Dr. Joseph H. Golden Golden Research & Consulting, Boulder, CO, USA

Transcription

1 Dr. Joseph H. Golden Golden Research & Consulting, Boulder, CO, USA

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6 Alberta Hail Problem Central Alberta: Thunderstorms 55-75% of summer (JJA) days. Hail on 50% of the days. Hail > walnut size 15% of the days. Point frequency between 2-6 days/yr. Widespread damage to crops and property Hail damage to property in 2012 was >$700 Million Hail damage to crops in 2012 was >$400 Million

7 Storm Tracks 2s

8 Physical Evaluation using Satellite Satellite observations have confirmed that seeded feeder clouds in Alberta have higher ice crystal concentrations and reduced supercooled liquid water, compared with similar non-seeded feeder clouds (see Rosenfeld figure next slide)

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10 Physical Evaluation using RADAR TITAN radar parameters such as Vertical Liquid Water (VIL), Vertical Integrated Hail Mass, and Kinetic Energy Flux have shown reduced storm intensity after seeding. See next slide for the storm on 12 August 2012 that showed a 40% reduction in VIL after seeding, but still caused >$500 Million in property damage in Calgary.

11 Storm Track & Vertical Integrated Liquid (VIL) Before Seeding After Seeding

12 Percent Physical Evaluation using RADAR Probability Plot of Hail-Vihm kg/m2 Lognormal - 90% CI SeedT B S Loc Scale N AD P <0.005 Compare Vertical Integrated Hail Mass for 12 August 2012 Storm Before Seeding (B) vs After Seeding (S) Hail-Vihm kg/m2 1000

13 ) Physical evaluation using Radar shows a reduction in storm intensity after seeding, supported by point ground observations. The hail loss ratios in the target area were less than expected for 13 of the past 17 years (except for 1996, 2009, 2010 and 2012). There has been a recent trend towards increased number of storms and severity across Alberta (and North America), which complicates evaluation efforts.

14 Results are not conclusive by statistical standards, therefore, no scientific proof and no guarantee. However, the results are within the range of positive benefitto-cost risk management standards for the private insurance companies. It takes a very small reduction in claims to justify the cost of the program. Hail is reduced, not eliminated. Therefore, the program continues to be funded.

15 Australia Snowy Hydro Project Permanent cloud-seeding gets green light A plan to boost snowfall in the New South Wales Snowy Mountains has passed through State Parliament. The Government announced last month that cloud seeding trials had proved successful and that it would seek to make the process permanent. The Member for Monaro, John Barilaro, says the trial resulted in a 14 per-cent increase in snowfall. He says the legislation will help safeguard the region's ski industry 15

16 SPERP-1 Dual-tracer Experiment SPERP is the first dual-tracer randomized experiment Patterned after the DRI Sierra Nevada experiments (Warburton et al, 1995,1996) Simultaneous release of In 2 O 3 and AgI Similar particle sizes and mass release rates Release rates monitored and controlled Ag:In ratio of 1 expected in snow via scavenging processes Ag/In ratio > 1 indicates nucleation by AgI Chai et al (1993) found mean Ag/In ratio of 5.5 in PG&E Almanor target area

17 CSG SPERP-1 target outlined in blue 13 CSGs with northernmost site noted by red arrow North Bulls Peak Expanded target includes area outlined in black Blue Calf Gauge Snow Site

18 Precipitation and Snow Sampling Site Double-fenced gauge Snow Board

19 Background Concentrations of Ag and In Site North of Primary Target with ~5% of samples having In > 1 PPT For In < 1 In +/- 2 std dev = 1 PPT For In < 1 Ag +/- 2 std dev = 4.6 PPT

20 Trace chemical response to seeding during an Australian experiment Ag is part of the ice nucleant (AgI) In is an non-ice nucleating tracer A ratio of Ag to In that exceeds one indicates ice nucleation by AgI is contributing to the snowfall

21 Overview and update on the evaluation of the WWMPP NCAR contributors: Dan Breed, Roy Rasmussen, Scott Landolt, Lulin Xue, Changhai Liu, Courtney Weeks, and others (technicians, software engineers, statisticians, ) Confidential and Proprietary. 2013, WWDC, UCAR, and WMI. All rights reserved.

