10 Dec 2007

Precipitation Uncertainty due to

Variations in Particle Parameters within a Simple Microphysics

Scheme

Dr. Matt GilmoreDAS/UIUC

(Photo Credit: NCAR/NSF)

Presented 10 Dec 2007

10 Dec 2007

Collaborators

• Jerry Straka - OU School of Meteorology

• Erik Rasmussen - OU/NSSL

Special Thanks toBob Wilhelmson, Adam Houston, Leigh Orf, Ted Mansell, Lou Wicker

Sue van den Heever

10 Dec 2007

Purpose

• Test precip. sensitivity to hail, snow, and rain particle characteristics in a single-moment bulk ice microphysics package (used in WRF)

• Can we trust precipitation forecasts from such models? Tornado forecasts?

• Motivate the use of more sophisticated microphysics

10 Dec 2007

National Hail Research Experiment

1972-1974, 1976, Grover, CO

(Photo Credit: NCAR/NSF)

• Examined the influence of cloud seeding on hailfall

(Photo Credit: NCAR/NSF)

Former division that worked on NHRE became MMM.

10 Dec 2007

“... the specific sources of error must be identified in current microphysical parameterizations, and physically based improvements to the model physics must be developed, particularly for ice formation...” (MMM/NCAR Science Plan, Oct. 2000)

Prediction of Precipitating Weather Systems - Cloud Microphysics and Precipitation

(Photo Credit: NCAR/NSF)

The Costner’s, April 1975

10 Dec 2007

Top-5 Severe Weather Hazardsthat Caused Crop and Property Damage

(1995 - 2000)

Average Cost PerYear(Billions of U.S. dollars)

Flood

Tornad

o

Hurrica

ne Hail

Drough

t$0.0

$1.0

$2.0

$3.027% 25%

11% 9% 8%

Total=$11.2 Billionper Year

(NWS Natural Hazard Statistics)

10 Dec 2007

Hail Vulnerability• Tobacco, tea, and soybeans ->

numerous 5 mm hail,(Changnon 1971, 1977, 1999)

• Other crops, farm animals, & property -> larger hail(Largest from supercells;

Changnon 2001)

• Could we someday forecast hail occurrence & its characteristics?

(Photo Credit: NCAR/NSF)

10 Dec 2007

Supercell Flooding Potential

• Hypothesized that supercells might contribute to climatology of extreme rainfall (100-year recurrence interval). (Smith et al. 2001, J. Hydrometeor.)

e.g., dense gauge network, max rainfall rates:Orlando, FL: 330 mm h–1 (26 Mar. 1992)Dallas, TX: 231 mm h–1 (5-6 May 1995)

Would cloud model flooding predictions help?

10 Dec 2007

Hail Climatology

Changnon and Changnon (1999)

Changnon (1996)

# Hail Days/year (1901-1994)

Mean Hail Diameter (cm)

10 Dec 2007

Hail Forecasting• Climatology

• Synoptic Pattern Based– See review articles by Longley and Thompson (1965); Johns and Doswell (1992).

• Sounding Based– Edwards and Thompson (1998) demonstrated thermodynamic parameters to be practically useless in hail severity forecasting: VIL, CAPE, maximum parcel level, EL, convective cloud depth, wet-bulb zero, freezing level. Suggested that forecasts of storm rotation be made. Progress?

• Reliable NWP cloud model forecasts? (We show …not yet)

10 Dec 2007

Numerical Experiments

• Straka Atmospheric 3-D Cloud Model (SAM). Similar to the N-COMMAS model.

• 6th order Crowley advection on scalars. 2nd order flux scheme on momentum.

• Prognostic equations for u, v, w, , p, TKE, qv, qc, qr. Optional ice physics.

10 Dec 2007

Model Set-up

• 91x91x22 km domain

• dx,dy = 1000 m, dz = 500 m

• 1°C perturbation bubble.

• Horizontally homogeneous environment

10 Dec 2007

Experimental Design• Idealized environments from Weisman and Klemp (1984).

HodographsT , Td Profile

CAPE=2200 J kg-1

10 Dec 2007

Experimental Design• Microphysics scheme:

– Three-class bulk ice scheme (ice, snow, and hail/ graupel). [Lin et al. (1983), “LFO”] Includes warm rain scheme for cloud and rain. Referred to herein as “3-ICE”

– Scheme described in Gilmore et al. (2004; MWR, August issue)

– Similar schemes still being used (Purdue-Lin and WSM-6)

10 Dec 2007

“3-ICE” Process Rates

• 6 species• Single moment

PRECIPITATION ON GROUND

RAINHAIL/GRAUPEL

WETOR DRYGROWTH

SNOW

CLOUDICE

CLOUDWATER

WATER VAPOR

qhcns, qhacs,

qhdpv

qvevr

qsdpv

qrmlh

qiint

qsacw, qsfw

qrmls

qrcnw, qracw, qh

acw qs

aci, qsfi,

qscni

qhfzr,

, qsacr

qhaci,

qvsbsqvsbh

qrshh

qwmliqwcdv

qvevw qvsbi

qidpv

qifzw

qraci,

qsacw

qiacr

qiacr,

qracs

qraci

qsacrqhacr,

10 Dec 2007

Many Limitations

• Species are constrained to a predefined distribution function which experiences bulk/average fallout.

• Scheme doesn’t predict number concentration or other moments.

• Inconsistencies in diameter’s used for different processes (see McFarquhar & Black 2004; Potter 1991)

• Efficiencies are assumed constant.

10 Dec 2007

Limitations (continued)

• Only a single graupel/hail (qh) species.

