Abstract

This paper presents an evaluation of the precipitation patterns and seedability of orographic clouds in Wyoming using SNOTEL precipitation data and a high-resolution multiyear model simulation over an 8-yr period. A key part of assessing the potential for cloud seeding is to understand the natural precipitation patterns and how often atmospheric conditions and clouds meet cloud-seeding criteria. The analysis shows that high-resolution model simulations are useful tools for studying patterns of orographic precipitation and establishing the seedability of clouds by providing information that is either missed by or not available from current observational networks. This study indicates that the ground-based seeding potential in some mountain ranges in Wyoming is limited by flow blocking and/or prevailing winds that were not normal to the barrier to produce upslope flow. Airborne seeding generally had the most potential for all of the mountain ranges that were studied.

1. Introduction

Since the seminal work on glaciogenic cloud seeding in the late 1940s (Schaefer 1946; Vonnegut 1947), a number of programs have investigated whether seeding winter orographic clouds with silver iodide (AgI) could produce additional snow. Evaluation of this hypothesis has been attempted over the last half century using randomized statistical experiments, observational studies, and numerical modeling of both natural and seeded clouds, but most programs to date have yielded inconclusive findings (National Research Council 2003; Garstang et al. 2005; Rauber et al. 2019). Yet, several operational cloud-seeding programs continue to exist in the United States, as well as internationally, aiming to mitigate water shortages and manage water resources.

In the last decade, new developments in observational and computer-modeling approaches have laid the foundation to advance the state of the science with regard to cloud seeding (Xue et al. 2013a,b; Geerts et al. 2013; Tessendorf et al. 2015a, 2019; French et al. 2018; Rasmussen et al. 2018; Rauber et al. 2019). Intriguing results from recent projects such as Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE; Tessendorf et al. 2019) have shown that, under the right conditions, cloud seeding with AgI enhances ice crystal production and local precipitation (French et al. 2018; Tessendorf et al. 2019; Friedrich et al. 2020). These results improve our understanding of the effectiveness of current cloud-seeding efforts, and they are attracting interest in the possibility that cloud seeding may be useful for locations that have not previously been considered for cloud seeding. This paper examines this possibility for five mountain ranges in Wyoming.

2. Background

After facing an extended drought in the western United States, Wyoming water users requested that the State of Wyoming investigate the potential for cloud seeding. As a result, in 2004 the Wyoming State Legislature, through the Wyoming Water Development Commission (WWDC), funded the first of a sequence of studies to examine the feasibility of cloud seeding in the state. The first feasibility study investigated the potential of cloud seeding to increase winter orographic precipitation in two regions: the Wind River Range (WRR) in west-central Wyoming and the Medicine Bow (MB) and Sierra Madre (SM) Ranges in south-central Wyoming (Fig. 1). These ranges were selected based upon previous studies that documented the presence of supercooled liquid water (SLW) in these regions (Auer and Veal 1970; Dirks 1973; Politovich and Vali 1983), as well as their importance for producing streamflow into three major river basins in the state: the Green, Wind–Bighorn, and North Platte Rivers. The results of this initial feasibility study (Weather Modification Inc. 2005) led to the state funding what would eventually be a 10-yr program to test the potential of cloud seeding in these regions. The program, known as the Wyoming Weather Modification Pilot Project (WWMPP), was initially funded in 2005 and concluded in 2014 (Breed et al. 2014; Rasmussen et al. 2018). The WWMPP included a sophisticated randomized statistical experiment along with physical measurements to assess the impact of cloud seeding [see Breed et al. (2014) for a complete project description]. It also included a detailed analysis to quantify the fraction of precipitation that fell under seedable conditions (Ritzman et al. 2015), which was used to put the results of the randomized statistical experiment into context.

Fig. 1.

Topography maps of (a) the full study domain (denoted by the area within the white dashed line) and (b)–(d) each of the regions of focus within the full domain. The Wyoming state border is illustrated as a thin black line in all panels. SNOTEL sites are labeled and denoted by magenta dots in (b)–(d).

Fig. 1.

Topography maps of (a) the full study domain (denoted by the area within the white dashed line) and (b)–(d) each of the regions of focus within the full domain. The Wyoming state border is illustrated as a thin black line in all panels. SNOTEL sites are labeled and denoted by magenta dots in (b)–(d).

After the conclusion of the WWMPP, the WWDC decided to provide funding for additional feasibility and program design studies on the potential for operational cloud-seeding programs in the following Wyoming mountain ranges: Wyoming Range (WYR), Bighorn Mountains (BH), Laramie Range, and the MB and SM Ranges (Fig. 1). The first of these new studies was a phase-II feasibility study to build upon the 2006 feasibility study (Griffith et al. 2006) utilizing new data and analysis tools that became available, in part due to cloud-seeding operations run by Idaho Power Company that had expanded into the far western reaches of Wyoming, including the Salt River Range (SRR) and parallel WYR. In 2015, three additional studies were funded, with the BH and Laramie Range studies focused on the feasibility of cloud seeding in these regions, and the MB and SM Ranges study focused on designing an operational cloud-seeding program to build upon what was learned from the WWMPP. Concurrent with the phase-II study, which focused on the WYR, the U.S. Bureau of Reclamation (USBR) funded the National Center for Atmospheric Research (NCAR) to examine the effectiveness of seeding in the WRR associated with the ongoing cloud-seeding program there. All of these studies1 included an assessment of the orographic precipitation behavior and frequency of conditions amenable for cloud seeding to estimate the potential of cloud seeding with AgI. This paper aims to estimate the potential for operational orographic cloud seeding using AgI during winter storms occurring over the SRR, WRR, BH, MB, and SM mountain ranges in Wyoming (Fig. 1).

3. Data and methods

The information needed to evaluate the potential for operational orographic cloud seeding using AgI include vertical profiles of atmospheric temperature, stability, winds, and SLW, as well as amount and frequency of surface precipitation. Observations of these variables are rare, especially in the western U.S. mountains, with the exception of Natural Resource Conservation Service (NRCS) SNOTEL gauge precipitation measurements. An alternative way to obtain this information is to utilize high-resolution model simulation output. The present study uses a NCAR-generated high-resolution model simulation (Liu et al. 2017) over an 8-yr period between 1 October 2000 and 30 September 2008 that includes all key atmospheric variables for the analysis as a surrogate for observational measurements in a similar way that Ritzman et al. (2015) did for the WWMPP.

a. High-resolution model simulation

The NCAR-generated model simulation (Liu et al. 2017) is a convection-permitting regional climate (RCM) simulation run using the Weather Research and Forecasting (WRF; Skamarock et al. 2008) Model over the contiguous United States (CONUS) domain (hereinafter referred to as the WRF-CONUS simulation; Liu et al. 2017). It was carried out as a follow-up study to a preceding high-resolution RCM study discussed in Rasmussen et al. (2011, 2014) that was shown to reasonably represent precipitation patterns in complex terrain (Ikeda et al. 2010; Liu et al. 2011). The model simulation has a horizontal grid spacing of 4 km over the CONUS and an output frequency of 3 h for three-dimensional (3D) data fields (e.g., atmospheric temperature, winds, various mixing ratios) and 1 h for two-dimensional fields (e.g., precipitation reaching the ground, near-surface air temperature). Table 1 lists the physical parameterizations used in the WRF-CONUS model simulation. The model was forced with 6-hourly European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim) data [see Liu et al. (2017) for a full description of the simulation setup].

Table 1.

WRF-CONUS model simulation physics options.

WRF-CONUS model simulation physics options.
WRF-CONUS model simulation physics options.

