OSPO

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

Synthetic Aperture Radar Imagery (NRCS)

jebidiah.jeffery — Thu, 17 Mar 2022 15:30:36 +0000

Synthetic Aperture Radar Imagery (NRCS)

Spaceborne Synthetic Aperture Radar (SAR) imagery maps the surface microwave radar reflectivity at resolutions from a sub-meter to 100 m depending on the particular SAR satellite and mode. Since a radar provides its own illumination, imagery is independent of the time of day. At typical radar frequencies, SARs can image through clouds, so SARs are considered "all-weather" instruments. Several geophysical parameters can be derived from SAR including sea surface wind speed.

jebidiah.jeffery Thu, 03/17/2022 - 11:30

Spaceborne Synthetic Aperture Radar (SAR) imagery maps the surface microwave radar reflectivity at resolutions from a sub-meter to 100 m depending on the particular SAR satellite and mode. Since a radar provides its own illumination, imagery is independent of the time of day. At typical radar frequencies, SARs can image through clouds, so SARs are considered "all-weather" instruments. Several gephysical parameters can be derived from SAR including sea surface wind speed.

For side-scanning SARs, the normalized radar cross section (NRCS) of the surface is proportional to the surface roughness on the scale of the radar wave length (from 3 to 30 cm). Ocean surface roughness on this scale is dependent on the local wind speed and direction. The higher the wind speed the larger the NRCS of the surface. The highest NRCS occurs when the radar look direction and wind direction are aligned and smallest when the radar looks cross wind.

The relationship between wind speed and direction and radar cross section is called the "geophysical model function." These functions are generally empirically determined.

Although we can predict an expected NRCS value for a given wind speed and direction, the reverse is not true. A specific NRCS value can be associated with many pairs of wind speed and direction. If we know the wind direction a priori, we can infer the wind speed.

For the operational SAR-derived wind speed products, we use the model directions from NOAA’s Global Forecast System (GFS) model on a 0.5 degree, latitude-longitude grid to initiate the retrievals. The wind speed retrievals are provided at 500 m resolution. These retrievals agree with independent wind speed estimates to better than 2 m/s.

Current users of these data include the US National Ice Center, the Alaska Weather Service, and the Naval Oceanographic Office.

A good review of SAR operational wind speed retrieval can be found at: http://tos.org/oceanography/assets/docs/26-2_monaldo.pdf

Product Families

Sea Surface Roughness

Monaldo, F.M., C.R. Jackson, and W.G. Pichel. 2013. Seasat to RADARSAT-2: Research to operations. Oceanography 26(2):34–45, http://dx.doi.org/10.5670/oceanog.2013.29
SAR Marine User Manual (2004) Christopher Jackson and John Apel, Editors

Measurements

Normalized Radar Cross-Section

Sea Surface Wind Speed

Processing Levels

Level 1

Level 2

Level 3

Spatial Coverage

Selective Acquisitions

Latency Groups

0 Hours <= 24 Hours (NRT)

Spatial Resolution Groups

0 m <= 100 m

100m < 2km

Spatial Resolution Details

NRCS: Variable, 0-10 m
Winds: 500 m

Platforms

Instruments

Processing Algorithms

SAROPS

Data Providers

Copernicus

CSA

NOAA

NESDIS

OSPO

STAR

Data Tool Links

CoastWatch Tools

CoastWatch Data Portal (select Spatial Search)

Data files: https://coastwatch.noaa.gov/data/pub0022/coastwatch/

Northern & Southern Hemisphere Products

NRCS Search Tool

XPOL Search Tool

Other Search Tools

RadarSat-2 + RCM SAR Winds Search Tool (MECB)

Sentinel-1 SAR Winds Search Tool (MECB)

RadarSat-2 + Sentinel-1 SAR Winds Search Tool (OSPO) [Formally Operational]

Sample Filenames

Filenames begin with satellite, data source, year, month, day, hour, minute, second, Julian seconds, center latitude, center longitude, polarization, delimited with an underscore NRCS: S1A_ESA_2016_12_07_18_18_57_0534449937_178.66E_51.85N_VV_C5_GFS05 CDF_nrcs_level2.png
Wind: S1A_ESA_2016_12_07_18_18_57_0534449937_178.66E_51.85N_VV_C5_GFS05 CDF_wind_level3.png

