TrendSLR: TrendSLR: A package providing improved techniques to estimate...
In TrendSLR: Estimating Trend, Velocity and Acceleration from Sea Level Records
Description
TrendSLR functions
References
Description
The “TrendSLR” package provides improved estimates of mean sea level (trend)
and associated real-time velocities and accelerations from individual, annual
average ocean water level data records. Improved trend estimates are based on
Singular Spectrum Analysis (SSA) methods. Various gap-filling options are included
to accommodate incomplete time series records along with a range of diagnostic
tools to investigate the SSA decomposition of the time series. A wide range of
screen and plot to file options are available within the package.
TrendSLR functions
The msl.trend function is one of the key functions of the package
deconstructing annual average time series into a trend and
associated velocities and accelerations, filling necessary internal structures
which facilitate all functions in this package. The fixed settings built into
this function are based on the detailed research and development summarised
in Watson (2016a,b; 2018).
The custom.trend function is the other key function which permits
customisation of key input parameters to enable improved isolation of trend
components (mean sea level) and estimated associated velocities and accelerations.
This function provides more flexibility for the expert analyst than the
msl.trend function with fixed inbuilt parameterisation.
References
Watson, P.J., 2016a. Identifying the best performing time series
analytics for sea-level research. In: Time Series Analysis and
Forecasting, Contributions to Statistics, pp. 261-278, ISBN 978-3-319-28725-6.
Springer International Publishing.
Watson, P.J., 2016b. How to improve estimates of real-time acceleration in
the mean sea level signal. In: Vila-Concejo, A., Bruce, E., Kennedy, D.M.,
and McCarroll, R.J. (eds.), Proceedings of the 14th International Coastal
Symposium (Sydney, Australia). Journal of Coastal Research,
Special Issue, No. 75, pp. 780-785. Coconut Creek (Florida), ISSN 0749-0208.
Watson, P.J., 2018. Improved Techniques to Estimate Mean Sea Level,
Velocity and Acceleration from Long Ocean Water Level Time Series to Augment
Sea Level (and Climate Change) Research. PhD Thesis, University of New South
Wales, Sydney, Australia.
TrendSLR documentation built on Aug. 7, 2019, 9:03 a.m.
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Description TrendSLR functions References
Description
The “TrendSLR” package provides improved estimates of mean sea level (trend) and associated real-time velocities and accelerations from individual, annual average ocean water level data records. Improved trend estimates are based on Singular Spectrum Analysis (SSA) methods. Various gap-filling options are included to accommodate incomplete time series records along with a range of diagnostic tools to investigate the SSA decomposition of the time series. A wide range of screen and plot to file options are available within the package.
TrendSLR functions
The msl.trend function is one of the key functions of the package
deconstructing annual average time series into a trend and
associated velocities and accelerations, filling necessary internal structures
which facilitate all functions in this package. The fixed settings built into
this function are based on the detailed research and development summarised
in Watson (2016a,b; 2018).
The custom.trend function is the other key function which permits
customisation of key input parameters to enable improved isolation of trend
components (mean sea level) and estimated associated velocities and accelerations.
This function provides more flexibility for the expert analyst than the
msl.trend function with fixed inbuilt parameterisation.
References
Watson, P.J., 2016a. Identifying the best performing time series analytics for sea-level research. In: Time Series Analysis and Forecasting, Contributions to Statistics, pp. 261-278, ISBN 978-3-319-28725-6. Springer International Publishing.
Watson, P.J., 2016b. How to improve estimates of real-time acceleration in the mean sea level signal. In: Vila-Concejo, A., Bruce, E., Kennedy, D.M., and McCarroll, R.J. (eds.), Proceedings of the 14th International Coastal Symposium (Sydney, Australia). Journal of Coastal Research, Special Issue, No. 75, pp. 780-785. Coconut Creek (Florida), ISSN 0749-0208.
Watson, P.J., 2018. Improved Techniques to Estimate Mean Sea Level, Velocity and Acceleration from Long Ocean Water Level Time Series to Augment Sea Level (and Climate Change) Research. PhD Thesis, University of New South Wales, Sydney, Australia.
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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