STAR

Global Water Classification from VIIRS

jwarner — Thu, 19 Dec 2024 21:27:36 +0000

Global Water Classification from VIIRS

The global water classification products are produced by the NOAA Ocean Color Science Team from the VIIRS-SNPP Level 2 Ocean Color data. Water classification data is 9km resolution, with daily, 8-day and monthly products generated.

jwarner Thu, 12/19/2024 - 16:27

The water classification product provides a grouping of global ocean color data into 23 distinct water classes based on bio-optical and biogeochemical characteristics. Water classification allows for the development and improvement of class-specific algorithms estimating bio-optical and biogeochemical properties of water (Cui et al., 2020; Jiang et al., 2020; Le et al., 2011; Moore et al., 2001; Uudeberg et al., 2020), and provides a framework for understanding satellite product uncertainties (Moore et al., 2009; Wei et al., 2016b) as well as the mechanisms controlling ocean biology, chemistry and physics (Longhurst, 1998; Longhurst et al., 1995; Martin Traykovski and Sosik, 2003; Oliver and Irwin, 2008).

To generate these data products, a new water classification algorithm was applied to the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) mission (2011–present), focusing on ocean remote sensing reflectance (R_rs(λ)) (or normalized water-leaving radiance, nL_w(λ)).

The following table shows the median for each of the biogeochemical and bio-optical properties in global surface waters by water class.

Water Class	Chl-a	a_ph*(443)	K_d(490)	SPM	a_pg(443)	b_bp(443)	a_ph(443)	a_dg(443)
1	0.0547	0.1401	0.0248	0.0528	0.0121	0.0019	0.0077	0.0044
2	0.1252	0.1074	0.0347	0.1098	0.0212	0.0022	0.0135	0.0078
3	0.202	0.0932	0.0463	0.1688	0.0309	0.0025	0.0189	0.0121
4	0.326	0.0772	0.0636	0.2466	0.0449	0.0029	0.0258	0.0193
5	0.4215	0.0609	0.0722	0.2998	0.0559	0.003	0.0254	0.029
6	0.5877	0.0636	0.0925	0.3543	0.0687	0.0036	0.0375	0.0307
7	0.6605	0.0457	0.0963	0.4082	0.0822	0.0038	0.0296	0.0504
8	0.9448	0.0516	0.1234	0.4598	0.0994	0.0043	0.0487	0.0492
9	0.9653	0.036	0.1233	0.5172	0.1141	0.0054	0.0334	0.078
10	1.4385	0.042	0.162	0.6191	0.1401	0.007	0.0591	0.0782
11	1.617	0.0499	0.2272	0.5934	0.1499	0.006	0.0789	0.0678
12	1.7774	0.032	0.1981	0.8035	0.1841	0.01	0.053	0.1242
13	2.9497	0.0413	0.4987	1.5881	0.2632	0.0105	0.1228	0.1366
14	2.8087	0.0353	0.6142	2.8692	0.3054	0.0228	0.0921	0.2032
15	3.0296	0.0252	0.301	1.0352	0.2943	0.0129	0.0716	0.2122
16	4.2528	0.0433	1.4246	7.9519	0.5585	0.0716	0.1883	0.3505
17	4.0711	0.0303	0.7734	3.4954	0.4767	0.0217	0.1125	0.3431
18	6.5827	0.0193	0.544	1.4592	0.5747	0.0168	0.1228	0.4399
19	6.4768	0.0434	2.5078	20.906	1.18	0.1492	0.3055	0.8409
20	11.566	0.0337	3.7803	53.549	2.4363	0.1879	0.4112	2.0425
21	9.3719	0.0304	1.3166	6.5699	0.9131	0.06	0.3045	0.5643
22	11.847	0.022	1.3258	6.6311	1.3354	0.0506	0.262	1.0603
23	13.435	0.0273	2.1316	13.718	2.1111	0.0918	0.376	1.7439

Temporal Start Date

February 1, 2012

Temporal Coverage

Daily, 8-Day, and Monthly

Product Families

Ocean Color

Jianwei Wei, Menghua Wang, Karlis Mikelsons, Lide Jiang, Susanne Kratzer, Zhongping Lee, Tim Moore, Heidi M. Sosik, Dimitry Van der Zande, Global satellite water classification data products over oceanic, coastal, and inland waters, Remote Sensing of Environment, Volume 282, 2022, 113233, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2022.113233.