Winds from Special Sensor Microwave Imager (SSM/I)

jebidiah.jeffery — Thu, 17 Mar 2022 15:29:52 +0000

Winds from Special Sensor Microwave Imager (SSM/I)

Defense Meteorological Satellite Program (DMSP) / Special Sensor Microwave Imager (SSM/I) Wind Speed

jebidiah.jeffery Thu, 03/17/2022 - 11:29

Temporal Coverage

Near real-time + 3 days

Product Families

Sea Surface Winds

Measurements

Sea Surface Wind Speed

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

0.33° (37 km)

Platforms

DMSP

Instruments

SSMIS

Data Providers

NOAA

NESDIS

OSPO

Data Tool Links

CoastWatch Near Real-Term Search

Sample Filenames

SSMI1630004as_WW.hdf

HTTPS

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

Vector Winds OSCAT-2 SCATSAT-1

jebidiah.jeffery — Thu, 17 Mar 2022 15:29:22 +0000

Vector Winds OSCAT-2 SCATSAT-1

Ocean Surface Vector Winds (OSVW; magnitude and direction) from the Ocean Scatterometer (OSCAT) on SCATSAT-1.

jebidiah.jeffery Thu, 03/17/2022 - 11:29

OSCAT-2, aboard the SCATSAT-1 mission of the Indian Space Research Organization (ISRO), is an active microwave sensor used to determine ocean surface level wind vectors through estimation of radar backscatter. The ISRO sensor data are processed by the NOAA OSVW team to estimate wind speed and direction in near real time with global coverage, every 4 hours with a spatial resolution of about 12.5 km. NOAA CoastWatch maps and distributes the OSVW products. See the "information" and "data access" tabs for more.

Temporal Coverage

Near real-time + 3 days

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

12.5 km

Platforms

ScatSat-1

Instruments

OSCAT

Data Providers

NOAA

NESDIS

OSPO

Data Tool Links

CoastWatch Data Portal

CoastWatch Near Real-Term Search

Sample Filenames

SS201630004as_WW.hdf

HTTPS

https://coastwatch.noaa.gov/pub/socd7/coastwatch/scatsat/netcdf/

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

Synthetic Aperture Radar (Surface Roughness) Winds

jebidiah.jeffery — Thu, 17 Mar 2022 15:27:13 +0000

Synthetic Aperture Radar (Surface Roughness) Winds

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

Spaceborne Synthetic Aperture Radar (SAR) imagery maps the surface microwave radar reflectivity at resolutions from a sub-meter to 100 m depending on the particular SAR satellite and mode. Since a radar provides its own illumination, imagery is independent of the time of day. At typical radar frequencies, SARs can image through clouds, so SARs are considered "all-weather" instruments. Several gephysical parameters can be derived from SAR including sea surface wind speed.

The relationship between wind speed and direction and radar cross section is called the "geophysical model function." These functions are generally empirically determined.

Current users of these data are the US National Ice Center, the Alaska Weather Service, and the Naval Oceanographic Office.

A good review of SAR operational wind speed retrieval can be found at: http://tos.org/oceanography/assets/docs/26-2_monaldo.pdf

Product Families

Sea Surface Roughness

Sea Surface Winds

Monaldo, F.M., C.R. Jackson, and W.G. Pichel. 2013. Seasat to RADARSAT-2: Research to operations. Oceanography 26(2):34–45, http://dx.doi.org/10.5670/oceanog.2013.29
SAR Marine User Manual (2004) Christopher Jackson and John Apel, Editors

Measurements

Normalized Radar Cross-Section

Sea Surface Wind Speed

Processing Levels

Level 1

Level 2

Level 3

Spatial Coverage

Selective Acquisitions

Latency Groups

0 Hours <= 24 Hours (NRT)

Latency Details

Less than 24 hours

Spatial Resolution Groups

0 m <= 100 m

100m < 2km

Spatial Resolution Details

NRCS: Variable, 0-10 m
Winds: 500 m

Platforms

Instruments

Data Providers

NOAA

NESDIS

OSPO

Data Tool Links

CoastWatch Tools

CoastWatch Near Real-Time Search

Other Search Tools

Radarsat Constellation Mission / Radarsat-2 (MECB)

