rfilltimegaps: Disaggregation in the time dimension through the use of...

In meteo: RFSI & STRK Interpolation for Meteo and Environmental Variables

Disaggregation in the time dimension through the use of splines for each pixel

Description

The function creates an object of STFDF-class class, spatio-temporal data with full space-time grid, from another STFDF-class and fills attribute data for missing values in time using splines.

Usage

rfilltimegaps(stfdf,
              tunits="day",
              attrname=1,
              ...)

Arguments

stfdf	STFDF-class; object with time information of minimum length 2, and gap in time dimension.
tunits	character; increment of the sequence used to generete time infromation for temporal gap. See 'Details'.
attrname	integer or character; varible from STFDF-class to be splined in time.
...	arguments passed to splinefun, function spline.

Details

tunits can be specified in several ways:

• A number, taken to be in seconds

• A object of class difftime

• A character string, containing one of "sec", "min", "hour", "day", "DSTday", "week", "month", "quarter" or "year". This can optionally be preceded by a (positive or negative) integer and a space, or followed by "s"

The difference between "day" and "DSTday" is that the former ignores changes to/from daylight savings time and the latter takes the same clock time each day. ("week" ignores DST (it is a period of 144 hours), but "7 DSTdays") can be used as an alternative. "month" and "year" allow for DST.)

Value

STFDF-class object with filled temporal gaps.

Author(s)

Milan Kilibarda [e-mail]

References

Kilibarda, M., T. Hengl, G. B. M. Heuvelink, B. Graeler, E. Pebesma, M. Percec Tadic, and B. Bajat (2014), Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution, J. Geophys. Res. Atmos., 119, 2294-2313, doi:10.1002/2013JD020803;

Kilibarda M., M. Percec Tadic, T. Hengl, J. Lukovic, B. Bajat - Spatial Statistics (2015), Global geographic and feature space coverage of temperature data in the context of spatio-temporal interpolation, doi:10.1016/j.spasta.2015.04.005.

See Also

rfillspgaps pred.strk

Examples

  data(nlmodis20110704)
  data(nlmodis20110712)
  data(NLpol)

  # fill spatial gaps
  library(raster)
  NLpol@proj4string <- nlmodis20110704@proj4string
  
  
  nlmodis20110704 <- rfillspgaps(nlmodis20110704,NLpol)
  nlmodis20110712 <- rfillspgaps(nlmodis20110712,NLpol)
  
  nlmodis20110704 <- as(nlmodis20110704,"SpatialPixelsDataFrame")
  names(nlmodis20110704)='m1'
  nlmodis20110712 <- as(nlmodis20110712,"SpatialPixelsDataFrame")
  names(nlmodis20110712)='m2'
  
  nlmodis20110704@data <- cbind(nlmodis20110704@data, nlmodis20110712@data)
  
  df<-reshape(nlmodis20110704@data , varying=list(1:2), v.names="modis",direction="long", 
            times=as.Date(c('2011-07-04','2011-07-12')), ids=1:dim(nlmodis20110704)[1])
  
  library(spacetime)
  stMODIS<- STFDF(as( nlmodis20110704, "SpatialPixels"), 
                  time= as.Date(c('2011-07-04','2011-07-12')), 
                  data.frame(modis=df[,'modis']))
  
  stplot(stMODIS, col.regions=bpy.colors())
  stMODIS <- rfilltimegaps(stMODIS)
  stplot(stMODIS, col.regions=bpy.colors())
  

meteo documentation built on May 29, 2024, 6:13 a.m.