FREddyPro (version 1.0)

cleanFluxes: Cleaning and de-spiking fluxes

Description

The main function for cleaning and de-spiking post-processed fluxes. There are five ways to clean/de-spike gas fluxes based on 1) QC flags, 2) standard deviation of negative and positive fluxes, 2) flux distribution for each hour of the day, 4) mean AGC value of the IRGA and 5) u* filtering. There are also three ways to clean heat and water fluxes based on 1) QC flags, 2) a threshold value and on 3) standard deviation of negative and positive fluxes.

Usage

cleanFluxes(data, gas = "co2_flux", qcFlag = 2, sdCor = FALSE, sdTimes =
1, distCor = FALSE, agcCor = FALSE, agcVal = NULL, ustar = NULL, plot
= FALSE, write = FALSE, outputFile, thresholdList = list(H = NULL, LE =
NULL, Tau = NULL, h2o = NULL), timesList = list(H = NULL, LE = NULL, Tau
= NULL, h2o = NULL), sunset = 19, sunrise = 6, na.value = "NaN")

Arguments

data

The name of the data frame to clean and de-spike.

gas

A character input giving the name of the gas to clean. The default value is for CO2.

qcFlag

The QC flag to clean. Default value is 2 using the Mauder and Foken (2004) flagging system. qcFlag=NULL will bypass the flag removal system and no data will be removed. Function can be used with other flagging systems (e.g., 1-9 by Foken 2003) but an array must be given of values to remove e.g., qcFlag=c(7,8,9).

sdCor

Logical. If TRUE de-spiking based on standard deviation is applied.

sdTimes

A number showing how many times the gas flux data have to be greater than the SD to remove them as spikes. The default value is 1 SD.

distCor

Logical. If TRUE gas flux data are de-spiked using a distribution calculated for every half hour.

agcCor

Logical. If TRUE data with a mean value between 50-60% are removed.

agcVal

A character input giving the name of the mean AGC in the data.

ustar

This input can be either logical or numeric. If logical and TRUE then ustar filtering is applied using the Papale et al. 2006 method. Sunset and sunrise times should also be provided for more accurate definition of the night time data.

plot

Logical. If TRUE two multiplot outputs are produced showing all major variables before and after cleaning and de-spiking.

write

Logical. If TRUE the new clean data frame will be written into a new csv file. The output file name should also be given.

outputFile

A character input giving the name of the output file to write the clean data. String can include full path name.

thresholdList

A list giving the threshold for which if greater and lower data will be removed. The list can include sensible heat (H), latent heat (LE), momentum flux (Tau), and water flux (h2o).

timesList

A list giving how many times a flux should be greater than the SD to be a spike. The list can include sensible heat (H), latent heat (LE), momentum flux (Tau), and water flux (h2o).

sunset

The time of sunset as a real number (0-23)

sunrise

The time of sunrise as a real number (0-23)

na.value

A string or a number showing NA values in the data set

Details

By default the function removes QC flag 2, assuming data were flagged for quality using Mauden and Foken (2004) system.

To clean data using standard deviation, the mean and standard deviation of negative and positive values is calculated separately. Data greater than a user-defined times of the standard deviation are removed

For distributional cleaning, the half-hourly destribution using the complete dataset and the 5th and 95th quantiles are calculated. Values outside these quantiles are removed.

Mean AGC is calculated by EddyPro if a diagnostics value is given during processing of raw data. AGC usually should be between 50 - 60%. Values outside the threshold will be removed. Use with caution as it may remove a large number of data

U* filtering is standard procedure. For the FREddyPro function we used the procedure described by Papale et al. 2006 . Air temperature is classes and within each temperature class u* is grouped in 20 classes. For more details see Papale et al. 2006.

References

Papale D, Reichstein M, Aubinet M, Canfora E, Bernhofer C, Kutsch W, Longdoz B, Rambal S, Valentini R, Vesala T & et al. (2006) Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: algorithms and uncertainty estimation. Biogeosciences, Copernicus GmbH, 3, 571 - 583

Examples

## Load the data
data(fluxes)

## Clean data using 3 times the SD for both gas and heat fluxes.
## Also use some thresholds for head fluxes.
fluxes=cleanFluxes(fluxes,sdCor=TRUE,sdTimes=3,distCor=TRUE,timesList=3,
thresholdList=list(H=c(-100,1000),LE=c(-100,1000)),plot=TRUE)