The precipitation and snowpack from this WRF-CONUS simulation was verified by comparison to SNOTEL data and showed that the model was able to realistically represent the observed precipitation (Liu et al. 2017). Herein, we show that the WRF-CONUS simulation is able to adequately represent the amount and spatial distribution of precipitation over the mountain ranges examined in this study using SNOTEL data, and that it can reasonably simulate the presence of liquid water by comparison with radiometer data.

b. SNOTEL data

SNOTEL observations provide a long-term record of precipitation data from gauges that weigh precipitation and snow water content via pressure-sensing snow pillows located at numerous sites throughout the western United States. These sites are owned and operated by the U.S. Department of Agriculture NRCS and are typically located at elevations between 2400 and 3600 m above mean sea level (MSL). Historical and real-time data are available from the NRCS website (http://www.wcc.nrcs.usda.gov/snow/). There are known measurement deficiencies of these gauges (e.g., Serreze et al. 1999, 2001; Johnson and Marks 2004) such as an undercatch of snowfall in the weighing gauges due to wind (Serreze et al. 2001; Yang et al. 1998; Rasmussen et al. 2012). The SNOTEL gauges are often located in a forest clearing where the wind speed is typically less than 2 m s−1, and an undercatch of approximately 10%–15% is expected (Yang et al. 1998). The SNOTEL data resolution is 0.1 in. (2.5 mm), making it difficult to study precipitation characteristics or verify model data on a subdaily basis. However, these data are suitable for use over monthly or longer periods. To evaluate the ability of the model simulation to adequately represent precipitation, as well as to understand the orographic precipitation behavior, SNOTEL precipitation gauge data were analyzed over three regions of the state. Region 1 contains the SRR, WYR, and WRR, Region 2 includes the BH, and Region 3 includes the MB and SM ranges (Fig. 1). To compare the observations with the model, precipitation accumulation from the model was matched to SNOTEL data by taking the inverse-distance weighted average of the four model data points closest to each SNOTEL site. These results show that the model represents the observed precipitation patterns and amounts well (see section 4 for more details).

c. Radiometer data and model comparison

Beginning in 2008, a two-channel microwave radiometer2 collected vertically integrated liquid water path (LWP) data at the Cedar Creek site west of the Medicine Bow Range (Fig. 2c) for the WWMPP (Breed et al. 2014; Ritzman et al. 2015). These data were compared with the LWP from the model for a 2-month period of overlap between the period of this study and the radiometer dataset. During this period, the observed LWP was entirely supercooled (given a surface temperature < 0°C) 84% of the time. This comparison shows that the model reasonably simulates the presence of supercooled liquid water (Fig. 3). In particular, the timing between the model and observational data is reasonable (Fig. 3b). The model tends to underpredict small amounts of observed liquid water and overpredict larger values relative to radiometer observations. The model captures the occurrences of observed LWP, but in some storms the model can overpredict or underpredict the amount of LWP. On average, the model predicted LWP to within 0.01 mm (standard deviation of 0.08 mm). Since the primary use of the model for this study is to determine whether or not liquid water was present, with less focus on the amount of SLW, we assume (as did Ritzman et al. 2015) that the model is able to adequately detect the presence of liquid water for the purposes of this study.

Fig. 2.

Topography maps defining the assessment areas studied in each region. The stippling indicates model grid points used in area-based analysis, and labels for each area are noted, with acronyms spelled out in the lower right legend. Sites used to analyze winds are indicated by black dots, and snow gauge sites used for the analyses are indicated by yellow dots. Note that all snow gauge sites are SNOTEL sites, with the exception that the snow gauge sites used for Region 3 are precipitation gauge sites from the WWMPP. Note that beginning in 2008 the Cedar Creek site (in Region 3) had a radiometer.

Fig. 2.

Topography maps defining the assessment areas studied in each region. The stippling indicates model grid points used in area-based analysis, and labels for each area are noted, with acronyms spelled out in the lower right legend. Sites used to analyze winds are indicated by black dots, and snow gauge sites used for the analyses are indicated by yellow dots. Note that all snow gauge sites are SNOTEL sites, with the exception that the snow gauge sites used for Region 3 are precipitation gauge sites from the WWMPP. Note that beginning in 2008 the Cedar Creek site (in Region 3) had a radiometer.

Fig. 3.

A comparison of observed liquid water path (red) vs that from the CONUS simulation (blue) (a) as a log-scale histogram showing LWP (mm) during all times in the 31 Jan–1 Apr 2008 period when radiometer data were available at the Cedar Creek site, and (b) as a time series of LWP during March 2008 showing the model and radiometer observations on the left axis and the difference between the two (green) on the right axis. Regular radiometer observations were only available during the 2000–08 period of this study at the Cedar Creek site between January and April 2008 as part of the first year of the WWMPP.

Fig. 3.

A comparison of observed liquid water path (red) vs that from the CONUS simulation (blue) (a) as a log-scale histogram showing LWP (mm) during all times in the 31 Jan–1 Apr 2008 period when radiometer data were available at the Cedar Creek site, and (b) as a time series of LWP during March 2008 showing the model and radiometer observations on the left axis and the difference between the two (green) on the right axis. Regular radiometer observations were only available during the 2000–08 period of this study at the Cedar Creek site between January and April 2008 as part of the first year of the WWMPP.

d. Cloud-seeding criteria

The hypothesized impacts of precipitation enhancement via cloud seeding with AgI requires certain atmospheric conditions to be present. The most basic requirements are that (i) the temperature in the cloud is amenable to the activation of AgI to form ice crystals; and (ii) that SLW is present to allow the ice crystals to grow rapidly by riming, diffusion, and aggregation. In addition to these basic requirements, another necessary condition is that the AgI can be dispersed into the cloud that meets these two basic criteria. This latter condition is location dependent and specific criteria should be set based upon characteristics of the mountain being targeted.

1) Basic seeding criteria

In many cases, natural orographic clouds have low ice crystal concentrations (<1 L−1) at temperatures warmer than 14°C (DeMott et al. 2010) unless secondary ice processes are active. However, AgI has been shown to activate at temperatures as warm as −6°C (DeMott 1995; DeMott 1997), with up to two orders of magnitude more effective activation by −8°C (DeMott 1997). As a result, the additional ice created by seeding a cloud with AgI has the chance to grow and deplete existing supercooled liquid water by diffusion and riming. The WWMPP research program required a 700-hPa temperature of −8°C or colder before performing any seeding experiments (Breed et al. 2014), based on the increased rate of AgI activation shown to occur at these temperatures (DeMott 1997). An analysis of how often seeding criteria were met for the WWMPP, conducted by Ritzman et al. (2015), utilized the same criteria as defined for the research program.

In contrast, many operational orographic cloud-seeding programs utilize a temperature threshold warmer than that used in the WWMPP research program in order to optimize the frequency of seeding opportunities. To ensure adequate AgI activation efficiency in this analysis, while being consistent with typical operational cloud-seeding program practices, the temperature threshold for this study was set at −6°C. Given that natural ice nucleation may become quite active at colder temperatures, the temperature criterion to determine a seedable cloud was limited to clouds warmer than −18°C.

In addition to a proper activation temperature for AgI, liquid water needs to be present for cloud seeding with AgI to work. The presence of SLW is a sign that natural precipitation processes are inefficient, and if additional ice crystals are nucleated (via AgI activation) they could grow at the expense of the SLW and fall out on the ground as additional snow. The criteria utilized in this study defined seedable LWC as greater than 0.01 g kg−1 within the temperature range from −6° to −18°C. In summary, both proper temperature and LWC criteria need to be met to determine seeding potential (Table 2). The WRF-CONUS model output utilized for this analysis were temperature and LWC mixing ratio (defined as cloud water mixing ratio).

Table 2.

Summary of the seeding criteria utilized in this study. The primary criteria represent the most basic conditions required for either ground-based or airborne seeding. The additional criteria apply only to ground-based seeding and are meant to ensure that AgI from ground-based generators could be transported into the cloud.

Summary of the seeding criteria utilized in this study. The primary criteria represent the most basic conditions required for either ground-based or airborne seeding. The additional criteria apply only to ground-based seeding and are meant to ensure that AgI from ground-based generators could be transported into the cloud.
Summary of the seeding criteria utilized in this study. The primary criteria represent the most basic conditions required for either ground-based or airborne seeding. The additional criteria apply only to ground-based seeding and are meant to ensure that AgI from ground-based generators could be transported into the cloud.

2) Dispersion criteria

Seeding with an aircraft allows the AgI to be released directly into the cloud and the flight track adjusted based upon the wind direction; therefore, only the basic seeding criteria need to be met to determine the seeding potential by aircraft seeding. However, for ground-based seeding, additional variables, such as atmospheric stability and winds, play a role in determining seeding potential because they impact how effectively AgI can be transported into the seedable cloud from ground-based generators. In addition, assessing wind direction is important for determining locations to site ground-based generators. Herein, we investigate common wind regimes to determine wind directions for use in the ground-seeding dispersion criteria as well as we assess the likelihood for seeding material released from a ground generator to flow into clouds over the mountain using the Froude number as an indicator. The additional criteria based upon wind direction and stability for assessing ground-based seeding are listed in Table 2.

