ASP summer colloquium 2006: The challenge of convective forecasting Microphysics of deep convection Axel Seifert Research Division of the German Weather.

ASP summer colloquium 2006: The challenge of convective forecasting

Microphysics of deep convection

Axel SeifertResearch Division of the German Weather Service

ASP summer colloquium 2006: The challenge of convective forecasting

Cloud microphysics?Cloud microphysical schemes have to describe the formation, growth and sedimentation of water particles (hydrometeors).

In deep convection this is especially complicated, since the particles have very different properties like geometrie, density, fall speed and size.

Cloud microphysics is difficult due to• complexity• non-linearity• lack of observations

ASP summer colloquium 2006: The challenge of convective forecasting

Cloud microphysical processes

Evaporation and condensation of cloud droplets are usually parameterized by a saturation adjustment scheme.

Autoconversion is an artificial process introduced by the separation of cloud droplets and rain. Parameterization of the process is quite difficult and many different schemes are available.

Evaporation of raindrops can be very important in convective systems, since it determines the strength of the cold pool. Parameterization is not easy, since evaporation is very size dependent.

Even for the warm rain processes a lot of things are unknown or in discussion for decades, like effects of mixing / entrainment on the cloud droplet distribution, effects of turbulence on coalescence, coalescence efficiencies, collisional breakup or the details of the nucleation process. In cloud models these problems are usually neglected.

ASP summer colloquium 2006: The challenge of convective forecasting

Cloud microphysical processesConversion processes, like snow to graupel conversion by riming, are very difficult to parameterize but very important in convective clouds.

Especially for snow and graupel the particle properties like particle density and fall speeds are important parameters. The assumption of a constant particle density is questionable.

Aggregation processes assume certain collision and sticking efficiencies, which are not well known.

Most schemes do not include hail processes like wet growth, partial melting or shedding (or only very simple parameterizations).

The so-called ice multiplication (or Hallet-Mossop process) may be very important, but is still not well understood

ASP summer colloquium 2006: The challenge of convective forecasting

Spectral (bin) microphysics

The particle size distribution f(x), with some measure of particle size x, is explicity calculated from

with

and

ASP summer colloquium 2006: The challenge of convective forecasting

The collision-coalescence kernel

The effects of in-cloud turbulence on the collision efficiency is still unknown. Estimates of various groups differ by almost an order in magnitude.

ASP summer colloquium 2006: The challenge of convective forecasting

Collisional Breakup of Drops

Binary droplet collision with We = 4 U2D/ = 106Re = 2UD/ = 100 B = b/D = 0.33

Binary droplet collision with We = 4 U2D/ = 106Re = 2UD/ = 100 B = b/D = 0.48

Binary droplet collision with We = 4 U2D/ = 106Re = 2UD/ = 100 B = b/D = 0.37

coalescence!

temporarycoalescence!

collisional breakup!

(Simulations by ITLR, University Stuttgart)

ASP summer colloquium 2006: The challenge of convective forecasting

Bulk microphysical schemes

Instead of f(x) only moments of the size distribution are predicted like the

liquid water content (or mixing ratio):

or the number concentration of particles:

ASP summer colloquium 2006: The challenge of convective forecasting

Bin vs. bulk microphysics

x {m,D}

ASP summer colloquium 2006: The challenge of convective forecasting

Increasing complexity of bulk microphysics

models over the last decades

Recently the first three-moment scheme has been published by Milbrandt and Yau (2005)

ASP summer colloquium 2006: The challenge of convective forecasting

Kessler‘s warm phase scheme

In 1969 Kessler published a very simple warm rain parameterization which is still used in many bulk schemes.

„As we know, water clouds sometimes persist for a long time without evidence of precipitation, but various measurements show that cloud amounts > 1 g/m3 are usually associated with production of precipitation. It seems reasonable to model nature in a system where the rate of cloud autoconversion increases with the cloud content but is zero for amounts below some threshold.“

(E. Kessler: On the Distribution and Continuity of Water Substance in Atmospheric Circulation , Meteor. Monogr. , 1969)

ASP summer colloquium 2006: The challenge of convective forecasting

Assuming a Gamma distribution forcloud droplets

the following autoconversion can be derived from the spectral collection equation

with a universal function

A double-moment warm phase scheme

(Seifert and Beheng 2001)

ASP summer colloquium 2006: The challenge of convective forecasting

The colored lines a solutions of theSpectral collection equation for variousinitial conditions.

The dashed line is the fit:

This function describes the broadening of the cloud droplet size distribution by collisions between cloud droplets.

