msl.trend(file, station_name = " ", fillgaps = 1, iter = 10000, plot = TRUE)
Warning: If input data files do not conform to these pre-conditions, the analysis will be terminated. It should be further noted that the existence of quasi 60 year oscillations in global mean sea level have been well recognised in the literature. Therefore, in order to be effective for climate change and sea level research, only input files with a minimum length exceeding 80 years have been considered in order that the package can identify and isloate such signals.
Note: This field can be left blank, however, it is retained for use in banner labelling of all plotting and pdf outputs.
igapfill
)
. The alternatives (2 and 3) are based on linear interpolation and cubic
spline interpolation, respectively (refer na.approx
).Note: Gap filled portions of the time series are denoted in red on the default screen plot. This is done specifically to provide ready visual observation to discern if the selected gap filling method provides an appropriate estimate within the gaps in keeping with the remainder of the historical record. Depending on the nature of the record and extent of gaps, some trial and error between alternatives might be necessary to optimise gap filling.
Warning: Although the default setting provides a more accurate basis for estimating error margins, the degree of iterations slows the analysis and can take several minutes to run.
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).
msl.forecast
, msl.plot
,
msl.pdf
, summary
, Balt
, s
.
# -------------------------------------------------------------------------
# Isolate trend from Baltimore record, filling gaps with spline interpolation and
# 500 iterations. Use raw 'Balt.csv' data file. Note: ordinarily user would call
# 'File.csv' direct from working directory using the following sample code:
# s <- msl.trend('Balt.csv', fillgaps = 3, iter = 500, 'BALTIMORE, USA') # DONT RUN
# -------------------------------------------------------------------------
data(s) # msl.trend object from above-mentioned example
str(s) # check structure of msl.trend object
msl.plot(s, type=2) # check screen output of gapfilling and trend estimate
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