The Froude number (Fr) expresses the ability of airflow to go over a mountain barrier (Smolarkiewicz et al. 1988; Rasmussen et al. 1989). The flow is mostly blocked by the barrier when Fr < 0.5. while the airflow will freely move over the barrier (unblocked) when Fr > 1 (Smolarkiewicz and Rotunno 1989). The Froude number used in this study is defined as

 
Fr=U/hN,

where U is the average wind speed (m s−1) perpendicular to the mountain barrier orientation over a depth of h (m), and N is an average of the Brunt–Väisälä frequency between the same depths following

 
N=(gθθz)1/2,

where g is the gravitational acceleration (m s−1) and θ is the ambient potential temperature (K).

To calculate Fr, the wind speed component perpendicular to each mountain range at each grid point lower than the peak of the range was used. The height h was calculated as the difference between the peak height of a range and the local height at each grid point. The local Brunt–Väisälä frequency N was then used to calculate the local Fr. Using this method, a 3D field of local Fr was generated.

For this analysis, Fr was used as a criterion to determine the potential for ground-based seeding material to be entrained into the clouds over the mountain barrier. Assuming that flow is completely blocked when Fr < 0.5, a minimum requirement for ground-based seeding would be that Fr > 0.5. Since flow may still be partially blocked for Fr between 0.5 to 1, we also investigated situations when Fr > 1 (Table 2).

4. Precipitation analysis

The 8-yr average SNOTEL measurements shown in Fig. 4 for each region indicate that both Region 1 (SRR, WYR, and WRR) and Region 2 (BH) had similar annual precipitation accumulation (615–630 mm), while the most annual precipitation accumulation (~ 850 mm) was observed in Region 3 (SM, MB). These data also indicate that wintertime (November–April) precipitation typically accounts for roughly 60% or more of the annual precipitation observed at SNOTEL gauge sites (Fig. 4). The exception to this is the BH in Region 2, where only 46% of the average annual (~615 mm) precipitation fell at SNOTEL sites during the wintertime months. The red curve in each plot shows that the WRF-CONUS simulation represents the average annual precipitation reasonably well in all regions, with the wintertime precipitation accumulation within the observed annual range. The interannual model variability (as represented by the error bars) was well within the observed interannual variability for all ranges in the winter.

Fig. 4.

Eight-year average precipitation accumulation (mm) over the course of the water year averaged across multiple SNOTEL sites in each region from the WRF-CONUS simulation (red) and SNOTEL gauge observations (black). Error bars indicate ±1 standard deviation from the 8-yr mean, which represents the year-to-year variation. The wintertime period is shaded in gray. The number of SNOTEL sites included in the calculation is listed in the inset table for each region, along with the total observed annual and wintertime precipitation accumulation and model bias.

Fig. 4.

Eight-year average precipitation accumulation (mm) over the course of the water year averaged across multiple SNOTEL sites in each region from the WRF-CONUS simulation (red) and SNOTEL gauge observations (black). Error bars indicate ±1 standard deviation from the 8-yr mean, which represents the year-to-year variation. The wintertime period is shaded in gray. The number of SNOTEL sites included in the calculation is listed in the inset table for each region, along with the total observed annual and wintertime precipitation accumulation and model bias.

The model simulation performance by individual SNOTEL site in each of these regions shows some scatter about, but generally remained close to, the 1:1 line (Fig. 5). In Region 1, however, there was one outlier site in the SRR that had a systematic low bias in the model for all eight years studied (Fig. 6), which led to a deviation from the 1:1 line in Fig. 5. This site, Willow Creek, was evaluated closely for instrumentation/site maintenance issues, but none were found. Based on discussions with the NRCS and local area water managers, the site is known for having higher snowfall than neighboring areas. We speculate that the site experiences large precipitation amounts due to impacts of the local terrain that are not resolved by the 4-km grid spacing in the model. Aside from that one notable outlier in Region 1, the comparison between model and SNOTEL is very good for nearly all of the SNOTEL locations.

Fig. 5.

Scatterplots of simulated vs observed annual (blue dots) and wintertime (red dots) precipitation accumulation (mm) for each year and at individual SNOTEL sites in each region. The 8-yr average annual (orange triangles) and wintertime (cyan triangles) simulated-vs-observed precipitation accumulation is also shown for individual SNOTEL sites in each region.

Fig. 5.

Scatterplots of simulated vs observed annual (blue dots) and wintertime (red dots) precipitation accumulation (mm) for each year and at individual SNOTEL sites in each region. The 8-yr average annual (orange triangles) and wintertime (cyan triangles) simulated-vs-observed precipitation accumulation is also shown for individual SNOTEL sites in each region.

Fig. 6.

Eight-year average (a), (b) annual and (c), (d) wintertime (November–April) simulated precipitation accumulation (mm) from the (left) WRF-CONUS simulation and (right) observed precipitation accumulation from SNOTEL gauges for Region 1. The black open circles in (a) and (c) represent the SNOTEL sites shown in (b) and (d).

Fig. 6.

Eight-year average (a), (b) annual and (c), (d) wintertime (November–April) simulated precipitation accumulation (mm) from the (left) WRF-CONUS simulation and (right) observed precipitation accumulation from SNOTEL gauges for Region 1. The black open circles in (a) and (c) represent the SNOTEL sites shown in (b) and (d).

In examining the spatial distribution of observed and simulated precipitation in Region 1, it is seen that the precipitation from both the model and SNOTEL increases with increasing elevation, with maximum precipitation at the highest elevation (Fig. 6). This is a commonly observed pattern in mountainous areas (e.g., Henry 1919; Spreen 1947; Daly et al. 1994; Rasmussen et al. 2011). The SRR exhibits more precipitation than the adjacent and parallel WYR, yet the WRR receives the most precipitation overall according to the model simulation. In the WRR, the model simulated some locations with up to 1300 mm of average annual accumulation. However, the WRR is lacking SNOTEL gauge sites in the highest elevation areas where the model simulated the greatest precipitation accumulation, both in winter and on the annual basis. This does not allow for the model-simulated values to be confirmed by these observations, yet it also means that estimates of precipitation in the WRR based upon SNOTEL data alone may be underestimated.

In Region 2, the model-simulated precipitation at each site compares fairly well to that at the individual sites (Fig. 7), as indicated also in (Fig. 5). However, in the BH the location of the maximum precipitation accumulation is not restricted to the highest-elevation terrain, as might be expected and observed in Region 1.

Fig. 7.

As in Fig. 6, but for Region 2.

Fig. 7.

As in Fig. 6, but for Region 2.

There is a simulated maximum around the highest elevations in the Cloud Peak area with roughly 1000 mm of average annual accumulation. The SNOTEL data also show a local maximum in this area, from the Cloud Peak gauge site (albeit the gauge indicated only ~800 mm). Yet, there is a second maximum in the northern end of the mountains in the model-simulated precipitation. Several of the northernmost SNOTEL sites (Bone Springs, Sucker Creek, and Bald Mountain) indicate generally higher annual precipitation, of similar amounts as observed at Cloud Peak, however there are no SNOTEL sites north of Bald Mountain to corroborate the greatest precipitation values shown in the model simulation. Also, it should be reiterated that the majority of precipitation in this region falls outside of the wintertime months, as illustrated by the low values of precipitation for both the model and SNOTEL in the wintertime panel of Fig. 7.

The spatial distribution of the model-simulated precipitation again mimics the topography in Region 3, with the greatest precipitation falling at the highest elevations (Fig. 8). Unlike other regions, SNOTEL gauge sites are available in these higher elevation areas to confirm the model-simulated maximum precipitation accumulation values of 1100–1300 mm. These maps indicate that the SM Range receives more annual and wintertime precipitation than the MB.

Fig. 8.

As in Fig. 6, but for Region 3. Black x symbols in (b) and (d) represent SNOTEL sites that did not have continuous data during the 8-yr period.

Fig. 8.

As in Fig. 6, but for Region 3. Black x symbols in (b) and (d) represent SNOTEL sites that did not have continuous data during the 8-yr period.

