TY - JOUR
T1 - Atmospheric flow indices and interannual Great Salt Lake variability
AU - Moon, Young Il
AU - Lall, Upmanu
PY - 1996/4
Y1 - 1996/4
N2 - This paper identifies connections between the time variability of the volume of the Great Salt Lake (GSL), Utah, and selected atmospheric circulation indices. The indices considered are the Southern Oscilation Index (SOI), the Pacific/North America (PNA) climatic pattern, and the Central North Pacific (CNP) index. We focus on interannual time scales. Low-frequency (interannual or interdecadal) relationships between the Great Salt Lake (GSL) volume change and atmospheric circulation indices are of importance because of their significance for the understanding and prediction of the GSL volume. We use Singular Spectral Analysis (SSA), Multichannel Singular Spectral Analysis (MSSA), and Multitaper Spectral Analysis [or Multitaper Method (MTM)], to identify persistent or nearly periodic patterns in time in each series. MSSA examines the joint modes of variability across the time series, while SSA decomposes each series into its component time patterns. MTM is used for identification of peaks and frequency band structure.
AB - This paper identifies connections between the time variability of the volume of the Great Salt Lake (GSL), Utah, and selected atmospheric circulation indices. The indices considered are the Southern Oscilation Index (SOI), the Pacific/North America (PNA) climatic pattern, and the Central North Pacific (CNP) index. We focus on interannual time scales. Low-frequency (interannual or interdecadal) relationships between the Great Salt Lake (GSL) volume change and atmospheric circulation indices are of importance because of their significance for the understanding and prediction of the GSL volume. We use Singular Spectral Analysis (SSA), Multichannel Singular Spectral Analysis (MSSA), and Multitaper Spectral Analysis [or Multitaper Method (MTM)], to identify persistent or nearly periodic patterns in time in each series. MSSA examines the joint modes of variability across the time series, while SSA decomposes each series into its component time patterns. MTM is used for identification of peaks and frequency band structure.
UR - http://www.scopus.com/inward/record.url?scp=0030112417&partnerID=8YFLogxK
U2 - 10.1061/(ASCE)1084-0699(1996)1:2(55)
DO - 10.1061/(ASCE)1084-0699(1996)1:2(55)
M3 - Article
AN - SCOPUS:0030112417
SN - 1084-0699
VL - 1
SP - 55
EP - 62
JO - Journal of Hydrologic Engineering - ASCE
JF - Journal of Hydrologic Engineering - ASCE
IS - 2
ER -