10.1175/JTECH-D-13-00121.1, www.star.nesdis.noaa.gov/sod/sst/iquam/

Measurements

Sea Surface Temperature - Polar-orbiting

Processing Levels

Level 2

Level 3

Spatial Coverage

Global

The ACSPO AVHRR FRAC 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

L2P: 3 hours
L3U: 3 hours

Spatial Resolution Groups

100m < 2km

2km+

Spatial Resolution Details

L2P: 1km @Nadir; ~6 km @swath edge
L3U: 0.02°

Platforms

MetOp

Instruments

AVHRR

Processing Algorithms

ACSPO

Data Providers

NOAA

NESDIS

STAR

Product DOI

https://doi.org/10.5067/GHMTA-2PS28

https://doi.org/10.5067/GHMTA-3US28

https://doi.org/10.5067/GHMTB-2PS28

https://doi.org/10.5067/GHMTB-3US28

https://doi.org/10.5067/GHMTC-2PS28

https://doi.org/10.5067/GHMTC-3US28

Data Tool Links

[14 Day Rotated Buffer]

L3U:

CoastWatch Data Portal

Sample Filenames

L2P: 20190809200000-OSPO-L2P_GHRSST-SSTsubskin-AVHRRF_MA-ACSPO_V2.70-v02.0-fv01.0.nc
L3U: 20190809200000-OSPO-L3U_GHRSST-SSTsubskin-AVHRRF_MA-ACSPO_V2.70-v02.0-fv01.0.nc

HTTPS

[14 Day Rotated Buffer]

L2P + L3U:

https://coastwatch.noaa.gov/data/pub0012/coastwatch/sst/nrt/avhrr_frac/

THREDDS

[14 Day Rotated Buffer]

L2P + L3U:

coastwatch.noaa.gov/thredds/socd/coastwatch/acspo/catalog_sst_acspo_frac_nrt.html

PO.DAAC Archive

AVHRRF_MA-STAR-L2P-v2.80: 10.5067/GHMTA-2PS28

AVHRRF_MA-STAR-L3U-v2.80: 10.5067/GHMTA-3US28

AVHRRF_MB-STAR-L2P-v2.80: 10.5067/GHMTB-2PS28

AVHRRF_MB-STAR-L3U-v2.80: 10.5067/GHMTB-3US28

AVHRRF_MC-STAR-L2P-v2.80: 10.5067/GHMTC-2PS28

AVHRRF_MC-STAR-L3U-v2.80: 10.5067/GHMTC-3US28

NCEI Archive

GHRSST-AVHRRF_MA-STAR-L2P: https://doi.org/10.25921/am4h-a956 (incomplete version of PO.DAAC 10.5067/GHMTA-2PS28)

GHRSST-AVHRRF_MA-STAR-L3U : https://doi.org/10.25921/rnn8-jh57 (incomplete version of PO.DAAC 10.5067/GHMTA-3US28)

GHRSST-AVHRRF_MB-OSPO-L2P: https://doi.org/10.25921/pakj-ez21 (incomplete version of PO.DAAC 10.5067/GHMTB-2PS28)

GHRSST-AVHRRF_MB-OSPO-L3U: https://doi.org/10.25921/ba3d-yz14 (incomplete version of PO.DAAC 10.5067/GHMTB-3US28)

GHRSST-AVHRRF_MC-OSPO-L2P: https://doi.org/10.25921/xcj3-sd43 (incomplete version of PO.DAAC 10.5067/GHMTC-2PS28)

GHRSST-AVHRRF_MC-OSPO-L3U: https://doi.org/10.25921/ktez-s353 (incomplete version of PO.DAAC 10.5067/GHMTC-3US28)

NOAA CoastWatch co-gridded VIIRS SST from ACSPO

jebidiah.jeffery — Thu, 21 Oct 2021 15:58:41 +0000

NOAA CoastWatch co-gridded VIIRS SST from ACSPO

NOAA CoastWatch co-gridded VIIRS SST products are Level 3 daily composites in compatible format and projection with other CoastWatch Level 3 sector and global products (such as CoastWatch sector VIIRS ocean color). Twenty four CoastWatch sector files mapped to equatorial projection that cover the globe (see Description tab for sector definitions) two polar stereographic sectors in 750 m nominal, native resolution and a global, 4 km resolution, single file mapped product is also available. These CoastWatch Level 3 products are generated from ACSPO L2P SST as input.

jebidiah.jeffery Thu, 10/21/2021 - 11:58

NOAA CoastWatch creates these co-gridded AVHRR SST Level 3 daily composites to be compatible in format and projection with the VIIRS Ocean Color Level 3 daily composites (Note VIIRS sector full resolution is 750m). The input for these CoastWatch Sector SST files is daily, near real time ACSPO SST for AVHRR.

