Variable Seasonal and Subseasonal Oscillations in Sea Level Anomaly Data and Their Impact on Prediction Accuracy

Kosek, W.; Niedzielski, Tomasz; Popiński, Waldemar; Zbylut-Górska, Maria; Wnęk, Agnieszka

doi:10.1007/1345_2015_74

Cited by 9 publications

(10 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They were subsequently modelled with the use of stochastic methods, as described in detail by Niedzielski and Miziński (2013) and Świerczyńska et al (2016). The physical background of the PH model with five harmonic components is based on the papers by Kosek (2001), Niedzielski and Kosek (2009) and Kosek et al (2016) who, using Fourier transform band pass filter, identified the most energetic periodic oscillations in sea level as a function of geographic location. Apart from the annual oscillation, which is associated with the annual cycle and is found to be broadband (Kosek et al 2016), Kosek (2001) found that semi-annual, 120-, 90-and 62-day components are more energetic in the Northern Hemisphere than in the Southern Hemisphere.…”

Section: Stochastic Models In Prognocean Plusmentioning

confidence: 99%

“…The physical background of the PH model with five harmonic components is based on the papers by Kosek (2001), Niedzielski and Kosek (2009) and Kosek et al (2016) who, using Fourier transform band pass filter, identified the most energetic periodic oscillations in sea level as a function of geographic location. Apart from the annual oscillation, which is associated with the annual cycle and is found to be broadband (Kosek et al 2016), Kosek (2001) found that semi-annual, 120-, 90-and 62-day components are more energetic in the Northern Hemisphere than in the Southern Hemisphere. In addition, the presence of these oscillations has been identified in sea level variation in the North Atlantic Ocean and some of its seas, mainly in the east-equatorial Atlantic Ocean, the North American Basin, the North Sea and the Baltic Sea (Kosek 2001).…”

Section: Stochastic Models In Prognocean Plusmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting the North Atlantic Oscillation index using altimetric sea level anomalies

Świerczyńska-Chlaściak

Niedzielski

2020

Acta Geod Geophys

View full text Add to dashboard Cite

The objective of this paper is to present a new approach for forecasting NAO index (NAOi) based on predictions of sea level anomalies (SLAs). We utilize significant correlations (Pearson's r up to 0.69) between sea surface height (SSH) calculated for the North Atlantic (15-65°N, basin-wide) and winter Hurrell NAOi, as shown by Esselborn and Eden (Geophys Res Lett 28:3473-3476, 2001). We consider the seasonal and monthly data of Hurrell NAOi, ranging from 1993 to 2017. Weekly prognoses of SLA are provided by the Prognocean Plus system which uses several data-based models to predict sea level variation. Our experiment consists of three steps: (1) we calculate correlation between the first principal component (PC1) of SSH/SLA data and NAOi, (2) we determine coefficients of a linear regression model which describes the relationship between winter NAOi and PC1 of SLA data (1993-2013), (3) we build two regression models in order to predict winter NAOi (by attaching SLA forecasts and applying coefficients of the fitted regression models). The resulting 3-month prognoses of winter NAOi are found to reveal mean absolute errors of 1.5 or less. The choice of method for preparing SLA data for principal component analysis is shown to have a stronger impact on the prediction performance than the selection of SLA prediction method itself.

show abstract

Section: Stochastic Models In Prognocean Plusmentioning

confidence: 99%

Section: Stochastic Models In Prognocean Plusmentioning

confidence: 99%

Forecasting the North Atlantic Oscillation index using altimetric sea level anomalies

Świerczyńska-Chlaściak

Niedzielski

2020

Acta Geod Geophys

View full text Add to dashboard Cite

show abstract

“…is the parabolic transmittance function in which λ is half of the window bandwidth and  is the frequency argument. The variable amplitudes and phases of the broadband oscillations can be computed by the FTBPF+HT combination (Kosek et al, 2015(Kosek et al, , 2020:…”

Section: Interannual and Intra-seasonal Oscillations In Lodr Aam Andmentioning

confidence: 99%

Interannual and Intra-seasonal Oscillations in the Length of Day, Atmospheric Angular Momentum and Atmospheric Surface Pressure Time Series Observed at Chosen Meteorological Stations Near the Equator

Kosek

Han

2020

Preprint

View full text Add to dashboard Cite

Abstract The atmospheric surface pressure time series of Madras, Darwin, and Tahiti together with non-tidal length-of-day (LODR) variations and axial component of atmospheric angular momentum (AAM) were analyzed by wavelet transform as well as the combination of the Fourier transform band pass filter with the Hilbert transform (FTBPF + HT) to detect interannual and intra-seasonal oscillations in them. It was found that annual oscillations in the atmospheric surface pressure variations of Darwin and Tahiti stations are in phase and are about 180o out of phase in the atmospheric surface pressure variations of Madras station. The phase of the annual oscillation in atmospheric surface pressure variations of Madras station is slightly greater (~ 20o) than the phase of the annual oscillation in the LODR time series. The amplitude and phase variations of the annual and semi-annual oscillations computed by the FTBPF + HT combination in LODR and the axial component of AAM are very similar. The mean amplitudes of the semi-annual oscillation in the atmospheric surface pressure variations of Madras and Tahiti are of the order of 0.4 hPa, the phases of these oscillations are stable and the amplitude of the semi-annual oscillation in the atmospheric surface pressure variations of Darwin is negligible due to unstable phase of this oscillation. The atmospheric surface pressure variations of Madras, Darwin, and Tahiti stations show similar amplitude wideband signals with a central period of ~ 4 years (cutoff periods ranging from about 2.2 to 20 years) related to El Niño phenomenon. The amplitude maxima of this signal corresponding to the strongest El Niño events in 1982-83, 1997-98, and 2014-15 are also present in amplitude variations of this signal in the LODR and AAM χ3 time series.

show abstract

“…The sea level anomalies based on satellite altimetry data available from AVISO website were analysed (Kosek et al, 2013). The time-frequency analysis based on the Fourier Transform filter of these data enables to detect ocean regions where the signal in sea level change is not linear.…”

Section: Sea Level Changementioning

confidence: 99%

Earth rotation and geodynamics

Bogusz

Brzeziński

Kosek

et al. 2015

Geodesy and Cartography

View full text Add to dashboard Cite

This paper presents the summary of research activities carried out in Poland in 2011-2014 in the field of Earth rotation and geodynamics by several Polish research institutions. It contains a summary of works on Earth rotation, including evaluation and prediction of its parameters and analysis of the related excitation data as well as research on associated geodynamic phenomena such as geocentre motion, global sea level change and hydrological processes. The second part of the paper deals with monitoring of geodynamic phenomena. It contains analysis of geodynamic networks of local, and regional scale using space (GNSS and SLR) techniques, Earth tides monitoring with gravimeters and water-tube hydrostatic clinometer, and the determination of secular variation of the Earth’ magnetic field.

show abstract

Variable Seasonal and Subseasonal Oscillations in Sea Level Anomaly Data and Their Impact on Prediction Accuracy

Cited by 9 publications

References 8 publications

Forecasting the North Atlantic Oscillation index using altimetric sea level anomalies

Forecasting the North Atlantic Oscillation index using altimetric sea level anomalies

Interannual and Intra-seasonal Oscillations in the Length of Day, Atmospheric Angular Momentum and Atmospheric Surface Pressure Time Series Observed at Chosen Meteorological Stations Near the Equator

Earth rotation and geodynamics

Contact Info

Product

Resources

About