bReeze (version 0.1-0)

clean: Clean faulty values

Description

Cleans faulty values of a met mast object, set or specified set of a met mast. Faulty values are replaced by NA.

Usage

clean(mast, set, v.avg.min=0.4, v.avg.max=50, 
  dir.clean=TRUE, turb.clean=TRUE, icing=FALSE)

Arguments

mast

Met mast object created by createMast. To be ignored, if a single dataset shall be cleaned.

set

Set object created by createSet (if no mast is given) or set number of met mast as integer value. To be ignored, if all datasets of mast shall be cleaned.

v.avg.min

Lower limit for wind speeds as numeric value. Default is 0.4 m/s.

v.avg.max

Upper limit for wind speeds as numeric value. Default is 50 m/s.

dir.clean

If TRUE, faulty wind direction values, i. e. dir.avg<0 or="" dir.avg>360, are excluded. Default is TRUE.

turb.clean

If TRUE, turbulence intensity values are excluded for wind speeds lower then 4 m/s. Default is TRUE.

icing

If TRUE, wind direction values are excluded, where standard deviation of wind direction is 0, assuming icing. Default is FALSE.

Value

• Returns the input met mast or dataset object with cleaned data.

encoding

UTF-8

See Also

createSet, createMast

Examples

# load and prepare data
data(winddata)
set40 <- createSet(height=40, v.avg=winddata[,2], v.std=winddata[,5],
dir.avg=winddata[,14])
set30 <- createSet(height=30, v.avg=winddata[,6], v.std=winddata[,9],
dir.avg=winddata[,16])
set20 <- createSet(height=20, v.avg=winddata[,10], v.std=winddata[,13])
ts <- formatTS(winddata[,1])
neubuerg <- createMast(time.stamp=ts, loc=NULL, desc=NULL, 
	set40=set40, set30=set30, set20=set20)

# clean faulty values of a met mast
neubuerg.clean <- clean(neubuerg)

# compare a subset of the original and cleaned data
neubuerg$sets$set40$data$v.avg[660:670]
neubuerg.clean$sets$set40$data$v.avg[660:670]


# clean faulty values of a dataset
set40.clean <- clean(set=set40)
  
# clean just one dataset of a met mast
neubuerg.clean.2 <- clean(mast=neubuerg, set=1)


# change lower wind speed limit 
neubuerg.clean.3 <- clean(neubuerg, v.avg.min=0.3)

# compare number of samples set to 'NA', due to lowered limit
length(which(is.na(neubuerg.clean$sets$set40$data$v.avg)==TRUE))
length(which(is.na(neubuerg.clean.3$sets$set40$data$v.avg)==TRUE))


# omit cleaning of turbulence intensity
neubuerg.clean.4 <- clean(neubuerg, turb.clean=FALSE)

# compare number of samples set to 'NA', due to turb.clean
neubuerg.clean$sets$set40$data$turb.int[75:100]
neubuerg.clean.4$sets$set40$data$turb.int[75:100]


# check whether icing is assumed for any samples
neubuerg.clean.5 <- clean(neubuerg, set=1, v.avg.min=0, v.avg.max=100, 
	dir.clean=FALSE, turb.clean=FALSE, icing=TRUE)
not.cleaned <- which(is.na(neubuerg$sets$set40$data$dir.avg)==TRUE)
cleaned <- which(is.na(neubuerg.clean.5$sets$set40$data$dir.avg)==TRUE)
length(cleaned)-length(cleaned)	# no icing here

# if icing is detected, the time stamp should be checked to exclude 
# implausible icing assumptions, e.g in summer
neubuerg.clean.5$time.stamp[cleaned]