Remote Sensing Systems is looking for a Satellite Oceanographer with at least 4 years of scientific programming experience in FORTRAN and MATLAB, IDL or Python.  A PhD is required.  For more information, please see our careers page or look at the posting on the AGU job website.

Remote Sensing Systems has just released the GPM Microwave Imager (GMI) ocean product suite, V8.1

RSS has stopped near-real-time processing of TMI.  The V7 RSS TMI data set extends from December 7, 1999 to December 31, 2014. 

Microwave OI SST Product Description


The through-cloud capabilities of satellite microwave radiometers provides a valuable picture of the global sea surface temperature (SST). To utilize this, scientists at Remote Sensing Systems have calculated two Optimally Interpolated (OI) SST daily products, one using only microwave data at 25 km resolution and one using microwave and IR data at 9 km resolution. These products are ideal for research activities in which a complete, daily SST map is more desirable than one with missing data due to orbital gaps or environmental conditions precluding SST retrieval.  The 9 km microwave plus infrared (MW_IR) OI SST product combines the through-cloud capabilities of the microwave data (MW) with the high spatial resolution of the IR SST data.  The 25 km MW-only OI SST product contains the SST measurements from all operational radiometers for a given day.   All OI SST values are corrected using a diurnal model to create a foundation SST that represents a 12 noon temperature.  Improved global daily NRT SSTs are useful for a wide range of scientific and operational activities.

Optimally Interpolated SST Products

Two optimally interpolated (OI) SST products are created from the microwave (MW) and infrared (IR) SSTs.




Time Span



40S to 40N < 2002

global >= 2002

1998-Jan to present





2002-Jun to present

Both products are updated several times daily in near-real time. These near-real-time data are intended as research for the Multi-sensor Improved SST (MISST) project, which is a US contribution to the Global Ocean Data Assimilation Experiment (GODAE) High-Resolution SST Pilot Project (GHRSST-PP).  The files have the extension .rt  until they have been fully processed and deemed to be of sufficient quality for research, at which point the extension changes to .v04.0

Sensor-specific Error Calculation

The following corrections and analyses of errors are necessary first steps towards producing both OI SST products. Each step is further described below.

Correcting for TMI’s Emissive Antenna

The antenna coating of the TMI sensor was oxidized in orbit soon after launch, causing errors in the TMI observations. A correction was developed (Wentz, 2001), but proved to be incomplete in removing the error. A bias still exists in TMI data, which is a function of local observation time (Gentemann, accepted JGR). To account for this, an additional correction is applied before TMI data are included in the OI analysis.  

Estimation and Removal of Diurnal Warming

Before blending the satellite data, we consider the data sampling of each instrument. For example, the sun synchronous orbit of MODIS and AMSR-E on Aqua yields retrievals at a local time of approximately 1:30 AM and 1:30 PM. During the daytime over-pass, solar heating of the ocean surface can cause warming of up to 3° C (Price et al, 1986, Yokohama, 1996). Currently, many OI SST algorithms either ignore daytime retrievals or assign them a higher error than nighttime retrievals. While simply removing the daytime retrievals from the objective analysis does prevent warm retrievals from 'contaminating' the final product, the number of samples can be reduced by half. In well-sampled regions this may not impact the final product, but the IR SSTs used in most analyses have large regions where few retrievals exist each month due to persistent cloud cover, making the daytime retrievals extremely valuable. Assigning the daytime retrievals a higher error (and therefore a smaller weight in the objective analysis) reduces diurnal 'contamination' of the data set, but at the risk of still including some component of diurnal warming. The OI SSTs include day and night observations. To optimally utilize daytime retrievals, a simple empirical model of diurnal warming was developed that depends on solar insolation, wind speed, and local time of observation (Gentemann, 2003). Solar insolation is calculated as a function of latitude and day of year; wind speed is simultaneously retrieved with SST from radiometer observations. Using this diurnal model, all SSTs are 'normalized' to a foundation SST.  For more information, see the GHRSST definition.  

Sensor Errors for OI Analysis

Microwave SST retrieval errors are mainly a function of wind speed and SST. These errors are added in a root-sum-squared sense to the daily standard deviation (STD) derived from buoy collocations to obtain a total retrieval error.

