Ocean fronts are narrow zones of intense dynamic activity that play an important role in global ocean–atmosphere interactions. Owing to their highly variable nature, both in space and time, they are notoriously difficult features to adequately sample using traditional in situ techniques. In this paper, the authors propose a new statistical modeling approach for detecting and monitoring ocean fronts from Advanced Very High Resolution Radiometer (AVHRR) SST satellite images that builds on a previous “front following” algorithm. Weighted local likelihood is used to provide a smooth, nonparametric description of spatial variations in the position, mean temperature, width, and temperature change of an individual front within an image. Weightings are provided by a Gaussian kernel function whose width is automatically determined by likelihood cross-validation. The statistical model fitting approach allows estimation of the uncertainty of each parameter to be quantified, a capability not possessed by other techniques. The algorithm is shown to be robust to noise and missing data in an image, problems that hamper many of the existing front-detection schemes. The approach is general and could be used with other remotely sensed datasets, model output, or data assimilation product
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