A double-moment warm phase scheme

(Seifert and Beheng 2001)

no rain no cloud

optimum atLc = 0.9 L

ASP summer colloquium 2006: The challenge of convective forecasting

A comparison of some warm phase autoconversion schemes

spectralKE1969BR1974BE1994SB2001

spectralKE1969BR1974BE1994SB2001

• For high LWC, as in deep convection, the differences are usually small• One-moment schemes cannot describe the effects of drop size on coalescence

mean radius of cloud droplets(near cloud base)

Liquid water content

ASP summer colloquium 2006: The challenge of convective forecasting

Parameterization of sedimentation:

An example how to derive a bulk scheme

ASP summer colloquium 2006: The challenge of convective forecasting

Fundamental parameterization assumption

ASP summer colloquium 2006: The challenge of convective forecasting

The sedimentation velocity for liquid water:

ASP summer colloquium 2006: The challenge of convective forecasting

An interesting result:

A linear PDE is parameterized by a nonlinear PDE!!

ASP summer colloquium 2006: The challenge of convective forecasting

Sedimentation Equations

Spectral microphysics:

One-moment scheme:

Two-moment scheme:

No gravitational sorting!

Has gravitational sorting!

ASP summer colloquium 2006: The challenge of convective forecasting

Idealized rainfall experiment

(Wacker and Seifert 2001)

Sedimentation of a layer of raindrops as described by the spectral equation,a one-moment scheme and a two-moment scheme.

ASP summer colloquium 2006: The challenge of convective forecasting

The 19 June 2002 IHOP “Mantle Echo” Case

(Wakimoto et al. 2004)

Weak echo region

ASP summer colloquium 2006: The challenge of convective forecasting

The two-moment scheme nicely reproduces the weak echo region and the observed reflectivities in the anvil region.

.

The simulation did not produce any surface precipitation, while the observed storm did give a significant amount of hail. Initialization problem or microphysics?

WRF Simulation of the IHOP “Mantle Echo” Case

(Fovell and Seifert 2005)

ASP summer colloquium 2006: The challenge of convective forecasting

WRF Simulation of the IHOP “Mantle Echo” Case

Even a tweaked Lin-type one-moment scheme was not able to give similar results.Different color scale between models and radar!

(Fovell and Seifert 2005)

ASP summer colloquium 2006: The challenge of convective forecasting

Conclusions from the IHOP “Mantle Echo” Case

Why do one-moment schemes have problems reproducing weak echo regions?

• Often Kessler-type autoconversion schemes are used with low threshold values which overestimates the speed of rain formation within the updraft

• Maybe more important, the D(q) or v(q) is inappropriate for the first raindrops within the updraft: Although the mixing ratios can be high, the raindrops are still small during the development stage.

• The exponential size distribution is almost only based on observations at the ground. Nobody really knows how the raindrop size distribution looks like in a 40 m/s updraft.

(Fovell and Seifert 2005)

ASP summer colloquium 2006: The challenge of convective forecasting

Evaporation of raindrops: Yet another problem for bulk

schemesThe (mass) evaporation rate of a single raindrop is proportional to the diameter of the drop:

For a one-moment bulk scheme we find

with

ASP summer colloquium 2006: The challenge of convective forecasting

Evaporation of raindrops: Even more problems in

convective rain

Especially in convective precipitation the raindrop size distribution f(D) is highly variable and not necessarily exponential. A better description is a Gamma distribution:

f(D) = N0 Dμ exp(-λD)

Zhang et al. (2001) measured μ vs. λ

λ = λ(qr) in mm-1

Problem: μ and N0 are highly variable and have a strong impact on evaporation and sedimentation

(see also Seifert 2006; Zhang et al. 2006)

high qr low qr

Two-moment schemes do not necessarily solve our problems!

ASP summer colloquium 2006: The challenge of convective forecasting

Sensitivity of deep convective storms to ice

microphysicsWhat does ice microphysics change compared to a simple warm rain scheme?

• More latent heat, higher updraft velocity and more condensate

• Slower precipitation formation of ice particle growth

• More precipitation at the ground in most regimes

• Different cold pool formation: The cold pool can be stronger and/or extend over a larger area.

• Higher mixing ratios at mid-levels and in the anvil, but this dependsvery much on the microphysics scheme.

(Gilmore et al. 2004a; and others)

ASP summer colloquium 2006: The challenge of convective forecasting

Sensitivity of deep convective storms to graupel properties

Effect of graupel density and PSD, i.e. size and fall speed, on supercells:

(Gilmore et al. 2004b)

ASP summer colloquium 2006: The challenge of convective forecasting

Sensitivity of deep convective storms to graupel properties

Effect of graupel density and PSD, i.e. size and fall speed, on supercells:

(Gilmore et al. 2004b)

Mass reaching surface:Decreases for small and/or low-density graupel compared to hail.

Mass aloft:Increases for small and/or low-density graupel compared to hail.

ASP summer colloquium 2006: The challenge of convective forecasting

A numerical study of CCN effects on different storm

typesWeisman and Klemp (1982)sensitivity study, but nowincluding CCN as a third external parameter:

Variation of 1. CAPE 2. vertical wind shear3. CCN concentration

Effects on total precipitation?