5. Analysis of seeding potential

The frequency of instances when seeding conditions occurred over the target areas were determined by analyzing the defined criteria needed for clouds to be seedable (recall section 3d). The analysis focused on two layers of the atmosphere: 1) the layer 0–1 km above ground level (AGL) was analyzed for ground-based seeding by averaging each criterion over that layer for each model grid point (4-km spacing) and output time (3 hourly), and 2) a 1-km-thick layer (Table 3) was analyzed for airborne seeding by averaging each criterion over that vertical layer as was done for the ground-seeding layer. It should be noted that for an aircraft to realistically fly in cloud near a mountain range, flight level limitations need to be considered (i.e., minimum safe altitudes). Given that the WRR and BH are the highest elevation mountains in this study, airborne seeding in clouds near them require flying at higher altitudes than other nearby mountain ranges. Therefore, the airborne-seeding layer was tailored to each mountain range assessed based upon instrument flight rules (IFR) Enroute Aeronautical Charts (https://www.faa.gov/air_traffic/flight_info/aeronav/digital_products/ifr/; see Table 3). These will be referred to as “realistic airborne seeding layers.” However, for the purpose of some of the analysis requiring a common level be examined (i.e., mapping), the 3–4 km MSL layer was used.

Table 3.

Summary of area-specific criteria used in this analysis, including the wind direction criteria for the ground-seeding layers and the vertical layer analyzed for airborne seeding in each area’s western slope. Wind direction ranges were selected to capture realistic upslope conditions that could carry seeding material into clouds over the mountain barrier. This was determined based upon the orientation and shape of each mountain barrier, as well as being informed by the wind regime analysis.

Summary of area-specific criteria used in this analysis, including the wind direction criteria for the ground-seeding layers and the vertical layer analyzed for airborne seeding in each area’s western slope. Wind direction ranges were selected to capture realistic upslope conditions that could carry seeding material into clouds over the mountain barrier. This was determined based upon the orientation and shape of each mountain barrier, as well as being informed by the wind regime analysis.
Summary of area-specific criteria used in this analysis, including the wind direction criteria for the ground-seeding layers and the vertical layer analyzed for airborne seeding in each area’s western slope. Wind direction ranges were selected to capture realistic upslope conditions that could carry seeding material into clouds over the mountain barrier. This was determined based upon the orientation and shape of each mountain barrier, as well as being informed by the wind regime analysis.

Using the 8-yr average model simulation output, the spatial distribution of where basic seeding criteria are most frequently met shows that the mountainous regions of northwestern Wyoming, including the Absaroka Range, WRR, SRR, and WYR, as well as the Park Range in north-central Colorado (south of Region 3), all meet the basic seeding criteria greater than 30% of the time in the wintertime months (Fig. 9). The MB and SM Ranges (Region 3), the Uinta Mountains in Utah, and the very highest elevations of the BH (Region 2) also meet these conditions over 20% of the time. The locations with the highest frequency of supercooled LWC at temperatures meeting the seeding criteria occur at the highest elevations.

Fig. 9.

Maps (covering the domain of study) of the frequency of time within the wintertime months of November–April that, on 8-yr average between 2000 and 2008, temperature and SLW cloud-seeding criteria are met in the (a) ground-seeding layer (0–1 km AGL) and (b) airborne-seeding layer (3–4 km MSL), based upon the WRF-CONUS simulation.

Fig. 9.

Maps (covering the domain of study) of the frequency of time within the wintertime months of November–April that, on 8-yr average between 2000 and 2008, temperature and SLW cloud-seeding criteria are met in the (a) ground-seeding layer (0–1 km AGL) and (b) airborne-seeding layer (3–4 km MSL), based upon the WRF-CONUS simulation.

a. Wind regimes

Understanding wind regimes is important to determining which side of the mountain should be targeted for seeding, especially for placing ground-based generators. The 700-hPa wind direction corresponds to the near or just below crest-level height of these mountain ranges and was analyzed when the temperature and SLW seeding criteria were met using output from the WRF-CONUS simulation at individually selected grid points surrounding the mountain ranges in each region (Fig. 2). To normalize the results by when precipitation occurred, a “representative SNOTEL site” for the given wind site was selected (Fig. 2). Part of this selection required the model precipitation at the SNOTEL site to have a reasonable comparison with the SNOTEL data (based upon the SNOTEL model evaluation performed as part of the precipitation analysis presented in section 4), which resulted in some site pairings having closer proximity than others. For each representative SNOTEL site selected, the model grid point nearest that SNOTEL site was used to determine the amount of precipitation that occurred.

For Region 1, the 700-hPa wind direction from the model simulation was examined to the west and east of each major mountain barrier when precipitation occurred over the mountains (Fig. 10). For the SRR/WYR, the 700-hPa wind direction during precipitating events is predominantly westerly and west-northwesterly. While less frequent, southwesterly winds also bring large amounts of precipitation to the region. There is a lack of any easterly component in the 700-hPa winds during the time period when precipitation impacted these mountain ranges.

Fig. 10.

Wind roses showing simulated 700-hPa wind direction at sites around Region 1 (see Fig. 2) when precipitation was simulated at nearby representative SNOTEL sites in the WRF-CONUS model simulation during wintertime months over the 8-yr period of study. The amount of precipitation (mm) is color coded according to the legend.

Fig. 10.

Wind roses showing simulated 700-hPa wind direction at sites around Region 1 (see Fig. 2) when precipitation was simulated at nearby representative SNOTEL sites in the WRF-CONUS model simulation during wintertime months over the 8-yr period of study. The amount of precipitation (mm) is color coded according to the legend.

For the WRR, the wind direction on the western slope of the barrier (i.e., Pinedale) is predominantly west-northwesterly when precipitation occurred on the west slope. However, given the orientation of the WRR, southwesterly flow provides a stronger upslope flow, and occurs in conjunction with large precipitation amounts. In contrast, while there are some north-northwesterly winds during precipitation events, these typically contribute very little precipitation.

At Lander, on the east side of the WRR, there is more variability in the wind direction during precipitating events. Due westerly and some southwesterly winds bring precipitation to the eastern slope, but this is likely spillover precipitation from those events. There is also a north and north-northeasterly wind component that occurs less frequently but contributes to large precipitation amounts. Because of the orientation of this mountain barrier, this leads to upslope conditions that brings precipitation to the eastern slope of the barrier, in contrast to negligible upslope conditions for the east slope of the WYR.

Interestingly, in Region 2 (Bighorn Mountains) the predominant 700-hPa wind direction on all sides of the mountain barrier when precipitation occurs in the mountains was consistently from the northwest (Fig. 11). This was especially true at Sheridan when precipitation was simulated at the Sucker Creek SNOTEL site in the northeastern area of the mountains. Northerly and northeasterly winds do bring precipitation to some areas of the BH, especially in the north and eastern sites (i.e., 20 Mile Creek, Sheridan, and especially Kaycee); however, these occur at such low frequencies that it is barely perceptible in Fig. 11. Nonetheless, similar to the WRR, these northerly and northeasterly events, when they occur, tend to produce a large amount of precipitation.

Fig. 11.

As in Fig. 10, for Region 2.

Fig. 11.

As in Fig. 10, for Region 2.

Precipitation in the SM Range in Region 3 occurs nearly equally under southwesterly through northwesterly wind flow, with no particular dominant direction (Fig. 12). In contrast, precipitation in the MB Range occurs predominantly under westerly winds, with southwesterly being only slightly less frequent. North and northeasterly winds bring precipitation to these mountain range very infrequently, even on the eastern slopes of these barriers (i.e., Riverside and Centennial).

Fig. 12.

As in Fig. 10, for Region 3.

Fig. 12.

As in Fig. 10, for Region 3.

b. Frequency of seeding conditions

Within each of the three regions of study, an analysis was performed to assess the frequency of seeding conditions on each slope of the mountain barrier. Target areas were defined for investigation (cross-hatched areas shown in Fig. 2) and the area-averaged values over each target area for each seeding criterion (for either ground-based- or airborne-seeding layers, see Tables 2 and 3) were produced at every model output time (3-hourly). Then, the frequency of time (over a given month or winter season) that the area-averaged conditions met the thresholds defined in Table 2 was determined. These frequencies were also normalized by when precipitation occurred, which was determined using model-simulated precipitation from a grid point nearest the “representative SNOTEL site” (that had a good comparison between model and SNOTEL observations) within the given area.

