Accession Number : ADA601139


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


Descriptive Note : Annual rept.


Corporate Author : CALIFORNIA UNIV LOS ANGELES INST OF GEOPHYSICS AND PLANETARY PHYSICS


Personal Author(s) : Ghil, Michael ; Chekroun, Mickael D ; Kondrashov, Dmitri ; Tippett, Michael K ; Robertson, Andrew W ; Camargo, Suzana J ; Cane, Mark ; Chen, Dake ; Kaplan, Alexey ; Kushnir, Yochanan ; Sobel, Adam ; Ting, Mingfang ; Yuan, Xiaojun


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a601139.pdf


Report Date : 30 Sep 2013


Pagination or Media Count : 21


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. The objectives are to demonstrate the ability of linear and nonlinear, stochastic-dynamic models to capture the dominant and most predictable portion of the climate system's variability. Improve the understanding and prediction of the low-frequency modes (LFMs) of variability such as the Madden-Julian Oscillation (MJO), El Nino-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO) and Pacific-North American (PNA) pattern. Validate LDMs based on data sets from observations, reanalyses and high-end simulations.


Descriptors :   *CLIMATE , *MODELS , *STOCHASTIC PROCESSES , *VARIATIONS , INDIAN OCEAN , LONG RANGE(TIME) , NORTH ATLANTIC OCEAN , OSCILLATION , PACIFIC OCEAN , PATTERNS , PREDICTIONS


Subject Categories : Meteorology
      Physical and Dynamic Oceanography


Distribution Statement : APPROVED FOR PUBLIC RELEASE