(Seifert and Beheng 2006)

ASP summer colloquium 2006: The challenge of convective forecasting

Total 3h-precipitation

supercell convection

multicell conv.

(Seifert and Beheng 2006)

ASP summer colloquium 2006: The challenge of convective forecasting

Total 3h-precipitationmaritime vs. cont. CCN

(Seifert and Beheng 2006)

ASP summer colloquium 2006: The challenge of convective forecasting

Total 3h-precipitationand rel. change for cont. CCN

(Seifert and Beheng 2006)

ASP summer colloquium 2006: The challenge of convective forecasting

Coupling of microphysics and dynamics!

(Seifert and Beheng 2006)

ASP summer colloquium 2006: The challenge of convective forecasting

Summary of microphysical problems

in convection-resolving NWP• Many fundamental problems in cloud microphysics are still unsolved.

• The lack of in-situ observations makes any progress very slow and difficult.

• Most of the current parameterization have been designed, operationally applied and tested for stratiform precipitation only.

• Most of the empirical relations used in the parameterizations are based on surface observation or measurements in stratiform cloud (or storm anvils, stratiform regions).

• Many basic parameterization assumptions, like N0=const., are at least questionable in convective clouds.

• Many processes which are currently neglected, or not well represented, may become important in deep convection (shedding, collisional breakup, ...).

• One-moment schemes might be insufficient to describe the variability of the size distributions in convective clouds.

• Two-moment schemes haven‘t been used long enough to make any conclusions.

• Spectral methods are overwhelmingly complicated and computationally expensive. Nevertheless, they suffer from our lack of understanding of the fundamental processes.

ASP summer colloquium 2006: The challenge of convective forecasting

Robert Fovell and Axel Seifert. 2005: The 19 June 2002 “Mantle Echo” Case: Sensitivity to Microphysics and Initiation. WRF Workshop 2005, Boulder

Matthew Gilmore, J.M. Straka and E.N. Rasmussen. 2004: Precipitation Uncertainty Due to Variations in Precipitation Particle Parameters within a Simple Microphysics Scheme. Monthly Weather Review: 132, pp. 2610–2627.

Matthew Gilmore, J.M. Straka and E.N. Rasmussen. 2004: Precipitation and Evolution Sensitivity in Simulated Deep Convective Storms: Comparisons between Liquid-Only and Simple Ice and Liquid Phase Microphysics. Monthly Weather Review: 132, pp. 1897–1916.

Jason Milbrandt and M.K. Yau. 2005: A Multimoment Bulk Microphysics Parameterization. Part I: Analysis of the Role of the Spectral Shape Parameter. Journal of the Atmospheric Sciences: Vol. 62, No. 9, pp. 3051–3064.

Jason Milbrandt and M.K. Yau. 2005: A Multimoment Bulk Microphysics Parameterization. Part II: A Proposed Three-Moment Closure and Scheme Description. Journal of the Atmospheric Sciences: 62, pp. 3065–3081.

Axel Seifert. 2005: On the Shape–Slope Relation of Drop Size Distributions in Convective Rain. Journal of Applied Meteorology: 44, No. 7, pp. 1146–1151.

Axel Seifert and K.D. Beheng. 2006: A two-moment cloud microphysics parameterization for mixed-phase clouds. Part I: Model description. Meteorol. Atmos. Phys., 92:45--66.

Axel Seifert and K.D. Beheng. 2006: A two-moment cloud microphysics parameterization for mixed-phase clouds. Part II: Maritime vs. continental deep convective storms. Meteorol. Atmos. Phys. , 92:67--88.

References

ASP summer colloquium 2006: The challenge of convective forecasting

Axel Seifert and K. D. Beheng. 2001: A double-moment parameterization for simulating autoconversion, accretion and selfcollection. Atmos. Res., 59-60:265—281.

Axel Seifert and Morris Weisman. 2005: A comparison of microphysical schemes for cloud-resolving NWP, WRF Workshop 2005, Boulder

Ulrike Wacker and Axel Seifert. 2001: Evolution of rain water profiles resulting from pure sedimentation: Spectral vs. parameterized description. Atmos. Res., 58:19--39.

Roger M. Wakimoto, Hanne V. Murphey, Robert G. Fovell and Wen-Chau Lee. 2004: Mantle Echoes Associated with Deep Convection: Observations and Numerical Simulations. Monthly Weather Review: 132, pp. 1701–1720.

Guifu Zhang, J. Vivekanadan and E.A. Brandes. 2001: A method for estimating rain rate and drop size distribution from polarimetric radar measurements. IEEE Trans. Geosci. Remote Sens., 39, 830-841.

Guifu Zhang, J. Sun and E.A. Brandes. 2006: Improving Parameterization of Rain Microphysics with Disdrometer and Radar Observations. Journal of the Atmospheric Sciences: Vol. 63, No. 4, pp. 1273–1290.

References (continued)