Ancillary Data

The algorithms for retrieval of ocean color radiometry and sea surface temperature from satellite radiometry make use of a number of ancillary data sources, in addition to the sensor observed radiometry. Examples include meteorological data (windspeed, surface pressure, relative humidity) and concentrations of atmospheric gases (water vapor, ozone, nitrogen dioxide). In general, the optimal ancillary data is not available when the satellite radiometry is first acquired, so the NASA OBPG processing uses best available ancillary data sources for processing satellite data in near realtime, and then reprocesses the data about one week later to refine the products using the optimal ancillary data.

The primary sources of ancillary data used in NASA ocean color and SST processing are described here. When a primary source is not available, a back-up source is used. If the primary nor it's backup source is available, the secondary source is used.

Dynamic Sources

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Our primary source for meteorological data is NCEP. We will transition to MERRA2 as our primary source in the future.

Primary NCEP Reanalysis 2 Meterological data:
The NCEP Reanalysis 2 product has more consistency over time (one fixed model is used to analyze the data) and includes more observations in it's analysis (it waits for more observations to arrive) than the real-time data does, at the cost of timeliness (data is available with a 3 - 4 day delay). This data is used to improve the quality of the real-time data (real-time has 1 degree resolution and the Reanalysis 2 has 2.5 degree, so the real-time fields are adjusted to match the reanalysis).
source: NCEP CPC
spatial resolution: 1 x 1 degrees (360 x 181 grid)
temporal resolution: Every 6 hours starting 0000 GMT
filename composition: NYYYYDDDHH_MET_NCEPR2_6h.hdf where YYYY=year, DDD=day-of-year, HH=hour-of-day, 00 to 18 GMT by 6. (e.g. N201603003_MET_NCEPR2_6h.hdf)
Secondary GMAO MERRA2 Meterological data:
Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) meterological data from NASA/Goddard's Global Modeling and Assimilation Office (GMAO)
source: GES DISC
spatial resolution: ?
temporal resolution: 1-hourly
filename composition:
post-processing notes: Various GEOS 2-D datasets are aggregated in order to generate the aforementioned OBPG product.
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Atmospheric Gases

Our primary source for ozone data is TOMS/OMI. We will transition to MERRA2 as our primary source in the future.

Primary Real-time Ozone:
The real time ozone data is derived from TOMS-like instrument data. Currently, we are using data from the AURA OMI instrument (AURAOMI), and prior to 2005, we used data from the Nimbus-7 (N7TOMS) and Earth Probe (EPTOMS) TOMS instruments. We apply a normalization to all the ozone data based on a de-trended merged SBUV record.
source: GES DISC
spatial resolution: 1.25° x 1° (288 x 180 grid)
temporal resolution: daily
filename composition: NYYYYDDD00_O3_III_24h.hdf where YYYY=year, DDD=day-of-year, III= instrument, N7TOMS, EPTOMS or AURAOMI. (e.g. N201918800_O3_AURAOMI_24h.hdf)
Secondary GMAO MERRA2 Aerosol data:
This dataset is based on the off-line "MERRAero" dataset that was integrated using meteorological fields from MERRA.
source: GES DISC
spatial resolution: 0.5° (lat) x 0.625° (lon) horizontal grid
temporal resolution: 1-hourly
filename composition: where YYYY=year, DDD=day of year (e.g.
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Surface Properties

Near-Real-Time DMSP SSMIS Daily Polar Gridded Sea Ice Concentrations:
This data set provides a Near-Real-Time (NRT) map of sea ice concentrations for both the Northern and Southern Hemispheres. The near-real-time passive microwave brightness temperature data that are used as input to this data set are acquired with the Special Sensor Microwave Imager/Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) satellites.
source: NSIDC
spatial resolution: North and South polar projections, variable resolution: 488x304 (North), 332x316 (South)
temporal resolution: daily
filename composition: NYYYYDDDHH_SEAICE_NSIDC_24h.hdf where YYYY=year, DDD=day-of-year, HH=hour. (e.g. N201919000_SEAICE_NSIDC_24h.hdf)
post-processing notes: The north and south datasets are merged together in order to generate the aforementioned OBPG product.
GHRSST Level 4 CMC Global Foundation Sea Surface Temperature:
This dataset merges infrared satellite SST at varying points in the time series from the (A)TSR series of radiometers from ERS-1, ERS-2 and Envisat, AVHRR from NOAA-16,17,18,19 and METOP-A, and microwave data from TMI, AMSR-E and Windsat in conjunction with in situ observations of SST from drifting buoys and ships from the ICOADS program. It uses the previous days analysis as the background field for the statistical interpolation used to assimilate the satellite and in situ observations. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.
source: PO.DAAC
spatial resolution: 0.2° (Latitude) x 0.2° (Longitude)
temporal resolution: daily
filename composition:, where YYYY=year, MM=month, DD=day, HH=hour, MM=Min, SS=seconds, L4=PODAAC product level,X=spatial resolution, GLOB=global coverage, VER=file version, (e.g.