Measurements

Water Classification

Processing Levels

Level 3

Spatial Coverage

Global

Latency Groups

24+ hours (Delayed)

Spatial Resolution Groups

2km+

Spatial Resolution Details

9km resolution

Platforms

NOAA

SNPP

Instruments

VIIRS

Processing Algorithms

MSL12

Data Providers

NOAA

NESDIS

STAR

Product DOI

https://doi.org/10.1016/j.rse.2022.113233

Data Tool Links

NOAA STAR Ocean Color Viewer (OCView)

Daily

8-Day

Monthly

HTTPS

NetCDF: https://www.star.nesdis.noaa.gov/data/pub0040/coastwatch/viirs/science/L3/global/wc/

Images: https://www.star.nesdis.noaa.gov/data/pub0040/coastwatch/viirs/science/L3/PNG/wc/

ERDDAP

NPP: https://coastwatch.noaa.gov/erddap/griddap/noaacwVIIRSNPPwaterclassDaily.html

N20: https://coastwatch.noaa.gov/erddap/griddap/noaacwVIIRSN20waterclassDaily.html

N21: https://coastwatch.noaa.gov/erddap/griddap/noaacwVIIRSN21waterclassDaily.html

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

Global

Latency Groups

24+ hours (Delayed)

Spatial Resolution Groups

2km+

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: MUNSTER_v1_eddytraject_multi_global_30days_s20241128_e20241227.nc

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: https://www.star.nesdis.noaa.gov/data/pub0054/coastwatch/products/eddy_tracking/netcdf/munster/eddy_tracks_monthly/

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 MSL12 multi-sensor DINEOF global 2 km gap-filled products: Chlorophyll-a, diffuse attenuation coefficient Kd(490), and suspended particulate matter (SPM)

michael.soracco — Tue, 23 Jan 2024 16:22:32 +0000

NOAA MSL12 multi-sensor DINEOF global 2 km gap-filled products: Chlorophyll-a, diffuse attenuation coefficient Kd(490), and suspended particulate matter (SPM)

The NOAA Multi-Sensor Level-1 to Level-2 (MSL12) Ocean Color, science quality, multi-sensor 2 km global gap-filled analysis includes chlorophyll-a, K_d(490), and SPM products. These global gap-free data are generated using the data interpolating empirical orthogonal function (DINEOF) method (Liu and Wang, 2023). This product currently uses data from 3 instruments: the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite, VIIRS on the NOAA-20 satellite, plus the Ocean and Land Colour Instrument (OLCI) on the Sentinel 3A satellite from the Copernicus program of the European Union.

michael.soracco Tue, 01/23/2024 - 11:22

Ocean Color satellite sensors measure visible light, at specific wavelengths, that leaves the surface of the ocean and arrives at the top of the atmosphere where the sensor (satellite platform) is located. From these visible spectral radiance measurements, along with simultaneous radiance measurements at the near-infrared (NIR) and the shortwave infrared (SWIR) wavelengths, the color of the ocean, or normalized water-leaving radiances (nL_w(λ)), can be calculated. Then, the nL_w(λ) spectra are used to derive other ocean properties such as the concentration of chlorophyll-a (chlor-a or Chl-a) (Wang and Son, 2016), which is the green pigment responsible for photosynthesis and therefore an indicator of the amount of phytoplankton biomass in the ocean water, the diffuse attenuation coefficient at 490 nm K_d(490) (Wang et al., 2009), and water suspended particulate matter (SPM) (Wei et al., 2021). K_d(490) and SPM can be related to water turbidity and clarity.

