Application of Singular Spectrum Analysis for Investigating Chaos in Sea Surface Temperature

Altimeter waveforms are usually contaminated due to nonmarine surfaces or inhomogeneous sea state conditions. The present work aimed to present how the singular spectrum analysis (SSA) can be used to reduce the noise level in Jason-1 altimeter waveforms to obtain SSA-denoised waveforms, improving the accuracy of a mean sea surface height (MSSH) model. Comparing the retracked sea surface heights (SSHs) by a 50% threshold retracker for the SSA-denoised waveforms with those for the raw waveforms, the results indicated that SSA allowed a noise reduction on Jason-1 waveforms, improving the accuracy of retracked SSHs. The MSSH model (called Model 1) over the South China Sea with a grid of 2′ × 2′ was established from the retracked SSHs of Jason-1 by the 50% threshold retracker for the SSA-denoised waveforms. Comparing Model 1 and Model 2 (established from the retracked SSHs by the 50% threshold retracker for the raw waveforms) with the CLS15 and DTU18 models in the South China Sea, it was found that the accuracy of Model 1 was higher than that of Model 2, which indicates that using SSA to reduce noise level in Jason-1 waveforms can effectively improve the accuracy of the MSSH model.

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An empirical stochastic model of sea-surface temperature and surface wind over the Southern Ocean

Ocean Science Discussions ◽

10.5194/osd-8-1891-2011 ◽

2011 ◽

Vol 8 (4) ◽

pp. 1891-1936

Author(s):

S. Kravtsov ◽

D. Kondrashov ◽

I. Kamenkovich ◽

M. Ghil

Keyword(s):

Southern Ocean ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Singular Spectrum Analysis ◽

Surface Wind ◽

Preliminary Evidence ◽

Sea Surface ◽

Satellite Measurements ◽

Synoptic Variability ◽

Ocean Region

Abstract. This study employs NASA's recent satellite measurements of sea-surface temperature (SST) and sea-level wind (SLW) with missing data filled-in by Singular Spectrum Analysis (SSA), to construct empirical models that capture both intrinsic and SST-dependent aspects of SLW variability. The model construction methodology uses a number of algorithmic innovations that are essential in providing stable estimates of model's propagator. The best model tested herein is able to faithfully represent the time scales and spatial patterns of anomalies associated with a number of distinct processes. These processes range from the daily synoptic variability to interannual signals presumably associated with oceanic or coupled dynamics. Comparing the simulations of an SLW model forced by the observed SST anomalies with the simulations of an SLW-only model provides preliminary evidence for the climatic behavior characterized by the ocean driving the atmosphere in the Southern Ocean region.

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Gap-Filling of MODIS Time Series Land Surface Temperature (LST) Products Using Singular Spectrum Analysis (SSA)

Atmosphere ◽

10.3390/atmos9090334 ◽

2018 ◽

Vol 9 (9) ◽

pp. 334 ◽

Cited By ~ 11

Author(s):

Hamid Ghafarian Malamiri ◽

Iman Rousta ◽

Haraldur Olafsson ◽

Hadi Zare ◽

Hao Zhang

Keyword(s):

Time Series ◽

Surface Temperature ◽

Land Surface Temperature ◽

Spectrum Analysis ◽

Land Surface ◽

Cloud Cover ◽

Singular Spectrum Analysis ◽

Series Data ◽

Study Region ◽

Singular Spectrum

Land surface temperature (LST) is a basic parameter in energy exchange between the land and the atmosphere, and is frequently used in many sciences such as climatology, hydrology, agriculture, ecology, etc. Time series of satellite LST data have usually deficient, missing, and unacceptable data caused by the presence of clouds in images, the presence of dust in the atmosphere, and sensor failure. In this study, the singular spectrum analysis (SSA) algorithm was used to resolve the problem of missing and outlier data caused by cloud cover. The region studied in the present research included an image frame of the Moderate Resolution Imaging Spectroradiometer (MODIS) with horizontal number 22 and vertical number 05 (h22v05). This image involved a large part of Iran, Turkmenistan, and the Caspian Sea. In this study, MODIS LST products (MOD11A1) were used during 2015 with approximately 1 km × 1 km spatial resolution and day/night LST data (daily temporal resolution). On average, the data have 36.37% gaps in each pixel profile with 730 day/night LST data. The results of the SSA algorithm in the reconstruction of LST images indicated a root mean square error (RMSE) of 2.95 Kelvin (K) between the original and reconstructed LST time series data in the study region. In general, the findings showed that the SSA algorithm using spatio-temporal interpolation can be effectively used to resolve the problem of missing data caused by cloud cover.

