1. The ASCII 2012 campaign: overview and early results AgI Seeding Cloud Impact Investigation 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 University of Wyoming NCAR University of Colorado University of Illinois Ningbo University funded by NSF AGS-1058426

2. 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

3. 2012 target ASCII target mountains 2008, 09, 13 target

4. Sierra Madre Medicine Bow Range ASCII seeding source: the 2007-14 Wyoming Weather Mod Pilot Project, a dual-mountain randomized project, evaluated by NCAR (Rasmussen, Breed)

5. ASCII 2012 experimental design

6. Battle Pass (elevation 3000 m)

7. dual-polarization x-band Doppler radar (DOW7) Battle Pass instruments

8. snow size distribution (>1 mm) and terminal velocity profiles of reflectivity and hydrometeor vertical velocity water vapor, temp profile, liquid water path passive microwave radiometer MRR profiling Ka-band radar Parsivel disdrometer

9. Yankee Hotplate snow rate Vaisala wxt520 (T, p, q, wind) snow photography, sampling for chemical analysis ceilometer mountain valley Battle Pass instruments

10. SPEC Cloud Particle Imager imaging of particles >20 micron Battle Pass instruments

11. 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

12. Medicine Bow Range non-simultaneous comparison NOSEED, then SEED identical flight pattern legs 2-5: treated legs NO SEEDINGSEEDING 54321 2009 02 18 flight sequence AgI generators on the ground leg 1: control leg

13. 2009 02 18 1726 UTC Medicine Bow Range Wyoming cloud base temperature -9°C cloud top temperature -26°C much liquid water in cloud

14. Battle Pass Bridger Peak 2012 02 21 2010 UTC Sierra Madre cloud base temp -8.4°C much liquid water in cloud (LWP ~0.22 mm) case study: 18 Feb 2009

15. pass 1 NOSEED pass 2 NOSEED leg 4 reflectivity (dBZ) 40 km airflow into the page Med Bow Range black line = radar blind zone (flight level) 40 km pass 3 SEED pass 4 SEED case study: 18 Feb 2009

16. Positive seeding effect confined to the boundary layer (~lowest 1 km) 18 Feb 2009: [seed – noseed] CFAD treated legs seed (2 passes) noseed (2 passes) think of blue as a positive SEED effect null hypothesis: this is natural variability

17. “Natural” storm intensity actually decreased during SEED period 18 Feb 2009: [seed – noseed] CFAD control leg seed (2 passes) noseed (2 passes)

18. SEED effect: all cases, all treated legs Sierra Madre 2012 9 cases (source: Bruce Boe) Medicine Bow 2008-09 7 cases height AGL (km)

19. MRR2 ground-based profiling radars

20. Sierra Madre 2012: 11 cases AgI generators control: upstream MRR treated: downstream MRR

21. case study: 18 Feb 2009: WRF LES (Xue) terrain map

22. case study: 18 Feb 2009: WRF LES (Xue) sounding comparison

23. case study: 18 Feb 2009: WRF LES (Xue) CFAD comparison

24. 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.

25. 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