Studies in the Atmospheric Sciences: (144 Lecture Notes in Statistics)

The need to understand and predict the processes that influence the Earth's atmosphere is one of the grand scientific challenges for the next century. Due to the complexity of observational data and the variability of geophysical phenomena at many spatial and temporal scales, statistical science has an important role to play in this effort. This volume is a series of case studies and review chapters that cover many of the recent developments in statistical methodology that are useful for interpreting atmospheric data. Although the applications in this volume target atmospheric sciences, the statistical methodology is general and addresses many common problems that arise in the analysis of spatial and spatio-temporal data. For this reason, it would also serve as a statistical source for graduate students and other researchers across a broad range of geophysical and environmental sciences. L. Mark Berliner is Professor of Statistics at Ohio State University. He has served as Project Leader for the Geophysical Statistics Project (GSP) at the National Center for Atmospheric Research (NCAR), 1995-97. Douglas Nychka is the current GSP Leader and a Senior Scientist at NCAR. Timothy Hoar is an Associate Scientist at NCAR.

Table of Contents:
1 Introduction.- 1 Statistics in the Climate and Weather Sciences.- 2 A Guide to this Volume.- 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.- 4 Ensemble Forecasting.- 5 Numerical Studies.- 6 Conclusions.- 3 Multivariate Spatial Models.- 1 Introduction.- 2 Cokriging.- 3 Kriging with External Drift.- 4 A Hierarchical Model.- 5 Miscellaneous Topics.- 6 Applications.- 7 Conclusions.- 4 Hierarchical Space—Time Dynamic Models.- 1 Introduction.- 2 Hierarchical Space—Time Dynamic Modeling.- 3 Tropical Wind Process.- 4 Ocean Wind Implementation.- 5 Discussion.- 5 Experimental Design for Spatial and Adaptive Observations.- 1 Introduction.- 2 Experimental Design: Spatial Fields.- 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.- 4 Continuous Basis Functions to Represent Ozone.- 5 Varying Coefficient Models.- 6 Discussion.- 7 Neural Networks: Cloud Parameterizations.- 1 Introduction.- 2 Cloud Parameterizations.- 3 Simulation of Cloud Cover.- 4 Conclusions and Future Work.- 8 Exploratory Statistical Analysis of Tropical Oceanic Convection Using Discrete Wavelet Transforms.- 1 Introduction.- 2 Description of the Dataset.- 3 Discrete Wavelets.- 4 Statistical Study of Cloud Systems.- 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.- 6 Implementing FDA + MARS for CAT Forecast.- 7 MARS as VariableSubset 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.- 4 Ocean Color Semivariograms.- 5 Spatial Patterns for the North Atlantic Ocean.- 6 Conclusions and Final Remarks.- References.