Bias Correction

Bias Correction and Model Output Statistics (MOS)

This section describes two approaches used to post-processing the direct output of the numerical modeling: Bias corection techniques and Model Output Statistics. As a difference with the Perfect Prog approach, these two methodologies use the same variable from the GCM (or RCM) and the local observations (e.g. 2-meters air temperature or precipitation amount).

Bias Correction:

In the downscaleR R-package, the user can find the main bias correction techniques used in the literature (delta method, qq-mapping, etc...) and other recently published (ISI-MIP). For a more detailed description, the user is referred to the main publications describing these techniques which are included at the end of this page.

All the bias correction methods implemented in downscaleR are included in the functions biasCorrection and isimip. These two functions work directly with the object obtained loading Observations and/or Simulations. Then, in our example we will use the observations and simulations obtained in the Practice 1B

# Example 2: Regional-Continental domain selections
pred <- loadECOMS(dataset = "CFSv2_seasonal_16", var = "tas", members = 1:2, lonLim = c(-15,35), latLim = c(32, 75), season = c(12,1,2), years = 2001:2010, leadMonth = 3, time = "DD")
Example.obs <- loadECOMS(dataset = "WFDEI", var = "tas", lonLim = c(floor(min(pred$xyCoords$x, na.rm = TRUE)),ceiling(max(pred$xyCoords$x, na.rm = TRUE))), latLim = c(floor(min(pred$xyCoords$y, na.rm = TRUE)),ceiling(max(pred$xyCoords$y, na.rm = TRUE))), season = c(12,1,2), years = 2001:2010)
# We should interpolate the observations to the grid of model: we use the method "nearest"
obs <- interpGridData(Example.obs, new.grid = pred$xyCoords, method = "nearest")
# We estimate the unbiased simulation
sim <- biasCorrection (obs, pred, pred, method = "unbiasing")

Dealing with precipitation, a frequency adaptation is implemented to alleviate the problem that arise when the dry day frequency in the raw model output is larger than in the observations, which would lead to a strong positive bias after the correction when using q-q map (Wilcke et al. 2013). More often the model overestimates the light precipitation frequency (drizzling-effect), which is caught by the q-q map automatically.

The methods available are qqmap, delta, unbiasing, scaling and Piani.

• === Delta

This is the simplest bias correction method, which consist on adding to the observations the mean change signal (delta method). According to Amengual et al. 2012 this would be the case g=1 and f=0. This method is applicable to any kind of variable but it is preferable to not apply it to bounded variables (e.g. precipitation, wind speed, etc.) because could be obtained values out of range.