cslice: Climatological slicing of dates
In sethmcg/climod: Bias correction and other tools for climate model output

Description Usage Arguments Details Value See Also

Given a sequence of dates, creates an object that indexes the sets of dates falling into each of a series of climatological moving windows.

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cslice(time, num = ceiling(year/inner), ratio = outer/inner,
  inner = year/num, outer = inner * ratio, year = yearlength(time),
  names = NULL, split = TRUE)

`time`	A vector of times, encoded as time elapsed since some start date.
`num`	The number of inner windows per year.
`ratio`	The length of the outer window as a multiple of the length of the inner window.
`inner`	The length of the inner window.
`outer`	The length of the outer window.
`year`	The length of the year.
`names`	(optional) A vector of names for the windows (e.g., names of months).
`split`	Logical: whether to split the indexes for each window into contiguous segments (i.e., grouped by year). Defaults to TRUE.

cslice generates indices for a set of inner and outer windows. The inner windows are contiguous and non-overlapping; the outer windows overlap and are centered on the inner windows. Applying an operation like the mean or the variance to the outer window gives a moving-window statistic for the set of inner windows. For example, if the inner windows are 1 day long and the outer windows are 30 days long, averaging over the outer windows generates a daily climatology with a 30-day filter, which is smoother and more robust than simply averaging over the Nth day of each year.

The windows generated by cslice are all of uniform size, and there are the same number of inner and outer windows. The slice and unslice functions (q.v.) use a cslice object to subset data for climatological operations.

Typically, when people generate climatologies they either don't use an inner window (e.g., when generating a monthly climatology from daily data) or they use an inner window with the same frequency as the data. However, allowing the length of the inner window to vary is useful for smoothing out irregularities and differences in the calendar. Using this approach to generate daily climatologies with 360 inner windows for multiple different datasets seamlessly accommodates model data with a 360-day calendar, model data with a noleap (365-day) calendar, and real world data with leap years. Likewise, it will also handle irregularly-spaced and missing timesteps.

Caveats: the function operates on dates encoded as time elapsed since an epoch (start time). When generating climatologies from multiple sources, be sure that the epochs match, or the climatologies may not align with one another. Using an inner window that's shorter than the sampling frequency of the data could result in some output values being based on many fewer inputs than others; think carefully about window sizes and the temporal structure of the data.

If the indexes are split into contiguous segments by year using the split argument, any outer window segment that doesn't contain any inner window indexes will be discarded. This happens when the first (last) outer window overlaps the start (end) of the dataset; discarding these segments ensures a one-to-one correspondence between inner and outer window segments and prevents some of the intermediate statistics created in KDDM bias-correction from being distorted by segments containing only a few points.

The size and number of the inner and outer windows can be specified using any two of the four parameters num, ratio, inner, and outer. By default, inner and outer are assumed to have units of days, but other units should work as long as they are consistent with the value of year.

See the KDDM bias-correction vignette for examples.

An object of class 'cslice' containing lists of indices associated with moving windows across multiple years and a list of the parameters specifying the object.

slice

sethmcg/climod documentation built on Nov. 19, 2021, 11:12 p.m.