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Application of the Singular Spectrum Analysis Technique to Study the Recent Hiatus on the Global Surface Temperature Record

PLoS ONE ◽

10.1371/journal.pone.0107222 ◽

2014 ◽

Vol 9 (9) ◽

pp. e107222 ◽

Cited By ~ 14

Author(s):

Diego Macias ◽

Adolf Stips ◽

Elisa Garcia-Gorriz

Keyword(s):

Surface Temperature ◽

Spectrum Analysis ◽

Singular Spectrum Analysis ◽

Temperature Record ◽

Global Surface Temperature ◽

Singular Spectrum ◽

Analysis Technique ◽

Global Surface ◽

Surface Temperature Record

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Decadal Variations of North Atlantic Sea Surface Temperature in Observations and CMIP3 Simulations*

Journal of Climate ◽

10.1175/2010jcli3598.1 ◽

2010 ◽

Vol 23 (17) ◽

pp. 4619-4636 ◽

Cited By ~ 10

Author(s):

Nathan Jamison ◽

Sergey Kravtsov

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Time Scales ◽

North Atlantic ◽

Climate Models ◽

Decadal Variability ◽

Singular Spectrum Analysis ◽

Global Climate Models ◽

Sea Surface ◽

Decadal Variations

Abstract This study evaluates the ability of the global climate models that compose phase 3 of the Coupled Model Intercomparison Project (CMIP3) to simulate intrinsic decadal variations detected in the observed North Atlantic sea surface temperature (SST) record via multichannel singular spectrum analysis (M-SSA). M-SSA identifies statistically significant signals in the observed SSTs, with time scales of 5–10, 10–15, and 15–30 yr; all of these signals have distinctive spatiotemporal characteristics and are consistent with previous studies. Many of the CMIP3 twentieth-century simulations are characterized by quasi-oscillatory behavior within one or more of the three observationally motivated frequency bands specified above; however, only a fraction of these models also capture the spatial patterns of the observed signals. The models best reproduce the observed quasi-regular SST variations in the high-frequency, 5–10-yr band, while the observed signals in the intermediate, 10–15-yr band have turned out to be most difficult to capture. A handful of models capture the patterns and, sometimes, the spectral character of the observed variability in the two or three bands simultaneously. These results imply that the decadal prediction skill of the models considered—to be estimated within the CMIP5 framework—would be stratified according to the models’ performance in capturing the time scales and patterns of the observed decadal SST variations. They also warrant further research into the dynamical causes of the observed and simulated decadal variability, as well as into apparent differences in the representation of these variations by individual CMIP3 models.

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Reconstruction of the Cloud-Free Time Series Satellite Observations of Land Surface Temperature (LST) Using Singular Spectrum Analysis (SSA)

10.20944/preprints201806.0327.v1 ◽

2018 ◽

Author(s):

Hamid Reza Ghafarian Malamiri ◽

Iman Rousta ◽

Haraldur Olafsson ◽

Hadi Zare ◽

Hao Zhang

Keyword(s):

Time Series ◽

Missing Data ◽

Surface Temperature ◽

Land Surface Temperature ◽

Spectrum Analysis ◽

Land Surface ◽

Cloud Cover ◽

Singular Spectrum Analysis ◽

Series Data ◽

Singular Spectrum

Land Surface Temperature (LST) is a basic parameter in energy exchange between the land and atmosphere and is frequently used in many sciences such as climatology, hydrology, agriculture, ecology, etc. LST time series data have usually deficient, missing and unacceptable data caused by the presence of clouds in images, presence of dust in atmosphere and sensor failure. In this study, Singular Spectrum Analysis (SSA) algorithm was used to resolve the problem of missing and outlier data caused by cloud cover. The region studied in the present research included an image frame of MODIS with horizontal number 22 and vertical number 05 (h22v05). This image involved a large part of Iran and Turkmenistan and Caspian Sea. In this study, MODIS LST sensor (MOD11A1) was used during 2015 with 1×1 Km spatial resolution and day/night LST data (daily temporal resolution). The results of the data quality showed that cloud cover caused 36.37% of missing data in the studied time series with 730 day/night LST images. Further, the results of SSA algorithm in reconstruction of LST images indicated the Root Mean Square Error (RMSE) of 2.95 K between the original and reconstructed data in LST time series in the study region. In general, the findings showed that SSA algorithm using spatio-temporal interpolation in LST time series can be effectively used to resolve the problem of missing data caused by cloud cover.

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