TrendSLR {TrendSLR}

TrendSLR: A package providing improved techniques to estimate mean sea level (trend), velocity and acceleration from sea level records.

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.