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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.

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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 theme
Topic theme

The 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
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)?

Ice nucleation


Diffusional growth/sublimation/melting colder]

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


Use of wave clouds as natural colder]

laboratories

Wave clouds:

Heymsfield et al. 2011



Aggregation
Aggregation ventilation

What we do know:

  • Stochastic Collection equation

  • Bulk representation - Ea


Aggregation1
Aggregation ventilation

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?


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

Courtesy: Richard Cotton


Ice saturation conditions aircraft observations

Only aggregation process is acting to change PSD

Use modelling to interpret results and estimate Ea.

e.g. Passarelli 1978, Mitchell 1988, Field et al. 2006.


Generate ice at the top

Combine observations from CPI1, 2 with modelling to estimate Ea

CPI1

Connolly et al 2012

CPI2


Secondary multiplication
Secondary multiplication aircraft observations

  • From 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
Secondary ice production aircraft observations

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

  • Need laboratory confirmation


Secondary ice production1
Secondary ice production aircraft observations

  • 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
Graupel/Hail production aircraft observations

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



Sedimentation
Sedimentation measurements

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?)


Irina Gorodetskaya measurements


Sedimentation measurements

Heymsfield and Westbrook

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

Protat and Williams 2011

0.1m/s residual in method: less than variability

Should this variability be represented?

Szyrmer and Zawadzki 2010

Disdrometer


PSD measurements

What 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
Recent Developments in Knowledge of ICE PSDs measurements

  • 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 measurements

  • 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.


Impact of psd shape on the far infrared spectrum baran 2007 qjrms 133 pgs 1425 1437

Theoretical BT differences between shapes of the PSD but D measurementse is the same

De=48 µm

Impact of PSD shape on the far-infraredspectrum (Baran, 2007 QJRMS 133, pgs 1425-1437)

De=8 µm

33 25 20 17 um

De=48 µm

TAFTS simulations: CIRCREX field campaign fall of 2013 spring 2014

100 67 50 40 um


New observations
New Observations measurements

  • 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-size measurements

What 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
Improving the representation of precipitation processes measurements

  • 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: measurements

  • 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: measurements

  • Determine collection efficiencies for ice aggregation and graupel/hail formation

  • Solution:

  • New laboratory work

  • More insitu Lagrangian flight studies


  • Requirement: measurements

  • 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


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