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William R. Cotton, Ray McAnelly, and Gustavo Carrió Colorado State University PowerPoint Presentation
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William R. Cotton, Ray McAnelly, and Gustavo Carrió Colorado State University

William R. Cotton, Ray McAnelly, and Gustavo Carrió Colorado State University

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William R. Cotton, Ray McAnelly, and Gustavo Carrió Colorado State University

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  1. Numerical Simulations of Snowpack Augmentation for Drought Mitigation Studies in the Colorado Rocky Mountains William R. Cotton, Ray McAnelly, and Gustavo Carrió Colorado State University Dept. of Atmospheric Science Fort Collins, Colorado

  2. Introduction In this research we apply the CSU Regional Atmospheric Modeling System (RAMS) to simulate cloud seeding operations supported by the Denver Water Department (DWD). The DWD’s cloud seeding program is operated by Western Weather Consultants, LLC .

  3. Hydrometeors that are explicitly represented include: • cloud droplets • raindrops • pristine vapor-grown ice crystals • snow or partially rimed ice crystals • aggregates • graupel • hail

  4. Activation curve for AgI

  5. RAMS 3-km grid with Target Area Seeding generator x Snotel site + Snowcourse site Target area boundary

  6. Control and Seeding Simulations • 30 selected cases from Nov. 2003 through March 2004. • Two cases are illustrated here.

  7. 24h Precip, Control Run 3-4 Nov 2003

  8. 24h Precip, Seed Run 3-4 Nov 2003

  9. 24h Seed-Control Precip, 3-4 Nov 2003

  10. 24h Precip, Control Run 11-12 Nov 2003

  11. 24h Precip, Seed Run 11-12 Nov 2003

  12. 24h Seed-Control Precip, 11-12 Nov 2003

  13. A Detailed Look 24h into Seeding Simulation, 3-4 Nov 2003 • Horizontal mapped fields. • Vertical cross sections.

  14. Precipitation rate field shows orographic maxima along major mountain barriers in SW'ly surface flow, some embedded convective maxima, and some organization into NE-SW bands. A 2h time sequence (not included) shows series of NE-SW bands moving E'd in advance of upper-level wave.

  15. Vertically-integrated total condensate. Shows the NE-SW banding and embedded convection a little more clearly at this time. One of the convective features is over the east-central portion of the Target Area.

  16. Vertically integrated available background IFN concentration (fairly uniform, on the order of 105 per m2. The maximum along the eastern edge is due to low-elev max concentrations in upslope flow along the Front Range.)

  17. Vertically integrated activated background IFN concentration -- for a no-seed run, this is the pristine ice concentration. Shows that the natural activation is occurring primarily on the upwind flank of topo features, with some banded organization.

  18. Available seeded IFN concentration in lowest model level. The approximately 15 small maxima indicate the source grid points where active seeding generators are located.

  19. Vertically integrated available seeded IFN concentration. Shows both the generator sources and the advected downwind plume.

  20. Vertically integrated activated seeded IFN concentration (contributes to pristine ice concentration along with activated background IFN). The activated seeded plume is primarily downwind of the Target Area to the lee of the Front Range.

  21. Available background IFN concentration. The initial field is largely a function of density, and is advected and diffused. There are no sources, and the only sink is when it is activated and becomes pristine ice.

  22. Available seeded IFN concentration. Shows the generator sources in the valleys within and adjacent to the Target Area.

  23. Activated background IFN concentration. Shows that natural pristine ice forms from the activation of background IFN in two primary temperature regimes, -10 to -12 C and -19 to -22C.

  24. Activated seeded IFN concentration. Maximum at -19C. This maximum is two orders of magnitude less than the maximum activated background IFN concentration in the previous figure.

  25. Total activated IFN concentration or pristine ice concentration. Because of the relatively low contribution from activated seeded IFN, this field is very similar to the activated background IFN concentration shown previously.

  26. Total condensate mixing ratio.

  27. Cloud mixing ratio -- very little supercooled liquid water over Target Area.

  28. Rain mixing ratio -- below 0C isotherm in central Colorado valleys.

  29. Pristine ice mixing ratio -- similar to the total activated IFN concentration field shown earlier.

  30. Snow mixing ratio, a pattern very similar to the pristine ice mixing ratio. Note that very little snow reaches the surface in the Target Area.

  31. Aggregate mixing ratio -- strongly dominates the total condensate mixing ratio shown earlier.

  32. Graupel mixing ratio, exists where supercooled liquid water exists in western Colorado in earlier figure, and coexists with rain in the central Colorado valleys.

  33. Hail mixing ratio, coexists with graupel and rain in central Colorado valleys.

  34. Summary • Model precipitation biases are much greater than differences between seed and no-seed amounts. • Seed minus no-seed precipitation amounts are consistently small. • Possible sources of model precipitation biases are: • Inadequate resolution of atmospheric dynamics and terrain, especially when embedded convection is prevalent. • Meyers formula for crystal concentrations over-predicts concentrations of natural ice crystals.

  35. Summary (cont.) • The small differences between seed and no-seed precipitation could be: • Real. • A result of over-prediction of natural precipitation using the Meyers formula. • A result of over-prediction of natural precipitation using the assumed CCN concentrations. • Over-prediction biases which could be a result of inadequate dynamic representation of the system due to coarse grid spacing thereby consuming supercooled water that could have been utilized by

  36. Recommendations • Future cloud seeding operations should include measurements of background IN, CCN, and giant CCN concentrations. • Tests should be made of the effects of increased model resolution on precipitation prediction and/or a sub-grid model representing embedded convection. • New statistical techniques need to be developed that include model simulated data along with observed precipitation amounts and other observable predictors.