Studies in the Atmospheric Sciences (eBook, PDF)

Redaktion: Berliner, Mark L.; Hoar, Timothy; Nychka, Douglas

• Format: PDF

Produktbeschreibung

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. 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. L. Mark Berliner is Professor of Statistics at Ohio State University.

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Produktdetails

• Verlag: Springer New York
• Seitenzahl: 199
• Erscheinungstermin: 6. Dezember 2012
• Englisch
• ISBN-13: 9781461221128
• Artikelnr.: 44052648

Inhaltsangabe

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.