Abstract: A nonlinear, probabilistic synoptic downscaling algorithm for daily precipitation series at multiple sites is presented. The expanded Bernoulli?gamma density network (EBDN) represents the conditional density of multisite ...

Abstract: Principal predictor analysis is a multivariate linear technique that fits between regression and canonical correlation analysis in terms of the complexity of its architecture. This study introduces a new neural network ...

Abstract: ogistical constraints can limit the number of global climate model (GCM) simulations considered in a climate change impact assessment. When dealing with annual or seasonal variables, one can visualize and manually select ...

Abstract: nivariate bias correction algorithms, such as quantile mapping, are used to address systematic biases in climate model output. Intervariable dependence structure (e.g., between different quantities like temperature and ...

Abstract: egression-guided clustering is introduced as a means of constructing circulation-to-environment synoptic climatological classifications. Rather than applying an unsupervised clustering algorithm to synoptic-scale atmospheric ...

Abstract: ecent improvements in forecast skill of the climate system by dynamical climate models could lead to improvements in seasonal streamflow predictions. This study evaluates the hydrologic prediction skill of a dynamical ...