Accession Number ADA572180
Title Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data-driven Approach.
Publication Date Sep 2012
Media Count 8p
Personal Author A. Robertson D. Kondrashov M. Ghil M. D. Chekroun M. K. Tippett
Abstract The long-term goal of this project is to quantify the extent to which reduced-order models can be used for the description, understanding and prediction of atmospheric, oceanic and sea ice variability on time scales of 1 12 months and beyond.
Keywords Climate
El nino-southern oscillation
Long range(Time)
Low dimensional models
Low frequency models
Madden-julian oscillation
Models
North atlantic oscillation
Oceans
Pacific-north american pattern
Predictions
Sea surface temperature
Seasonal variations
Stochastic processes
Stochastic-dynamic models


 
Source Agency Non Paid ADAS
NTIS Subject Category 47C - Physical & Chemical Oceanography
Corporate Author California Univ., Los Angeles. Inst. of Geophysics and Planetary Physics.
Document Type Technical report
Title Note Annual rept.
NTIS Issue Number 1316
Contract Number N00014-12-1-0911

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