Book contents
- Frontmatter
- Contents
- Preface
- Acronyms and abbreviations
- Principal symbols
- 1 Introduction
- 2 The governing systems of equations
- 3 Numerical solutions to the equations
- 4 Physical-process parameterizations
- 5 Modeling surface processes
- 6 Model initialization
- 7 Ensemble methods
- 8 Predictability
- 9 Verification methods
- 10 Experimental design in model-based research
- 11 Techniques for analyzing model output
- 12 Operational numerical weather prediction
- 13 Statistical post processing of model output
- 14 Coupled special-applications models
- 15 Computational fluid-dynamics models
- 16 Climate modeling and downscaling
- Appendix: Suggested code structure and experiments for a simple shallow-fluid model
- References
- Index
10 - Experimental design in model-based research
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Contents
- Preface
- Acronyms and abbreviations
- Principal symbols
- 1 Introduction
- 2 The governing systems of equations
- 3 Numerical solutions to the equations
- 4 Physical-process parameterizations
- 5 Modeling surface processes
- 6 Model initialization
- 7 Ensemble methods
- 8 Predictability
- 9 Verification methods
- 10 Experimental design in model-based research
- 11 Techniques for analyzing model output
- 12 Operational numerical weather prediction
- 13 Statistical post processing of model output
- 14 Coupled special-applications models
- 15 Computational fluid-dynamics models
- 16 Climate modeling and downscaling
- Appendix: Suggested code structure and experiments for a simple shallow-fluid model
- References
- Index
Summary
The aim of this chapter is to provide a few examples of some common methods for using models in research studies. Other chapters also discuss experimental designs in the context of the specific subject being discussed. For example, there are many places in Chapter 16 describing experimental methods related to modeling studies of climate change. The summary here is far from complete because experimental methods are obviously closely tied to the objectives of a research project, which can vary widely. Nevertheless, the methods summarized are in wide use, and their strengths and limitations should be understood.
Case studies for physical-process analysis
Model simulations, generally for short time periods, are often used to study some aspect of a meteorological phenomenon. Sometimes the purpose is to better understand the predictability of a process, in terms of the necessary physical-process parameterizations or initial conditions. This is treated in Section 10.7 on predictive-skill studies. More often, the purpose is to use the model to help better understand the dynamics or kinematics of a physical process. The model is integrated from an initialization that is based on observations at the beginning of the study period. A next step in the process is to confirm that a good correspondence exists between the model simulation and the observations that are available during the simulation period. Good verification of the model skill at these observation locations is typically considered to be justification for believing the simulation in the space and time gaps between the observations.
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- Numerical Weather and Climate Prediction , pp. 321 - 342Publisher: Cambridge University PressPrint publication year: 2010