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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 K1 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 utt 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 K1 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