Introduction
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 created two Optimally Interpolated (OI) SST daily products, one using only microwave data (MW) at 25 km resolution and one using microwave and infrared data (MW_IR) 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 25 km MW OI SST product contains the SST measurements from all operational radiometers. The 9 km MW_IR OI SST product combines the through-cloud capabilities of the microwave data with the high spatial resolution and near-coastal capability of the infrared SST data. All SST values are adjusted using a diurnal model to create a foundation SST. 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.
| Sensors | Spatial Coverage | Time Coverage | |
|---|---|---|---|
| MW |
TMI, AMSR-E, AMSR2, WindSat, GMI |
< 2002-06-01: 40 °S to 40 °N >= 2002-06-01: global |
1998-01-01 to present |
| MW_IR |
MW: TMI, AMSR-E, AMSR2, WindSat, GMI IR: MODIS-Terra, MODIS-Aqua, VIIRS-NPP, VIIRS-N20 |
global |
2002-06-01 to present |
Both products are produced 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.gz or rt.nc
until they have been fully processed and deemed to be of sufficient
quality for research, at which point the extension changes to
v05.1.gz or fv05.1.nc.
Version 5.1 Product
The version 5.1 product, introduced in 2022, includes the following significant changes from the version 5.0 product:
- VIIRS-N20 is added to the MW_IR product
- The sensor-specific error statistics (SSES) for each microwave sensor are updated
- Updated versions of the input files are used, including AMSR2 v8.2
- Deficiencies in the OI processing have been addressed
Both version 5.1 and version 5.0 products are currently available, but the version 5.0 product will cease production soon. The format of the version 5.1 netCDF and bytemap files are in the same format as the version 5.0 files.
Sensor-Specific Errors for OI Analysis
Microwave SST retrieval errors are mainly a function of wind speed and SST. The estimated SST error for each sensor is included in the OI analysis. The OI technique ensures that SST measurements with a larger error are weighted less.
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, 2004). 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 model of diurnal warming is used that depends on solar insolation, wind speed, and local time of observation. 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 converted to a foundation SST. For more information, see the GHRSST definition.
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 standard deviation of all nearby measurements is calculated using all data within one day and 100 km of the cell. SSTs falling outside of 3 standard deviations are flagged and not used for the OI SST product. This outlier removal is repeated again. 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. A similar process to the rain contamination removes spurious cloud contaminated retrievals from the infrared SST.
Undetected sea ice can yield erroneous SST values at high latitudes. For the MW product, we use available radiometer data to construct a sea ice mask around land. 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 concentration data from OSI SAF to flag data with sea ice. All SST values flagged with sea ice are set to -1.8 °C.
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 3 days and 100 km are used in determining the weights used in our methodology.
An analysis error term is produced, which ranges from 0 to 1. The error may be interpreted as a metric of how "far away" the observations used are in space and time from the desired grid cell. High values indicate that observations further from the grid cell are used to produce the SST value and should be used with caution.
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.
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The second set of images (above) tracks retreating Antarctic sea ice over ten days. Here we see probable ice causing warm artifacts up to ~5 °C.
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 values will be exactly the data from the day before.
Daily browse imagery for the TMI, AMSR-E, AMSR2, WindSat, or GMI instrument products can show the observations available on any given day that are incorporated into the daily OI SST products.
OI SST File Formats
Both the MW and MW_IR OI SST products are distributed in two file formats: GDS-compliant netCDF and a binary "bytemap" format. In both formats, the data are produced on an Earth-centered grid using a plate carrée projection. The grid sizes are 1440 by 720 for MW (a grid spacing of 0.25 degrees, or approximately 25 km near the equator) and 4096 by 2048 for MW_IR (a grid spacing approximately 9 km near the equator).
| NetCDF | Bytemaps | |
|---|---|---|
| MW | HTTPS access FTP access | HTTPS access FTP access |
| MW_IR | HTTPS access FTP access | HTTPS access FTP access |
Interim products (files ending in rt.nc or
rt.gz) typically do not yet contain all available
observations and are updated several times per day until the data
become "final" (ending in fv05.1.nc or
v05.1.gz). However, even these "final" files continue to
get updated since the input files also may be updated. Typically,
after about a month behind present, the OI SST product will stabilize
and no longer require updating.
