Topic 5:Precipitation Formation

Co leaders: Paul Field, Andy Heymsfield, Jerry Straka

Contributors:Sara Pousse-Nottelmann; Alexandria Johnson; Andrea Flossmann;;

Charmain Franklin; Irina Gorodetskaya; Ismail Gultepe; Jonny Crosier; Martina Krämer; Paul Lawson; Ulrike Lohmann; Wolfram

Wobrock

Topic themeThe aim of Topic 5 is to examine ice processes involved in precipitation formation.

The objectives of the group are to:

• Assess what is known about each process • Identify what we know less about • Suggest ways to close these gaps through:

•identifying methodologies for measuring these processes (including insitu and remote sensing platforms).

•identifying required observations

Recap – previous BAMS article• What are the respective roles of homogeneous and heterogeneous

nucleation under different ambient conditions?• What is the relationship between IN and ice crystal concentration?• When do secondary ice formation processes become important,

what are the mechanisms for secondary ice formation, and on what do these processes depend?

• What are the freezing mechanisms below205 K?• What are the optical properties of ice crystals as a function of habit?• What are the sedimentation velocities of ice crystals as a function of

habit?• What are the spatial scales of cirrus cloud inhomogeneities?• What is the value of the accommodation coefficient for ice and does

it vary with temperature and humidity?

Processes

Just consider processes involving ice

• Ice nucleation – [massive subject area – also See TOPIC 1• Diffusional growth/sublimation/evaporation/melting • Aggregation• Secondary ice production (splintering/breakup) (also see

TOPIC3) • Riming/graupel/hail formation• Sedimentation: fallspeed, mass

• Aerosol-cloud interactions: Inadvertent Weather Modification?

Ice nucleation

[Consider conditions warmer than T=-35C – see topic 1 for colder]

What we do know:

At T>-35C, immersion freezing is dominant process (Lab, field work e.g. ICE-L, AIDA, CFD results)

Dust (size>0.5microns) correlates well with IN concentrations suggesting that dust is a good IN (DeMott et al. 2010)

Lab tests have isolated Feldspar as the active component in dust (Atkinson et al. 2013)

What we know less about:

Are other aerosol types are important (e.g. bio, soot) ?

Do we need to worry about time dependence (e.g. Vali)?

Diffusional growth/sublimation/melting

What do we know:

• Use electrostatic analogy

• In the last 3 years – models developed that use different habits (Harrington)

• Estimates of capacitance from modelling of mass flux to particle (Westbrook et al. 2008) and using aircraft observations (Field et al 2008)

• Earlier lab estimates from Bailey+Hallett.

What we know less about

• No new estimates of ventilation parameters

• Few estimates of capacitance of complex shapes

Wave clouds:Heymsfield et al. 2011

Use of wave clouds as naturallaboratories

Westbrook and Heymsfield 2011– testing of capacitance and ventilation

Aggregation

What we do know: Stochastic Collection equation

Bulk representation - Ea

Aggregation

Need to know:

• PSD, V, K(I,j)

What we know less about:

• K(i,j) – what is its form? Is sweep out kernel adequate model?

• What is aggregation efficiency, Ea, as function of T, E-field ,etc?

Courtesy: Richard Cotton

Obtaining ice particle model parameters with in-situ aircraft observations

•Lagrangian spiral descent

•Drift with horizontal wind

•Descend with mean speed of ice crystals

•Repeatedly sample same ice crystals as they aggregate

Use modelling to interpret results and estimate Ea.e.g. Passarelli 1978, Mitchell 1988, Field et al. 2006.

Ice saturation conditionsOnly aggregation process is acting to change PSD

-The Manchester Ice Cloud Chamber (MICC) is a fall-tube 10 m tall and 1 m in diameter.

CPI1

CPI2

Generate ice at the top

Combine observations from CPI1, 2 with modelling to estimate Ea

Connolly et al 2012

Secondary multiplicationFrom AMS Glossary

Hallett–Mossop process(Also called rime splintering.) One of the mechanisms thought to be responsible for secondary ice production, when ice crystal concentrations in clouds well in excess (x 10 000) of the ice nucleus concentration are found.

Ice particles are produced in the range of temperature -3° to -8°C (with a maximum at -4°C) as graupel grows by accretion provided the cloud droplet spectrum contains appropriate numbers of droplets smaller than 12 μm and greater than 25 μm. About 50 splinters are produced per milligram of accreted ice.

