Titles Methods Predictors predictands Better Tereza B即x Daily rainfall Cavazos and Hewitson. ??? Daily Precipitation Winter (W): P=f(z5, q7, slp); P=f(z7,q7, Summer(S): P=f(z7, g7, u8); P=f(z7,q7,飞8) Winter (W): P=f(th1, g7, z7, Summer(S):P=f(z7, 7, th1, Dynamicl Nested RCM Hydrological cycle R. Cano. A Self- Organizing Maps for PCA daily Temperature(T), relative daily precipitation(Pp), s Statistical Downscal umid ity(H), Geopotential (Z maximum wind speed in Short-Range Weathe and U, V wind components at (Rx), and insolation(In) Rodruez and SEX P re levels(300, 500 700,850 Gu ferret 25, and 1000 mb) at 00, 06 12. 18 and 24 UTC atistic2 D.J. Sailo A neural network approach s for downscaling upper level winds, temperatur Daily mean wind speed THu,XLi,to local downscaling of Power: cut-in/shut-down J.N. Rosen, GCM output for as threshold wind power imp lications of climate change (2000359 378 Assessment of Climate/Change ImpactsChapter 6 Assessment of Climate/Change Impacts File name Authors, year Titles Methods Predictors predictands Better predictors Tereza Cavazos and Bruce Hewitson, ??? Relative Performance of Empirical Predictors of Daily Precipitation RPCA to get top 10 PCs ANNs for downscaling 29 variables Best: For midlatitude: Winter (W): P = ƒ (z5, q7, slp); P = ƒ (z7, q7, slp) Summer (S): P = ƒ (z7, q7, u8); P = ƒ (z7, q7, v8) For Tropical: Winter (W): P = ƒ (th1, q7, z7, v0) Summer (S): P = ƒ (z7, q7, th1, u8) Daily rainfall Dynamic1 Nested RCM Hydrolocgical cycle Statistic1 R. Cano, A. S. Cofino, M.A. Rodruez and J.M. Gutierrez Self-Organizing Maps for Statistical Downscaling in Short-Range Weather Forecast PCA, Self-organized map daily Temperature (T), relative Humidity (H), Geopotential (Z) and U, V wind components at six pressure levels (300, 500, 700, 850, 925, and 1000 mb) at 00, 06, 12, 18, and 24 UTC daily precipitation (Pp), maximum wind speed (Rx), and insolation (In) Statistic2 D.J. Sailor, T. Hu, X. Li, J.N. Rosen, Renewable Energy 19 (2000) 359- 378 A neural network approach to local downscaling of GCM output for assessing wind power implications of climate change ANNs for downscaling upper level winds, temperature, sea level pressure Daily mean wind speed Power: cut-in/shut-down threshold