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A limited number of complex numerical models that simulate the Earth's atmosphere, ocean, and land processes are the primary tool to study how climate may change over the next century due to anthropogenic emissions of greenhouse gases. A standard assumption is that these climate models are random samples from a distribution of possible models centered(More)
0 Abstract A Bayesian statistical model is proposed that combines information from a multi-model ensemble of atmosphere-ocean general circulation models and observations to determine probability distributions of future temperature change on a regional scale. The posterior distributions derived from the statistical assumptions incorporate the criteria of(More)
Many geophysical problems are characterized by high-dimensional, nonlinear systems and pose difficult challenges for real-time data assimilation (updating) and forecasting. The present work builds on the ensemble Kalman filter (EnsKF) with the goal of producing ensemble filtering techniques applicable to non-Gaussian densities and high-dimensional systems.(More)
Quantification of precipitation extremes is important for flood planning purposes, and a common measure of extreme events is the r-year return-level. We present a method for producing maps of precipitation return levels and uncertainty measures and apply to a Colorado region. Separate hierarchical models are constructed for the intensity and the frequency(More)
Projections of future climate change caused by increasing greenhouse gases depend critically on numerical climate models coupling the ocean and atmosphere (GCMs). However, different models differ substantially in their projections, which raises the question of how the different models can best be combined into a probability distribution of future climate(More)
Nonlinear dynamical systems often exhibit chaos, which is characterized by sensitive dependence on initial values or more precisely by a positive Lyapunov exponent. Recognizing and quantifying chaos in time series represents an important step toward understanding the nature of random behavior and revealing the extent to which short-term forecasts may be(More)
Numerical weather prediction is characterized by high-dimensional, nonlinear systems and poses difficult challenges for real-time data assimilation (updating) and forecasting. The goal of this work is to build on the ensemble Kalman filter (EnsKF) to produce ensemble filtering techniques applicable to non-Gaussian densities in high dimensions. Two filtering(More)
1. INTRODUCTION Currently, the only high-quality, high-resolution temperature and precipitation data sets for the continental United States suitable for use on climatological time scales are for mean values. None yet exist that represent sequential monthly values over an extended historical period. Such data sets would enable, for example: transient(More)
Nonlinear regression is a useful statistical tool, relating observed data and a nonlinear function of unknown parameters. When the parameter-dependent nonlinear function is computationally intensive , a straightforward regression analysis by maximum likelihood is not feasible. The method presented in this paper proposes to construct a faster running(More)