The Application of High Resolution Dynamical-Numerical Models as a Tool to Infer Climate Statistics

Abstract

The grant The Application of High Resolution Dynamical-Numerical Models as a Tool to Infer Climate Statistics was designed to investigate the use of dynamical-numerical models as a tool for the inference of climate statistics. In addition this grant would develop a procedure that could be implemented at department of defense centers (particularly AFCCC). The main objective of this work was to determine the feasibility of using a dynamical-numerical model for the generation of climate statistics. Other objectives of the pro-posed work were: (1) Estimate the quality of climate statistics generated from a dynamical-numerical model, (2) Determine the change in quality of the statistics with varying amounts of observational data assimilated into the model, (3) Estimate the sensitivity of the statistics to the choice of numerical model. (4) Determine the variations in the quality of the statistics with differing simulation strategies, and (5) Investigate algorithms to estimate quantities not directly generated by the model (i.e., visibility, icing, etc.). The major effort of this work involved the simulations for January and July for ten years. The details of the results of these simulations are found in the accompanying technical reports. Here we will mainly address the objectives listed above.

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Document Details

Document Type
Technical Report
Publication Date
Jun 14, 1998
Accession Number
ADA384543

Entities

People

  • Charles E. Graves
  • John Zack

Organizations

  • Saint Louis University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Atmospheric Sciences
  • Boundaries
  • Boundary Layer
  • Climate
  • Data Science
  • Demographic Cohorts
  • Department Of Defense
  • High Resolution
  • Information Science
  • Sensitivity
  • Simulations
  • Standards
  • Statistics
  • Surface Energy
  • Surface Properties

Fields of Study

  • Mathematics

Readers

  • Computational Modeling and Simulation

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference