Traditional regression-based sea surface temperature (SST) retrieval schemes, such as those currently employed to generate level-2 (L2) products for modis and viirs, are inadequate to properly…

This federal contract award, valued at $818,551.87, was granted to the University of Maryland, College Park by the National Aeronautics and Space Administration (NASA) to develop a new physical deterministic Sea Surface Temperature (SST) retrieval scheme and cloud detection algorithm. The proposal aims to extend the university's prior research on these techniques and establish a facility for large-scale pilot processing of MODIS and VIIRS satellite data to generate improved Level-2 SST products. The project seeks to achieve significant reductions in retrieval errors and increases in data coverage compared to current operational SST products. This award does not have a set-aside designation. The University of Maryland, College Park is a subunit of the University System of Maryland, which has a history of providing a range of research, engineering, and information technology services to various federal agencies through both contracts and grants.

Traditional regression-based sea surface temperature (SST) retrieval schemes, such as those currently employed to generate level-2 (L2) products for modis and viirs, are inadequate to properly characterize the dynamic ocean, i.e. different boundary currents, mesoscale and downwelling eddies etc., as well as the highly dynamic coastal ocean, as evidenced by several publications. A combination of radiative transfer, a mathematically deterministic approach to the solution for inverse problem, and atmospheric profiles information from numerical weather prediction (NWP) offers the prospect to account for local retrieval conditions and obtain a more uniform product with superior accuracy. Building on nasas investments in physical oceanography research, a physical deterministic SST (pdsst) retrieval scheme is now reaching maturation. One of the unprecedented capabilities of our pdsst is that IT can use aerosol profiles in addition to atmospheric profile information in the forward modeling, and further allows for adjustment of the aerosol burden by including IT as a retrieved element. Cloud detection is a vital part of SST retrieval processing. An innovative cloud and error masking (CEM) algorithm has been developed, combining the functional spectral differences and radiative transfer based cloud detection tests, especially the unique functional double difference tests, and has led to substantial improvements in information content. Improvement in information content refers to a dual benefit of increased data coverage (reduced false alarms) and detection of actual cloud contamination (improved detection rate). However, there remains scope for improvement achieving 100% detection with a 0% false alarm rate is beyond present capabilities. The theoretical approaches of the pdsst and CEM algorithms are established by our several peer-reviewed publications. The pdsst retrieval suite, when implemented on data from modis-aqua (and goes imager), demonstrates the superiority of this approach with an overall 3-4 times information gain (e.g. rmse reduction from 0.52 k to 0.35 k and data coverage enhanced from ~9% to ~19% for modis as compared to NASA operational SST products). Our research to date has been performed on matchup datasets, these include a substantial amount of verification data which serve as a ready point of reference for the development of algorithms for full-swath radiances. This proposal aims to extend our research by developing a facility for large-scale pilot processing (i.e. full-swath and global data) at university of maryland, to identify issues and develop solutions regarding application of pdsst retrieval suite to global full-swath data. The major tasks, which will be included in this project, are: processing of modis-aqua, modis-terra, and viirs data to develop L2 SST products using our pdsst suite, development of the user interface to disseminate data to users, incorporating user feedback, research on algorithm improvements, a routine validation capability with respect to in situ, and regular comparison analysis with other prevalent L2 and level-4 SST products. This proposal also aims to establish a paradigm shift in satellite inverse methods for operational processing. Aditionally, since the majority of current imagers designed with multiple channels are like the key IR channels in modis, and this knowledge can be easily transferred to new sensors because instrument-specific coefficients and any error covariance are not required. The success of this project should enable a new ERA of ocean science development due to significant error reduction in the end-product and a dramatic increase in data coverage (compared to other prevalent methodologies), in conjunction with better error estimation at the pixel level using deterministic method