Static Sources

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Meterological Climatology:
This data set is used when none of the dynamic meteorological files or backup sources are available.
temporal resolution: monthly
filename: met_climatology_v2014.hdf
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Atmospheric Gases

Ozone Climatology:
This data set is used when none of the dynamic ozone files or backup sources are available.
temporal resolution: monthly
filename: ozone_climatology_v2014.hdf
NO2 Climatology
We currently use a monthly climatology of NO2 based on the Aura OMI data. Currently, only the climatology is used in ocean color processing. The climatology is created from the daily OMI NO2 files. The daily files of NO2, as it comes from the DAAC has many gaps and only has good coverage when averaged into monthly files.
source: GES DISC
spatial resolution: 0.25 x 0.25 degrees (1440 x 720 grid)
temporal resolution: Monthly, not year-specific
filename: no2_climatology_v2013.hdf
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Surface Properties

ETOPO1 1 Arc-Minute Global Relief Model
Compact form of ETOPO1 Ice Surface topography and bathymetry, with additional dataset for surface altitude of inland water bodies.
source: NOAA
spatial resolution: 1 x 1 arcminutes (21601 x 10801 grid)
Mixed Layer Depth
Mixed layer depth is a type of atmospheric boundary layer characterized by vigorous turbulence tending to stir and uniformly mix, primarily in the vertical, quantities such as conservative tracer concentrations, potential temperature, and momentum or wind speed.
filename: mld_climatology_woa1994.hdf
Sea Ice Climatology:
This data set is used when the dynamic Sea Ice file is not available.
temporal resolution: monthly
filename: ice_climatology.hdf
Sea Surface Salinity Climatology
filename: sss_climatology_woa2009.hdf
Sea Surface Temperature Climatology
filename: sst_climatology.hdf
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Global Land/Water Mask
Simple Land/Water mask generated using the GMT command:
grdlandmask -I15s -Rd -Df -N1/0
source: GSHHG
spatial resolution:15 x 15 arcseconds (86401 x 43201 grid)
Contiguous United States Land/Water Mask
Water mask in geographic projection for the Contiguous United States, covering latitudes 23 to 55 degrees and longitudes -127 to -65 degrees. Most values were aggregated to 2-arcsec resolution from Shuttle Radar Topography Mission 1-arcsecond data; missing or erroneous values were corrected with data from other sources.
source: SRTM
spatial resolution: 2 x 2 arcseconds (111601 x 57601 grid)
Global Land/Water Flags
Global Land/Water flags in geographic projection, with water classified into one of 7 types. Original 15-arcsecond files aggregated into a single global file, using 30-arcsec values where higher resolution is unavailable. Used only for VIIRS geolocation processing.
source: LandSeaMask_DEM
spatial resolution: 15 x 15 arcseconds (86400 x 43200 grid)
Shallow Water Mask
filename: watermask.dat
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Water Spectra Mask
This file contains the entire range of wavelengths of electromagnetic radiation related to water absorption and scattering coefficients.


Derber, J.C., D.F. Parrish, and S.J. Lord, 1991:L The new global operational analysis system at the National Meteorological Center. Wea. Froecasting, 6, 538-547.

Kanamitsu, M., W. Ebisuzaki, J Woollen, S.-K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP-DOE AMIP-II Reanalysis (R-2), Bull. Amer. Meteor. Soc., 83, 1631-1643.

Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., et al. (2011), The ERA-Interim reanalysis configuration and performance of the data assimilation system. Q.J.R. Meteorol. Soc., 137: 553-597. doi: 10.1002/qj.828

Stammnes, P. (Ed),(2002). OMI Algorithm Theoretical Basis Document, Volume III, Clouds, Aerosols, and Surface UV Irradiance