A new approach to parameterizeice-phase cloud microphysics

The Predicted Particle Properties (P3) Scheme

WWOSC 2014Montreal, CanadaAugust 17, 2014

Hugh Morrison1 and Jason Milbrandt2

1National Center for Atmospheric Research, Boulder, CO, USA2Environment Canada, Montreal, Canada

Environment Canada’sHigh Resolution (2.5-km) Deterministic Prediction System

Column-maximum REFLECTIVITY*

* Computed from microphysics (Milbrandt-Yau 2-moment) Experimental implementation: summer 2014

Operational implementation: 2015

N (D)

D [ m]100

[m-3 m-1]

20 40 60 800

101

100

10-1

10-2

Microphysics Parameterization SchemesULTIMATE GOAL: Predict evolution of hydrometeor size distributions

N (D)

D [ m]100

[m-3 m-1]

20 40 60 800

101

100

10-1

10-2

Bin-resolving: Bulk:DeDNDN 0)(i

I

i

NDN

1

)(

N (D)

D [ m]100

[m-3 m-1]

20 40 60 800

101

100

10-1

10-2

(spectral)

1 m3

(unit volume)

Radius [cm]Bin-resolving coalescence model

Berry and Reinhardt (1974)

RAINCLOUD

Mas

s D

ensi

ty [

g m

-3 (

lnr)

-1]

Tim

e [m

in]

BACKGROUND – Representation of Hydrometeors

Historical development of bulk scheme approach:

• start with liquid-only (“cloud” and “rain”)

Example of Observed Ice Particles

Photos c/o Alexei Korolev

BACKGROUND – Representation of Hydrometeors

Historical development of bulk scheme approach:

• start with liquid-only (“cloud” and “rain”)

• add ice-phase (“ice”)

GRAUPELg = 400 kg m-3

V = agDbg

HAILh = 900 kg m-3

V = ahDbh

“SNOW”s = 100 kg m-3

V = asDbs

abrupt / unphysical

conversions

CLOUD ICEs = 500 kg m-3

V = aiDbi

Problems with pre-defined categories:

1. Conversions between categories are ad-hoc

2. Conversions lead to large, discrete changes in particle properties

etc. …

Traditional approach of bulk microphysics schemes:

BACKGROUND – Representation of Hydrometeors

The simulation of ice-containing cloud systems is oftenvery sensitive to how ice is partitioned among categories

MOR-hail (only)

MY2 - hail (only)

MOR-graupel (only)

MY2-baseline (g + h)

Morrison and Milbrandt (2011), MWR

Microphysics Schemes:MOR: Morrison et al. (2005, 2009)MY2: Milbrandt and Yau (2005)

• idealized 1-km WRF simulations (em_quarter_ss)

• base reflectivity

There is a paradigm shiftin the parameterization of ice-phase microphysics

Increased emphasis on the prediction ofhydrometeor properties

Recent developments:

• 2-moment – Ziegler (1985), Ferrier (1994), Reisner et al. (1998), etc.

• 3-moment – Milbrandt and Yau (2005)

• predicted rime fraction – Morrison and Grabowski (2008)

• predicted crystal habit – Harrington et al. (2013)

• predicted graupel density – Connolly et al. (2005), Mansell et al. (2010),

Milbrandt and Morrison (2013)

BACKGROUND – Representation of Hydrometeors

Partial mitigation to the problems with pre-defined categories

SQUEAK!

QUACK!

• has a label that says “DUCK”• big, round eyes• yellow, wing-like appendages• plastic exterior, hollow interior• no feet• makes a “squeak” noise

• has no label• small, round eyes• white, wing-like appendages• feathery exterior, meaty interior• webbed feet• makes a “quack” noise

Which of the following is more duck-like?

IF IT QUACKS LIKE A DUCK …

DUCK

Based on a conceptually different approach to parameterize ice-phase.

