Download Precipitation downscaling with SDSM over Rio de la Plata basin

Precipitation Downscaling
with SDSM
over Rio de la Plata Basin
Mario Bidegain and Madeleine Renom
• Rio de la Plata Basin RPB and selected stations
• The Statistical DownScaling Model (SDSM)
• Downscaling precipitation process
• Preliminary results
RIO DE LA PLATA BASIN (RPB)
and selected stations
The Statistical DownScaling Model
SDSM
Downscaling of Precipitation
Precipitation occurrence process
An nth order, two state process governs daily
precipitation occurrence, with serially independent
precipitation amounts on wet days
π t = xβ + ε
where t is the conditional probability of a wet day, X is
a K1 vector of standard Gaussian (i.e., normally
distributed, with zero mean and unit variance)
explanatory variables,  is the coefficient matrix, and 
is random noise.
Precipitation occurrence (cont.)
The binary event of precipitation, Pt or no precipitation
is determined by
Pt = {
1,
if utt
0,
otherwise
where ut denotes uniform independent random forcing
for the occurrence process (probability density f[u] =1,
0 u 1).
Notes:
• Autocorrelation is incorporated implicitly by predictors
• Separate equations may be specified for each month
Conditional variables
Conditional variables, including nonzero precipitation
amounts rt are simulated by
rt = zβ + ε
where Z is a K1 vector of standard Gaussian (i.e.,
normally distributed, with zero mean and unit variance)
explanatory variables,  is the coefficient matrix, and 
is an error term which is modelled stochastically (by
assuming zero mean and variance equal to model
standard error).
Conditional variables (cont.)
Many conditional variables as precipitation amounts are
strongly skewed to the right. Therefore, a range of
transformations for rt are available in SDSM (Version 2.3
and later), including exponential, fourth root, and inverse
normal.
Illustration of the inverse normal transformation
Process to calibrate SDSM and
generate downscaled series
NCEP
GCMs
Global daily reanalysis
1948-2003
Global daily outputs
2000-2100
Selection of
predictand variables
(webpage NCEP)
Predictand variables
Station daily
observed precipitation
1996-2001
SDSM
Model Calibrated
at location
1996-2001
SDSM
Statistical
downscaling
2000-2100
Monthly daily mean of
precipitation
observed vs. generated
Monthly daily variance of
precipitation
observed vs. generated
Preliminary results
• SDSM provides a technique of scenario construction that
complements other methods (dynamic downscaling)
• Daily precipitation amount at individual stations is the most
problematic variable to downscale
• SDSM should not be used uncritically as a “black box” (evaluate
all relationships using independent data), the local knowledge is
an invaluable source of information when determining sensible
combinations of predictors
• The plausibility of all SDSM scenarios depends on the realism of
the climate model forcing