Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data-driven Approach

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. Prepared in cooperation with Columbia University, Palisad...

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Bibliographic Details
Main Authors: Ghil, Michael, Chekroun, Mickael D, Kondrashov, Dmitri, Tippett, Michael K, Robertson, Andrew, Camargo, Suzana J, Cane, Mark, Chen, Dake, Kaplan, Alexey, Kushnir, Yochanan, Sobel, Adam, Ting, Mingfang, Yuan, Xiaojun
Other Authors: CALIFORNIA UNIV LOS ANGELES INST OF GEOPHYSICS AND PLANETARY PHYSICS
Format: Text
Language:English
Published: 2012
Subjects:
Physical and Dynamic Oceanography
*CLIMATE
*OCEANS
*SEASONAL VARIATIONS
*STOCHASTIC PROCESSES
LONG RANGE(TIME)
MODELS
PREDICTIONS
EL NINO-SOUTHERN OSCILLATION
LOW DIMENSIONAL MODELS
LOW FREQUENCY MODELS
MADDEN-JULIAN OSCILLATION
NORTH ATLANTIC OSCILLATION
PACIFIC-NORTH AMERICAN PATTERN
STOCHASTIC-DYNAMIC MODELS
SEA SURFACE TEMPERATURE
Pacific
Palisades
North Atlantic
North Atlantic oscillation
Sea ice
Online Access:http://www.dtic.mil/docs/citations/ADA572180
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA572180
Description
Summary: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. Prepared in cooperation with Columbia University, Palisades, NY.

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