High Resolution Cloud Free SST
JPL Ultra High Resolution Cloud Free Sea Surface Temperature
JPL SST (NASA Jet Propulsion Laboratory) is our premium Sea Temperature product.
It uses data from a wide variety of satellites, including those that are not affected by cloud cover. Advanced algorithms are applied to the data to produce a 1km product with no gaps due to cloud cover. See technical description for more info
We can produce JPL in Grib for the global region, but due to the very large size of the source data and the cost of retrieval and processing we have limited our operational products to the areas below.
Contact us if you are seeking access to a non-operational area.
Sample image - Gulfstream
A daily, global Sea Surface Temperature (SST) data set is produced at 1-km (also known as ultra-high resolution) by the JPL ROMS (Regional Ocean Modeling System) group. Based on the level 2 preprocessed (L2P) data products from the Global High-Resolution Sea Surface Temperature (SST) Pilot Project (GHRSST-PP), the input SST data include infrared (IR) sensors (e.g., Advanced Very High Resolution Radiometer or AVHRR, Moderate Resolution Imaging Spectroradiometer or MODIS, Advanced Along-Track Scanning Radiometer or AATSR) with a spatial resolution of 1~2 km, the Geostationary Environmental Satellites (GOES, METOP) with a spatial resolution of 6 km, and microwave sensors (e.g., Advanced Microwave Scanning Radiometer Earth Observing System or AMSR-E, Tropical Rainfall Measuring Mission Microwave Imager or TMI) with a spatial resolution of 25 km. The in-situ SSTs obtained from the Global Ocean Data Assimilation Experiment (GODAE) server consisting of thousands of daily ship and buoy SST measurements are also used. The goal is to provide a global optimal estimate of SST at the highest possible spatial resolution. In order to combine all the available SST data sets at various spatial resolutions, we have developed a multi-scale two-dimensional variational (MS-2DVAR) blending algorithm. This MS-2DVAR algorithm is characterized by inhomogeneous and anisotropic background error covariances, which are of particular importance for coastal oceans.