Sentinel-1 SAR Winds Search Tool (MECB)

Radarsat Constellation Mission + RadarSat-2 + Sentinel-1 SAR Winds Search Tool (OSPO) [Formally Operational]

Sample Filenames

Filenames begin with satellite, data source, year, month, day, hour, minute, second, Julian seconds, center latitude, center longitude, polarization, delimited with an underscore

NRCS: S1A_ESA_2016_12_07_18_18_57_0534449937_178.66E_51.85N_VV_C5_GFS05 CDF_nrcs_level2.png
Wind: S1A_ESA_2016_12_07_18_18_57_0534449937_178.66E_51.85N_VV_C5_GFS05 CDF_wind_level3.png

THREDDS

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

NCEI Archive

SAR Winds:

https://www.ncei.noaa.gov/products/sar-wind-data-quality-monitoring

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. doi:10.1175/2010JCLI3294.1
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. doi:10.1109/tip.2004.838703
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. doi:10.1175/BAMS-84-5-645

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

VIIRS - Sea Ice Concentration, Ice Thickness, Ice Surface Temperature

jebidiah.jeffery — Thu, 17 Mar 2022 14:32:41 +0000

VIIRS - Sea Ice Concentration, Ice Thickness, Ice Surface Temperature

VIIRS Sea Ice Concentration, Sea Ice Thickness and Ice Surface Temperature NOAA CoastWatch/OceanWatch/PolarWatch Level 3, Daily and daily 4-day rolling merged, mapped composites in both polar stereographic projections and CW sector mapped for Northern and Southern hemispheres.

jebidiah.jeffery Thu, 03/17/2022 - 10:32

VIIRS Sea Ice Concentration, Sea Ice Thickness and Ice Surface Temperature, NOAA CoastWatch/OceanWatch/PolarWatch Level 3, Daily and daily 4-day rolling merge, mapped composites are nominally at native spatial resolution (~750 m), clear sky only.

North Polar (NP06) and South Polar (SP06) are Polar Stereographic projected daily composites
Six Northern hemisphere and six Southern hemisphere high latitude CoastWatch VIIRS sectors (see sector map below)
- North Sectors: UZ, VZ, WZ, XZ, YZ, ZZ
- South Sectors: UW, VW, WU, XW, YW, ZW

Map of CoastWatch regional sectors

Data Provider: JPSS Center for Satellite Applications and Research Cryosphere Team

STAR Cryosphere Environmental Data Record Team Lead: Jeff Key

Sea ice age is defined as the time that has passed since the formation of the surface layer of an ice covered region of the ocean.

Sea ice concentration is the fraction of a horizontal cell covered by ice.

NDE Enterprise Ice Age/Thickness Algorithm

One-dimensional Thermodynamic Ice Model (OTIM):

Based on the surface energy budget at thermo-equilibrium state
Independent of historical records for ice thickness and age estimation
Capable of retrieving daytime and nighttime sea and lake ice thickness under both clear and cloudy sky conditions

IDPS Sea Ice Concentration is an intermediate product (IP)

Algorithm computes ice fractions based on ice and water tie points determined from VIIRS I1 (0.64 µm) and I2 (0.865 µm) reflectance and surface temperature from the VIIRS Surface Temperature IP

Enterprise Sea Ice Concentration Algorithm

Use threshold tests to detect possible ice cover: Daytime and Nighttime
Use a tie-point algorithm to derive reflectance (temperature) for pure ice pixel, and then calculate ice concentration

https://stratus.ssec.wisc.edu/cgi-bin/amsr2snow_prod?seaice

Temporal Coverage

Daily and
Daily 4-day rolling merge

Product Families

Polar Sea Ice

Liu, Yinghui and Key, Jeffrey R., NOAA/NESDIS Center for Satellite Applications and Research, Algorithm Theoretical Basis Document: Ice Surface Temperature, Ice Concentration, and Ice Cover from VIIRS, ABI, and METimage, Version 1.4, 15 June 2020.