NetCDF Format
The netCDF format follows the GHRSST Data Specifications, version 2.0 (GDS2). Each daily L4 file contains the analyzed SST, the analysis error, the sea ice fraction, and mask information.
The mask variable follows GDS conventions for L4 data and are defined below.
| bit 0 | ocean = 1, not ocean = 0 |
| bit 1 | land = 1, not land = 0 |
| bit 2 | unused |
| bit 3 | ice = 1, no ice = 0 |
| bit 4 | unused |
| bit 5 | IR data used = 1 (only for MW_IR product), not used = 0 |
| bit 6 | MW data used = 1, not used = 0 |
| bits 7 to 15 | unused |
The MW product additionally uses a combination of bits 0 and 1: when both are set, the cell is a near-coastal region. Although not technically land, it is too close to land for any microwave sensors to report an SST. Since these regions may contain IR observations, the MW_IR product does not have cells with bits 0 and 1 simultaneously set.
NetCDF file names follow these conventions:
| MW | YYYYMMDD120000-REMSS-L4_GHRSST-SSTfnd-MW_OI-GLOB-v02.0-fv05.1.nc
|
| MW_IR | YYYYMMDD120000-REMSS-L4_GHRSST-SSTfnd-MW_IR_OI-GLOB-v02.0-fv05.1.nc
|
Where YYYYMMDD is the date
(e.g., 20170101). Interim (near real-time) files end
with -fv05.1-rt.nc.
Bytemap Format
Each daily bytemap consists of three two-dimensional data arrays consisting of 1) the SSTs, 2) OI error estimates, and 3) data masking information. Each array element is a single unsigned byte and is in little-endian format. The arrays are sized as 1440 by 720 for MW or 4096 by 2048 for MW_IR.
The mask array uses the following bit values to flag the data:
| bit 0 | land = 1, no land = 0 |
| bit 1 | ice = 1, no ice = 0 |
| bit 2 | IR data used = 1 (only for MW_IR product), 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) |
| bit 6 | unused |
| bit 7 | unused |
(Mask bit 5 is unused for the version 5 product, but was used for version 4.)
Bytemap file names follow these conventions:
| MW | mw.fusion.yyyy.doy.ver.gz |
| 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" (January 1), "032" (February 1), etc. | ||||
ver |
version |
|
For the MW product, the center of the first cell of the 1440 column and 720 row map is at 0.125 °E, -89.875 °N. The center of the second cell is 0.375 °E, -89.875 °N. For the MW_IR product, the center of the first cell of the 4096 column and 2048 row map is at 0.044 °E, -89.956 °N. The center of the second cell is 0.132 °E, -89.912 °N.
Valid SST values are stored as single unsigned bytes ranging from 0 to 250, with values 251 to 255 reserved for masking information. Some of the read routines supplied insert the following specific values into the SST and error data arrays to signify invalid SST values due to:
| 252 | sea ice |
| 254 | missing data |
| 255 | land |
The data is stored in a packed format. Bytes with values from 0 to 250 are unpacked with the following scaling factors and additive offsets:
| SST | (byte value * 0.15) | + -3.0 | yields | temperature | between | -3.0 and 34.5 °C |
|---|---|---|---|---|---|---|
| Error | (byte value * 0.005) | + 0.0 | yields | error value | between | 0.0 and 1.0 |
All binary data files are compressed with gzip to reduce size and decrease transfer time.
Read Routines
Read routines for the bytemap format data are available in IDL,
Matlab, Fortran, Python, and C++
at: data.remss.com/SST/daily_v04.0/read_routines_v04.0.
These read routines (dated Aug 2014 or later) read the version 4.0, 5.0, or
5.1 MW 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.
The netCDF format data are self-describing and are readable with a variety of tools.
Acknowledgements
Microwave OI SST data are produced by Remote Sensing Systems and sponsored by National Oceanographic Partnership Program (NOPP) and the NASA Earth Science Physical Oceanography Program. Data are available at www.remss.com.
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.