22 CONCEPTUAL MODEL OF OROGRAPHIC WINTER CLOUD SEEDING The existence (and duration) of seeding conditions: AgI-effective temperatures and supercooled liquid water Generation and release of seeding material (AgI IN quantity and effectiveness) Transport of material to seedable cloud conditions such that any effects on precipitation will likely be measurable in the target area or site Initiation and growth of ice crystals into snow Measuring snowfall (and other characteristics of snow that might identify seeding effects), or streamflow

23 Evaluation Measures for the WWMPP Physical studies that examine processes important to the seeding concepts. Airborne measurements: background conditions (aerosols, thermodynamics, liquid water, ); plume transport; ASCII piggyback experiment (in situ observations) Remote sensing: radiometer; radar (including ASCII airborne w-band radar studies); ceilometer; satellite Ground-based observations: state variables; precipitation; snow chemistry; plume detection

24 Evaluation Measures for the WWMPP Numerical modeling studies. WRF RTFDDA forecast model (2-km grid): provides a medium-term detailed base for verification, precipitation, hydrological, and various seeding scenario studies. Fine-scale modeling ( m grids) for plume transport, verification of modeled/simulated seeding, physical processes Selected WRF runs for: seeding evaluation in the Winds; comparison to the randomized seeding cases; downwind effects; etc.

25 Evaluation Measures for the WWMPP Randomized experiment: quantitative assessment. Concentrated in the Medicine Bow and Sierra Madre Ranges Build a population of seeded and non-seeded cases in a cross-over design Precipitation measurements (high-resolution) are compared statistically

26 Randomized Seeding Experiment 1) Crossover design two ranges (Medicine Bows and Sierra Madres) with similar conditions for seeding (temperature, SLW, seeding generator placements). Randomly select one range for seeding; the other becomes the control (non-seeded). 2) Besides target gauge sites in each range, there are additional control gauge sites for decreasing the effects of variability in snowfall (between ranges and within ranges) 3) Seeding period: 4-hr 4) Response variable: 4-hr accumulation of liquid equivalent precipitation (beginning 30-min after seeding starts) 5) Buffer period: 4-hr

27 Rain over sea The rain is suppressed in these shallow clouds when clouds ingest the urban aerosols :03Z 1500 m 3 o C

28 Precipitation water loss at the central Sierra Nevada reach 4 x10 9 m 3 year -1. Hydrological loss? Height [km] West 1.14 East Givati A. and D. Rosenfeld, 2004: Quantifying precipitation suppression due to air Pollution. Journal of Applied meteorology 43,

29 Accepted, 9/13 Atmos.Research Extra Area Effects of Cloud Seeding - An Updated Assessment DeFelice, TP. a, Golden, J. b, Griffith, D. c, Woodley, W. d, Rosenfeld, D. e, Breed, D. f, Solak, M. c, Boe, B. g a HYS, Marriottsville, MD USA b Golden Research and Consulting, Boulder CO USA c North American Weather Consultants (NAWC), Sandy UT USA d Woodley Weather Consultants, Littleton CO USA e Hebrew University Jerusalem, Israel f National Center Atmospheric Research, Boulder, CO USA g Weather Modification Inc., Fargo ND USA For submission to Atmospheric Research 2013 Corresponding Author: Tom DeFelice, HYS, clddoc@verizon.net ABSTRACT This paper examines the commonly-held hypothesis that cloud seeding reduces precipitation in regions adjacent to seeding target areas, sometimes referred to as downwind but more correctly referred to as extra area effects ( the Robbing Peter to pay Paul hypothesis). The overall concept in the potential creation of extra area effects from seeding is illustrated with respect to the hydrologic cycle, which includes both dynamical and microphysical processes. For the first time, results were synthesized from five operational and research weather modification experiments, including winter orographic snowpack enhancement and summer experiments to enhance rainfall. One of the most surprising aspects of these results is that extra area seeding effects on precipitation appear to be uniformly positive (5-15% increases, perhaps greater for some convective systems) for both the winter and summer seeding projects examined in this paper. The spatial extent of the positive extra area seeding effects may extend to a couple hundred kilometers for winter orographic seeding projects and summer convective seeding projects (such as North Dakota, Texas, Thailand). Both microphysical and dynamical effects of seeding appear to be contributors to these extra area effects. Future work needs to incorporate larger data sets from some of the larger more sustained projects with advanced cloud models and tracer experiments.