ATBD (PDF, ATBD_EPS_Cryosphere_IST_IceCover_v1.4.pdf)

Wang, Xuanji and Key, Jeffrey R., NOAA/NESDIS Center for Satellite Applications and Research, Algorithm Theoretical Basis Document: ABI and VIIRS Ice Thickness and Age, Version 1.0, 17 December 2015.

Liu, Yinghui and Key, Jeffrey R.,, NOAA/NESDIS Center for Satellite Applications and Research, Algorithm Theoretical Basis Document: Ice Surface Temperature, Ice Concentration, and Ice Cover, Version 1.0, 17 December 2015.

EDR Long Term-Monitoring

Data Viewer at STAR (JSTAR Mapper)
University of Wisconsin Cryo Team CIMSS website

Measurements

Sea Ice Concentration

Sea Ice Temperature

Sea Ice Thickness

Visible And Near IR Radiometry

Processing Levels

Level 3

Spatial Coverage

CoastWatch Global Sectors

Polar (Artic/Antarctic)

Latency Groups

0 Hours <= 24 Hours (NRT)

Latency Details

about 12 to 24 h

Spatial Resolution Groups

100m < 2km

Spatial Resolution Details

Nominal 750 m

Platforms

NOAA

SNPP

Instruments

VIIRS

Data Providers

NOAA

NESDIS

OSPO

STAR

CoastWatch

Cryosphere Team

Data Tool Links

CoastWatch Near Real-Time Search Tool

S-NPP & NOAA-20:

Sample Filenames

VXSCCW_B2019203_C244_011341-233757_NP06_edgemask_IceConc.nc
VXSCCW_B2019203_C103_011341-232508_UZ00_edgemask_IceConc.nc
VXSACW_B2019203_C246_002354-224644_SP06_edgemask_IceThickness.nc
VXSCCW_B2019203_C102_003351-224644_VW00_edgemask_IceSrfTemp.nc
VXSCCW is
B2019201 is Year and day of year as BYYYYdoy
C### is number of granules in composite
Series of 6-digit numbers are the range of granule start times included in composite
UZ00, for example, is sector indicator (see Description tab for sector map) for equatorial projection
NP06 or SP06 is indicator for polar stereographic projection from the 6 sectors regions (North or South)
edgemask
Product DescriptionIceConc = Ice Concentration
IceThickness = Ice Thickness
IceSrfTemp = Ice Surface Temperature

North Polar (NP06) and South Polar (SP06) are Polar Stereographic projected daily composites from the 6 CoastWatch VIIRS sectors

UZ, VZ, WZ, XZ, YZ, ZZ Northern high latitude CoastWatch VIIRS sectors (see sector map below)

UW, VW, WU, XW, YW, ZW Southern high latitude CoastWatch VIIRS sectors (see sector map below)

THREDDS

Aggregated and Per-File View

Ice Thickness:

S-NPP:
- Daily merge: https://www.star.nesdis.noaa.gov/thredds/socd/coastwatch/catalog_viirs_npp_ice_thick_daily.html
- Daily 4-day rolling merge: https://coastwatch.noaa.gov/thredds/socd/coastwatch/catalog_viirs_npp_ice_thick_4day.html
NOAA-20:
- Daily merge: https://coastwatch.noaa.gov/thredds/socd/coastwatch/catalog_viirs_n20_ice_thick_daily.html
- Daily 4-day rolling merge: https://coastwatch.noaa.gov/thredds/socd/coastwatch/catalog_viirs_n20_ice_thick_4day.html

Ice Surface Temperature:

S-NPP:
- Daily merge: https://www.star.nesdis.noaa.gov/thredds/socd/coastwatch/catalog_viirs_npp_ice_temp_daily.html
- Daily 4-day rolling merge: https://coastwatch.noaa.gov/thredds/socd/coastwatch/catalog_viirs_npp_ice_temp_4day.html
NOAA-20:
- Daily merge: https://coastwatch.noaa.gov/thredds/socd/coastwatch/catalog_viirs_n20_ice_temp_daily.html
- Daily 4-day rolling merge: https://coastwatch.noaa.gov/thredds/socd/coastwatch/catalog_viirs_n20_ice_temp_4day.html

