Statistical Field Estimation and Scale Estimation for Complex Coastal Regions and Archipelagos

Abstract

A fundamental requirement in realistic computational geophysical fluid dynamics is the optimal estimation of gridded fields and of spatial-temporal scales directly from the spatially irregular and multivariate data sets that are collected by varied instruments and sampling schemes. In this work, we derive and utilize new schemes for the mapping and dynamical inference of ocean fields in complex multiply-connected domains, study the computational properties of our new mapping schemes, and derive and investigate new schemes for adaptive estimation of spatial and temporal scales. Adaptive methodologies for the spatial-temporal scale estimation are proposed. The ultimate goal of all these methods would be to create maps of spatial and temporal scales that evolve as new ocean data are fed to the scheme. This would potentially be a significant advance to the ocean community for better understanding and sampling of ocean processes.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2009
Accession Number
ADA507865

Entities

People

  • Arpit Agarwal

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Archipelagoes
  • Coastal Regions
  • Computational Fluid Dynamics
  • Computational Science
  • Computations
  • Differential Equations
  • Fluid Dynamics
  • Islands
  • Landforms
  • Mathematical Filters
  • Oceanography
  • Partial Differential Equations
  • Ridges
  • Terrain
  • Theorems
  • Topography
  • Two Dimensional

Fields of Study

  • Environmental science

Readers

  • Approximation Theory.
  • Coastal Oceanography
  • Distributed Systems and Data Platform Development

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms