New Methods For Predictability Analysis
Abstract
LONG-TERM GOAL. The long-term goal of this work is to improve atmospheric and oceanic deterministic forecast capability by maximizing the accuracy of the initial state estimate from which a forecast is made while minimizing observational and computational costs required to obtain the initial state. OBJECTIVES. The present objective of this work is to increase fundamental understanding of error dynamics and develop practical methods for computing error statistics. We intend to reduce the dimension of the error system so that error statistics can be practically obtained using forecast products. In addition from knowledge of the statistical structure of the time dependent error field we intend to determine the areas in space and time where observational resources can be most effectively applied. APPROACH. Small error dynamics is governed by linear equations but because these equations are non-normal and time dependent theoretical tools for analyzing error growth have only recently been obtained. Our approach is to apply these recent theoretical advances in non-normal time-dependent stability analysis to the forecast error problem. Methods of modern control theory, specifically balanced Hankel operator truncation methods, will be used to reduce the dimension of the error system.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 30, 1999
- Accession Number
- ADA629738
Entities
People
- Brian Farrell
Organizations
- Harvard University