![Basics... Representation
• BULK representation types:
• Inverse exponential: Marshall and
Palmer (1948)
• As rainfall rate increases, so does
number of large particles
Diameter (mm)
N
D
(m
-3
mm
-1
)
n (D) = n0e−λD
0 ≤ D ≤ Dmax
λ= 41 R-0.21
, R [mm h-1
], λ [cm-1
]
N = 8x104
m-3
cm-1
D = particle diameter
N = # particles per unit volume
λ = Slope parameter
n0 = Intercept parameter (max # of particles per volume at D=0)
• Raindrops
• Snow
• Graupel
• Hail
In double-moment schemes, this becomes a variable](https://image.slidesharecdn.com/microphysics-250210112312-07cc6c77/85/WRF-model-Cloud-Microphysics-paramterization-13-320.jpg)
WRF model Cloud Microphysics paramterization
- 1. Microphysics Parameterizations 1 Nov 2010 (“Sub” for next 2 lectures) Wendi Kaufeld
- 2. Sources for these lectures... • Your Stensrud Parameterization Schemes book • Rogers & Yau: A Short Course in Cloud Physics • WRF User’s website: past WRF Workshop presentations • Notes from ATMS 597P, Matt Gilmore’s Cloud Microphysics Parameterization class • Notes from ATMS 501, Greg McFarquhar’s Physical Meteorology class • Comet module: “How Models Produce Precipitation and Clouds” http://www.meted.ucar.edu/nwp/model_precipandclouds/
- 3. Basics... • Parameterization: • AMS Glossary = “The representation, in a dynamic model, of physical effects in terms of admittedly oversimplified parameters, rather than realistically requiring such effects to be consequences of the dynamics of the system.” • Black Box syndrome: • The meteorological cancer of researchers • Ignorance of assumptions, processes, implementations within the parameterization Blindly choosing a parameterization? Inconceivable!
- 4. • So many schemes... Why does the microphysics parameterization you choose matter?
- 5. • Why do microphysical parameterizations matter? • Spatial distribution of precipitation Gilmore et al. (2004b) Kessler, Lin (no ice), and Lin (ice)
- 6. • Why do microphysical parameterizations matter? • Domain-total precipitation • Behavior can change through the course of development Kaufeld – MS Thesis (2010) WRFv3.0.1 WRFv3.2 WRF-Chem responses to total aerosol, v3.0.1 & 3.2
- 7. Gilmore et al. (2004a) • Why do microphysical parameterizations matter? • Vertical distribution of mass (hydrometeors) Vertical distribution of latent heating Varying only intercept param. & graupel density, individually
- 8. Gilmore et al. (2004a) • Why do microphysical parameterizations matter? • Ultimately can dictate evolution of system Varying only intercept param. & graupel density, individually
- 9. • Microphysics: • An emulation of the processes by which moisture is removed from the air, based on other thermodynamic and kinematic fields represented within a model • Attempting to accurately account for sub-grid scale updrafts, clouds, and precipitation Basics... Terminology Trouble in looking at only one output variable: illusion of getting it right for the wrong reasons!
- 10. Basics... Interaction • Convective Parameterizations + Microphysics Parameterizations? • CP: redistribution of Temperature, Moisture (reduce instability) • Resolve sub-grid updrafts due to convection • MP: Limited by CP • High resolution: convection (updrafts) can be explicity modeled, and no sub-grid emulation of convection is required • Convective Parameterization obsolete! • 1-2 km resolution reasonable for this assumption, though others suggest much higher resolution may be required (Bryan 2003) • Results feed back into other schemes: radiation
- 11. Basics... Terminology • Hydrometeors • Species (types): • Cloud Droplets (QCLOUD) – no terminal velocity • Raindrops (QRAIN) • Ice Crystals and Aggregates (QICE) • Rimed Ice Particles, Graupel, Hail (QGRAUP) • Habits? • Scales represented? • Shapes? • Non-hydrometeors: • Aerosol vs. CN vs. CCN vs IN Not in most WRF configurations represent this!