LIQUID PHASE: 2 categories, 2-moment:

qc, qr, Nc, Nr

ICE PHASE: 1 category, 4 prognostic variables:

qi, qrim, Ni, Brim

predicts wide range of properties (and thus types of ice)Compared to traditional (ice-phase) schemes, P3:• avoids some necessary evils (category conversion, pre-defined properties)

• has self-consistent physics• is better linked to observations• is more computationally efficient

New Bulk Microphysics Parameterization:

Predicted Particle Properties (P3) Scheme*

* Morrison and Milbrandt (2014)J. Atmos. Sci (in press)

Mesoscale Model Results

3D Squall Line case:(June 20, 2007 central Oklahoma)

• WRF_v3.4.1, x = 1 km, z ~ 250-300 m, 112 x 612 x 24 km domain• initial sounding from observations• convection initiated by u-convergence• no radiation, surface fluxes

2007 OK Squall Line: Base Reflectivity (1 km AGL, t = 6 h)

MOR-G

MOR-H

THO

Observations

WSM6

WDM6P3

dBZ

MY2

Morrison et al. (2014)J. Atmos. Sci (in press)

WRF Results: Line-averaged Reflectivity (t = 6 h)

ObservationsdBZ

MOR-G

MOR-H

THO

WSM6

WDM6P3

MY2

DERIVED Ice Physical Properties

Fr ~ 0-0.1 ~ 900 kg m-3V ~ 0.3 m s-1

Dm ~ 100 μm small crystals

Fr ~ 0 ~ 50 kg m-3V ~ 1 m s-1

Dm ~ 3 mm aggregates

Fr ~ 1 ~ 900 kg m-3

V > 10 m s-1

Dm ~ 5 mm hail

etc.

Fr

VmDm

ρp

Vertical cross section of model

fields (t = 6 h)

P3 SIMULATION

Precipitation rate at 1 km height

Time-averaged from 6-7 h

Low-density, unrimed snow

Rimed snow/ low-density graupel

Small, dense ice

Z qi

qrqc

Fr

Vm Dm

ρp

Z

P3 SIMULATION

Vertical cross section of model fields (t = 24 h)

2014 OU CAPS Ensemble (HWT)

1 km Reflectivity, 22 UTC 8 May, 201422 h forecast

OBSMOR-GP3

THO MY2-v2 MY2-v1

http://hwt.nssl.noaa.gov/Spring_2014

Scheme Squall line case

(x = 1 km)

Orographic case

(x = 3 km)

# prognostic

variables

P3* 0.436 (1.043) 0.686 (1.013) 7

MY2 0.621 (1.485) 1.012 (1.495) 12

MOR-H 0.503 (1.203) 0.813 (1.200) 9

THO 0.477 (1.141) 0.795 (1.174) 7

WSM6 0.418 (1.000) 0.677 (1.000) 5

WDM6 0.489 (1.170) 0.777 (1.148) 8

• Average wall clock time per model time step (units of seconds.)

• Times relative to those of WSM6 are indicated parenthetically.

Timing Tests for 3D WRF Simulations

P3* is one of the fastest schemes in WRF

*1 ice-phase category version

Based on a conceptually different approach to parameterize ice-phase.

LIQUID PHASE: 2 categories, 2-moment:

qc, qr, Nc, Nr

ICE PHASE: 1 category, 4 prognostic variables:

qi, qrim, Ni, Brim

predicts wide range of properties (and thus types of ice)

New Bulk Microphysics Parameterization:

Predicted Particle Properties (P3) Scheme*

* Morrison and Milbrandt (2014)J. Atmos. Sci (in press)

The single “free (ice-phase) category” version shows very promising early results

Multi-category version*

LIQUID PHASE: 2 categories, 2-moment: qc, qr, Nc, Nr

ICE PHASE: category 1: qi_1, qrim_1, Ni_1, Brim_1

category 2: qi_2, qrim_2, Ni_2, Brim_2

category 3: qi_3, qrim_3, Ni_3, Brim_3

… …

predicts wide range of properties (and thus types of ice) – as before –

BUT now allows for different types of ice in the same grid point

New Bulk Microphysics Parameterization:

Predicted Particle Properties (P3) Scheme

* Milbrandt and Morrison (2015) (in preparation)

Conclusions

• The P3 approach introduces a conceptual departure from current bulk microphysics schemes.

• Preliminary results – idealized, real-case simulations, and real-time forecasts – are very promising…

• Further developments to the P3 scheme will include:• more predicted properties

+ spectral dispersion (3-moment)+ liquid fraction+ etc…