Studies in the Atmospheric Sciences

by Mark L. Berliner (Editor), Douglas Nychka (Editor), Timothy Hoar (Editor)

Overview

The National Center for Atmospheric Research (NCAR) in Boulder, Colorado, is a major institution performing research in the environmental sciences. After an open competition, the National Science Foundation gave its financial sup­ port to NCAR for the establishment of a much-needed statistics/probability program in the geophysical sciences. The result is the NCAR Geophysical Statistics Project (GSP). The primary mission of this program is: • To foster excellence in the use of modern statistical science in the geo­ physical and environmental sciences. • To attract and support statistical scientists for collaborative research. To fulfill its mission, GSP engages in research in statistical science and its application to the atmospheric and allied sciences. It supports a variety of collaborative efforts between statistical scientists and disciplinary scientists. Senior statisticians, both members of and visitors to GSP, work in conjunc­ tion with researchers at NCAR and elsewhere in the application of current statistical methods as well as in. the development of new statistical models and techniques. Senior GSP members and NCAR researchers also collaborate in the mentoring of junior members, typically post-doctoral level or statistics graduate students.

Product Details

ISBN-13:	9780387987576
Publisher:	Springer New York
Publication date:	03/30/2000
Series:	Lecture Notes in Statistics , #144
Edition description:	Softcover reprint of the original 1st ed. 2000
Pages:	199
Product dimensions:	6.10(w) x 9.25(h) x 0.20(d)

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.