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
11 - Techniques for analyzing model output
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
Background
This chapter describes methods for (1) the graphical display and interpretation of model output, and observations; (2) the calculation of derived variables from model output, which can help in the analysis of processes; and (3) the mathematical processing of model output, which can reveal properties and patterns that are not apparent from the dependent variables themselves. The comparison of the model output with observations is a type of analysis of course, but Chapter 9 on model verification is devoted to this subject. Also, the application of post-processing algorithms, for example to remove systematic error, is a special type of mathematical processing of the output, and this subject is treated in Chapter 13.
Graphical methods for displaying and interpreting model output and observations
Much of the material in this section is covered in courses on meteorological analysis; however, it is provided here because many students of NWP have not had such a course available to them. More in-depth material can be found in texts such as Saucier (1955) and Bluestein (1992a,b).
There have been so many creative ways of displaying model output, and comparing it with observations, that it is impossible to present a thorough treatment here. Nevertheless, some examples will be provided and the student is encouraged to review the literature and become familiar with typical techniques (see chapter Problems 1 and 3). This subject is important because successfully publishing research, whether it is model-based or not, depends on displaying the results in an easily and quickly understood format.
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- Information
- Numerical Weather and Climate Prediction , pp. 343 - 357Publisher: Cambridge University PressPrint publication year: 2010