Statistical Modelling and Variability of the Subtropical Front, New Zealand

Ocean fronts are narrow zones of intense dynamic activity that play an important role in global ocean-atmosphere interactions. Of particular significance is the circumglobal frontal system of the Southern Ocean where intermediate water masses are formed, heat, salt, nutrients and momentum are redistributed and carbon dioxide is absorbed. The northern limit of this frontal band is marked by the Subtropical Front, where subtropical gyre water convergences with colder subantarctic water. Owing to their highly variable nature, both in space and time, ocean fronts are notoriously difficult features to adequately sample using traditional in-situ techniques. We therefore propose a new and innovative statistical modelling approach to detecting and monitoring ocean fronts from AVHRR SST images. Weighted local likelihood is used to provide a nonparametric description of spatial variations in the position and strength of individual fronts within an image. Although we use the new algorithm on AVHRR data it is suitable for other satellite data or model output. The algorithm is used to study the spatial and temporal variability of a localized section of the Subtropical Front past New Zealand, known locally as the Southland Front. Twenty-one years (January 1985 to December 2005) of estimates of the front’s position, temperature and strength are examined using cross correlation and wavelet analysis to investigate the role that remote atmospheric and oceanic forcing relating to the El Nino-Southern Oscillation may play in interannual frontal variability. Cold (warm) anomalies are observed at the Southland Front three to four months after peak El Nino (La Nina) events. The gradient of the front changes one to two seasons in advance of extreme ENSO events suggesting that it may be used as a precursor to changes in the Southern Oscillation. There are strong seasonal dependencies to the correlation between ENSO indices and frontal characteristics. In addition, the frequency and phase relationships are inconsistent indicating that no one physical mechanism or mode of climate variability is responsible for the teleconnection

Learning the dynamics of deformable objects and recursive boundary estimation using curve evolution techniques

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 161-176).The primary objective of this thesis is to develop robust algorithms for the incorporation of statistical information in the problem of estimating object boundaries in image data. We propose two primary algorithms, one which jointly estimates the underlying field and boundary in a static image and another which performs image segmentation across a temporal sequence. Some motivating applications come from the earth sciences and medical imaging. In particular, we examine the problems of oceanic front and sea surface temperature estimation in oceanography, soil boundary and moisture estimation in hydrology, and left ventricle boundary estimation across a cardiac cycle in medical imaging. To accomplish joint estimation in a static image, we introduce a variational technique that incorporates the spatial statistics of the underlying field to segment the boundary and estimate the field on either side of the boundary. For image segmentation across a sequence of frames, we propose a method for learning the dynamics of a deformable boundary that uses these learned dynamics to recursively estimate the boundary in each frame over time. In the recursive estimation algorithm, we extend the traditional particle filtering approach by applying sample-based methods to a complex shape space.(cont.) We find a low-dimensional representation for this shape-shape to make the learning of the dynamics tractable and then incorporate curve evolution into the state estimates to recursively estimate the boundaries. Experimental results are obtained on cardiac magnetic resonance images, sea surface temperature data, and soil moisture maps. Although we focus on these application areas, the underlying mathematical principles posed in the thesis are general enough that they can be applied to other applications as well. We analyze the algorithms on data of differing quality, with both high and low SNR data and also full and sparse observations.by Walter Sun.Ph.D

Space-time interpolation of oceanic fronts

Oceanic temperature fronts observed through composite infrared images from the AVHRR satellite data are fragmented due mostly to cloud occlusion. The sampling frequency of such frontal position observations tends to be insufficiently high to resolve dynamics of the meandering features associated with the frontal contour, so that contour reconstruction using a standard space-time smoothing often leads to introduction of spurious features. Augmenting space-time smoothing with a simple point-feature detection/matching scheme, however, can dramatically improve the reconstruction product. This paper presents such a motion-compensated interpolation algorithm, for reconstruction of open contours evolving in time given fragmented position data. The reconstruction task is formulated as an optimization problem, and a time-sequential solution which adaptively estimates feature motion is provided. The resulting algorithm reliably interpolates position measurements of the surface temperature fronts associated with the highly convoluted portions of strong ocean currents such as the Gulf Stream and Kuroshio