In REddyProc: Post Processing of (Half-)Hourly Eddy-Covariance Measurements

library(knitr)
#rmarkdown::render("vignettes/gapFilling.Rmd")
opts_knit$set(root.dir = '..')
opts_chunk$set(
    #, fig.align = "center"
    #, fig.width = 3.27, fig.height = 2.5, dev.args = list(pointsize = 10)
    #,cache = TRUE
    #, fig.width = 4.3, fig.height = 3.2, dev.args = list(pointsize = 10)
    #, fig.width = 6.3, fig.height = 6.2, dev.args = list(pointsize = 10)
    # works with html but causes problems with latex
    #,out.extra = 'style = "display:block; margin: auto"' 
    )
knit_hooks$set(spar = function(before, options, envir) {
    if (before) {
        par(las = 1 )                   #also y axis labels horizontal
        par(mar = c(2.0,3.3,0,0) + 0.3 )  #margins
        par(tck = 0.02 )                          #axe-tick length inside plots             
        par(mgp = c(1.1,0.2,0) )  #positioning of axis title, axis labels, axis
     }
})

#themeTw <- theme_bw(base_size = 10) + theme(axis.title = element_text(size = 9))
bgiDir <- "~/bgi"

Extended usage of the gap filling algorithm

#+++ load libraries used in this vignette
library(REddyProc)
library(dplyr)
#+++ Add time stamp in POSIX time format and filter long runs of equal values 
EddyDataWithPosix <- fConvertTimeToPosix(
  filterLongRuns(Example_DETha98, "NEE")
  , 'YDH', Year = 'Year', Day = 'DoY', Hour = 'Hour')

#+++ Add some (non-sense) example vectors:
#+++ Quality flag vector (e.g. from applying ustar filter)
QF <- rep(c(1, 0, 1, 0, 1, 0, 0, 0, 0, 0), nrow(EddyDataWithPosix) / 10)
#+++ Dummy step function vector to simulate e.g. high / low water table
Step <- ifelse(EddyDataWithPosix$DoY < 200 | EddyDataWithPosix$DoY > 250, 0, 1)

#+++ Initialize new sEddyProc processing class with more columns
EProc <- sEddyProc$new(
  'DE-Tha', cbind(EddyDataWithPosix, Step = Step, QF = QF)
  , c('NEE', 'LE', 'H', 'Rg', 'Tair', 'Tsoil', 'rH', 'VPD', 'QF', 'Step'))

#+++ Gap fill variable with (non-default) variables and limits
# including preselection of data with quality flag QF == 0
EProc$sMDSGapFill(
  'LE', QFVar = 'QF', QFValue = 0, V1 = 'Rg', T1 = 30, V2 = 'Tsoil', T2 = 2
  , 'Step', 0.1)

#+++ Use individual gap filling subroutines with different window sizes
# and up to five variables and limits
EProc$sFillInit('NEE') #Initialize 'NEE' as variable to fill
Result_Step1.F <- EProc$sFillLUT(
  3, 'Rg', 50, 'rH', 30, 'Tair', 2.5, 'Tsoil', 2, 'Step', 0.5)
Result_Step2.F <- EProc$sFillLUT(6, 'Tair', 2.5, 'VPD', 3, 'Step', 0.5)
Result_Step3.F <- EProc$sFillMDC(3)
#Individual fill result columns are called 'VAR_...'
EProc$sPlotHHFluxesY('VAR_fall', Year = 1998)

Explicit demonstration of MDS algorithm for NEE gap filling

EProcMDS <- sEddyProc$new(
  'DE-Tha', EddyDataWithPosix, c('NEE', 'Rg', 'Tair', 'VPD'))
#Initialize 'NEE' as variable to fill
EProcMDS$sFillInit('NEE')
# Set variables and tolerance intervals
V1 = 'Rg'; T1 = 50 # Global radiation 'Rg' within +/-50 W m-2
V2 = 'VPD'; T2 = 5 # Vapour pressure deficit 'VPD' within 5 hPa
V3 = 'Tair'; T3 = 2.5 # Air temperature 'Tair' within +/-2.5 degC
# Step 1: Look-up table with window size +/-7 days
ResultStep1 <- EProcMDS$sFillLUT(7, V1, T1, V2, T2, V3, T3)
# Step 2: Look-up table with window size +/-14 days
ResultStep2 <- EProcMDS$sFillLUT(14, V1, T1, V2, T2, V3, T3)
# Step 3: Look-up table with window size +/-7 days, Rg only
ResultStep3 <- EProcMDS$sFillLUT(7, V1, T1)
# Step 4: Mean diurnal course with window size 0 (same day)
ResultStep4 <- EProcMDS$sFillMDC(0)
# Step 5: Mean diurnal course with window size +/-1, +/-2 days
ResultStep5a <- EProcMDS$sFillMDC(1)
ResultStep5b <- EProcMDS$sFillMDC(2)
# Step 6: Look-up table with window size +/-21, +/-28, ..., +/-70
for (WinDays in seq(21, 70, 7) ) 
  ResultStep6 <- EProcMDS$sFillLUT(WinDays, V1, T1, V2, T2, V3, T3)
# Step 7: Look-up table with window size +/-14, +/-21, ..., +/-70, Rg only
for (WinDays in seq(14, 70, 7) ) 
  ResultStep7 <- EProcMDS$sFillLUT(WinDays, V1, T1)
# Step 8: Mean diurnal course with window size +/-7, +/-14, ..., +/-210 days
for (WinDays in seq(7, 210, 7) ) 
  ResultStep8 <- EProcMDS$sFillMDC(WinDays)
# Export results, columns are named 'VAR_'
FilledEddyData <- EProcMDS$sExportResults()
head(FilledEddyData)