Multi-sensor Ultra-high Resolution (MUR) SST field
In various scientific and operational applications, the sea surface temperature (SST) is presented at very different spatial and temporal scales. For example, a global SST mean over a long time period is often examined in climate studies, while an SST snapshot of sub-kilometer resolution may be desired in some biological studies. Also, when used as a boundary condition of atmospheric dynamics (such as weather forecast) models, the SST field is often resolved only down to a spatial scale that matches the numerical resolution in the particular model in order to avoid spurious dynamical behaviors.
Resolution and span in space and time of SST analysis are thus usually application dependent. Our project focuses on analysis of the satellite-based measurements to address these variety of needs for SST.
Satellite-based SST data are irregularly-sampled by different sensor types. Geostationary satellites have fine temporal resolutions but cover only limited geographical regions, while orbiting satellites can have global coverage’s by compromising temporal sampling.
The microwave (MW) sensors have typically coarser 25-km resolution than the infra-red (IR) sensors which can resolve down to a 1-km scale. However, the IR-based measurements are prone to data voids due to cloud contamination, which does not affect MW sensors nearly as much.
To deal with the data voids which can be both persistent in time and recurrent over particular geographical regions, we employ a motion-compensated analysis technique to reduce temporal smearing of small-scale coherent patterns. Also, to merge satellite measurements with drastically different spatial resolution and coverage, we employ a wavelet-based, multi-resolution analysis technique to ensure consistency of our analysis with the self-similar (power-law) characteristics observed empirically over a wide range of wavenumber spectrum.
Our goal is to implement a system to analyze and deliver the SST products "on demand" with subsetting and near real-time capabilities.
Mike Chin - PI, NASA"s Jet Propulsion Laboratory
Distributed by NASA"s Physical Oceanography Distributed Active Archive Center
Last Updated: Nov 15, 2017 at 12:11 PM EST