Additional Quality Control

Some rain contaminated SSTs exist in the microwave data. At the edges of rain cells, there is often undetected rain that causes a biased SST retrieval. Two tests attempt to remove rain contaminated SSTs. First, at each SST retrieval the STD is calculated using all data within one day and 100 km of the cell. SSTs falling outside of 3 STDs are flagged and removed from the data set. This process is then further repeated to remove outliers. Next, the SST is compared to the previous day's OI SST value. Any SSTs within 100 km of a rain pixel that are more than 0.6 C warmer than the previous day's OI SST value are removed.

Some cloud contaminated SSTs exist in the infrared data. At the edges of cloud cells, there is often undetected cloud that causes a biased SST retrieval. We use a similar test as described above, to remove spurious cloud contaminated retrievals from the infrared SST.

Undetected sea ice can yield erroneous SST values at high latitudes.  We use available radiometer data to construct a sea ice mask around land.  This is applied to the MW-only product.  For the MW_IR product, an additional sea ice data set is needed to determine the presence of sea ice closer to land.  We use sea ice data from OSI SAF to flag cells with sea ice.  There are times when the product does not have sufficient information to identify the sea ice.  We translate this information to our MASK layer in bit 5 which contains locations near land where sea ice may or may not be present.  

Optimum Interpolation (OI)

After characterizing the errors listed above, the SSTs are blended together using the OI scheme described in Reynolds and Smith (1994). OI is a widely utilized method in oceanography and meteorology that makes use of the statistical properties of irregularly spaced data (in time and space) to interpolate the data onto a regularly sampled grid. For each dataset included in the analysis, error characteristics must be understood or at least estimated.

A first-guess field, the previous day's OI SST, is employed to calculate data increments, which are all nearby data minus the first-guess field. The new SST estimate is formed by a weighted sum of increments, with the weights calculated by the OI method, added to the first guess data. Correlation scales of 4 days and 100 km are used in determining the weights used in our methodology.

Known Problems

Undetected Sea Ice

Undetected sea ice causes some unrealistically warm SST values to appear in these products. The problem is most apparent near ice edges, especially as the ice edge advances or retreats.

The first set of images (below) illustrates the problem occurring in the Beaufort Sea, Arctic Ocean, over a seven day period. In the images on the left, the ~4° C (light blue, circled) SSTs are probably artifacts of a thin layer of sea ice or slush. As the sea ice solidifies, it becomes more accurately identified as the images progress in time towards the right.

undetected sea ice undetected sea ice undetected sea ice undetected sea ice undetected sea ice undetected sea ice undetected sea ice
color bar
undetected sea ice undetected sea ice undetected sea ice undetected sea ice undetected sea ice undetected sea ice undetected sea ice undetected sea ice undetected sea ice undetected sea ice

The second set of images (above) tracks retreating Antarctic sea ice over ten days. Here we see probable ice causing up to ~5° C warm artifacts.

Missing Data

Satellite instruments are occasionally unavailable. Near-real-time OI SST products will be created for the current day, even if no new observations exist. The OI method utilizes a first guess field, which for this analysis is the previous day's OI SST. If there are no new observations, the new SST estimate is the previous day's SST.  This means if MW data are missing, the MW OI SST will look exactly like the map from the day before.

Instrument observations are known to be missing for the following dates:


Missing Data

# Days


1999.01.04 - 1999.01.05
2001.08.14 - 2001.08.16
2013.11.12 - 2013.11.14
2014.01.25 - 2014.01.28



2002.07.30 - 2002.08.07
2002.09.13 - 2002.09.19
2003.10.30 - 2003.11.05
2010.02.03 - 2010.02.04



2003.07.15 - 2003.07.19
2003.12.06 - 2003.12.09
2004.01.28 - 2004.01.30
2004.03.01 - 2004.03.03
2004.07.13 - 2004.07.17
2004.10.15 - 2004.10.20
2005.01.27 - 2005.02.01
2005.02.14 - 2005.06.16
2005.09.28 - 2005.10.04
2005.10.28 - 2005.11.07
2006.01.22 - 2006.02.01
2006.05.04 - 2006.05.06
2006.07.29 - 2006.08.01
2006.08.10 - 2006.08.17
2006.09.02 - 2006.09.05
2006.10.08 - 2006.10.10
2007.04.03 - 2007.04.05
2007.06.09 - 2007.08.06
2007.09.19 - 2007.09.22
2007.12.21 - 2007.12.25
2008.02.29 - 2008.03.02
2008.03.29 - 2008.03.30
2008.04.02 - 2008.04.03
2008.06.10 - 2008.06.30
2009.06.12 - 2009.06.13
2009.08.21 - 2009.08.23
2009.09.02 - 2009.09.03
2010.01.08 - 2010.01.14
2010.11.14 - 2010.11.15
2011.05.23 - 2011.05.24
2014.05.11 - 2014.05.13