The NOAA Multi-Sensor Level-1 to Level-2 (MSL12) Ocean Color, science quality, multi-sensor 2km global gap-filled analysis includes chlorophyll-a, K_d(490), and SPM products. These global gap-free data are generated using the data interpolating empirical orthogonal function (DINEOF) method (Liu and Wang, 2023). The data that go into these three products currently come from 3 instruments: the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite, VIIRS aboard the NOAA-20 satellite, plus the Ocean and Land Colour Instrument (OLCI) on the Sentinel 3A satellite from the Copernicus program of the European Union. NOAA gets the OLCI Level-1B data from EUMETSAT, and ocean color data at NOAA are processed using the same NOAA MSL12 ocean color data processing system developed by the NOAA/STAR Ocean Color Team (Wang et al., 2013).

Mikelsons and Wang (2019) explains the approach to the Level-3, multi-sensor merged, global daily data product served by CoastWatch at ~4 km spatial resolution in NetCDF format. SNPP and NOAA-20 operate along the same Sun-synchronous polar orbit that crosses the equator at about 13:30 local time—both satellites travel around Earth from pole to pole in such a way that they observe the same areas at about the same time of day, no matter the season. There is about a 50-minute delay between the paths of NOAA-20 and SNPP, so NOAA-20’s path runs between two adjacent SNPP orbital paths and vice versa. Thus, the overlap of the spatial coverages in the two VIIRS sensors automatically fills each instrument’s data gaps (Mikelsons and Wang, 2019). In addition, ocean color data from the VIIRS SNPP and NOAA-20 have the same spatial and temporal resolution, and these data are processed using the same algorithm and software package (i.e., MSL12). Therefore, the statistics of their ocean color products are very similar, and the data can be merged into a global resolution data set directly without adjustment (Liu and Wang, 2019). These multi-sensor daily merged products are derived from MSL12 for VIIRS SNPP and NOAA-20, as well as OLCI-Sentinel-3A (Liu and Wang, 2023).

Note that proper adjustments are applied to Chl-a, K_d(490), and SPM algorithms so that consistent ocean biological and biogeochemical products can be derived from the three satellite sensors (VIIRS-SNPP, VIIRS-NOAA-20, and OLCI-Sentinel-3A) (Wang et al., 2020).

The EOS article by Liu and Wang (2019) explains the approach to the Level-4, multi-sensor, gap-filled analysis for the earlier, 2-sensor product (VIIRS SNPP plus VIIRS NOAA-20; also available on CoastWatch). "Even after the datasets from the two satellites are merged, some gaps remain. To complete the picture, the gap-filling application uses a mathematical technique based on the data interpolating empirical orthogonal function (DINEOF) (Alvera-Azcarate et al., 2005; Beckers and Rixen, 2003). This technique exploits the coherency over location and time of the data from the two satellites to infer a value at the missing location." CoastWatch now serves this 3-sensor-derived daily global gap-filled Chl-a, K_d(490), and SPM products at 2 km spatial resolution in NetCDF. Go to the data access tab on this page for access.

Please read Liu and Wang (2023) for details about the global gap-free ocean color products of Chl-a, K_d(490), and SPM derived from multi-satellite measurements of VIIRS-SNPP, VIIRS-NOAA-20, and OLCI-Sentinel-3A.

Temporal Start Date

February 9, 2018

Temporal Coverage

Daily

Product Families

Ocean Color

Liu and Wang, 2023 (Liu, X. and M. Wang, "High spatial resolution gap-free global and regional ocean color products", IEEE Trans. Geosci. Remote Sens., 61, 4204118 (2023). doi:10.1109/tgrs.2023.3271465)

Liu, X. and M. Wang, "Global daily gap-free ocean color products from multi-satellite measurements", Int. J. Appl. Earth Obs. Geoinf., 108, 102714 (2022). doi:10.1016/j.jag.2022.102714

Wei, J., M. Wang, L. Jiang, X. Yu, K. Mikelsons, and F. Shen, "Global estimation of suspended particulate matter from satellite ocean color imagery", Journal of Geophysical Research: Oceans, 126, e2021JC017303 (2021). doi:10.1029/2021JC017303

Liu, X. and M. Wang, "Filling the gaps in ocean maps", EOS, 100 (2019). doi:10.1029/2019EO136548

Liu, X., and M. Wang (2019), Filling the gaps of missing data in the merged VIIRS SNPP/NOAA-20 ocean color product using the DINEOF method, Remote Sens., 11, 178, https://doi.org/10.3390/rs11020178.