• Pick parameters such as the particle intercepts, densities, and fall velocity coefficients a priori

(There are many more “gotchas”… but we will focus on the two above)

10 Dec 2007

NHRE-Motivated Sensitivity Testing

Relative Frequency

104 106 108

Intercept (dm-3 mm-1 )

.06

.12

Knight et al. (1982)

10 Dec 2007

Intercept Observations

Hail/graupel intercept (noh) (dm-3

mm-1 )

References

103 – 105 (hail) Federer and Waldvogel (1975), Dennis et al. (1971), and Spahn (1976)

102 – 106 Cheng et al. (1985)

104 – 108

graupel/hailKnight et al. (1982)

10 Dec 2007

Particle Density Observations

Density (kg m-3)

Hail: 700 to 900Graupel: 50 to

890

Hail: 400

Reference

Pruppacher and Klett (1978)

• D. Zrnić, personal communication

10 Dec 2007

Species Size Distributions

Standard LFOparamsno

1

104

106

102

108

Particle Diameter (mm)

0 10 20 30 40

rainsnowN2ρ93N ρ94N ρ95N ρ94N ρ45N ρ46N ρ47N ρ48N ρ4

a) Number Conc. per mm bin

n(D) {count dm

–3 mm

–1}

10 Dec 2007

Species Fallspeeds

• Larger and more dense fall faster

10 155

Snow

Water Content (g m–3)

Rain

N5ρ4

3N ρ9

4N ρ9

5N ρ9

6N ρ4 7N ρ48N ρ4

4N ρ4

5

10

15

20

25

Vt (m s–1)

c) Mass-weighted mean fall speed

rainsnowN2ρ93N ρ94N ρ95N ρ94N ρ45N ρ46N ρ47N ρ48N ρ4

10 Dec 2007 4

x3x

10 Dec 2007 2 h accumulation of qr & qh

10 Dec 2007

N3ρ9

PeasDimesGolfballs

10 Dec 2007

N4ρ9

PeasDimes

10 Dec 2007

N5ρ4

Peas

10 Dec 2007

Hail counts over a 100 m2 area

10 Dec 2007

Midlevel Storm Structure and Surface Gust Front Evolution

t=30t=60 t=90t=120

10 Dec 2007

Midlevel Storm Structure and Surface Gust Front Evolution

N8

t=30t=60 t=90t=120Warmer!

10 Dec 2007

Getting microphysics “right” is important for simulating

the right downdraft

Z ~ 500 m

Z = 0 m

10 Dec 2007

N8

Low-level Storm Structure and Surface Gust Frontt=60 min (Us=50)

10 Dec 2007

Temporally and Horizontally-AveragedVertical Profiles

Average Water Content (g m-3 ).10 .15 .20.05 .250 .30 .05 .10

Hail/Graupel

Rain

Snow Cloud ice

CloudWater

10

8

6

4

2

12

14

.035

Z(km)

10 Dec 2007

Temporally and Horizontally-AveragedVertical Profiles (cont’d)

Z(km)

N3ρ94N ρ94N ρ45N ρ95N ρ46N ρ47N ρ48N ρ4

LFO warm rain

10

8

6

4

2

12

14

.10 .15 .20.05 .25 .30

Total Species

( Average Water Content g m-3 )

10 Dec 2007

Thus, N8r4 case has larger massof qh but spread over larger areaSmaller fall velocities

10 Dec 2007

Second Sensitivity Test

• When the intercepts for the following species were increased individually by 2 orders of magnitude, then total precipitation fall decreased by...– Rain (N6 to N8): 3 Tg – Snow (N5 to N7): 4 Tg– Large Ice (N4 to N6): 12 Tg

• Greatest sensitivity to qh

10 Dec 2007

Third Sensitivity Test

• Change the ground-relative (GR) motion but not the vertical wind shear...

• Result: point rainfall and hailfall doubles although total system mass preserved

New GR-Motion: subtract 10 m s–1 from u windspeeds

10 Dec 2007

Results Summary• Mass fallout changes by a factor of 4 when the “large ice” distribution is varied within its observational limits. Weaker shear shows differences of a factor of 3. Us=20 m/s cases (not shown) had factor of 2.

• Thus, sensitive to the vertical wind shear

• Model precipitation is more sensitive to changes in the “large ice” distribution than the snow or rain distribution.

• Precipitation depth is also a function of GR-storm motion.

10 Dec 2007

Conclusion

• While simple bulk microphysics schemes such as LFO can be “tuned” and may have value in a research mode, they are probably not well suited for precipitation forecasts.

10 Dec 2007

Future Work: Improved schemes

Multiple Moments for 3 or 4 ice categories• e.g., Ferrier (1994),

Siefert and Beheng (2001)

More ice categories (necessary - McCumber et al.)

More liquid and ice categories, more moments, particle density prediction,

Straka and Gilmore (2008)

10 Dec 2007

Two Moment, 5-ice, 3-

rain scheme

• Straka and Gilmore (2008)

– Will this reduce the precipitation uncertainty?

10 Dec 2007

(Photo Credit: NCAR/NSF)

Joe and Jill Newham, April 1975

Thanks for your attention!

10 Dec 2007

10 Dec 2007

Rates t = 30 min

10 Dec 2007

Supplemental Figures

Rates t = 30 min

10 Dec 2007

Rates t = 90 min

10 Dec 2007

Supplemental Figures

Rates t = 90 min

10 Dec 2007

Conventional versus Dual-Polarization Radar

Light Rain

Wet Snow

Heavy Rain

Large Drops

Moderate Rain

Vertical Ice

Horizontal Ice

Dry Snow

Hail

Graupel/Small Hail

Rain/Hail

Conventional Radar Reflectivity

Hydrometeor Classification

Cimmaron, OK, az=148.2°,2316 UTC 6 June 1996. (Adapted from Zrnic et al. 2001).