Ice Concentration:

S-NPP:
- Daily merge: https://www.star.nesdis.noaa.gov/thredds/socd/coastwatch/catalog_viirs_npp_ice_conc_daily.html
- Daily 4-day rolling merge: https://coastwatch.noaa.gov/thredds/socd/coastwatch/catalog_viirs_npp_ice_conc_4day.html
NOAA-20:
- Daily merge: https://coastwatch.noaa.gov/thredds/socd/coastwatch/catalog_viirs_n20_ice_conc_daily.html
- Daily 4-day rolling merge: https://coastwatch.noaa.gov/thredds/socd/coastwatch/catalog_viirs_n20_ice_conc_4day.html

Top Level Sea Ice Catalog

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

ERDDAP

S-NPP:

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

NOAA-20:

** Not yet available **

AMSR2 - Sea Ice Concentration

jebidiah.jeffery — Thu, 17 Mar 2022 14:32:01 +0000

AMSR2 - Sea Ice Concentration

NOAA CoastWatch / OceanWatch / PolarWatch Sea Ice Concentration, Level 3, daily and daily 4-day rolling merge, polar stereographic mapped composites - AMSR-2

jebidiah.jeffery Thu, 03/17/2022 - 10:32

The second Advanced Microwave Scanning Radiometer 2 (AMSR-2) on JAXA's Global Climate Observation Mission - Water 1 (GCOM-W1)
Daily composites and daily 4-day rolling merge composites
10 km spatial resolution
Polar Stereographic projections for North Pole and South Pole
Microwave not impacted by clouds
Source data from NOAA OSPO; NOGAPS algorithm developed by NOAA STAR (Paul Chang, Lead)

Temporal Coverage

Daily and
Daily 4-day rolling merge

Product Families

Polar Sea Ice

STAR Development (Paul Chang, Lead)

https://manati.star.nesdis.noaa.gov/gcom

OSPO Monitoring and Documentation

https://www.ospo.noaa.gov/Products/atmosphere/gpds/algo.html

Measurements

Sea Ice Concentration

Processing Levels

Level 3

Spatial Coverage

Polar (Artic/Antarctic)

Latency Groups

0 Hours <= 24 Hours (NRT)

Latency Details

Approximately 12 to 24 hours

Spatial Resolution Groups

2km+

Spatial Resolution Details

10 km

Platforms

GCOM-W

Instruments

AMSR2

Data Providers

JAXA

NASA

NOAA

NESDIS

OSPO

Data Tool Links

CoastWatch Near Real-Time Search

Sample Filenames

GWXCW_W2019203_C15_002140_020039_033940_051540_065439_083040_100940_114840_132440_150341_164242_182143_200342_214543_224240_NP06_IceConc.nc

Where:
GWXCW is
W2019203 is W year and day of year: WYYYYdoy
C15 indicates number of granules in composite (generally 14 or 15)
Series of 6 digit numbers are start times each granule included in the composite
NP06 (or SP06) indicates mapped to Polar Stereographic Projection for North Pole (NP06) or South Pole (SP06)
Ice Conc is product name = Ice Concentration
.nc is NetCDF format (CF compliant)

THREDDS

Aggregated and Per-file Views:

https://www.star.nesdis.noaa.gov/thredds/socd/coastwatch/catalog_amsr2_gw1_ice_conc_daily.html

Top Level Sea Ice Catalog:

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

ERDDAP

https://coastwatch.noaa.gov/erddap/search/index.html?page=1&itemsPerPage=1000&searchFor=Ice+AMSR2

Seviri (MSG) - Geostationary - Level 3

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

Seviri (MSG) - Geostationary - Level 3

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

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

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

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

Algorithms

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

Short Names

GOES_L3

Temporal Coverage

Near real-time + 3 days

Product Families

Sea Surface Temperature

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