Hogan et al 2002

Secondary ice production

Extension to riming snow (Hogan et al. 2002, Crosier et al. 2011 , Crawford et al. 2012 )

Need laboratory confirmation

Secondary ice production

New lab work by Knight at -5C

“There appears to be an ice multiplication mechanism in these conditions that does not involve riming.”

Recent studies have looked at crystal fragmentation using aircraft data (Schwarzenboeck et al. 2009, earlier proposal from Vardiman)

Graupel/Hail production

If latent heating during riming leads to T~0C then wet growth (droplets flow before freezing, air bubbles escape, ice is clear), otherwise dry growth (air bubbles included, ice is translucent

Exponential size distribution parameters based on surface measurements

Sedimentation

What we know

• Is a control on the amount of ice in the atmosphere – important for radiation, precip and water vapour.

• Represented by v-D power laws based on direct observation and/or Best number-Reynolds number parametrization (e.g. Mitchell and Heymsfield)

• Variable (+- factor of 2? for same size across many powerlaws )

What we know less about

• How to include variability of v-D in models (do we need to?)

Sedimentation

Protat and Williams 20110.1m/s residual in method: less than variability

Should this variability be represented?

Szyrmer and Zawadzki 2010Disdrometer

Heymsfield and Westbrook Used tank and lab data to combine drag data with the Best-Re number method of estimating Vt (2010)

PSDWhat we do know

• PSD has an exponential tail

• Scalable (possibly due to aggregation controlling its evolution)

• Past measurements affected by shattering

What we know less about

• What does the small end of the PSD look like? Where should the mode be centred?

• Graupel/hail size distributions in cloud

• how do ice number concentrations relate to IN concentrations? How does secondary ice affect this?

Recent Developments in Knowledge of ICE PSDs

• New Probes, etc– CDP– SID-2 and SID-3– HOLODEC– 2D-S, 3VCPI– CIP Grey– HVPS-3– Probe Tips

• Characterizing Probe Response– Ice Shattering in Wind Tunnels, Observations (Korolev, Lawson)– Ice scattering models

PSD

Recent papers:• Tian et al (2010 JAS) suggests that lognormal

distributions are best – challenging the exponential tail idea.

• Woods et al (2008 JAS) grouped PSD according to habit. Exponential fit parameters a function of T and habit

• Mitchell et al (2010 JAS) combine remote sensing with PSD representations to retrieve the sub-100 microns size range of the PSD.

• More papers using remote sensing to test PSDs? Anyone?

New Observations• DOE ARM Projects (ISDAC)• NASA Cirrus Studies (MACPEX)• NASA GPM Studies• Hurricanes (GRIP, PREDICT)• UK Met Office• Balloon-Borne Launches (Sweden)• Laboratory (AIDA)• Contrail, Hole Punch Obs.

Mass-sizeWhat do we know:

• m-D power law representation

• Aggregrates mass ~D2 - insensitive to monomer habit (Westbrook et al)

Estimate mass-size relationship through:

Direct measurement of individual particles in lab or captured outside

Equating bulk ice water measurement from CVI (e.g. Heymsfield et al . 2010) or Nevzorov probe with integrated size distribution (e.g. Cotton et al. 2010)

• m-D relations highly variable

What we know less about:

• How to implement variability into models. Do we need to – or is a mean representation sufficient?

Improving the representation of precipitation processes

Requirement:

Ice particle properties (m, v, Cap, vent) AND their variability as a function of spatial scale

Solution:

Improved sensitivity and range of bulk ice water probes.

Additional statistics to provide spatial coherence

Develop cloud microphysics representations that can use variability

Requirement:

Validation of PSD representation (for ice and graupel/hail)

Solution:

Multi-sensor comparisons with same volume of ice cloud (e.g. Radar, lidar, microwave)

Measurements of insitu graupel distributions

Requirement:

Determine collection efficiencies for ice aggregation and graupel/hail formation

Solution:

New laboratory work

More insitu Lagrangian flight studies

Requirement:

Improved Ice nucleation treatment in models

Solution:

Continued laboratory and field work

Determine whether time dependence needs to be represented

Explore usefulness of including prognostic IN in models