Overview

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.

Full Product Details

Author:   Mark L. Berliner ,  Douglas Nychka ,  Timothy Hoar
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of the original 1st ed. 2000
Volume:   144
Dimensions:   Width: 15.50cm , Height: 1.10cm , Length: 23.50cm
Weight:   0.335kg
ISBN:  

9780387987576

ISBN 10:   0387987576
Pages:   199
Publication Date:   30 March 2000
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Awaiting stock  
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Table of Contents

1 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 Scale.- 4.3 Effect of Sampling Resolution.- 5 Spatial Patterns for the North Atlantic Ocean.- 6 Conclusions and Final Remarks.- References.

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