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Numerical Simulations of Snowpack Augmentation for Drought Mitigation in the Colorado Rocky Mountains

Explore the research project evaluating cloud seeding in Colorado Rockies using RAMS, assess model performance and precipitation biases, and study warm temperature bias along with ESRI's mapping tools.

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Numerical Simulations of Snowpack Augmentation for Drought Mitigation in the Colorado Rocky Mountains

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  1. Numerical Simulations of Snowpack Augmentation for Drought Mitigation Studies in the Colorado Rocky Mountains San Diego, CA January 12, 2005 Colorado WDMP Project Overview Presented by: Curt Hartzell, CCM, Project Consultant Joe Busto, Colorado Water Conservation Board Ray McAnelly, Colorado State University Gustavo Carrio, Colorado state University PI for CSU Research Team: Dr. Bill Cotton

  2. INTRODUCTION The Colorado WDMP research project was joined with the Denver Water Department’s operational cloud seeding program in the central Colorado Rocky Mountains for the 2003-2004 winter season. DWD’s cloud seeding program was operated by Western Weather Consultants, LLC - Larry Hjermstad Up to 56 ground-based seeding generators were used to release silver Iodide over a mountainous target area of about 3700 sq. km. WWC seeding generator site V1 (Ellison), elevation 7,088 ft, looking SE

  3. GOAL To provide a physical evaluation of the operational cloud seeding using the well-established Colorado State University Regional Atmospheric Modeling System (RAMS), with a fine 3-km horizontal grid spacing covering the entire seeding area. RAMS 3-km grid with target area boundary, towns, seeding generator locations, Snotel sites

  4. OBJECTIVES Use RAMS to develop a better understanding of the transport and dispersion of seeding materials and to provide guidance as to what meteorological conditions are most favorable for augmenting snowfall from orographic clouds over the Colorado mountains. ESRI’s ArcView 3D Analyst was used to create a 3D model of the region immediately surrounding and including the cloud seeding target area (looking northwest)

  5. PROJECT WEBSITE CSU implemented a website for the Colorado WDMP at http://rams.atmos.colostate.edu/clseeding/ • menu • Rear-time Forecasts (no-seed model runs based on 00Z Eta data) • Networks (towns, seeding generators, snowfall observational sites) • Daily Precipitation Maps (control no-seed and after-the-fact simulated seed) • Data (Snotel & Snow Course Snow Water Equivalent 24-hr precipitation) • Evaluation & Studies (simulated seeding, particle transport, statistical) • GIS Maps (particle concentration and simulated precipitation) • Progress Reports (required quarterly technical progress reports) • Meetings & Conference Calls (Colorado WDMP related) • Conferences & Workshops (related to the Colorado WDMP) • Related Publications

  6. OBSERVATIONS Snotel Sites For detailed statistical evaluation: 12 in target area 18 non-target area Also included in some analyses: 31 other non-target area sites

  7. RAMS REAL-TIME FORECAST • Real-time no-seed model runs based on 00Z Eta initialization data were posted on CSU website. • Model output on website were used by DWD’s seeding contractor for daily cloud seeding operations. • 2-hr forecast products through the 48-hr forecast period provided guidance for the seeding operations. • Evaluation of model performance was done throughout the 2003-2004 winter operational cloud seeding period. • Simulated no-seed precipitation over-prediction bias and a low-level warm temperature bias were noted. • Model fixes were implemented in mid-February 2004. • After-the-fact control no-seed simulations were rerun for 152 days (November 2003 - March 2004).

  8. SIMULATED PRECIPITATION OVER-PREDICTION BIAS Simulated 24-hr precipitation from the daily control runs was used to establish no-seed (control) precipitation for individual events and monthly and seasonal totals. 30 operational cloud seeding days were selected from November 2003 through March 2004 for evaluation studies. Comparison with 61 Snotel observations shows that the forecast runs generally simulated the spatial distribution well, but with an over-prediction bias for precipitation amounts (factor of 1.88). • Possible sources of model precipitation biases are: • Inadequate resolution of atmospheric dynamics and terrain, especially when embedded convection is prevalent. • The Meyers formula used in the project for crystal concentrations over-predicts concentrations of natural ice crystals.

  9. SIMULATED PRECIPITATION OVER-PREDICTION BIAS Plot of 30-day total control-run precipitation extracted at 61 Snotel sites vs. the 30-day total observed precipitation.

  10. LOW-LEVEL WARM TEMPERATURE BIAS • Real-time use of RAMS output revealed a significant warm-temperature bias from the surface to above mountaintop level. • Model fixes implemented February 14, 2004 reduced but did not eliminate this warm bias. Two case studies were subsequently done to evaluate the magnitude of the warm temperature bias . The model forecast temperatures at the 700 mb level were compared to 3 sounding stations in the 12km Grid 2 (DNR, GJT, RIW) • Case 1 (040129.00) - model had about a +1.0C bias • Case 2 (040205.00) - model had about a +1.8C bias • The case studies suggest that there is still a slight warm temperature bias of +1 to +2 C in the prime seeding levels near mountain ridge tops.

