Despite the fact that the non-linear sea-surface temperature (NLSST) atmospheric correction algorithm has been used in one form or another for the derivation of the SST from satellite measurements…

This is a grant award to the University of Miami Rosenstiel School of Marine and Atmospheric Science (RSMAS) to conduct research on optimizing sea surface temperature (SST) retrieval algorithms from satellite data. The $684,632.50 grant, issued by the NASA Shared Services Center, will fund work to develop and test advanced SST estimation techniques based on optimal estimation (OE) methods, with the goal of improving accuracy compared to the existing non-linear SST (NLSST) algorithm. The research will leverage MODIS and VIIRS satellite data and also evaluate the accuracy of atmospheric water vapor retrievals. There is no set-aside designation for this grant. RSMAS has a history of collaborating with federal agencies like NOAA, NASA, and USGS on oceanographic and atmospheric research projects through contract and grant awards. The Division's subcontractor, Tritium Laboratory, also holds an existing $1.128 million indefinite delivery contract with the U.S. Geological Survey for low-level tritium analysis services.

Despite the fact that the non-linear sea-surface temperature (NLSST) atmospheric correction algorithm has been used in one form or another for the derivation of the SST from satellite measurements in the infrared for over two decades, IT has proved itself to be very robust and difficult to improve on. However in recent years the benefit of optimum estimation (OE) approaches to SST retrieval have been demonstrated. Work on the precursor grant has shown that optimal estimation of SST from modis produces values that are comparable to those of the NLSST, when compared to the temperatures from drifting buoys, in most situations, but are better than the NLSST values in difficult atmospheric situations, including towards the edges of the swath, and in conditions where the cloudscreening algorithms have returned a quality flag that usually leads to the data being discarded. The OE algorithms also return an estimate of the atmospheric perceptible water which in the past has been discarded, but we plan to assess the accuracies of this as, in principle, algorithm improvements aimed at decreasing errors in SST should also increase the accuracies of the water vapor retrieval. Furthermore, we propose to use additional spectral measurements that contain information about the state of the atmosphere. We also propose to extend the techniques developed for modis to the measurements of viirs.