These SST products are intended for users who seek AVHRR ACSPO SST data co-registered with CoastWatch ocean color data from AVHRR and are not the NOAA operational L3U ACSPO products. The CoastWatch co-gridded products are available in regional Sectors at full 1.2 kilometer AVHRR resolution and as global products at 4 km resolution. The regional products consist of 24 sectors (see Sector map below) mapped to the VIIRS ocean color sectors, and polar stereographic regions NP06 and SP06 mapped identically to the CoastWatch VIIRS ice products. The 4 km global product is also mapped identically to the corresponding CoastWatch VIIRS ocean color product.

CoastWatch Regional Sectors

These products start with the operational ACSPO L2P product (in the canonical ten-minute granule form), ingest that for CoastWatch processing, and then georegister and mask those granule-level products. The mask applied is 0xC002.That rejects pixels flagged 0x40, 0x80, and 0xC0 (anything but confident clear) and pixels flagged 0x02 (land). The L3 masked granule products are aggregated to a daily product on the same grid on the basis of the last pixel seen. The 4 km global product is created by averaging the non-null values for the pixels of the daily sector maps in a 5x5 grid.

Note: CoastWatch also distributes a gap-filled polar plus geosynchronous blended daily SST analysis product suite at 5 km resolution.

Short Names

CW Sector SST

Temporal Coverage

Daily

Product Families

Sea Surface Temperature

Measurements

Sea Surface Temperature - Polar-orbiting

Processing Levels

Level 3

Spatial Coverage

Global

CoastWatch Global Sectors

Latency Groups

0 Hours <= 24 Hours (NRT)

Latency Details

~12-24 h

Spatial Resolution Groups

100m < 2km

2km+

Spatial Resolution Details

Global in 24 CoastWatch Sectors (24 separate files at nominal 750 m resolution)
Global 4 km (in 1 file)

Platforms

Instruments

Processing Algorithms

ACSPO

Data Providers

NOAA

NESDIS

STAR

SST Team

Data Tool Links

CoastWatch Data Portal

HTTPS

NPP:

https://coastwatch.noaa.gov/data/pub0021/coastwatch/sst_acspo/viirs/npp/night/global/daily/

N20:

https://coastwatch.noaa.gov/data/pub0021/coastwatch/sst_acspo/viirs/n20/night/global/daily/

N21:

https://coastwatch.noaa.gov/data/pub0021/coastwatch/sst_acspo/viirs/n21/night/global/daily/

THREDDS

Top Level

Global+Sector:

https://www.star.nesdis.noaa.gov/thredds/socd/coastwatch/catalog_coastw…

CONUS:

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

GLOBAL

NPP:

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

N20:

https://coastwatch.noaa.gov/thredds/socd/coastwatch/catalog_coastwatch_sst_acspo_viirs_n20_night_conus_daily.html

N21:

https://coastwatch.noaa.gov/thredds/socd/coastwatch/catalog_coastwatch_sst_acspo_viirs_n21_night_conus_daily.html

Sectors

NPP:

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

N20:

https://coastwatch.noaa.gov/thredds/socd/coastwatch/catalog_coastwatch_sst_acspo_viirs_n20_night_conus_daily.html

N21:

https://coastwatch.noaa.gov/thredds/socd/coastwatch/catalog_coastwatch_sst_acspo_viirs_n21_night_conus_daily.html

ACSPO Global 0.02º Gridded Super-collated SST and Thermal Fronts from Low-Earth-Orbiting Platforms (L3S-LEO)

jebidiah.jeffery — Thu, 21 Oct 2021 15:47:25 +0000

ACSPO Global 0.02º Gridded Super-collated SST and Thermal Fronts from Low-Earth-Orbiting Platforms (L3S-LEO)

NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) L3S-LEO SST is a family of multisensor gridded ("L3") 0.02º resolution super-collated ("S") products. The L3S-LEO family is organized into three lines: PM, AM and Daily. The AM and PM lines correspond to 9:30am/pm and 1:30am/pm equator crossing times, respectively. The Daily line combines PM and AM (day and night) SSTs into a single daily L3S SST that is normalized to 1:30am viewing conditions.

jebidiah.jeffery Thu, 10/21/2021 - 11:47

The NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) L3S-LEO is a family of multisensor super-collated (L3S) gridded 0.02º resolution SST products from low earth orbit (LEO) satellites. The L3S-LEO family is organized into three lines: PM, AM and Daily. The AM (mid-morning orbit) and PM (afternoon orbit) lines contain SST data from satellites with 9:30am/pm and 1:30am/pm equator crossing times, respectively. Both AM and PM lines are split into day-time (solar zenith angle < 90º) and night-time (solar zenith angle >= 90º) files, resulting in 4 files per day in total, which approximately cover the 24 hours diurnal cycle in 4 points. The Daily (DY) line combines PM and AM (day and night) SSTs into a single daily L3S SST that is normalized to 1:30am viewing conditions using a statistical model informed by surface wind speed and shortwave radiation.

