Book contents
- Frontmatter
- Contents
- List of contributors
- Foreword
- Preface
- 1 Introduction
- 2 The Factor Separation Methodology and the fractional approach
- 3 Investigation of the Factor Separation features for basic mathematical functions
- 4 Factor Separation Methodology and paleoclimates
- 5 Meso-meteorology: Factor Separation examples in atmospheric meso-scale motions
- 6 Using the Alpert–Stein Factor Separation Methodology for land-use land-cover change impacts on weather and climate process with the Regional Atmospheric Modeling System
- 7 Application of Factor Separation to heavy rainfall and cyclogenesis: Mediterranean examples
- 8 Experience in applying the Alpert–Stein Factor Separation Methodology to assessing urban land-use and aerosol impacts on precipitation
- 9 Free and forced thermocline oscillations in Lake Tanganyika
- 10 Application of the Factor Separation Methodology to quantify the effect of waste heat, vapor and pollution on cumulus convection
- 11 The use of the Alpert–Stein Factor Separation Methodology for climate variable interaction studies in hydrological land surface models and crop yield models
- 12 Linear model for the sea breeze
- 13 Experience and conclusions from the Alpert–Stein Factor Separation Methodology
- 14 Tagging systematic errors arising from different components of dynamics and physics in forecast models
- 15 Some difficulties and prospects
- 16 Summary
- Appendix: References employing the Alpert–Stein Factor Separation Methodology
- References
- Index
15 - Some difficulties and prospects
Published online by Cambridge University Press: 03 May 2011
- Frontmatter
- Contents
- List of contributors
- Foreword
- Preface
- 1 Introduction
- 2 The Factor Separation Methodology and the fractional approach
- 3 Investigation of the Factor Separation features for basic mathematical functions
- 4 Factor Separation Methodology and paleoclimates
- 5 Meso-meteorology: Factor Separation examples in atmospheric meso-scale motions
- 6 Using the Alpert–Stein Factor Separation Methodology for land-use land-cover change impacts on weather and climate process with the Regional Atmospheric Modeling System
- 7 Application of Factor Separation to heavy rainfall and cyclogenesis: Mediterranean examples
- 8 Experience in applying the Alpert–Stein Factor Separation Methodology to assessing urban land-use and aerosol impacts on precipitation
- 9 Free and forced thermocline oscillations in Lake Tanganyika
- 10 Application of the Factor Separation Methodology to quantify the effect of waste heat, vapor and pollution on cumulus convection
- 11 The use of the Alpert–Stein Factor Separation Methodology for climate variable interaction studies in hydrological land surface models and crop yield models
- 12 Linear model for the sea breeze
- 13 Experience and conclusions from the Alpert–Stein Factor Separation Methodology
- 14 Tagging systematic errors arising from different components of dynamics and physics in forecast models
- 15 Some difficulties and prospects
- 16 Summary
- Appendix: References employing the Alpert–Stein Factor Separation Methodology
- References
- Index
Summary
This chapter discusses several difficulties as well as several prospects that are related to the FS methodology and that were not treated explicitly earlier in the book. First, the problem of dealing with a large number of factors is common to atmospheric research but becomes a serious computational burden when 2n simulations are required. The most important point here is that we can limit our calculation to second-order interactions at the first stage and choose the most important factors, and only then continue toward a full factor separation.
Another somewhat related problem is how to deal with unchosen factors and with factor dependency. A further related problem is the fractional treatment that allows a finer analysis of potential non-linear effects of some factors. Particularly, how to reveal a significant variation in the effect of some factor due to change in a potential threshold.
A comparison of the FS against a different statistical factorial modeling (FM) method is performed. It is shown that the FS method has two clear advantages over the FM method. First, the sum of all 2n contributions in an n-factor problem equals the full run result. This allows a closure of the separation as well as a percentage-wise analysis of all 2n contributions that sum up to 100%. Second, the contribution by the zero- or basic-state is calculated and is one of the 2n contributions. It is shown here and in many earlier FS studies that in the atmosphere the zero-state contribution can be significant and should be calculated.
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- Factor Separation in the AtmosphereApplications and Future Prospects, pp. 237 - 244Publisher: Cambridge University PressPrint publication year: 2011
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