Mikelsons, K., and M. Wang (2019), Optimal satellite orbit configuration for global ocean color product coverage, Opt. Express, 27, A445–A457, https://doi.org/10.1364/OE.27.00A445

Mikelsons, K., and M. Wang (2018), Interactive online maps make satellite ocean data accessible, Eos Trans. AGU, 99, https://doi.org/10.1029/2018EO096563.

Liu, X. and M. Wang, "Gap filling of missing data for VIIRS global ocean color product using the DINEOF method", IEEE Trans. Geosci. Remote Sens., 56, 4464-4476 (2018). doi:10.1109/TGRS.2018.2820423

Wang, M. and S. Son, "VIIRS-derived chlorophyll-a using the ocean color index method", Remote Sens. Environ., 182, 141-149 (2016). doi:10.1016/j.rse.2016.05.001

Wang, M., et al. (2013), Impact of VIIRS SDR performance on ocean color products, J. Geophys. Res. Atmos., 118, 10,347–10,360, https://doi.org/10.1002/jgrd.50793.

Wang, M., S. Son, and L. W. Harding Jr., “Retrieval of diffuse attenuation coefficient in the Chesapeake Bay and turbid ocean regions for satellite ocean color applications,” J. Geophys. Res., 114, C10011, https://doi.org/10.1029/2009JC005286, 2009.

Wang, M., L. Jiang, S. Son, X. Liu, and K. J. Voss, “Deriving consistent ocean biological and biogeochemical products from multiple satellite ocean color sensors,” Opt. Express, 28, 2661–2682, 2020. https://doi.org/10.1364/OE.376238

Alvera-Azcarate, A., et al. (2005), Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions: Application to the Adriatic Sea, Ocean Model., 9, 325–346, https://doi.org/10.1016/j.ocemod.2004.08.001.

Beckers, J., and M. Rixen (2003), EOF calculations and data filling from incomplete oceanographic data sets, J. Atmos. Oceanic Technol., 20, 1,839–1,856, https://doi.org/10.1175/1520-0426(2003)020<1839:ECADFF>2.0.CO;2.

Algorithm Theoretical Basis Document (ATBD)

Wang, M., X. Liu, L. Jiang and S. Son, "The VIIRS Ocean Color Products", Algorithm Theoretical Basis Document Version 1.0, 68 pp., June 2017.

For more MSL12 processing documentation, please go to documentation tab here.

Measurements

Chlorophyll-a Concentration

Diffuse Attenuation Coefficients

Suspended Particulate Matter

Processing Levels

Level 4

Spatial Coverage

Global

Latency Groups

24+ hours (Delayed)

Latency Details

Daily, ~24h to 48h

Spatial Resolution Groups

100m < 2km

Spatial Resolution Details

2km

Platforms

Instruments

Processing Algorithms

MSL12

Data Providers

NOAA

NESDIS

STAR

CoastWatch

Data Tool Links

CoastWatch Data Portal:

Sample Filenames

V2022141_A1_WW00_chlora_2km.nc

V2022141_A1_WW00_kd490_2km.nc

V2022141_A1_WW00_spm_2km.nc

HTTPS

Chlorophyll-a:

https://coastwatch.noaa.gov/data/pub0040/coastwatch/viirs/science/L3/global/chlora/dineof2km/

K_d(490):

https://coastwatch.noaa.gov/data/pub0040/coastwatch/viirs/science/L3/global/kd/dineof2km/

Suspended Particulate Matter (SPM):

https://coastwatch.noaa.gov/data/pub0040/coastwatch/viirs/science/L3/global/spm/dineof2km/

THREDDS

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

ERDDAP

https://coastwatch.noaa.gov/erddap/search/index.html?page=1&itemsPerPage=1000&searchFor=2km+dineof