Here, only the western slope regions are presented, since the frequency of easterly upslope conditions is much less frequent, if not negligible, in all study regions as shown by the wind regime analysis (section 5a). However, it should be noted that in the regions where easterly upslope conditions are not negligible, such as the WRR and BH, operational cloud seeding to target these conditions could be pursued.

In the west slope areas in all ranges, basic seeding criteria (temperature and LWC) are met 15%–30% of the time in the ground-seeding layer (0–1 km AGL) for any given month between November–March (Fig. 13a), with the most frequent opportunities between December–February. The exception to this is the BH, which exhibited lower frequencies in every month compared to the other regions and had its peak monthly frequencies (of near 20%) occur later in winter during February and March (Fig. 13a).

Fig. 13.

Bar charts illustrating the frequency of time that ground-based cloud-seeding criteria were met, on 8-yr average, by wintertime month based upon the WRF-CONUS model simulation in the five western regions of each mountain range (see Fig. 2). (a) The frequency that the primary seeding criteria (temperature and SLW only) were met; (b) all ground-based seeding criteria (wind direction and Fr > 0.5) are included; (c) all ground-based seeding criteria (wind direction and Fr > 1.0) are included. The shaded portion of the bars indicate the fraction of time that conditions were met and precipitation did not occur.

Fig. 13.

Bar charts illustrating the frequency of time that ground-based cloud-seeding criteria were met, on 8-yr average, by wintertime month based upon the WRF-CONUS model simulation in the five western regions of each mountain range (see Fig. 2). (a) The frequency that the primary seeding criteria (temperature and SLW only) were met; (b) all ground-based seeding criteria (wind direction and Fr > 0.5) are included; (c) all ground-based seeding criteria (wind direction and Fr > 1.0) are included. The shaded portion of the bars indicate the fraction of time that conditions were met and precipitation did not occur.

When dispersion criteria for ground-based seeding are considered (wind direction, which varies depending on the orientation of the mountain barrier as listed in Table 3, and Fr > 0.5 or > 1.0), these monthly frequencies are reduced (Figs. 13b,c). In the WRR and BH, the monthly frequency of occurrence was reduced substantially. For example, the peak frequency in the month of December for the WRR was reduced from near 30% to less than 20%, and a similar reduction occurred in the other months. For the BH, the peak frequency in the month of February was reduced from just over 20% to 10% using Fr > 0.5 and to less than 5% using Fr > 1.0 (Fig. 13). In fact, the monthly frequency for ground-based seeding in the BH is less than 10% of any given wintertime month using a Fr > 0.5 threshold, and well less than 5% in any wintertime month using a Fr > 1.0 threshold. The ground-seeding potential in these two mountain ranges is reduced due to frequent blocking situations that would prevent the ground-released AgI to be transported over the mountain barrier, which was especially apparent in the BH (Fig. 14), and due to the frequency of wind conditions that are not perpendicular to the mountain barrier (Figs. 10,11). In fact, 53% of the time when precipitation occurred over the BH, Fr was less than 0.5 indicating low-level flow blocking, while Fr < 0.5 occurred less than 20% of the time when precipitation occurred in the other mountain ranges (Fig. 14). Moreover, the dominant wind direction along the west slope of the WRR is northwesterly (Fig. 10b), which is nearly parallel to the WRR. For the remaining areas, the reduction due to the additional ground-seeding dispersion criteria was not as dramatic, but a reduction was observed. Nonetheless, after all criteria for ground-based seeding are considered, all areas except the BH have monthly frequencies for ground-based cloud seeding between 15% and 25% between the peak months of December–March.

Fig. 14.

Cumulative distributions of Froude number for each of the five western regions of each mountain range (see Fig. 2) for all times (solid) and only those times when precipitation occurred over the mountain range (dotted).

Fig. 14.

Cumulative distributions of Froude number for each of the five western regions of each mountain range (see Fig. 2) for all times (solid) and only those times when precipitation occurred over the mountain range (dotted).

The frequency of airborne-seeding conditions is variable depending on what vertical layer of the atmosphere is being assessed. Recall that the WRR and Bighorns Mountains have higher peak elevations than the other ranges included in this study. As such, the airborne-seeding layer that could realistically be flown by an aircraft for cloud-seeding operations is 4–5 and 3.5–4.5 km MSL, respectively, for those ranges; higher than the layer at 3–4 km MSL assessed in all other ranges (Table 3). It is clear that the frequency for seeding conditions in those higher airborne layers for the WRR and BH are reduced compared to the conditions in the other ranges for the layer at 3–4 km MSL (Fig. 15).

Fig. 15.

Bar charts illustrating the frequency of time that airborne cloud-seeding criteria were met at realistic flight layers, on 8-yr average, by wintertime month based upon the WRF-CONUS model simulation in the five western regions of each mountain range (see Fig. 2). The shaded portion of the bars indicate the fraction of time that conditions were met and precipitation did not occur.

Fig. 15.

Bar charts illustrating the frequency of time that airborne cloud-seeding criteria were met at realistic flight layers, on 8-yr average, by wintertime month based upon the WRF-CONUS model simulation in the five western regions of each mountain range (see Fig. 2). The shaded portion of the bars indicate the fraction of time that conditions were met and precipitation did not occur.

For the WRR and BH, most winter months have close to 10% of the month meeting seeding conditions in the realistic flight layer, whereas it increases to 15%–20% by March and April, respectively (Fig. 15). The peak in March and April is interesting in that this period is when ground-based seeding criteria are met less frequently, and therefore suggests airborne seeding could extend the overall period of time that seeding could take place targeting these ranges. For the other ranges with realistic airborne layers of 3–4 km MSL, the SM and MB have fairly consistent monthly frequencies for airborne seeding of around 15%–20%, while the SRR exhibits a peak season between December–March of up to 25% (Fig. 15).

There is some variability in the frequency that ground or airborne-seeding conditions are met from year to year in a given region during the wintertime months of November–April (Fig. 16). This is most notable for airborne-seeding criteria, especially in the MB area, which exhibited a change from near 13%–23% in 2001–02 to the next winter in 2002–03. The frequency for ground-based seeding in the BH is far reduced compared to the other areas analyzed. On average, ground-based-seeding criteria are met approximately 15% of the wintertime months in the western slopes of the mountain regions analyzed, except for the BH where it is less than 5%. Airborne-seeding criteria were met, on average, close to 20% of the wintertime months for the SRR, SM, and MB Ranges, while they were met approximately 13% of the wintertime on average for the WRR and BH. As described above, one reason these two areas have less frequent airborne-seeding opportunities is the higher altitude that an aircraft must fly in cloud near these mountains.

Fig. 16.

Bar charts illustrating the frequency of time that (a) ground-based and (b) airborne cloud-seeding criteria were met in each of the 8 years simulated, as well as the 8-yr average (rightmost bars in the plot), based upon the WRF-CONUS model simulation in the five western regions of each mountain range (see Fig. 2). In (a), the frequency that the ground-based seeding criteria were met is using all relevant ground-seeding criteria and Fr >1. The shaded portion of the bars indicate the fraction of time that conditions were met and precipitation did not occur.

Fig. 16.

Bar charts illustrating the frequency of time that (a) ground-based and (b) airborne cloud-seeding criteria were met in each of the 8 years simulated, as well as the 8-yr average (rightmost bars in the plot), based upon the WRF-CONUS model simulation in the five western regions of each mountain range (see Fig. 2). In (a), the frequency that the ground-based seeding criteria were met is using all relevant ground-seeding criteria and Fr >1. The shaded portion of the bars indicate the fraction of time that conditions were met and precipitation did not occur.

c. Fraction of precipitation that could be seeded

The above discussion focused on how frequently conditions for seeding are met. It is also important to determine how much of the precipitation that falls in a given winter could be impacted by cloud seeding (Ritzman et al. 2015). The total (liquid equivalent) precipitation that was simulated at the representative SNOTEL sites between November–April is illustrated in Fig. 17. The portion of this precipitation that occurred during conditions suitable for ground (Fig. 17a) or airborne (Fig. 17b) seeding are colored (unshaded) on each bar and is labeled as a fraction of total precipitation atop each bar. On average, over the eight years studied, generally less than 50% of the wintertime precipitation in any given area fell when ground-based seeding criteria were met, and was as low as only 7% in the BH (using the Fr > 1 criterion; for Fr > 0.5 it was 19% for the BH while all other ranges did not change by more than 4 percentage points, not shown). This means that less than half of the precipitation that fell could be impacted by ground-based seeding.

