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

Abstract

Long-term goals and objectives: 1. Carry out cooperative real-time (sub)-mesoscale data-driven predictions with adaptive sampling and research and evaluate skill measures 2. Advance scientific understanding of 3D upwelling/relaxation dynamics and carry out budget analyses as possible (multi-balances, sensitivity studies, parameterizations, predictability) 3. Determine details of three metrics for adaptive sampling (coverage, dynamics, uncertainties) and develop schemes and exercise software for their integrated use. Approach: 1. Further modeling system improvements and skill metrics a. Re-analyses and Multi-model comparison and combination 2. Ocean Dynamics a. Ocean Flux and Term Balances: AN Budgets, Tidal effects, Eddying off AN shelf, Undercurrent and CC dynamics/interactions, Coastal trapped waves, etc b. Study impact of larger-scale effects shown today on AN shelf 3. Data Assimilation (DA), Uncertainty and Predictive capability 4. Energy and Vorticity Analysis (EVA), Scale estimation and LCS for dynamics, sampling and DA.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2009
Accession Number
ADA527062

Entities

People

  • Pierre F. J. Lermusiaux

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Assimilation
  • Boundaries
  • Delphi Method
  • Dynamics
  • Elevation
  • Engineering
  • Environmental Assessment
  • Equations
  • Mechanical Engineering
  • Observation
  • Oceans
  • Regions
  • Sampling
  • Simulations
  • Surface Temperature
  • Uncertainty

Fields of Study

  • Environmental science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Coastal Oceanography