Optimal initial perturbations for El Nino ensemble prediction with ensemble Kalman filter SCIE SCOPUS

Cited 13 time in WEB OF SCIENCE Cited 13 time in Scopus
Title
Optimal initial perturbations for El Nino ensemble prediction with ensemble Kalman filter
Author(s)
Ham, Yoo-Geun; Kug, Jong-Seong; Kang, In-Sik
Publication Year
2009-12
Abstract
A method for selecting optimal initial perturbations is developed within the framework of an ensemble Kalman filter (EnKF). Among the initial conditions generated by EnKF, ensemble members with fast growing perturbations are selected to optimize the ENSO seasonal forecast skills. Seasonal forecast experiments show that the forecast skills with the selected ensemble members are significantly improved compared with other ensemble members for up to 1-year lead forecasts. In addition, it is found that there is a strong relationship between the forecast skill improvements and flow-dependent instability. That is, correlation skills are significantly improved over the region where the predictable signal is relatively small (i.e. an inverse relationship). It is also shown that forecast skills are significantly improved during ENSO onset and decay phases, which are the most unpredictable periods among the ENSO events.
ISSN
0930-7575
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/4231
DOI
10.1007/s00382-009-0582-z
Bibliographic Citation
CLIMATE DYNAMICS, v.33, no.7-8, pp.959 - 973, 2009
Publisher
SPRINGER
Subject
SINGULAR VECTOR ANALYSIS; HYBRID COUPLED MODEL; ATMOSPHERIC DATA ASSIMILATION; TROPICAL PACIFIC; WIND STRESS; OPTIMAL-GROWTH; BRED VECTORS; OCEAN MODEL; PREDICTABILITY; ENSO
Keywords
Ensemble Kalman filter; Seasonal prediction; Optimal initial perturbation; Ensemble prediction
Type
Article
Language
English
Document Type
Article
Publisher
SPRINGER
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