  11. RAMS EXTENDED TO INCLUDE SEEDING EFFECTS Post-season RAMS seed simulations were preformed for each of the 86 days on which seeding operations were conducted during November 2003 - March 2004. Simulated sources of silver iodide (AgI) were at specified low-level model grid points in accordance with the timing and magnitude of AgI release at each seeding generator as recorded in WWC’s operational seeding logs. The AgI was treated as a second predictive IFN field with its own activation characteristics. All other aspects of the seed simulation runs were identical to the control (no-seed) simulation design.

  12. Seeding Activation Curve for AgI WWC used a 4% silver iodide (AgI) solution with sodium iodide (NaI) as a carrier in acetone along with about 1% moth balls to improve nuclei activation between -2.5C and -8.0C

  13. COMPARISON OF SIMULATED NO-SEED & SEED PRECIPITATION The differences in 24-hr precip between the seed and no-seed control simulations are within 1% averaged over the target area. The daily difference patterns are organized in bands, resembling longitudinal rolls that extend upwind, downwind, and laterally of the seeding target area. Dr. Cotton commented that these roll patterns suggest a possible weak dynamic response to seeding. Difference in total precip (Seed-Control) for the 30 selected days. Seeding generator sites are plotted.

  14. COMPARISON OF SIMULATED NO-SEED & SEED PRECIPITATION • The small differences between RAMS seed and no-seed simulated precipitation could be because: • The background CCN and IFN concentrations are unknown; therefore, the results are at the mercy of specified background concentrations. • The model under-predicts supercooled liquid water content in the lower portion of clouds over the target area, thereby reducing seedability. • An unforeseen dynamic response that appears to result in large areas of slightly suppressed precipitation in the target area and small regions of slightly enhanced precipitation. • The low-level warm temperature bias results in delayed AgI nuclei activation, fewer activated nuclei, and less time for crystals to grow and snow to fall in the target area. • The transport and diffusion of seeding material from generator sites is getting into the clouds too far downwind of the generator sites.

  15. LAGRANGIAN PARTICLE DISEPSION ANALYSIS The Lagrangian particle dispersion analysis for the Colorado WDMP project has not been completed. The 30 seeding days selected for evaluation span the full range of meteorological regimes that are conducive to snowfall in the project target area. Six of the 30 cases are being analyzed in detail to determine if and how efficiently the seeding material is getting into the simulated clouds under different wind and stability regimes. Example of a seeding simulation - available 20-hr AgI concentration in lowest model layer.

  16. MULTIPLE REGRESSION BLOCK PERMUTATION ANALYSIS The MRBP analysis code was provided by Dr. Paul Mielke, and modified by Gustavo Carrió at CSU for specific use on the Colorado WDMP project. This code computes the test statistic and associated P-value of a randomized block experiment. Model skill for predicting precipitation was evaluated for the 30 selected days using MRBP statistics. Analyses were conducted for observed daily precipitation at 30 Snotel sites vs. 24-hr control run precipitation extracted from those locations, and for observed vs. seed run simulated precipitation at the same. Mielke’s comments on the results of the MRBP analysis are that the model is describing the no-seed and seed simulations equally well. While the signal of the fits is strong (all P-values about 1.0E-6 or less), the agreement measures are not outstanding (all fall between 0.18 and 0.26).

  17. SUMMARY The Colorado WDMP project applied the CSU RAMS to simulate cloud seeding operations supported by the DWD. Daily RAMS forecast simulations initialized with 00Z Eta data were posted on a CSU website developed for the research project. RAMS was extended to include seeding effects; except for the added AgI release, the seed and control simulations were identical. Model precipitation biases are much greater than differences between seed and no-seed amounts. Model low-level warm temperature biases were estimated to be about +1 to +2 C near mountain ridge tops (700 mb level). 24-hr seed minus control no-seed simulated precipitation differences are consistently small.

  18. RECOMMENDATIONS FOR FUTURE RESEARCH Joined research and cloud seeding operations should include measurements of background IN, CCN, & giant CCN concentrations. RAMS as used on the operational winter orographic cloud seeding project needs the development of a sub-grid cumulus scheme. • Colorado WDMP research areas needing further study: • Defining meteorological conditions favorable for cloud seeding. • Lagrangian particle dispersion analyses. • Seed vs. no-seed simulated precipitation difference analyses. • RAMS forecast simulated precipitation over-prediction biases. • RAMS low-level warm temperature biases during cold conditions. • Add 4th grid (e.g. 1km) for seed & control runs. • Understand and correct RAMS deficiencies.

  19. The End

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