msltrend: msltrend: A package providing improved techniques to estimate...

Description msltrend functions References

Description

The 'msltrend' package provides improved estimates of trend (mean sea level) and associated real-time velocities and accelerations from long (minimum 80 years), individual, annual average ocean water level data records. Improved trend estimates are based on Singular Spectrum Analysis methods. Various gap-filling options are included to accommodate incomplete time series records. The package also contains a forecasting module to consider the implication of user defined quantum of sea level rise between the end of the available historical record and the year 2100. A wide range of screen and pdf plotting options are available within the package.

msltrend functions

The msl.trend function is the key entry point to the package deconstructing annual average time series data records into a trend and associated real-time velocities and accelerations, filling necessary internal structures which facilitate all functions in this package (Refer Watson 2016a,b for more detail).

The msl.forecast function enables a user defined quantum of sea level rise to be added from the end of the deconstructed historical record to the year 2100. Similalrly, this function estimates real-time velocities and accelerations from the start of the available historical record to the year 2100.

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, ISBN 978-3-319-28725-6, Springer International Publishing (in press).

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. Coconut Creek (Florida), ISSN 0749-0208 (in press).


msltrend documentation built on May 2, 2019, 5:41 a.m.

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