gap.fillview {TrendSLR}R Documentation

Inspect gap-filling options for mean sea level records.

Description

Inspect gap-filling options for mean sea level records.

Usage

gap.fillview(object, station_name = " ", fillgaps = 1)

Arguments

object

an annual average mean sea level time series (refer ts) with water levels (in millimetres). Missing data must be denoted by “NA”. Missing data and maximum missing data gap are limited to 15% and 5%, respectively, of the data record. These data input constraints have been based on the research work of Watson (2018) to best preserve the integrity and primary characteristics of the data set for mean sea level analysis. To ensure maximum flexibility for the data analyst, gaps beyond these margins are permitted to be filled but a warning will appear at the console when these advised limits are exceeded.

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 long period oscillations in global mean sea level have been well recognised in the literature (eg. Chambers et al. (2012); Minobe (1999)). Therefore, in order to be effective for climate change and sea level research, time series input files are recommended to have a minimum length of at least 80 years in order that the analysis can identify and isloate such signals. Time series less than 80 years in length will be analysed but a warning will be displayed.

station_name

character string, providing the name of the data record.

Note: This field can be left blank, however, it is retained for use in banner labelling of all plotting and pdf outputs.

fillgaps

numeric, provides 5 alternative gap filling procedures for missing data. The following options are available:

  • 1: The default procedure is based on iterative gap filling using Singular Spectrum Analysis (refer igapfill);

  • 2: linear interpolation (refer na.approx);

  • 3: Cubic spline interpolation (refer na.approx);

  • 4: Stineman's interpolation (refer na.interpolation); and

  • 5: Weighted moving average (refer na.ma).

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.

Details

This function permits visual screen checking of various gap-filling options prior to undertaking the full trend analysis (refer msl.trend). The returned object can also be used directly as input to the custom.trend function.

Value

An object of class “gap.fillview” is returned with the following elements:

$Station.Name:

the name of the data record.

$Summary:

a summary data frame of relevant parameters relating to the inputted annual average data set and filled time series, including:

$Fillgaps:

the procedure used to fill the time series.

References

Chambers, D.P., Merrifield, M.A., and Nerem, R.S., 2012. Is there a 60 year oscillation in global mean sea level? Geophysical Research Letters, 39(18).

Minobe, S., 1999. Resonance in bidecadal and pentadecadal climate oscillations over the North Pacific: Role in climatic regime shifts. Geophysical Research Letters, 26(7), pp.855-858.

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.

See Also

msl.trend, igapfill, na.approx, na.interpolation, na.ma, ts.

Examples

# -------------------------------------------------------------------------
# View different options for filling the Baltimore annual mean sea level record.
# -------------------------------------------------------------------------

data(Balt) # Baltimore mean sea level record
ts1 <- ts(Balt[2], start = Balt[1, 1]) # create time series input object

g <- gap.fillview(ts1, station_name = "Baltimore", fillgaps = 1) # SSA
g <- gap.fillview(ts1, station_name = "Baltimore", fillgaps = 2) # Linear interpolation
g <- gap.fillview(ts1, station_name = "Baltimore", fillgaps = 3) # Cubic spline interpolation
g <- gap.fillview(ts1, station_name = "Baltimore", fillgaps = 4) # Stineman's interpolation
g <- gap.fillview(ts1, station_name = "Baltimore", fillgaps = 5) # Weighted moving average

str(g) # Check structure of outputted object
[Package TrendSLR version 1.0 Index]