getBiasFactor {hyfo}R Documentation

Get bias factor for multi/operational/real time bias correction.

Description

When you do multi/operational/real time bias correction. It's too expensive to input hindcast and obs every time. Especially when you have a long period of hindcast and obs, but only a short period of frc, it's too unecessary to read and compute hindcast and obs everytime. Therefore, biasFactor is designed. Using getBiasFactor, you can get the biasFactor with hindcast and observation, then you can use applyBiasFactor to apply the biasFactor to different forecasts.

Usage

getBiasFactor(
  hindcast,
  obs,
  method = "scaling",
  scaleType = "multi",
  preci = FALSE,
  prThreshold = 0,
  extrapolate = "no"
)

## S4 method for signature 'data.frame,data.frame'
getBiasFactor(
  hindcast,
  obs,
  method = "scaling",
  scaleType = "multi",
  preci = FALSE,
  prThreshold = 0,
  extrapolate = "no"
)

## S4 method for signature 'list,list'
getBiasFactor(
  hindcast,
  obs,
  method = "scaling",
  scaleType = "multi",
  preci = FALSE,
  prThreshold = 0,
  extrapolate = "no"
)

Arguments

hindcast

a hyfo grid data output or a dataframe(time series) consists of Date column and one or more value columns, representing the hindcast data. This data will be used in the calibration of the forecast, so it's better to have the same date period as observation data. Check details for more information.

obs

a hyfo grid data output or a dataframe (time series) consists of Date column and one or more value columns, representing the observation data.

method

bias correct method, including 'delta', 'scaling'...,default method is 'scaling'.

scaleType

only when the method "scaling" is chosen, scaleType will be available. Two different types of scaling method, 'add' and 'multi', which means additive and multiplicative scaling method, default is 'multi'. More info check details.

preci

If the precipitation is biascorrected, then you have to assign preci = TRUE. Since for precipitation, some biascorrect methods may not apply to, or some methods are specially for precipitation. Default is FALSE, refer to details.

prThreshold

The minimum value that is considered as a non-zero precipitation. Default to 1 (assuming mm).

extrapolate

When use 'eqm' method, and 'no' is set, modified frc is bounded by the range of obs. If 'constant' is set, modified frc is not bounded by the range of obs. Default is 'no'.

Details

Information about the method and how biasCorrect works can be found in biasCorrect

why use biasFactor

As for forecasting, for daily data, there is usually no need to have different bias factor every different day. You can calculate one bisa factor using a long period of hindcast and obs, and apply that factor to different frc.

For example,

You have 10 years of hindcast and observation. you want to do bias correction for some forecasting product, e.g. system 4. For system 4, each month, you will get a new forecast about the future 6 months. So if you want to do the real time bias correction, you have to take the 10 years of hindcast and observation data with you, and run biasCorrect every time you get a new forecast. That's too expensive.

For some practical use in forecasting, there isn't a so high demand for accuracy. E.g., Maybe for February and March, you can use the same biasFactor, no need to do the computation again.

It is a generic function, if in your case you need to debug, please see ?debug() for how to debug S4 method.

Author(s)

Yuanchao Xu xuyuanchao37@gmail.com

References

Bias correction methods come from biasCorrection from dowscaleR

See Also

biasCorrect for method used in bias correction. applyBiasFactor, for the second part.

Examples


######## hyfo grid file biascorrection
########

# If your input is obtained by \code{loadNcdf}, you can also directly biascorrect
# the file.

# First load ncdf file.
filePath <- system.file("extdata", "tnc.nc", package = "hyfo")
varname <- getNcdfVar(filePath)    
nc <- loadNcdf(filePath, varname)

data(tgridData)
# Since the example data, has some NA values, the process will include some warning #message, 
# which can be ignored in this case.



# Then we will use nc data as forecasting data, and use itself as hindcast data,
# use tgridData as observation.

biasFactor <- getBiasFactor(nc, tgridData)
newFrc <- applyBiasFactor(nc, biasFactor)
   
biasFactor <- getBiasFactor(nc, tgridData, method = 'eqm', extrapolate = 'constant',
preci = TRUE)
# This method needs obs input.
newFrc <- applyBiasFactor(nc, biasFactor, obs = tgridData)

biasFactor <- getBiasFactor(nc, tgridData, method = 'gqm', preci = TRUE)
newFrc <- applyBiasFactor(nc, biasFactor) 


######## Time series biascorrection
########

# Use the time series from testdl as an example, we take frc, hindcast and obs from testdl.
data(testdl)

# common period has to be extracted in order to better train the forecast.

datalist <- extractPeriod(testdl, startDate = '1994-1-1', endDate = '1995-10-1')

frc <- datalist[[1]]
hindcast <- datalist[[2]]
obs <- datalist[[3]]


# The data used here is just for example, so there could be negative data.

# default method is scaling
biasFactor <- getBiasFactor(hindcast, obs)
frc_new <- applyBiasFactor(frc, biasFactor)

# for precipitation data, extra process needs to be executed, so you have to tell
# the program to it is a precipitation data.

biasFactor <- getBiasFactor(hindcast, obs, preci = TRUE)
frc_new1 <- applyBiasFactor(frc, biasFactor)

# You can use other methods to biascorrect, e.g. delta method. 
biasFactor <- getBiasFactor(hindcast, obs, method = 'delta')
# delta method needs obs input.
frc_new2 <- applyBiasFactor(frc, biasFactor, obs = obs)

# 
biasFactor <- getBiasFactor(hindcast, obs, method = 'eqm', preci = TRUE)
# eqm needs obs input
frc_new3 <- applyBiasFactor(frc, biasFactor, obs = obs)

biasFactor <- getBiasFactor(hindcast, obs, method = 'gqm', preci = TRUE)
frc_new4 <- applyBiasFactor(frc, biasFactor)

plotTS(obs, frc, frc_new, frc_new1, frc_new2, frc_new3, frc_new4, plot = 'cum')

# You can also give name to this input list.
TSlist <- list(obs, frc, frc_new, frc_new1, frc_new2, frc_new3, frc_new4)
names(TSlist) <- c('obs', 'frc', 'delta', 'delta_preci', 'scale', 'eqm', 'gqm')
plotTS(list = TSlist, plot = 'cum')


# If the forecasts you extracted only has incontinuous data for certain months and years, e.g.,
# for seasonal forecasting, forecasts only provide 3-6 months data, so the case can be 
# for example Dec, Jan and Feb of every year from year 1999-2005.
# In such case, you need to extract certain months and years from observed time series.
# extractPeriod() can be then used.
  
  



# More examples can be found in the user manual on https://yuanchao-xu.github.io/hyfo/
[Package hyfo version 1.4.6 Index]