Inhaltsangabe1 Introduction.- 1 Statistics in the Climate and Weather Sciences.- 2 A Guide to this Volume.- 2.1 Chapter Outline.- 2.2 What Is Missing?.- 3 Software, Datasets, and the Web Companion.- 2 A Statistical Perspective on Data Assimilation in Numerical Models.- 1 Introduction.- 2 Assimilation and Penalized Least Squares.- 3 Time-Dependent Assimilation Methods.- 3.1 The Kalman Filter.- 4 Ensemble Forecasting.- 4.1 Methodology.- 4.2 Statistical Perspective.- 5 Numerical Studies.- 5.1 Precipitation Model.- 6 Conclusions.- 3 Multivariate Spatial Models.- 1 Introduction.- 1.1 Motivating Example: Ozone and Meteorology.- 1.2 Optimal Spatial Prediction-Kriging.- 1.3 Universal Kriging.- 1.4 Multivariate Approaches.- 2 Cokriging.- 3 Kriging with External Drift.- 3.1 Prediction Variance Misspecification in the KED Model.- 4 A Hierarchical Model.- 5 Miscellaneous Topics.- 5.1 K-Variate Extensions.- 5.2 Latent Process Models.- 5.3 Modeling Orthogonal Contrasts.- 5.4 Multivariate Space-Time Models.- 6 Applications.- 6.1 Modeling Wind Fields.- 6.2 Modeling Temperature Fields.- 7 Conclusions.- 4 Hierarchical Space-Time Dynamic Models.- 1 Introduction.- 1.1 A Brief Review of Space-Time Modeling.- 1.2 Space-Time Dynamic Models.- 2 Hierarchical Space-Time Dynamic Modeling.- 2.1 A General Space-Time Dynamic Model.- 2.2 Reformulated Space-Time Dynamic Model.- 2.3 A Bayesian Model.- 3 Tropical Wind Process.- 3.1 Deterministic View.- 3.2 Stochastic View.- 3.3 Combined Stochastic/Dynamic View.- 3.4 Physically Informative Priors.- 4 Ocean Wind Implementation.- 4.1 Results.- 5 Discussion.- 5 Experimental Design for Spatial and Adaptive Observations.- 1 Introduction.- 1.1 Scientific Background.- 1.2 Overview of Statistical Design.- 2 Experimental Design: Spatial Fields.- 2.1 Optimal Design.- 2.2 Special Solutions.- 2.3 Greedy Algorithms.- 3 Experimental Design in Space-Time.- 4 Discussion.- 6 Seasonal Variation in Stratospheric Ozone Levels, a Functional Data Analysis Study.- 1 Introduction.- 2 Stratospheric Ozone Data.- 3 Principal Component Analysis.- 3.1 Principal Component Analysis of the Interpolated Profiles.- 4 Continuous Basis Functions to Represent Ozone.- 4.1 Basis Function Coefficients.- 5 Varying Coefficient Models.- 6 Discussion.- 7 Neural Networks: Cloud Parameterizations.- 1 Introduction.- 1.1 The Essence of Parameterization.- 1.2 Neural Networks and Fitting Nonlinear Models.- 2 Cloud Parameterizations.- 2.1 A Preliminary Model.- 2.2 Toward a GCM Cloud Parameterization.- 3 Simulation of Cloud Cover.- 4 Conclusions and Future Work.- 8 Exploratory Statistical Analysis of Tropical Oceanic Convection Using Discrete Wavelet Transforms.- 1 Introduction.- 1.1 Atmospheric Motivation.- 1.2 Statistical Motivation.- 1.3 Objectives.- 2 Description of the Dataset.- 2.1 Cloud System Regimes.- 3 Discrete Wavelets.- 3.1 Wavelets in One Dimension.- 3.2 Discrete Wavelet Transform in Two Dimensions.- 4 Statistical Study of Cloud Systems.- 4.1 Detecting Squall-Lines.- 4.2 The Orientation Problem.- 4.3 Examples.- 4.4 Identifying the Scattered Cloud Regime.- 5 Conclusions and Future Work.- 9 Predicting Clear-Air Turbulence.- 1 Introduction.- 2 Indices Derived from the RUC-60 Model.- 3 Data Structure.- 4 The Single Index Approach.- 5 Modeling Strategy.- 5.1 MARS.- 5.2 FDA.- 5.3 FDA + MARS Algorithm.- 6 Implementing FDA + MARS for CAT Forecast.- 6.1 Training the Procedure: In-Sample Performances.- 6.2 Testing the Procedure: Prediction Ability.- 7 MARS as Variable Subset Selection.- 8 Conclusions.- 10 Spatial Structure of the SeaWiFS Ocean Color Data for the North Atlantic Ocean.- 1 Introduction.- 2 SeaWiFS Ocean Color Data.- 3 Semivariograms and Other Tools in Spatial Statistics.- 3.1 Stationarity and Isotropy.- 3.2 Estimating the Empirical Semivariogram.- 3.3 Semivariogram Parameters: Nugget, Sill, and Range.- 3.4 Some Isotropic Semivariogram Models.- 4 Ocean Color Semivariograms.- 4.1 Spatial Analysis for the Ocean Chlorophyll.- 4.2 Semivariograms in the Original

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