Abstract: A mixture model is a flexible probability density estimation technique, consisting of a linear combination of k component densities. Such a model is applied to estimate clustering in Northern Hemisphere (NH) 700-mb ...

Abstract: A hidden Markov model (HMM) is used to describe daily rainfall occurrence at 10 gauge stations in the state of Ceará in northeast Brazil during the February?April wet season 1975?2002. The model assumes that rainfall ...

Abstract: Bayesian hidden Markov model (HMM) for climate downscaling of multisite daily precipitation is presented. A generalized linear model (GLM) component allows exogenous variables to directly influence the distributional ...

Abstract: A probabilistic tracking model is introduced that identifies storm tracks from feature vectors that are extracted from meteorological analysis data. The model assumes that the genesis and lysis times of each track are ...

Abstract: A new probabilistic clustering technique, based on a regression mixture model, is used to describe tropical cyclone trajectories in the western North Pacific. Each component of the mixture model consists of a quadratic ...

Abstract: A new probabilistic clustering method, based on a regression mixture model, is used to describe tropical cyclone (TC) propagation in the western North Pacific (WNP). Seven clusters were obtained and described in Part I of ...

Abstract: Understanding the physical drivers of seasonal hydroclimatic variability and improving predictive skill remains a challenge with important socioeconomic and environmental implications for many regions around the world. ...

Abstract: A Markov random field (MRF) statistical model is introduced, developed, and validated for detecting the east Pacific intertropical convergence zone in instantaneous satellite data from May through October. The MRF statistical ...