The AM product is produced from three (currently two active) AVHRR FRACs (flown aboard METOP-A, B and C). The PM product is produced from three VIIRSs (flown aboard NPP, N20, N21) and MODIS (onboard Aqua; included until Dec 31 2022). The DY product is produced by combining all four L3S-LEO-AM/PM day/night SSTs. SST from Terra MODIS is also included in DY in until 31 Dec 2021.

All L3S-LEO products contain information about thermal fronts, which is included in ACSPO files in two new variables ‘sst_front_position’ and ‘sst_gradient_magnitude’. The first variable is a binary indicator of SST front position. It is set to ‘1’ where a front is present, and to ‘0’ elsewhere. The second variable is the SST gradient magnitude (in units of K/km).

L3S-LEO data are provided in GHRSST Data Specification v2 (GDS2) NetCDF format, as L3S (gridded, super-collated, 0.02º). We plan to create an L3S-GEO line, and eventually consolidate the L3S-LEO and L3S-GEO into one sensor-agnostic L3S SST product.

All ACSPO L3S-LEO products provided here are mature and validated. Please contact the CoastWatch helpdesk with any questions.

Short Names

ACSPO-L3S-LEO-PM-v2.81

ACSPO-L3S-LEO-AM-v2.80

ACSPO-L3S-LEO-Daily-v2.81

Temporal Start Date

February 24, 2000

Product Families

Sea Surface Temperature

Algorithms

Jonasson, O.; Gladkova, I.; Ignatov, A. Towards global daily gridded super-collated SST product from low earth orbiting satellites (L3S-LEO-Daily) at NOAA. Proc. SPIE 2022, 12118, 1211805, doi:10.1117/12.2620103, [pdf paper (link)], [mp4 presentation (link)], [ppt presentation (link)].
Jonasson, O.; Gladkova, I.; Ignatov, A.; Kihai, Y. Algorithmic Improvements and Consistency Checks of the NOAA Global Gridded Super-Collated SSTs from Low Earth Orbiting Satellites (L3S-LEO). Proc. SPIE 2021, 11752, 1175202, doi:10.1117/12.2585819, [pdf paper (link)], [mp4 presentation (link)], [ppt presentation (link)].
Jonasson, O.; Gladkova, I.; Ignatov, A.; Kihai, Y. Progress with Development of Global Gridded Super-Collated SST Products from Low Earth Orbiting Satellites (L3S-LEO) at NOAA. Proc. SPIE 2020, 11420, 1142002. doi:10.1117/12.2551819. [pdf paper (link)], [mp4 presentation (link)], [ppt presentation (link)].
Gladkova, I.; Ignatov, A.; Pennybacker, M.; Kihai, Y. Towards High-Resolution Multi-Sensor Gridded ACSPO SST Product: Reducing Residual Cloud Contamination. Proc. SPIE 2019, 11014, 110140L. doi:10.1117/12.2518462, [pdf paper (link)], [ppt presentation (link)].
Gladkova, I., A. Ignatov, M. Pennybacker, Y. Kihai, 2019: Towards High-Resolution Multi-Sensor Gridded ACSPO L3S SST Product. GHRSTT XX: Science Team Meeting, June 2019, Frascati, Italy [ppt presentation (link)].

Monitoring

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: Xu, F., A. Ignatov, 2014: In situ SST Quality Monitor (iQuam). JTech, 31, 164-180, doi:10.1175/JTECH-D-13-00121.1.
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 [ppt presentation 21.5Mb (link)].
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 - Polar-orbiting

Processing Levels

Level 3

Spatial Coverage

Global

The ACSPO L3S-LEO SST data are provided by NOAA STAR. We strongly recommend contacting Olafur.Jonasson@noaa.gov or Alex.Ignatov@noaa.gov, before the data are used for any publication or presentation.

Latency Groups

0 Hours <= 24 Hours (NRT)

Latency Details

24 hours

Spatial Resolution Groups

2km+

Spatial Resolution Details

0.02°

Platforms

Instruments

Processing Algorithms

ACSPO

Data Providers

NOAA

NESDIS

STAR

Data Tool Links

Routine Research Production (source: STAR)
(Best effort, most recent ACSPO release, last 90 days)

PM L3S: Day Night

AM L3S: Day Night

Daily L3S: Day+Night Fronts

Reanalysis (RAN) Research Production (source: STAR)
(delayed 2 months, science quality)

PM L3S: Day Night

AM L3S: Day Night

Daily L3S: Day+Night Fronts

Sample Filenames

Near Real-Time (NRT) Operational Production (source: OSPO)

