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6 I I I I I I I I I I I I I I I I I I II 1.5 Denver Water Operational Cloud Seeding Program Due to continuing drought conditions in the central Colorado Rocky Mountains, in the fall of 2002 Denver Water (DW) contracted with Western Weather Consultants, LLC (WWC) to expand the Vail/Beaver Creek (BC) Ski Resort Program to the east and southeast to cover the watersheds within DW's water collection system. The DW 2002-2003 Program's target area included the Blue, Fraser, Williams Fork, and Upper South Platte River basins above 9,000 feet elevation, The DW 2002-2003 Program consisted of 43 manually-operated c1oud- seeding generators (38 new generator sites plus 5 existing generator sites from the Vail/BC Program). Cloud-seeding activities were performed from early November 2002 through April 7, 2003. There were a total of 25,433 hours of seeding at an average seeding rate of 5.92 grams of silver iodide (Agl) per seeding hour. Summary information on independent evaluations of the DW 2002-2003 Program is at http://www.denverwater.orQ/couldseedinQ.html. 1.6 Colorado State University Regional Atmospheric Modeling System The Regional Atmospheric Modeling System (RAMS) was develop€id at Colorado State University (CSU). RAMS has been used at CSU for real- time forecasting since 1991 (Cotton et aI., 1994), Gaudet and Cotton (1998) showed that explicit bulk microphysics improved the forecasting of the areal extent and maximum amount of precipitation, especially when compared to the SNOTEL automatic pillow-sensor stations, which are found at locations more representative of the model topography, For the month of April 1995, a series of 24-hour accumulated precipitation forecasts was generated with both the c1ump- bucket and microphysics versions of the forecast model. Both sets of output were compared to a set of 167 community-based station reports, and to a set of 32 SNOTEL stations. Climatological station precipitation forecasts were improved on the average by correcting for the difference between a station's actual elevation and the cell-averaged topography used by the model. The model had more problems with the precise timing and geographical location of the precipitation features, probably due in part to the influence of other model physics, the failure of the model to resolve adequately wintertime convection events, and lack of mesoscale detail in the initializations, WetzHI et al. (2003) further demonstrated RAMS accuracy in predicting snowfall amounlts in high-mountain terrain, specifically the Park Range of Colorado. As in the Gaudet and Cotton (1998) study, the best agreement occurred at the higher elevation sites and the worst in the valleys, This could be related to the inability of the model to represent the valley features correctly since emphasis is placed on getting the mountain high terrain forcing in the model. In addition, RAMS exhibited a warm-temperature bias, which may be a consequence of using Eta model forecast data for initialization and nudging: the Eta model is known to have such a warm temperature bias,