For example, AMSR-E was unavailable September 13-19, 2002. For these dates, the MW OI SST product accurately represents detailed daily SSTs in the TMI range (±40°), but at latitudes greater than 40° the OI SST values change little because no AMSR-E observations were available. As more satellites are added to the analysis, the chance of this 'frozen' data diminishes. All OI analyses suffer from this problem.

Daily browse imagery for the TMIAMSR-E, AMSR2, or WindSat instrument products can show the observations available on any given day that are incorporated into the daily OI SST products.

Gridded Binary Data OI SST File Format

Each binary SST data file available at RSS consists of three 2-dimensional data arrays consisting of 1) single byte values representing a given day's SSTs, 2) interpolation ERROR estimates, and 3) data MASKing information. The size of the gridded data arrays for the two products are:  MW-only(1440 by 720,  approximately 0.25 deg grid resolution),   MW-IR (4096 by 2048, approximately 9 km grid resolution).  

The MASK array consists of single byte values with have bit values: 

Bit values of MASK array in OI SST products
 leftmost bit (bit 0)  land=1, no land=0
 bit 1  ice = 1, no ice =0
 bit 2  IR data used for SST =1 (applies to MW-IR),  not used =0
 bit 3  MW data used =1, not used =0
 bit 4  bad data=1, good data =0
 bit 5

 unclassified sea ice region near land =1,  no ice = 0

 (sea ice may or may not be present)


Interim products ("rt") are updated several times per day until the data become final ("v04.0").

File names follow these conventions:

SST Product

Directory Path

Fiile Name

MW daily/mw/ mw.fusion.yyyy.doy.ver.gz
MW_IR daily/mw_ir/ mw_ir.fusion.yyyy.doy.ver.gz

Where "yyyy", "doy", and "ver" stand for:

yyyy year "2002", "2003", etc.
doy day of year "001" (Jan-1), "002" (Jan-2), etc.
ver version
"rt" = near real time (interim product)
"v04.0"  = version 4 (final product)

For the MW-only product, the center of the first cell of the 1440 column and 720 row map is at 0.125 E longitude and -89.875 latitude. The center of the second cell is 0.375 E longitude, -89.875 latitude.

For the MW-IR product, the center of the first cell of the 4096 column and 2048 row map is at 0.044 E longitude and -89.956 latitude. The center of the second cell is 0.132 E longitude, -89.912 latitude.

Good SST values are stored as single bytes ranging from 0 to 255.   Some of the read routines supplied insert the following specific values into the SST and ERROR data arrays to signify missing SST values due to:

0 to 250 = valid SST data
252 = sea ice
254 = missing data
255 = land mass

Byte values 0 - 250 need to be scaled to obtain standard units:

SST: (byte value * 0.15) - 3.0 offset yields temperature between -3.0 and 34.5 °C
Error: (bye value * 0.005)   0.0 offset yields error value between  0.0 and 1.0

Thus, to convert SST byte values (0 - 250, inclusive) to degrees Centigrade, multiply by .15, then subtract 3.

All binary data files have gzip compression to reduce size and decrease transfer time.

Read Routines:

Read routines are available in IDL, Matlab, Fortran, Python and C++  at: These read routines (dated Aug 2014 or later) read the version 4.0 MW-only and MW-IR SST files.  Be sure to use the verify.txt file to check that your program works correctly after altering to your needs.


Microwave OI SST data are produced by Remote Sensing Systems and sponsored by National Oceanographic Partnership Program (NOPP), the NASA Earth Science Physical Oceanography Program, and the NASA MEaSUREs DISCOVER Project. Data are available at

Research into SST blending, diurnal warming, observation errors, and near real-time validation of MW OI SST is supported by the NASA Earth Science Physical Oceanography Program (Dr. Eric Lindstrom) and the NASA Earth Science AMSR-E Science Team.

The distribution, web-interface, and visualization tools for these data sets are supported by the NASA Earth Science MEaSUREs Project.