EOF analysis of a time series with application to tsunami detection
Elena Tolkova

TL;DR
This paper introduces a modified EOF analysis method for non-stationary time series, enabling accurate tidal removal and short-term tsunami prediction from buoy data without prior location-specific knowledge.
Contribution
A novel EOF formalism for non-stationary processes is developed, improving tidal de-tiding and tsunami detection capabilities in buoy records.
Findings
EOFs can decompose 1-day tidal fragments at any location.
The method separates tsunami waves from tides with millimeter accuracy.
Short-term tidal predictions are possible without prior local tidal knowledge.
Abstract
Fragments of deep-ocean tidal records up to 3 days long belong to the same functional sub-space, regardless of the record's origin. The tidal sub-space basis can be derived via Empirical Orthogonal Function (EOF) analysis of a tidal record of a single buoy. Decomposition of a tsunami buoy record in a functional space of tidal EOFs presents an efficient tool for a short-term tidal forecast, as well as for an accurate tidal removal (Tolkova, E. 2009. Principal Component Analysis of Tsunami Buoy Record: Tide Prediction and Removal. Dyn. Atmos. Oceans, 46 (1-4): 62-82.) EOF analysis of a time series, however, assumes that the time series represents a stationary (in the weak sense) process. In the present work, a modification of one-dimensional EOF formalism not restricted to stationary processes is introduced. With this modification, the EOF-based de-tiding/forecasting technique can be…
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