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									   Cloud Seeding for Snowfall Enhancement:
Concepts, Evidence of Effects and New Evaluation
                  Techniques


       Cloud Seeding Research Symposium
              Melbourne, Australia
                 7-9 May 2007



                   Arlen W. Huggins
                   Desert Research
                        Institute
                  Reno, Nevada, USA
   Cloud Seeding for Snowfall Enhancement:
Concepts, Evidence of Effects and New Evaluation
                  Techniques


      Review a conceptual model
      Details of the steps in the model
      Examples of research results
      Trace chemical evaluation techniques
      Needs in future research
   A Brief Review of Winter Seeding Concepts

 Seeding material must be reliably produced
 Seeding material must be successfully transported to
clouds over the intended target
    Clouds must contain supercooled liquid water
 Sufficient dispersion of seeding material
    Significant cloud volume must affected by ice nuclei, so
    Significant numbers of ice crystals can be formed
 Seeding material must reach the temperature needed
for substantial ice crystal formation
    Depends of seeding material
 Ice crystals must reside in cloud long enough for growth
and fallout over the target area
Conceptual Diagram of Orographic Cloud Seeding

            Ground-based seeding with silver iodide




    -10C

    -5C
Ice-forming Activity of Seeding Materials
Instrumentation
         Availability of supercooled liquid water
 An excess of SLW is needed at relatively cold temperatures
 Studies over many mountainous areas have shown
    SLW is present at some stage on nearly every winter storm
    SLW exhibits considerable temporal and spatial variability
    SLW is found mainly over the windward slope and can extend upwind
    Maximum SLW exists from below mountain crest to ~1km above
 SLW temperature
    Depends a lot on barrier height and geographic location
    Rocky Mountains: SLW base -2 to -10 C SLW top -10 to -15 C
    Sierra Nevada: SLW base often > 0 C SLW top -12 C or higher
 Seasonal SLW flux often 50 – 100% of seasonal snowfall
    Suggests significant cloud seeding potential
SLW over a mountain barrier
     Transport and Dispersion of Seeding Material

 Verification of T and D is Critical
    Documented in several research studies of 1970s, 1980s and 1990s
    Key element in success of randomized Bridger Range experiment
    Consistently successful T&D from high altitude generators
        Generators at least halfway up the windward slope


Methods of verification
    Aircraft or ground-based detection of tracer gases
    Aircraft or ground-based ice nucleus counters
    Dispersion models for feasibility assessments (with verification)
    Trace chemical analysis of snowfall from the target
 T and D Examples:
Measurements from a
      fixed site




  Ice Nuclei Counts
  (NCAR counter)



SLW also verified
(Microwave radiometer)
     T and D
   Examples:
 Measurements                       Aircraft
                                    Detection
  from mobile
    platforms

Wasatch Plateau
AgI seeding from
a single site

Tracer gas and
ice nuclei
measurements            Ground
                        Detection
Plume dimension
similar to results in
other areas
      Cloud Microphysical Responses to Seeding

 Verification of the initiation, growth and fallout of ice crystals
    Strong evidence from ground-based seeding experiments in Bridger
   Range (MT), Grand Mesa (CO) and Wasatch Plateau (UT)
    Significant IC enhancement (>5x background) found in seeding
   plumes
    Best evidence found in cloud regions colder than -9 C with cloud tops
   warmer than -20 C.


 Method of verification
    Aircraft or ground-based particle imaging probes
    Aircraft detection required flying within 300 m of mountain peaks
    Ground-base instruments at fixed location, or mobile
Measurements
of microphysical
  effects from
    seeding:
    Use of fixed
  instrument sites,
aircraft instruments,
and mobile ground-
  based platforms
Microphysical
seeding effect
  examples


  Wasatch Plateau
  AgI seeding from
  a single site

  Aircraft data show
  aerosol and ice
  crystal seeding
  plumes
 6 km or 16.7 min downwind
 of seeding site
Microphysical
seeding effect
  examples


Wasatch Plateau
AgI seeding from
a single site
                                 3
                             2
Aircraft data show
aerosol and ice
crystal seeding
plumes
15 km or 41.7 min downwind
of seeding site
Microphysical
seeding effect
  examples

                    3rd Peak Pass 7
2nd   Peak Pass 7
             Microphysical seeding effect examples:
Time after
 seeding
                     An aircraft case study


 10 min



 19 min



 22 min



 30 min



 39 min
              Seeding Effects in Precipitation

 Last link in the “chain” and hardest to verify
    Physical evidence from ground-based seeding experiments on the
   Grand Mesa (CO) and Wasatch Plateau (UT)
    Statistical evidence from randomized experiments in Bridger Range
   and northern Sierra Nevada – supporting physical evidence
    One randomized propane case in UT with significant results


