Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Foundations of microphysical parameterizations
- 3 Cloud-droplet and cloud-ice crystal nucleation
- 4 Saturation adjustment
- 5 Vapor diffusion growth of liquid-water drops
- 6 Vapor diffusion growth of ice-water crystals and particles
- 7 Collection growth
- 8 Drop breakup
- 9 Autoconversions and conversions
- 10 Hail growth
- 11 Melting of ice
- 12 Microphysical parameterization problems and solutions
- 13 Model dynamics and finite differences
- Appendix
- References
- Index
9 - Autoconversions and conversions
Published online by Cambridge University Press: 23 November 2009
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Foundations of microphysical parameterizations
- 3 Cloud-droplet and cloud-ice crystal nucleation
- 4 Saturation adjustment
- 5 Vapor diffusion growth of liquid-water drops
- 6 Vapor diffusion growth of ice-water crystals and particles
- 7 Collection growth
- 8 Drop breakup
- 9 Autoconversions and conversions
- 10 Hail growth
- 11 Melting of ice
- 12 Microphysical parameterization problems and solutions
- 13 Model dynamics and finite differences
- Appendix
- References
- Index
Summary
Introduction
An autoconversion or conversion scheme represents hydrometeors changing from one species/habit to another. The change could be a phase change such as homogeneous freezing of water drops. Or it could be a change within a phase, but a change of species dependent on diameter, such as cloud droplets to drizzle or raindrops. It also could be a graupel of one density becoming more or less dense and subsequently reclassified as a different density owing to the riming it experienced.
One reason that autoconversions and conversions are so difficult to parameterize is that autoconversions and conversions are not well-observed processes, though they can be simulated approximately using a hybrid bin model (see Feingold et al. 1998). Furthermore, conversions of ice crystals or snow aggregates to graupels of particular densities are terribly difficult to parameterize, as there are few accurate measurements in nature or from the laboratory on this topic.
Multi-dimensional, Eulerian models incorporate autoconversion and conversion schemes of varying complexity to try to capture the physics changes on the sub-grid scale in terms of grid-scale quantities, much the way turbulence is parameterized (Stull 1988). This has been done for cloud, mesoscale, synoptic, and global models with complexity usually decreasing with increasing scale (Wisner et al. 1972; Koenig and Murray 1976; Cotton et al. 1982; Cotton et al. 1986; Cotton et al. 2001; Lin et al. 1983; Farley et al. 1989; Ferrier 1994, Straka and Mansell 2005 among many others) (this is the nature of microphysical parameterizations).
- Type
- Chapter
- Information
- Cloud and Precipitation MicrophysicsPrinciples and Parameterizations, pp. 253 - 292Publisher: Cambridge University PressPrint publication year: 2009