sEddyProc_sEstimateUstarScenarios.RdEstimate the distribution of u* threshold by bootstrapping over data
sEddyProc_sEstimateUstarScenarios(ctrlUstarEst = usControlUstarEst(),
ctrlUstarSub = usControlUstarSubsetting(),
UstarColName = "Ustar", NEEColName = "NEE",
TempColName = "Tair", RgColName = "Rg",
..., seasonFactor = usCreateSeasonFactorMonth(sDATA$sDateTime),
nSample = 200L, probs = c(0.05, 0.5,
0.95), isVerbose = TRUE, suppressWarningsAfterFirst = TRUE)control parameters
for estimating uStar on a single binned series,
see usControlUstarEst
control parameters
for subsetting time series (number of temperature and Ustar classes
...), see usControlUstarSubsetting
column name for UStar
column name for NEE
column name for air temperature
column name for solar radiation for omitting night time data
further arguments to sEddyProc_sEstUstarThreshold
factor of seasons to split (data is resampled only within the seasons)
the number of repetitions in the bootstrap
the quantiles of the bootstrap sample to return. Default is the 5%, median and 95% of the bootstrap
set to FALSE to omit printing progress
set to FALSE to show also warnings for all bootstrap estimates instead of only the first bootstrap sample
The choice of the criterion for sufficiently turbulent conditions (u * > chosen threshold) introduces large uncertainties in calculations based on gap-filled Eddy data. Hence, it is good practice to compare derived quantities based on gap-filled data using a range of u * threshold estimates.
This method explores the probability density of the threshold by
repeating its estimation
on a bootstrapped sample.
By default it returns the 90% confidence interval (argument probs).
For larger intervals the sample number need to be
increased (argument probs).
updated class. Request results by
sEddyProc_sGetEstimatedUstarThresholdDistribution