Fig. 17.

Bar charts illustrating the total wintertime precipitation (mm) simulated at each representative SNOTEL site in each of the 8 years simulated, as well as the 8-yr average (rightmost bars in the plot), based upon the WRF-CONUS model simulation for the five western regions of each mountain range (see Fig. 2). The fraction of the total precipitation that occurred when (a) ground-based and (b) airborne cloud-seeding criteria were met is colored on each bar, and the portion that did not meet seeding criteria are shaded in the upper portion of each bar. In addition, the fraction of total precipitation (expressed as a percentage) that occurred when seeding criteria were met is listed atop each bar. In (a), the fraction of precipitation for when the ground-based seeding criteria were met is using all relevant ground-seeding criteria and Fr > 1.

Fig. 17.

Bar charts illustrating the total wintertime precipitation (mm) simulated at each representative SNOTEL site in each of the 8 years simulated, as well as the 8-yr average (rightmost bars in the plot), based upon the WRF-CONUS model simulation for the five western regions of each mountain range (see Fig. 2). The fraction of the total precipitation that occurred when (a) ground-based and (b) airborne cloud-seeding criteria were met is colored on each bar, and the portion that did not meet seeding criteria are shaded in the upper portion of each bar. In addition, the fraction of total precipitation (expressed as a percentage) that occurred when seeding criteria were met is listed atop each bar. In (a), the fraction of precipitation for when the ground-based seeding criteria were met is using all relevant ground-seeding criteria and Fr > 1.

In the SM and MB Ranges, where Ritzman et al. (2015) had examined this question using criteria set forth by the WWMPP research program, the 8-yr average results herein indicate that 35% of wintertime precipitation fell under conditions that could be targeted by ground-based seeding (Fig. 17a). This result is slightly higher than the estimates of 27%–30% found by Ritzman et al. (2015), as would be expected given the more lenient operational seeding criteria employed herein (notably the warmer temperature threshold utilized in this study). On the other hand, 40%–65% of precipitation generally falls when airborne-seeding criteria are met, with the most in the SRR, SM, and MB Ranges (Fig. 17b). Airborne seeding in the BH has the potential to impact only 39% of what falls in the winter on 8-yr average, compared to 65% of precipitation in the SRR. It should be noted that the total precipitation estimates considered herein are based upon model-simulated precipitation at representative SNOTEL sites. The model indicated that higher elevations that do not have SNOTEL gauges exhibited greater wintertime precipitation amounts than those indicated here, especially in the WRR mountains, so these values could be underestimates of the actual amount of precipitation that could be seeded for the WRR (recall Figs. 68).

6. Statewide potential for cloud seeding

The analysis presented above quantifies how often and how much precipitation falls when typical operational seeding criteria were met in the specific regions of study. There are other areas across the state of Wyoming that exhibit potential for cloud seeding as well (recall Fig. 9) that could be further investigated for a possible cloud-seeding program. Besides the frequency of time that cloud-seeding criteria are met (i.e., Fig. 9), the mean amount of SLW over a winter (November–April) season (calculated at every grid point) can be used to illustrate the locations with the maximum potential for precipitation enhancement from AgI cloud seeding. The mean SLW compared to precipitation that occurs can provide an estimate of how efficient precipitation is produced in a given region, where more SLW relative to precipitation might indicate less efficient storms and thereby more potential for enhancement by cloud seeding. This analysis highlights areas that may hold the most potential for precipitation enhancement from cloud seeding, but it should be noted that this is based upon the model estimates of SLW, which as shown in section 3c, can be overpredicted (or underpredicted) in some storms.

The column-integrated SLW path (SLWP) at each grid point in each 3-hourly output of the simulation between November–April were averaged and then the 8-yr average SLWP mapped in Fig. 18. The regions in Wyoming with the most mean SLWP over the wintertime period are the mountainous regions in west-central Wyoming, including the SRR, WRR, and the Teton Range, as well as the SM and MB Ranges in south-central Wyoming (Fig. 18). Most of these regions also coincide with areas of greater wintertime precipitation, except the SRR, which has less precipitation compared to the other areas with greater mean SLWP (Fig. 18). This suggests that the SRR may be an area with lower natural precipitation efficiency, and therefore particularly amenable to precipitation enhancement by cloud seeding. While the BH and the Uinta Mountains of Utah (far lower left in map) both had a relatively high frequency of meeting seeding criteria (Fig. 9), both have quite low mean SLWP in the model simulation, indicating they may have less potential yield from cloud seeding. Interestingly, the Uinta Mountains have considerable wintertime precipitation, but less mean SLWP, suggesting that the precipitation efficiency in this region is fairly good. This is in agreement with results presented in Eidhammer et al. (2018). On the other hand, the BH has lower overall wintertime precipitation and low mean SLWP. For any areas that have high mean SLWP that have not already undergone a cloud-seeding feasibility study, such as the Park Range, the Teton Range, Absaroka Range, and area west of the Teton Range, feasibility studies would need to be conducted to determine the actual potential of cloud-seeding technology for enhancing precipitation in those regions.

Fig. 18.

Map of the 8-yr average wintertime (November–April) (a) average SLWP (mm) and (b) precipitation accumulation (mm) over the full analysis domain.

Fig. 18.

Map of the 8-yr average wintertime (November–April) (a) average SLWP (mm) and (b) precipitation accumulation (mm) over the full analysis domain.

7. Summary and discussion

In this study, the precipitation patterns and seedability of orographic clouds in Wyoming were evaluated using SNOTEL precipitation data and the WRF-CONUS model simulation (Liu et al. 2017) over an 8-yr period. The analysis revealed that high-resolution model simulations can adequately simulate the precipitation and SLW in regions of complex terrain as compared to available observations. This supports the findings of Ikeda et al. (2010), Rasmussen et al. (2011), Ritzman et al. (2015), and Liu et al. (2017). As a result, high-resolution model simulations are very useful tools for studying patterns of orographic precipitation and establishing the seedability of clouds in regions of complex terrain where observations of key atmospheric variables, such as SLW, are limited at best.

Some mountain ranges exhibit more frequent SLW than others, which impacts the frequency that seedable conditions are encountered in a given location. The seedability of a given mountain range depends on the orientation and shape of the mountain relative to the predominant wind flow and atmospheric stability. When low-level flow blocking occurs, it prevents AgI released from ground-based generators from reaching the clouds, and in the case of severely blocked flow, it also minimizes the frequency of SLW clouds forming on the mountain, as in the case of the BH. In locations where low-level flow blocking is common, airborne seeding may be more suitable given the aircraft releases AgI directly into the cloud. It is important to note, however, that not all airborne “seedable” hours can feasibly be seeded, especially with a single aircraft operation that has limited flight times. Further analysis is needed to assess the duration of each seedable period to determine how many of the seedable hours could effectively be seeded by an aircraft.

In most mountain ranges, the greatest precipitation often falls at the highest elevations. The BH mountains are an exception, however, as the WRF-CONUS model simulation indicated that the location of maximum precipitation accumulation is not solely coincident with the highest elevation terrain. Rather, the model indicated a precipitation maximum at two locations in the BH: at the highest elevation (Cloud Peak) and a second maximum at lower elevations in the northern end of the mountains. No SNOTEL sites exist in the far northern end of the BH to corroborate this model result. Moreover, in the WRR, SNOTEL gauges are not located at the highest elevations where the WRF-CONUS model indicated most of the precipitation falls. As a result, SNOTEL analysis alone may give an incomplete picture of precipitation accumulation totals and spatial distribution in some of these regions. This finding supports the notion put forward in Lundquist et al. (2019) that we may now be in an era where high-resolution models are surpassing the skill of our observational networks.

While this analysis focused on five mountain ranges in Wyoming, other areas across the broader region of study may have potential for cloud seeding to enhance precipitation, based upon the average amount of SLWP in the model simulation over the wintertime period. Feasibility studies to investigate how to target these areas would need to be conducted to help water managers assess the viability of cloud seeding in those areas. These techniques utilizing new high-resolution modeling capabilities are invaluable to assessing the feasibility of cloud seeding before starting a new cloud-seeding program.

