Physical deterministic sea surface temperature (PDSST) retrieval scheme is built on radiative transfer forward model and a mathematically deterministic approach to the solution for inverse problem. This requires atmospheric profiles information from Numerical Weather Prediction (NWP), which offers the prospect to account for local retrieval conditions and yields a more uniform product with superior accuracy. One of the unprecedented capabilities of the PDSST scheme is that it can use aerosol profiles in addition to atmospheric profiles information for the forward modeling, and also 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 functional double difference tests are unique. These advancements have led to substantial improvements in information retrieval from expensive satellite measurement. This improvement refers to a dual benefit of increased data coverage (reduced false alarms) and detection of actual cloud contamination (improved detection rate). The PDSST retrieval suite, is combining the PDSST retrieval scheme and CEM, demonstrates the superiority of this approach with an overall ~3-4 times information gain when implemented on data from MODIS-Aqua and GOES Imager. For example, RMSE reduction from 0.52 K to 0.35 K and data coverage enhanced from ~9% to ~19% as compared to NASA operational MODIS-AQUA SST products.
NASA has been providing global sea-surface temperatures (SST) from MODIS on daily to decadal periods, and these are extensively used for a wide range of atmospheric and oceanic studies. However, the retrieval quality and cloud detection are somewhat problematic. We will present a new physical deterministic algorithm based on truncated total least squares (TTLS) using multiple channels for SST retrieval from MODIS measurements in conjunction with a new cloud detection scheme using a radiative transfer model atop a functional spectral difference method. The TTLS method is a new addition to improve the information content of the retrieval compared to our earlier work using modified total least squares (MTLS). A systematic study is conducted to ascertain the optimal channel selection from the 16 channels in the thermal IR of MODIS. Our new algorithm can reduce average RMSE of ~50% while increasing the average data coverage by ~50% compared to the operationally available MODIS SST.
The MODIS advanced sensor contains 16 channels in the thermal infrared band, makes it an attractive instrument for atmospheric and oceanic sciences. Even for satellite-derived sea surface temperature (SST) retrievals, the dynamics of atmospheric conditions are intended to be characterized by the satellite measurement sufficiently to retrieve good quality SST. The Group for High Resolution SST (GHRSST), which is an international scientific body, provides MODIS SST to date using only two and/or three channels by employing regression method. The few coefficients used in regression based retrieval methods are unable to compensate for wide atmospheric variability and as a result, significant errors are embedded in the retrieved SST. We will demonstrate in this work that the MODIS SST can be retrieved with approximately double the accuracy compared GHRSST operational SST, by using more channels and our physical deterministic-based modified total least squares (MTLS) method. This study also includes the SST4/NLSST and optimal estimation based SST retrieval for comparison purposes. The information content and noise analysis of these retrievals, and the retrieval error due to the quality of cloud detection is discussed.