Forman, B. A., and R. H. Reichle:
"Estimating Passive Microwave Brightness Temperature over Snow-covered Land in North America Using a Land Surface Model and a Support Vector Machine"
Presentation at the Eastern Snow Conference, Huntsville, ON, Canada, 2013.

Abstract:
Recent research by Forman et al. [2013] and Xue and Forman [2013] demonstrates the ability of an artificial neural network (ANN) to predict passive microwave (PMW) brightness temperatures (Tb) over snow-covered land as measured by the Advanced Microwave Sounding Radiometer (AMSR-E). The eventual goal is to use the ANN as an observation operator within an existing, ensemble-based data assimilation framework where model estimates are merged with AMSR-E Tb measurements with the aim of improving snow water equivalent estimates across regional and continental scales. The results from this current study suggest an alternative form of machine learning the support vector machine (SVM) outperforms the ANN for all frequencies and polarizations evaluated in the study. Improvements in the SVM predictions (relative to the ANN) were found in both forested and non-forested regions as well as regions where the snowpack is relatively thin and ephemeral. Additionally, the SVM predictions (relative to the ANN) captured much more of the fine-scale (i.e., daily) temporal variability in the AMSR-E Tb observations. These finding suggest machine learning applications such as a SVM could serve as an effective observation operator within a land data assimilation framework.


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NASA-GSFC / GMAO / Rolf Reichle