Acknowledgments

This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement 1852977. This work does not constitute the opinions of the State of Wyoming, the Wyoming Water Development Commission, or the Wyoming Water Development Office. This study was funded by the Wyoming Water Development Commission under Contracts RN052914F and O5SC0296312 and the U.S. Bureau of Reclamation under Contract R11AC80816.

REFERENCES

REFERENCES
Auer
,
A. H.
, Jr.
, and
D. L.
Veal
,
1970
:
An investigation of liquid water-ice content budgets within orographic cap clouds
.
J. Atmos. Res.
,
4
,
59
64
.
Breed
,
D.
,
R.
Rasmussen
,
C.
Weeks
,
B.
Boe
, and
T.
Deshler
,
2014
:
Evaluating winter orographic cloud seeding: Design of the Wyoming Weather Modification Pilot Project (WWMPP)
.
J. Appl. Meteor. Climatol.
,
53
,
282
299
, https://doi.org/10.1175/JAMC-D-13-0128.1.
Daly
,
C.
,
R. P.
Neilson
, and
D. L.
Phillips
,
1994
:
A statistical-topographic model for mapping climatological precipitation over mountainous terrain
.
J. Appl. Meteor.
,
33
,
140
158
, https://doi.org/10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2.
DeMott
,
P. J.
,
1995
:
Quantitative descriptions of ice formation mechanisms of silver iodide-type aerosols
.
Atmos. Res.
,
38
,
63
99
, https://doi.org/10.1016/0169-8095(94)00088-U.
DeMott
,
P. J.
,
1997
:
Report to North Dakota Atmospheric Resource Board and Weather Modification Incorporated on tests of the ice nucleating ability of aerosols produced by the Lohse Airborne Generator. Colorado State University Dept. of Atmospheric Science Rep., 15 pp
.
DeMott
,
P. J.
, and et al
,
2010
:
Predicting global atmospheric ice nuclei distributions and their impacts on climate
.
Proc. Natl. Acad. Sci. USA
,
107
,
11 217
11 222
, https://doi.org/10.1073/pnas.0910818107.
Dirks
,
R. A.
,
1973
:
The precipitation efficiency of orographic clouds
.
J. Atmos. Res
,
7
,
177
184
.
Eidhammer
,
T.
,
V.
Grubisic
,
R.
Rasmussen
, and
K.
Ikeda
,
2018
:
Winter precipitation efficiency of mountain ranges in the Colorado Rockies under climate change
.
J. Geophys. Res. Atmos.
,
123
,
2573
2590
, https://doi.org/10.1002/2017JD027995.
French
,
J. R.
, and et al
,
2018
:
Precipitation formation from orographic cloud seeding
.
Proc. Natl. Acad. Sci.
,
115
,
1168
1173
, https://doi.org/10.1073/pnas.1716995115.
Friedrich
,
K.
, and et al
,
2020
:
Making snow—Quantifying snowfall from orographic cloud seeding
.
Proc. Natl. Acad. Sci.
,
117
,
5190
5195
, https://doi.org/10.1073/pnas.1917204117.
Garstang
,
M.
,
R.
Bruintjes
,
R.
Serafin
,
H.
Orville
,
B.
Boe
,
W.
Cotton
, and
J.
Warburton
,
2005
:
Weather modification: Finding common ground
.
Bull. Amer. Meteor. Soc.
,
86
,
647
656
, https://doi.org/10.1175/BAMS-86-5-647.
Geerts
,
B.
, and et al
,
2013
:
The AgI Seeding Cloud Impact Investigation (ASCII) campaign 2012: Overview and preliminary results
.
J. Wea. Modif.
,
45
,
24
43
.
Griffith
,
D. A.
,
M. E.
Solak
,
D. P.
Yorty
,
A. W.
Huggins
,
D.
Koracin
,
2006
:
Level II Weather Modification Feasibility Study for the Salt River and Wyoming Ranges, Wyoming. Wyoming Water Development Commission Final Rep., 265 pp.
, http://library.wrds.uwyo.edu/wwdcrept/Wyoming/Salt_River_Wyoming_Range-Level_II_Weather_Modification_Feasibility_Study-Final_Report-2006.pdf.
Henry
,
A. J.
,
1919
:
Increase of precipitation with altitude
.
Mon. Wea. Rev.
,
47
,
33
41
, https://doi.org/10.1175/1520-0493(1919)47<33:IOPWA>2.0.CO;2.
Hong
,
S.-Y.
,
Y.
Noh
, and
J.
Dudhia
,
2006
:
A new vertical diffusion package with an explicit treatment of entrainment processes
.
Mon. Wea. Rev.
,
134
,
2318
2341
, https://doi.org/10.1175/MWR3199.1.
Iacono
,
M. J.
,
J. S.
Delamere
,
E. J.
Mlawer
,
M. W.
Shephard
,
S. A.
Clough
, and
W. D.
Collins
,
2008
:
Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models
.
J. Geophys. Res.
,
113
,
D13103
, https://doi.org/10.1029/2008JD009944.
Ikeda
,
K.
, and et al
,
2010
:
Simulation of seasonal snowfall over Colorado
.
Atmos. Res.
,
97
,
462
477
, https://doi.org/10.1016/j.atmosres.2010.04.010.
Johnson
,
J. B.
, and
D.
Marks
,
2004
:
The detection and correction of snow-water equivalent pressure sensor errors
.
Hydrol. Processes
,
18
,
3513
3525
, https://doi.org/10.1002/hyp.5795.
Liu
,
C.
,
K.
Ikeda
,
G.
Thompson
,
R.
Rasmussen
, and
J.
Dudhia
,
2011
:
High-resolution simulations of wintertime precipitation in the Colorado Headwaters region: Sensitivity to physics parameterizations
.
Mon. Wea. Rev.
,
139
,
3533
3553
, https://doi.org/10.1175/MWR-D-11-00009.1.
Liu
,
C.
, and et al
,
2017
:
Continental-scale convection-permitting modeling of the current and future climate of North America
.
Climate Dyn.
,
49
,
71
95
, https://doi.org/10.1007/s00382-016-3327-9.
Lundquist
,
J.
,
M.
Hughes
,
E.
Gutmann
, and
S.
Kapnick
,
2019
:
Our skill in modeling mountain rain and snow is bypassing the skill of our observational networks
.
Bull. Amer. Meteor. Soc.
,
100
,
2473
2490
, https://doi.org/10.1175/BAMS-D-19-0001.1.
McDonough
,
F.
, and et al
,
2017
:
Weather modification level III feasibility study Laramie Range siting and design. Wyoming Water Development Commission Final Rep., 222 pp.
, http://wwdc.state.wy.us/weathermod/Laramie_Range_Final_Report_5-4-17.pdf.
National Research Council
,
2003
:
Critical Issues in Weather Modification Research. National Academies Press, 123 pp.
, https://doi.org/10.17226/10829.
Niu
,
G.-Y.
, and et al
,
2011
:
The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements
.
J. Geophys. Res.
,
116
,
D12109
, https://doi.org/10.1029/2010JD015139.
Politovich
,
M. K.
, and
G.
Vali
,
1983
:
Observations of liquid water in orographic clouds over Elk mountain
.
J. Atmos. Sci.
,
40
,
1300
1312
, https://doi.org/10.1175/1520-0469(1983)040<1300:OOLWIO>2.0.CO;2.
Rasmussen
,
R. M.
,
P. K.
Smolarkiewicz
, and
J.
Warner
,
1989
:
On the dynamics of Hawaiian cloud bands: Comparison of model results with observations and island climatology
.
J. Atmos. Sci.
,
46
,
1589
1608
, https://doi.org/10.1175/1520-0469(1989)046<1589:OTDOHC>2.0.CO;2.
Rasmussen
,
R. M.
, and et al
,
2011
:
High-resolution coupled climate runoff simulations of seasonal snowfall over Colorado: A process study of current and warmer climate
.
J. Climate
,
24
,
3015
3048
, https://doi.org/10.1175/2010JCLI3985.1.
Rasmussen
,
R. M.
, and et al
,
2012
:
How well are we measuring snow: The NOAA/FAA/NCAR Winter Precipitation Test Bed
.
Bull. Amer. Meteor. Soc.
,
93
,
811
829
, https://doi.org/10.1175/BAMS-D-11-00052.1.
Rasmussen
,
R. M.
, and et al
,
2014
:
Climate change impacts on the water balance of the Colorado headwaters: High-resolution regional climate model simulations
.
J. Hydrometeor.
,
15
,
1091
1116
, https://doi.org/10.1175/JHM-D-13-0118.1.
Rasmussen
,
R. M.
, and et al
,
2018
:
Evaluation of the Wyoming Weather Modification Pilot Project (WWMPP) using two approaches: Traditional statistics and ensemble modeling
.
J. Appl. Meteor. Climatol.
,
57
,
2639
2660
, https://doi.org/10.1175/JAMC-D-17-0335.1.
Rauber
,
R. M.
, and et al
,
2019
:
Wintertime orographic cloud seeding: A review
.
J. Appl. Meteor. Climatol.
,
58
,
2117
2140
, https://doi.org/10.1175/JAMC-D-18-0341.1.
Ritzman
,
J.
,
T.
Deshler
,
K.
Ikeda
, and
R.
Rasmussen
,
2015
:
Estimating the fraction of winter orographic precipitation produced under conditions meeting the seeding criteria for the Wyoming Weather Modification Pilot Project
.
J. Appl. Meteor. Climatol.
,
54
,
1202
1215
, https://doi.org/10.1175/JAMC-D-14-0163.1.
Schaefer
,
V. J.
,
1946
:
The production of ice crystals in a cloud of supercooled water droplets
.
Science
,
104
,
457
459
, https://doi.org/10.1126/science.104.2707.457.
Serreze
,
M. C.
,
M. P.
Clark
,
R. L.
Armstrong
,
D. A.
McGinnis
, and
R. S.
Pulwarty
,
1999
:
Characteristics of the western United States snowpack from snowpack telemetry (SNOTEL) data
.
Water Resour. Res.
,
35
,
2145
2160
, https://doi.org/10.1029/1999WR900090.
Serreze
,
M. C.
,
M. P.
Clark
, and
A.
Frei
,
2001
:
Characteristics of large snowfall events in the montane western United States as examined using snowpack telemetry (SNOTEL) data
.
Water Resour. Res.
,
37
,
675
688
, https://doi.org/10.1029/2000WR900307.
Skamarock
,
W. C.
, and et al
,
2008
:
A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp.
, http://doi.org/10.5065/D68S4MVH.
Smolarkiewicz
,
P. K.
, and
R.
Rotunno
,
1989
:
Low Froude number flow past three-dimensional obstacles. Part I: Baroclinically generated lee vortices
.
J. Atmos. Sci.
,
46
,
1154
1164
, https://doi.org/10.1175/1520-0469(1989)046<1154:LFNFPT>2.0.CO;2.
Smolarkiewicz
,
P. K.
,
R. M.
Rasmussen
, and
T. L.
Clark
,
1988
:
On the dynamics of Hawaiian cloud bands: Island forcing
.
J. Atmos. Sci.
,
45
,
1872
1905
, https://doi.org/10.1175/1520-0469(1988)045<1872:OTDOHC>2.0.CO;2.
Spreen
,
W. C.
,
1947
:
A determination of the effect of topography upon precipitation
.
Trans. Amer. Geophys. Union
,
28
,
285
290
, https://doi.org/10.1029/TR028i002p00285.
Tessendorf
,
S. A.
,
B.
Boe
,
B.
Geerts
,
M. J.
Manton
,
S.
Parkinson
, and
R.
Rasmussen
,
2015a
:
The future of winter orographic cloud seeding, a view from scientists and stakeholders
.
Bull. Amer. Meteor. Soc.
,
96
,
2195
2198
, https://doi.org/10.1175/BAMS-D-15-00146.1.
Tessendorf
,
S. A.
,
L.
Xue
,
D.
Breed
,
C.
Weeks
,
K.
Ikeda
,
D.
Axisa
, and
R.
Rasmussen
,
2015b
:
Evaluation of weather modification modeling in the Wind River Range, WY. U.S. Bureau of Reclamation Final Rep., 124 pp.
, https://www.usbr.gov/research/docs/WRR.pdf.
Tessendorf
,
S. A.
, and et al
,
2016
:
Wyoming Level II Weather Modification Feasibility–Wyoming Range Level II Phase II Study. Wyoming Water Development Commission Final Rep., 194 pp.
, http://wwdc.state.wy.us/weathermod/NCAR-Weather_Modification_Feasibility_Wyoming_Range_Study-Level_II-Final_Report-5_3_16.pdf.
Tessendorf
,
S. A.
, and et al
,
2017a
:
Wyoming Level II Weather Modification–Bighorn Mountains Siting and Design Study. Wyoming Water Development Commission Final Rep., 288 pp.
, http://wwdc.state.wy.us/weathermod/BighornMtns_Final_Report_5-4-17.pdf.
Tessendorf
,
S. A.
, and et al
,
2017b
:
Wyoming Level II Weather Modification–Medicine Bow/Sierra Madre Ranges Final Design and Permitting Study. Wyoming Water Development Commission Final Rep., 299 pp.
, http://library.wrds.uwyo.edu/wwdcrept/Wyoming/Medicine_Bow_Sierra_Madre-Weather_Modification_Final_Design_Permitting-Final_Report-2017.pdf.
Tessendorf
,
S. A.
, and et al
,
2019
:
A transformational approach to winter orographic weather modification research: The SNOWIE project
.
Bull. Amer. Meteor. Soc.
,
100
,
71
92
, https://doi.org/10.1175/BAMS-D-17-0152.1.
Thompson
,
G.
, and
T.
Eidhammer
,
2014
:
A study of aerosol impacts on clouds and precipitation development in a large winter cyclone
.
J. Atmos. Sci.
,
71
,
3636
3658
, https://doi.org/10.1175/JAS-D-13-0305.1.
Vonnegut
,
B.
,
1947
:
The nucleation of ice formation by silver iodide
.
J. Appl. Phys.
,
18
,
593
595
, https://doi.org/10.1063/1.1697813.
Weather Modification Inc.
,
2005
:
Wyoming Level II Weather Modification Feasibility Study. Wyoming Water Development Commission Final Rep., 151 pp.
, http://library.wrds.uwyo.edu/wwdcrept/Wyoming/Wyoming-Level_II_Weather_Modification_Feasibility_Study-Final_Report-2005.pdf.
Xue
,
L.
, and et al
,
2013a
:
Implementation of a silver iodide cloud seeding parameterization in WRF: Part I: Model description and idealized 2D sensitivity tests
.
J. Appl. Meteor. Climatol.
,
52
,
1433
1457
, https://doi.org/10.1175/JAMC-D-12-0148.1.
Xue
,
L.
,
S. A.
Tessendorf
,
E.
Nelson
,
R.
Rasmussen
,
D.
Breed
,
S.
Parkinson
,
P.
Holbrook
, and
D.
Blestrud
,
2013b
:
Implementation of a silver iodide cloud seeding parameterization in WRF: Part II: 3D simulations of actual seeding events and sensitivity tests
.
J. Appl. Meteor. Climatol.
,
52
,
1458
1476
, https://doi.org/10.1175/JAMC-D-12-0149.1.
Yang
,
D.
,
B. E.
Goodison
,
J. R.
Metcalfe
,
V. S.
Golubev
,
R.
Bates
,
T.
Pangburn
, and
C. L.
Hanson
,
1998
:
Accuracy of NWS 8″ standard nonrecording precipitation gauge: Results and application of WMO intercomparison
.
J. Atmos. Oceanic Technol.
,
15
,
54
68
, https://doi.org/10.1175/1520-0426(1998)015<0054:AONSNP>2.0.CO;2.
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Footnotes

1

The results of the Laramie Range study are documented in a report to the WWDC submitted by the Desert Research Institute (McDonough et al. 2017). The results of the studies for the other ranges that NCAR was funded to study (SRR/WYR, WRR, BH, MB, and SM) are presented herein, as well as documented in reports provided to the WWDC (Tessendorf et al. 2016, 2017a,b) and the USBR (Tessendorf et al. 2015b).

2

Radiometrics WVR-1100 series.