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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.
Sommario
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.
Riassunto
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.
