Description Usage Arguments Details Value Examples
use the inverse 'dtcwt' to correct errors in scale, anisotropy and direction
1 2 |
x |
a list of equally sized matrices, the first element is assumed to be the observation |
xmin |
values smaller than |
log |
logical, do you want to log-transfrom the data? (recommended for precipitation) |
rsm |
number of pixels which are linearly smoothed at the edge |
Nx |
size to which the data is extended in x-direction, has to be a whole power of 2 |
Ny |
size to which the data is extended in y-direction, has to be a whole power of 2 |
J |
largest scale considered |
boundaries |
how to handle the boundary conditions, either "pad", "mirror" or "periodic" |
direction |
if |
The algorithm performs the following steps:
remove values below xmin
if log=TRUE
log-transform all fields
set all fields to zero mean, unit variance
apply dtcwt
to all fields
loop over forecasts and scales. If direction=TRUE
loop over the six directions. Multiply forecast energy at each location by the ratio of total observed energy to total forecast energy at that scale (and possibly direction)
apply idtcwt
to all forecasts
reset means and variance of the forecasts to their original values
if log=TRUE
invert the log-transform
return the list of corrected fields
an object of class sadforecast
1 2 3 4 | data(rrain)
ra <- as.sadforecast( list( rrain[2,1,,], rrain[3,1,,], rrain[3,2,,], rrain[3,3,,] ) )
ra_c <- sadcorrect( ra, rsm=10 )
plot(ra_c)
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