 Methods of verification
    Ground-based particle imaging probes
    Precipitation gauges
    Radar occasionally useful
    Statistical assessments of target area precipitation
  Radar detection of
    seeding plume
from Wasatch Plateau
case that documented
aerosol and ice crystal
       plumes
Precipitation from
gauges inside and
 outside seeding
      plume
              Some of the Best Evidence
               of Precipitation Increases
 Physical evidence from case studies
    Wasatch Plateau (UT) experiments (1990s, 2004)
       Ground releases of silver iodide and liquid propane
       Precipitation rate increases of a few hundredths to > 1 mm/hour
    Grand Mesa (CO) 1990s
       Ground and aircraft releases of silver iodide
       Precipitation rates in seeded periods >> than unseeded periods
 Statistical results with supporting physical evidence
    Bridger Range randomized experiment (1970s)
       Double ratio analysis showed 15% increase in target
       Increases in target were much greater in cold storms
       Increases of 15% found within a few km of the source
    Lake Almanor randomized experiment (1960s)
       Statistically significant increase found with cold storm category
       Supported by later trace chemical evaluations
     Summary Points on Wintertime Cloud
            Seeding Research

 All the links in the chain of the conceptual model have
  been verified in physical case studies
 Ice crystal and precipitation enhancement have been
  verified through physical observations
 Precipitation enhancement has been documented by
  statistical methods in several projects where results were
  validated by physical measurements
 Research has revealed situations when cloud seeding is
  ineffective
 Research has not supplied all the answers to every
  meteorological situation where cloud seeding is applied
 Use of trace chemistry in evaluating cloud
              seeding projects
 The element silver in silver iodide has a very low background
  concentration in snowfall.
 Analyzing target area precipitation for evidence of Ag above
  background is one means of evaluating targeting effectiveness.
 In a randomized seeding project using a target and control
  design trace chemistry can be used to verify that the control
  area is unaffected by seeding.
 Can be used to address environmental concerns regarding Ag
  in snow, soil, water supplies, etc.
 Non-ice nucleating particles used in combination with AgI can
  be used to differentiate between nucleation and scavenging
  processes in target area snowfall.
 A seeding material ‘tagged’ with a trace element can be used to
  differentiate between seeding methods, like aircraft versus
  ground seeding.
Map of Ag/In Ratios (Almanor in northern Sierra Nevada)
 Ag/In ratio > 1 indicates Ag was removed by nucleation process
  Targeting Effectiveness for Project in southern Sierra Nevada




Map shows
percentages of snow
samples with Ag above
background during the
1994 season

Triangles are ground
generator sites




                        Primary Target
A new evaluation method based on snow chemistry analysis
     and high resolution precipitation measurements

• Dual tracer approach using AgI and In2O3
• Snow profile sites collocated with high resolution (~0.01 inch or less)
  recording precipitation gauges
• Trace chemical analysis defines sites with and without seeding
  effects (Ag/In ratio > expected)
• Gauge records used to define time period of seeding effect
• Profile without seeding effect used as no-seed (control) site
    – Analogous to comparing precipitation measurements inside and outside
      documented seeding plume locations
    – Trace chemistry is used to define the “plume”
• Similar time periods compared at “seeded” and “non-seeded” sites
  to compute the enhancement at the seeded site
• Technique can (potentially) be applied on a storm-by-storm basis
  and results integrated over a target area for an entire season
Targeting Effectiveness for
2005 Season of the Snowy
Precipitation Enhancement
Research Project (SPERP)



     Map shows
     percentages of snow
     samples with Ag/In ratio
     above expected value

     Squares are ground
     generator sites


             Primary Target
Targeting Effectiveness and
 Estimates of Precipitation
Increases for 2005 Season
of SPERP (based on snow
   chemistry technique)
 Map shows PRELIMINARY
 results of estimated
 precipitation increases
 (blue circles)

 Squares are ground
 generator sites
Comparison of results from
 2004 and 2005 SPERP
        Seasons


  2005 season had overall
 better targeting than 2004
  2005 precipitation
 enhancement estimates
 were higher, but data
 quality was lower
  2005 precipitation
 estimates were based on
 Ag/In vs ∆P relationship
 found in 2004
       Some Thoughts on What is Still Needed
 An evaluation of new or existing projects (which have not
  done so) to document the steps in the conceptual model
 Conduct additional randomized experiments – the number
  with significant results and supporting physical data is
  quite small particularly in the past 20 years
    Relatively small scale experiments to keep costs down
    Use accepted statistical methods to determine the magnitude of
     seeding effects – predictor variables to strengthen the analyses
     and reduce the number of experiments needed
    Support statistical studies with observations sufficient to allow
     understanding of the physical processes
 Make use of advances in modeling and remote sensing to
  further our understanding of natural and/or modified cloud
  and precipitation processes

								
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