- 12. Basics... Representation • How to represent these hydrometeors (and non-hydrometeors)? • PARTICLE SIZE DISTRIBUTIONS • BULK representation types: • Inverse exponential (Marshall-Palmer) • Lognormal • Gamma function • BIN representation: • No specified distribution • Particle distribution divided into a finite number of categories • “Moments” • 1 = mass, 2 = number, 3 = reflectivity
- 13. Basics... Representation • BULK representation types: • Inverse exponential: Marshall and Palmer (1948) • As rainfall rate increases, so does number of large particles Diameter (mm) N D (m -3 mm -1 ) n (D) = n0e−λD 0 ≤ D ≤ Dmax λ= 41 R-0.21 , R [mm h-1 ], λ [cm-1 ] N = 8x104 m-3 cm-1 D = particle diameter N = # particles per unit volume λ = Slope parameter n0 = Intercept parameter (max # of particles per volume at D=0) • Raindrops • Snow • Graupel • Hail In double-moment schemes, this becomes a variable
- 14. Basics... Representation • BULK representation types: • Gamma distribution • Small particle size relies heavily upon μ Diameter (mm) N D (m -3 mm -1 ) n(D) = n0Dμ e−λD 0 ≤ D ≤ Dmax μ can be positive or negative • Raindrops • Snow • Graupel • Hail In double-moment schemes, this becomes a variable D = particle diameter N = # particles per unit volume λ = Slope parameter n0 = Intercept parameter (max # of particles per volume at D=0)
- 15. „Recently the first three-moment scheme has been published by Milbrandt and Yau (2005)“ Stensrud cites one by Clark (1974) • BULK representation types: increasing in complexity! (courtesy Seifert 2006)
- 16. Basics... Representation Bulk Advantages • Fewer number of prognostic variables = Computationally cheap! • Easy to integrate • Tweakable parameters Bulk Limitations • Cannot represent more than one distribution at a time (different distributions may exist in different parts of the cloud/domain) • “Frozen” distributions for single-moment schemes
- 17. Basics... Representation Bin Advantages • More realistic • Processes that depend on size distribution (Terminal Velocity aggregation) better represented • Represent specific parameterizations & particle interactions • Allows for bimodal (+)distributions – and for them to vary Bin Limitations • Very computationally expensive!!! • Difficult to validate • Knowledge of ice phase physics is lacking essentially, tests the limits of our current scientific understanding and resources
- 18. Basics... Representation Single-Moment Advantages • Computationally efficient Single-Moment Limitations • Inherent uncertainty due to fixed parameters • Situational dependence Double-Moment Advantages • Mass and number are independent: can represent different environments! • Less “parameter-tuning” Double-Moment Limitations • Difficult to validate • Mass and Number are independent: very sensible to use with bin scheme
- 19. Basics... Representation • What’s “better” for YOUR research – • a BULK or BIN parameterization? • SINGLE, or DOUBLE moment (mass, number, both)? Small Group ACTIVITY 5 minutes: meet with small group 5 minutes: meet with larger group Pick group spokesperson for larger group Things to think about... -- what are you interested in forecasting/representing? -- what time & spatial scales are important to you? -- computational resources
- 20. Ideal MP scheme: • Includes all relevant processes and hydrometeor types • Perfect parameterizations • Infinitely small grid size explicitly resolving each particle • Easy to see why this is not currently possible... •Parameterizations appear to be situationally dependent •Limitations on computational power So what does WRF have to offer?
- 21. WRF: MP Schemes Available* * PUBLICLY available! Many more in development ALL BULK SCHEMES
Editor's Notes
- #4: Different hydrometeors represented Warm: Kessler // Cold also? Single-moment vs. double-moment Black Box syndrome... Too complicated to deal with? Lots added within last couple of years – more developments on the way.
- #9: Hierarchy of model needs to produce “realistic” precipitation: actually other factors are “more” important than mp scheme!!! (Because it depends on the predicted quantities, u,v,T,q)
- #11: Habits: where things get interesting.
- #12: Bulk types... Differing degrees of complexity
- #13: Represent most hydrometeors in this fashion. * ONLY MANIFESTS IN AVERAGES: not direct observations
- #14: Represent most hydrometeors in this fashion. * ONLY MANIFESTS IN AVERAGES: not direct observations
- #19: Habits: where things get interesting.
- #21: # of hydrometeors Types of hydrometeors Consideration of different types of cloud droplets? HOW MANY OF THESE CAN INCLUDE DEPENDENCE ON AEROSOL?