30 DeFelice, Golden, Griffith, Woodley, Rosenfeld, Breed, Solak, Boe, 2013 Atmos Res. (on-line as of 9/5/13) Extra Area Effects of Cloud Seeding - An Updated Assessment Joe Golden, T. DeFelice, D. Griffith, W. Woodley, D. Rosenfeld, D. Breed, M. Solak, B. Boe AGENDA Introductory Note Objective Highlight(s) Closing Remarks

31 EXTRA-AREA SEEDING EFFECTS Objective Investigate the evidence that cloud seeding increases precipitation in the intended target areas and beyond

32 Highlights Hypothesis: Actual seeding activities to increase precipitation generally increase precipitation amounts in target areas and also increase them beyond the intended target areas. * Common Misconception * WMA/ASCE jointly-sponsored, international workshop (Las Vegas, NV on April 27, 2012)

33 Summary of Experimental Cases (from DeFelice Experiment name Central Southern Utah (Case 1) Central Southern Utah (Case 1) Seeding Period , excl-uding , excl-uding Type of experimental units Season (Dec.- Mar.) Season (Dec.- Mar.) Number of experimental units Random- et al., 2013) ization Method evaluation scheme 25 seasons No Historical target control, Ground based precipitation 34 seasons No Historical target control, Ground based precipitation of Indicated effect (% at specified distance downwind of Target or at time following seeding) +14 (Target-T) +12 (T+40 km) +42 (40-80 km) +19 ( km) +16 ( km) + 6 ( km) -2 ( km) +14 (Target-T) +17 (T+80 km) +21 ( km) + 7 ( km) Reference Solak et al. (2003) WMA/ ASCE 2012 Las Vegas workshop HP (Case 2) Cells (25 km radius)- Radar EA (Case 2) Cells- Radar Santa Barbara II, Phase I, ground (Case 3) Winter Seasons Convectio n bands 635 No Radar selected controls 306 No Radar selected controls 56 seed, 51 notseeded Yes Ground observations of band precipitation +82 ( 2 h) +62 ( 3 h) +77 ( 6 h) +53 (12 h) +104 ( 2 h) +84 ( 3 h) +80 ( 6 h) +59 (12 h) +50 (0-50 km) ( km) +50 ( km) Woodley & Rosenfeld (2004) Woodley & Rosenfeld (2004) Griffith et al. (2005) Santa Barbara II, Phase II Airborne (Case 3) Winter Seasons Convection bands 18 seed, 27 notseeded Yes Ground observations of band precipitation +50 (0-50 km) ( km) ( km) Griffith et al. (2005)

34 Challenges The results should be verified and strengthened by randomized seeding efforts that are conducted in conjunction with realistic high-resolution cloud modeling that can simulate cloud seeding and transport of seeding agents, tracer studies, and confirmatory physical measurements.

35 Closing Remarks: extra area seeding effects appear to be uniformly positive (5-15% increases, perhaps larger for some convective systems) for both winter and summer seeding projects. These results run counter to widely held misconceptions over the years and to previous NAS/NRC assessments (e.g., Garstang et al., 2005) No apparent regional impacts on the water balance, nor on the natural precipitation on a regional scale. So cloud seeding would not dry up the atmosphere or lead to summer drought,

36 Closing Remarks (cont): The NRC (2003) report supports these conclusions, suggesting that the question about extended area effects likely will become better defined and understood as more is learned about the global water balance and as new tools enable the cloud scientist to better understand clouds and their response to seeding. We thank Eric Wise, UND and his MS Thesis Advisor, Dr. Paul Kucera (now at NCAR), for providing a copy Eric s MS thesis. Special thanks are also extended to Drs. Jean Dessens, Claude Berthet, and the others who made workshop presentations and the excellent discussions those inspired.