20240930120000-OSPO-L3S_GHRSST-SSTsubskin-LEO_AM_D-ACSPO_V2.80-v02.0-fv01.0.nc
20240930120000-OSPO-L3S_GHRSST-SSTsubskin-LEO_AM_N-ACSPO_V2.80-v02.0-fv01.0.nc
20240930120000-OSPO-L3S_GHRSST-SSTsubskin-LEO_PM_D-ACSPO_V2.80-v02.0-fv01.0.nc
20240930120000-OSPO-L3S_GHRSST-SSTsubskin-LEO_PM_N-ACSPO_V2.80-v02.0-fv01.0.nc

Research Production (source: STAR)

20200330120000-STAR-L3S_GHRSST-SSTsubskin-LEO_AM_N-ACSPO_V2.80-v02.0-fv01.0.nc
20200330120000-STAR-L3S_GHRSST-SSTsubskin-LEO_AM_D-ACSPO_V2.80-v02.0-fv01.0.nc
20200330120000-STAR-L3S_GHRSST-SSTsubskin-LEO_PM_N-ACSPO_V2.81-v02.0-fv01.0.nc
20200330120000-STAR-L3S_GHRSST-SSTsubskin-LEO_PM_D-ACSPO_V2.81-v02.0-fv01.0.nc
20200330120000-STAR-L3S_GHRSST-SSTsubskin-LEO_Daily-ACSPO_V2.81-v02.0-fv01.0.nc

HTTPS

Near Real-Time (NRT) Operational Production (source: OSPO)

AM,PM L3S (last 15 days):

https://coastwatch.noaa.gov/data/pub0053/coastwatch/sst/nrt/l3s/

Routine Research Production (source: STAR)
(Best effort, most recent ACSPO release, last 90 days)

PM L3S:

https://coastwatch.noaa.gov/pub/socd2/coastwatch/sst/nrt/l3s/leo/pm

AM L3S:

https://coastwatch.noaa.gov/pub/socd2/coastwatch/sst/nrt/l3s/leo/am

Daily L3S:

https://coastwatch.noaa.gov/pub/socd2/coastwatch/sst/nrt/l3s/leo/daily

Reanalysis (RAN) Research Production (source: STAR)
(delayed 2 months, science quality)

PM L3S:

https://coastwatch.noaa.gov/pub/socd2/coastwatch/sst/ran/l3s/leo/pm

AM L3S:

https://coastwatch.noaa.gov/pub/socd2/coastwatch/sst/ran/l3s/leo/am

Daily L3S:

https://coastwatch.noaa.gov/pub/socd2/coastwatch/sst/ran/l3s/leo/daily

Production sources

STAR - Center for Satellite Applications and Research

OSPO - Office of Satellite and Product Operations

Data content description

*NRT - Near-Real Time data
*RAN - Reanalysis of L3S-LEO-PM (Jul'2002-present), L3S-LEO-AM (Dec'2006-present), and L3S-LEO-DY (Feb'2000-present). Note that reanalysis data is produced on a 8-week delay.

Please acknowledge "NOAA CoastWatch/OceanWatch" when you use data from our site and cite the particular dataset DOI as appropriate.

THREDDS

Routine Research Production (source: STAR)
(Best effort, most recent ACSPO release, last 90 days)

https://coastwatch.noaa.gov/thredds/socd/coastwatch/acspo/catalog_sst_acspo_l3s_nrt.html

Reanalysis (RAN) Research Production (source: STAR)
(delayed 2 months, science quality)

https://coastwatch.noaa.gov/thredds/socd/coastwatch/acspo/catalog_sst_acspo_l3s_ran.html

ERDDAP

Near Real-Time (NRT) Research Production (source: STAR)
(Best effort, most recent ACSPO release, last 90 days)

Daily L3S:

https://coastwatch.noaa.gov/erddap/griddap/noaacwLEOACSPOSSTL3SnrtKDaily.html (Kelvin)

https://coastwatch.noaa.gov/erddap/griddap/noaacwLEOACSPOSSTL3SnrtCDaily.html (degrees Celsius)

Reanalysis (RAN) Research Production (source: STAR)
(delayed 2 months, science quality)

Daily L3S:

https://coastwatch.noaa.gov/erddap/griddap/noaacwLEOACSPOSSTL3SKDaily.html (Kelvin)

https://coastwatch.noaa.gov/erddap/griddap/noaacwLEOACSPOSSTL3SCDaily.html (degrees Celsius)

PO.DAAC Archive

NRT data is overwritten with RAN 'scientific quality' SST data with a 2-month delay

L3S-AM: 10.5067/GHLAM-3SS28

L3S-PM: 10.5067/GHLPM-3S281

L3S-Daily: 10.5067/GHLDY-3S281

NCEI Archive

NRT data is overwritten with RAN 'scientific quality' SST data with a 2-month delay

L3S-AM: 10.25921/0twk-7h57

L3S-PM: 10.25921/fddx-gq76