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Hierarchical Space—Time Dynamic Models

  • Christopher K. Wikle
Part of the Lecture Notes in Statistics book series (LNS, volume 144)

Abstract

Virtually all atmospheric and oceanographic processes (e.g., wind, temperature, sea surface temperature, moisture) involve variability over space and time. For example, consider surface wind fields over the tropical oceans. Such fields are important factors in many processes that are of critical interest to the general public. These include tropical storm (hurricane) formation and maturation, and the development and strengthening of the El Niño-La Niña climate phenomena. One only need examine the governing partial differential equations for wind processes, or selected spatial and/or temporal averages of them, to see that mathematical and statistical descriptions of these dynamical processes depend on complicated temporal and spatial relationships. Furthermore, observations of geophysical processes typically include measurement errors and are often temporally and spatially incomplete. Both of these features can obscure the signal of interest.

Keywords

Kalman Filter Wind Field Prediction Location Wavelet Basis Function Equatorial Shallow Water 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York, Inc. 2000

Authors and Affiliations

  • Christopher K. Wikle
    • 1
  1. 1.University of MissouriColumbiaUSA

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