The ASCII 2012 campaign:
overview and early results
AgI Seeding Cloud Impact Investigation
funded by NSF AGS-1058426
University of Wyoming
NCAR
University of Colorado
University of Illinois
Ningbo University
Bart Geerts
presented 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 Boe
AMS Planned and Inadvertent Weather Modification Conference, 9 Jan 2013
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
ASCII target mountains
2008, 09, 13 target
2012 target
ASCII seeding source: the 2007-14 Wyoming Weather Mod
Pilot Project, a dual-mountain randomized project, evaluated
by NCAR (Rasmussen, Breed)
ASCII 2012
experimental design
Battle Pass (elevation 3000 m)
Battle Pass instruments
dual-polarization x-band Doppler radar (DOW7)
Battle Pass instruments
Parsivel disdrometer
snow size distribution
(>1 mm)
and terminal velocity
passive microwave
radiometer
water vapor, temp
profile, liquid water
path
MRR profiling
Ka-band radar
profiles of reflectivity
and hydrometeor
vertical velocity
Yankee
Hotplate
snow rate
mountain
Battle Pass instruments
snow photography,
sampling for
chemical analysis
mountain
Vaisala
wxt520
ceilometer
(T, p, q, wind)
valley
mountain
Battle Pass instruments
imaging of particles >20 micron
SPEC Cloud Particle
Imager
UW King Air remote sensors
•
WCR (3 mm, W-band)
– three antennas
–
–
–
–
•
Wyoming Cloud Radar
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 Lidar
AgI generators
on the ground
non-simultaneous comparison
NOSEED, then SEED
identical flight pattern
2009 02 18 flight sequence
NO SEEDING
SEEDING
Medicine Bow
54321 Range
54321
54321
54321
2009 02 18 1726 UTC
Medicine Bow Range
Wyoming
cloud base temperature -9°C
cloud top temperature -26°C
much liquid water in cloud
2012 02 21
case study: 18 Feb 2009
2010 UTC
Sierra Madre
cloud base temp -8.4°C
much liquid water in cloud
(LWP ~0.22 mm)
Bridger Peak
Battle Pass
black line = radar blind zone (flight level)
leg 4 reflectivity (dBZ)
40 km
airflow into
the page
pass 2
NOSEED
pass 3
SEED
40 km
pass 4
SEED
case study: 18 Feb 2009
Med Bow Range
pass 1
NOSEED
18 Feb 2009: [seed – noseed] CFAD
treated legs
think of blue as a positive SEED effect
null hypothesis: this is natural variability
seed (2 passes)
noseed (2 passes)
Positive seeding effect confined to the boundary layer (~lowest 1 km)
18 Feb 2009: [seed – noseed] CFAD
control leg
seed (2 passes)
noseed (2 passes)
“Natural” storm intensity actually decreased during SEED period
SEED effect: all cases, all treated legs
Sierra Madre 2012
9 cases
ASCII Sierra Madre 12
Med Bow 08-09
height AGL (km)
number of IOPs
4
3
2
1
0
-19
-17
-15 -13 -11 -9
-7
700 mb temperature (°C)
-5
-3
(source: Bruce Boe)
Medicine Bow 2008-09
7 cases
ground-based profiling radars
MRR2
ground-based profiling radars
Sierra Madre 2012: 11 cases
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
B. related to AgI seeding: model validation
to evaluate WRF_Large Eddy Simulations with point
seeding module
work by Lulin Xue, Roy Rasmussen