The ASCII 2012 campaign: overview and early results

AgI Seeding Cloud Impact Investigation

Bart Geertspresented by: Xia Chu

contributions by: Katja Friedrich, Terry Deshler, David Kristovich, Joshua Wurman, Larry Oolman, Samuel Haimov, Qun Miao, Dan

Breed, Roy Rasmussen, Lulin Xue, Binod Pokharel, Yang Yang, Bruce BoeAMS Planned and Inadvertent Weather Modification Conference, 9 Jan 2013

University of WyomingNCARUniversity of ColoradoUniversity of Illinois Ningbo University

funded by NSF AGS-1058426

ASCII’s core goal

to gain insight into how glaciogenic seeding alters cloud microphysical processes in orographic clouds, using

– new instruments both airborne and ground-based– LES modeling with resolved microphysics

2012 target

ASCII target mountains

2008, 09, 13 target

Sierra Madre

Medicine Bow RangeASCII seeding source: the 2007-14 Wyoming Weather Mod

Pilot Project, a dual-mountain randomized project, evaluated by NCAR (Rasmussen, Breed)

ASCII 2012experimental design

Battle Pass (elevation 3000 m)

dual-polarization x-band Doppler radar (DOW7)

Battle Pass instruments

Battle Pass instruments

snow size distribution (>1 mm)

and terminal velocity

profiles of reflectivityand hydrometeor vertical velocity

water vapor, temp profile, liquid water

path

passive microwaveradiometer

MRR profiling Ka-band radarParsivel disdrometer

Yankee Hotplate

snow rate

Vaisalawxt520

(T, p, q, wind)

snow photography, sampling for

chemical analysis

ceilometer

mountain mountain mountainvalley

Battle Pass instruments

SPEC Cloud Particle Imager

imaging of particles >20 micron

Battle Pass instruments

UW King Air remote sensors

Wyoming Cloud Lidar

• WCR (3 mm, W-band)– three antennas– pulse width 250 ns, sampled at 15 m– max range 6 km– minimum detectable signal (@ 1 km): ~-30

dBZ– reflectivity is dominated by ice crystals

• WCL:– down-looking only– backscatter power– depolarization ratio

Wyoming Cloud Radar

Medicine Bow Rangenon-simultaneous comparison

NOSEED, then SEEDidentical flight pattern

legs 2

-5: tr

eated

legs

NO SEEDING SEEDING54321 54321 54321 54321

2009 02 18 flight sequence

AgI generatorson the ground

leg 1:

contr

ol leg

2009 02 18 1726 UTCMedicine Bow Range

Wyomingcloud base temperature -9°Ccloud top temperature -26°Cmuch liquid water in cloud

Battle PassBridger Peak

2012 02 21 2010 UTCSierra Madre

cloud base temp -8.4°Cmuch liquid water in cloud

(LWP ~0.22 mm)

case study: 18 Feb 2009

pass 1NOSEED

pass 2NOSEED

leg 4 reflectivity (dBZ)

40 kmairflow into the

page

Med Bow Range

black line = radar blind zone (flight level)

40 km

pass 3SEED

pass 4SEED

case study: 18 Feb 2009

Positive seeding effect confined to the boundary layer (~lowest 1 km)

18 Feb 2009: [seed – noseed] CFADtreated legs

seed (2 passes)noseed (2 passes)

think of blue as a positive SEED effect null hypothesis: this is natural variability

“Natural” storm intensity actually decreased during SEED period

18 Feb 2009: [seed – noseed] CFADcontrol leg

seed (2 passes)noseed (2 passes)

SEED effect: all cases, all treated legs

Sierra Madre 20129 cases

-19

-18

-17

-16

-15

-14

-13

-12

-11

-10

-9 -8 -7 -6 -5 -4 -30

1

2

3

4ASCII Sierra Madre 12Med Bow 08-09

700 mb temperature (°C)

num

ber o

f IO

Ps

(source: Bruce Boe)

Medicine Bow 2008-09 7 cases

heig

ht A

GL (k

m)

MRR2

ground-based profiling radars

ground-based profiling radarsSierra Madre 2012: 11 cases

AgI generators

control: upstream MRR treated: downstream MRR

case study: 18 Feb 2009: WRF LES (Xue)

terrain map

case study: 18 Feb 2009: WRF LES (Xue)

sounding comparison

case study: 18 Feb 2009: WRF LES (Xue)

CFAD comparison

Conclusions• Ground-based glaciogenic seeding of orographic clouds

may significantly increase reflectivity in the boundary layer, and thus snowfall on the ground.

• Profiling radar evidence is based on 3 types of comparisons:– non-simultaneous: treated flight legs (change within the BL)– nearly-simultaneous: control flight legs (upwind of generator)– simultaneous: ground-based radars

• 100 m Large Eddy Simulation over mountain range shows strong, but very shallow seeding effect.

• Net impact of AgI seeding over a season is typically much smaller, because many poor cases are included. Suitable conditions for seeding appear to be quite rare.

specific ASCII objectives

to evaluate WRF_Large Eddy Simulations with point seeding module

B. related to AgI seeding: model validation

work by Lulin Xue, Roy Rasmussen