Optimal Asset Distribution for Environmental Assessment and Forecasting Based on Observations, Adaptive Sampling, and Numerical Prediction

Abstract

The objective of this Multi-University Research Initiative (MURI) grant, subtitled, "The Adaptive Sampling and Prediction System (ASAP)" was to learn how to deploy, direct, and utilize autonomous vehicles [and other mobile sensing platforms] most efficiently to sample the ocean, assimilate the data into numerical models in real or near-real time, and predict future conditions with minimal error. The scientific goal was to close the heat budget for a control volume surrounding a three-dimensional coastal upwelling center, and identify via the magnitude of the terms the relative importance of the surface fluxes, boundary layer processes, alongshore advection, and mesoscale interactions in determining the temperature changes within the box. The work resulted in seven publications in refereed journals authored or co-authored by the Pis, and the eighth is in advanced preparation.

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Document Details

Document Type
Technical Report
Publication Date
Mar 18, 2013
Accession Number
ADA582359

Entities

People

  • Frederick L. Bahr
  • Steven R. Ramp

Organizations

  • Monterey Bay Aquarium Research Institute

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Aircrafts
  • Autonomous Vehicles
  • Boundaries
  • Boundary Layer
  • Climate Change
  • Delphi Method
  • Environmental Assessment
  • Heat Flux
  • Observation
  • Ocean Observing Systems
  • Oceans
  • Sampling
  • Three Dimensional
  • Underwater Acoustics
  • Unmanned Vehicles
  • Upwelling
  • Vehicles

Fields of Study

  • Environmental science

Readers

  • Library and Information Science
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers
  • Research Science/Academic Research

Technology Areas

  • Autonomy