This lecture contains basic information about atmospheric circulation, climate, and climate change. The role of greenhouse gases and other forcings is emphasized. Climate movie. L96Sens The basic equations of fluid dynamics are described, and their application to climate systems. The implementation of computer models to solve the equations is described with a view to helping a statistical audience understand the strengths and weaknesses of such physically based models. Scenarios i. Global climate models are relatively coarse, and in order to make regional projections it is necessary to develop regional models, or use other forms of downscaling global models to a finer resolution.

Smoothers File. Spatial processes and geophysical data Geophysical data typically come as spatio-temporal fields living on at least approximately a rotating sphere. Among the tasks for the statistician is estimation e. The processes involved are nonstationary in space and time, and usually space and time are not separable, and our statistical models need to take this into account. File updated. R code 1. R code 2. North American data. What is a spline? Common tasks in statistical climatology are estimation of trends in space and time.

Linear models are common in climate science, but nonparametric nonlinear smoothers are better descriptions of the complicated functional forms of these trends. There are many classes of splines, many defined as solutions to constrained optimization problems. Some large sample theory for Kriging and splines The large sample theory for Kriging and splines were developed by Stein and Wahba, respectively. The theory makes clear what assumptions are most important in spatial analysis and unifies the methods of kernel smoothing, variational methods and Kriging.

Mesquita slides. Spatial models for large data sets For large data sets the statistical computations to estimate parameters, form spatial predictions and to quantify the uncertainty may not be feasible given current computational resources. There are different approaches to deal with this problem, such as developing sparse matrix approximations to the covariances, or using Markov random field approaches. In either case these practical solutions modify the spatial model and it is important to understand how they change the statistical assumptions. Introduction to Linear Regression Analysis.

Douglas C. Financial Modelling. Daniel Wetterau. Ann G. Electrical Load Forecasting.

## List of Possible Supervisors

Bruce P. Circular Statistics in R. Arthur Pewsey.

Response Surface Methodology. Raymond H. Machine Learning in Python. Michael Bowles. Marc Kery. Using the Weibull Distribution. John I. Applied Spatial Data Analysis with R.

### IBM Opens Quantum Computing Center; Announces 53-Qubit Machine

Roger S. Image Registration for Remote Sensing. Jacqueline Le Moigne. Statistics for Earth and Environmental Scientists. John H.

- Using computation to understand statistics through climatology?
- The old new thing: practical development throughout the evolution of Windows!
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- Ilya Polyak, Computational Statistics in Climatology--德国Springer公司期刊数据库.

Mathematical Modeling. Elsevier Science. Handbook of Monte Carlo Methods.

Dirk P. Stefano M. Alain Zuur. Numerical Methods in Finance and Economics. Paolo Brandimarte. Mineral Resource Estimation.

## Computational statistics in climatology

Clayton V. Generalized Additive Models. Simon N. Time Series Analysis and Its Applications. Robert H. Ionut Florescu.

- Welcome to the world’s largest climate modelling experiment.
- Modelling Non-Stationary Time Series: A Multivariate Approach.
- Greenhouse Technology and Management;
- Computational Statistics in Climatology!

Analyzing Compositional Data with R. Gerald van den Boogaart. Data Mining Techniques in Sensor Networks. Annalisa Appice.

Statistical Methods in the Atmospheric Sciences. Daniel S. Frank Schorfheide. Understanding Biplots.

## Computational statistics in climatology [1996]

John C. Multispectral Satellite Image Understanding. Kim L. A Course in Statistics with R. Prabhanjan N. Estimation of Uncertainty of Wind Energy Predictions. David Zastrau. Statistics for Chemical and Process Engineers. Yuri A. Applied Multivariate Statistics with R. Daniel Zelterman. Applied Statistical Modeling and Data Analytics. Srikanta Mishra. Scientific Inference. Simon Vaughan. Enrico Zio. Modeling Uncertainty in the Earth Sciences. Jef Caers. Bayesian Inference for Probabilistic Risk Assessment. Dana Kelly. Franco Pavese. Fabrice D.

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The Improbability Principle. David J. Greg N. Allan Bluman. Statistics in Medicine. Optimal Learning. Warren B. Statistical Data Analysis for the Physical Sciences. Adrian Bevan. How to be a Quantitative Ecologist. Jason Matthiopoulos. Scientific Method in Brief. Hugh G. Design of Coastal Structures and Sea Defenses.