NOAA MSL12 multi-sensor DINEOF global 9km gap-filled products: Chlorophyll-a, diffuse attenuation coefficient Kd(490), and suspended particulate matter (SPM)

jebidiah.jeffery — Fri, 28 Oct 2022 14:47:01 +0000

NOAA MSL12 multi-sensor DINEOF global 9km gap-filled products: Chlorophyll-a, diffuse attenuation coefficient Kd(490), and suspended particulate matter (SPM)

The NOAA Multi-Sensor Level-1 to Level-2 (MSL12) Ocean Color, science quality, multi-sensor global gap-filled analysis includes chlorophyll-a, K_d(490), and SPM products. These global gap-free data are generated using the data interpolating empirical orthogonal function (DINEOF) method (Liu and Wang, 2022). The data that go into this product currently come from 3 instruments: the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite, VIIRS on the NOAA-20 satellite, plus the Ocean and Land Colour Instrument (OLCI) on the Sentinel 3A satellite from the Copernicus program of the European Union.

jebidiah.jeffery Fri, 10/28/2022 - 10:47

The NOAA Multi-Sensor Level-1 to Level-2 (MSL12) Ocean Color, science quality, multi-sensor global gap-filled analysis includes chlorophyll-a, K_d(490), and SPM products. These global gap-free data are generated using the data interpolating empirical orthogonal function (DINEOF) method (Liu and Wang, 2022). The data that go into these three products currently come from 3 instruments: the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite, VIIRS aboard the NOAA-20 satellite, plus the Ocean and Land Colour Instrument (OLCI) on the Sentinel 3A satellite from the Copernicus program of the European Union. NOAA gets the OLCI Level-1B data from EUMETSAT, and ocean color data at NOAA are processed using the same NOAA MSL12 ocean color data processing system developed by the NOAA/STAR Ocean Color Team (Wang et al., 2013).

Mikelsons and Wang (2019) explains the approach to the Level-3, multi-sensor merged, global daily data product served by CoastWatch at ~4 km spatial resolution in NetCDF format. SNPP and NOAA-20 operate along the same Sun-synchronous polar orbit that crosses the equator at about 13:30 local time—both satellites travel around Earth from pole to pole in such a way that they observe the same areas at about the same time of day, no matter the season. There is about a 50-minute delay between the paths of NOAA-20 and SNPP, so NOAA-20’s path runs between two adjacent SNPP orbital paths and vice versa. Thus, the overlap of the spatial coverages in the two VIIRS sensors automatically fills each instrument’s data gaps (Mikelsons and Wang, 2019). In addition, ocean color data from the VIIRS SNPP and NOAA-20 have the same spatial and temporal resolution, and these data are processed using the same algorithm and software package (i.e., MSL12). Therefore, the statistics of their ocean color products are very similar, and the data can be merged into a global 9-kilometer resolution data set directly without adjustment (Liu and Wang, 2019). These multi-sensor daily merged products are derived from MSL12 for VIIRS SNPP and NOAA-20, as well as OLCI-Sentinel-3A (Liu and Wang, 2022).

The EOS article by Liu and Wang (2019) explains the approach to the Level-4, multi-sensor, gap-filled analysis for the earlier, 2-sensor product (VIIRS SNPP plus VIIRS NOAA-20; also available on CoastWatch). "Even after the datasets from the two satellites are merged, some gaps remain. To complete the picture, the gap-filling application uses a mathematical technique based on the data interpolating empirical orthogonal function (DINEOF) (Alvera-Azcarate et al., 2005; Beckers and Rixen, 2003). This technique exploits the coherency over location and time of the data from the two satellites to infer a value at the missing location." CoastWatch now serves this 3-sensor-derived daily global gap-filled Chl-a, K_d(490), ans SPM products at 9 km spatial resolution in NetCDF. Go to the data access tab on this page for access.

Please read Liu and Wang (2022) for details about the global gap-free ocean color products of Chl-a, K_d(490), and SPM derived from multi-satellite measurements of VIIRS-SNPP, VIIRS-NOAA-20, and OLCI-Sentinel-3A.