An Assessment of Winter Orographic Precipitation and Cloud-Seeding Potential in Wyoming

JULY 2020 T E S S E N D O R F E T A L . 1217

SARAH A. TESSENDORF,KYOKO IKEDA,COURTNEY WEEKS,ROY RASMUSSEN, JAMIE WOLFF, AND LULIN XUE Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado

(Manuscript received 4 September 2019, in ﬁnal form 6 April 2020)

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 (SNOWIE; Tessendorf et al. 2019) have shown that, under the right conditions, cloud seeding with AgI en- Since the seminal work on glaciogenic cloud seeding hances ice crystal production and local precipitation in the late 1940s (Schaefer 1946; Vonnegut 1947), a (French et al. 2018; Tessendorf et al. 2019; Friedrich number of programs have investigated whether seeding et al. 2020). These results improve our understanding of winter orographic clouds with silver iodide (AgI) could the effectiveness of current cloud-seeding efforts, and produce additional snow. Evaluation of this hypothesis they are attracting interest in the possibility that cloud has been attempted over the last half century using seeding may be useful for locations that have not pre- randomized statistical experiments, observational stud- viously been considered for cloud seeding. This paper ies, and numerical modeling of both natural and seeded examines this possibility for five mountain ranges in clouds, but most programs to date have yielded inconclu- Wyoming. sive 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 2. Background States, as well as internationally, aiming to mitigate After facing an extended drought in the western water shortages and manage water resources. United States, Wyoming water users requested that the In the last decade, new developments in observational State of Wyoming investigate the potential for cloud and computer-modeling approaches have laid the foun- seeding. As a result, in 2004 the Wyoming State dation to advance the state of the science with regard Legislature, through the Wyoming Water Development to cloud seeding (Xue et al. 2013a,b; Geerts et al. 2013; Commission (WWDC), funded the first of a sequence of Tessendorf et al. 2015a, 2019; French et al. 2018; studies to examine the feasibility of cloud seeding in the Rasmussen et al. 2018; Rauber et al. 2019). Intriguing state. The first feasibility study investigated the potential results from recent projects such as Seeded and Natural of cloud seeding to increase winter orographic precipi- Orographic Wintertime Clouds: The Idaho Experiment tation in two regions: the Wind River Range (WRR) in west-central Wyoming and the Medicine Bow (MB) and Corresponding author: Sarah A. Tessendorf, [email protected] Sierra Madre (SM) Ranges in south-central Wyoming

DOI: 10.1175/JAMC-D-19-0219.1 Ó 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Unauthenticated | Downloaded 10/04/21 01:43 AM UTC 1218 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY VOLUME 59

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). These ranges were selected based upon previous their importance for producing streamﬂow into three studies that documented the presence of supercooled major river basins in the state: the Green, Wind–Bighorn, liquid water (SLW) in these regions (Auer and Veal and North Platte Rivers. The results of this initial 1970; Dirks 1973; Politovich and Vali 1983), as well as feasibility study (Weather Modiﬁcation Inc. 2005)led

Unauthenticated | Downloaded 10/04/21 01:43 AM UTC JULY 2020 T E S S E N D O R F E T A L . 1219 to the state funding what would eventually be a 10-yr SRR, WRR, BH, MB, and SM mountain ranges in program to test the potential of cloud seeding in these Wyoming (Fig. 1). regions. The program, known as the Wyoming Weather Modification Pilot Project (WWMPP), was initially 3. Data and methods funded in 2005 and concluded in 2014 (Breed et al. 2014; Rasmussen et al. 2018). The WWMPP included The information needed to evaluate the potential for a sophisticated randomized statistical experiment along operational orographic cloud seeding using AgI include with physical measurements to assess the impact of vertical profiles of atmospheric temperature, stability, cloud seeding [see Breed et al. (2014) for a complete winds, and SLW, as well as amount and frequency of project description]. It also included a detailed analysis surface precipitation. Observations of these variables to quantify the fraction of precipitation that fell under are rare, especially in the western U.S. mountains, with seedable conditions (Ritzman et al. 2015), which was the exception of Natural Resource Conservation Service used to put the results of the randomized statistical ex- (NRCS) SNOTEL gauge precipitation measurements. periment into context. An alternative way to obtain this information is to utilize After the conclusion of the WWMPP, the WWDC high-resolution model simulation output. The present study decided to provide funding for additional feasibility uses a NCAR-generated high-resolution model simulation and program design studies on the potential for opera- (Liu et al. 2017) over an 8-yr period between 1 October tional cloud-seeding programs in the following Wyoming 2000 and 30 September 2008 that includes all key atmo- mountain ranges: Wyoming Range (WYR), Bighorn spheric variables for the analysis as a surrogate for ob- Mountains (BH), Laramie Range, and the MB and SM servational measurements in a similar way that Ritzman Ranges (Fig. 1). The first of these new studies was a et al. (2015) did for the WWMPP. phase-II feasibility study to build upon the 2006 feasi- a. High-resolution model simulation bility study (Griffith et al. 2006) utilizing new data and analysis tools that became available, in part due to The NCAR-generated model simulation (Liu et al. 2017) cloud-seeding operations run by Idaho Power Company is a convection-permitting regional climate (RCM) simu- that had expanded into the far western reaches of lation run using the Weather Research and Forecasting Wyoming, including the Salt River Range (SRR) and (WRF; Skamarock et al. 2008) Model over the contiguous parallel WYR. In 2015, three additional studies were United States (CONUS) domain (hereinafter referred to as funded, with the BH and Laramie Range studies focused the WRF-CONUS simulation; Liu et al. 2017). It was car- on the feasibility of cloud seeding in these regions, and ried out as a follow-up study to a preceding high-resolution the MB and SM Ranges study focused on designing an RCM study discussed in Rasmussen et al. (2011, 2014) that operational cloud-seeding program to build upon what was shown to reasonably represent precipitation pat- was learned from the WWMPP. Concurrent with the terns in complex terrain (Ikeda et al. 2010; Liu et al. phase-II study, which focused on the WYR, the U.S. 2011). The model simulation has a horizontal grid Bureau of Reclamation (USBR) funded the National spacing of 4 km over the CONUS and an output fre- Center for Atmospheric Research (NCAR) to examine quency of 3 h for three-dimensional (3D) data fields the effectiveness of seeding in the WRR associated with (e.g., atmospheric temperature, winds, various mixing the ongoing cloud-seeding program there. All of these ratios) and 1 h for two-dimensional fields (e.g., pre- studies1 included an assessment of the orographic cipitation reaching the ground, near-surface air tem- precipitation behavior and frequency of conditions perature). Table 1 lists the physical parameterizations amenable for cloud seeding to estimate the potential used in the WRF-CONUS model simulation. The of cloud seeding with AgI. This paper aims to estimate model was forced with 6-hourly European Centre for the potential for operational orographic cloud seeding Medium-Range Weather Forecasts interim reanalysis using AgI during winter storms occurring over the (ERA-Interim) data [see Liu et al. (2017) for a full description of the simulation setup]. The precipitation and snowpack from this WRF-CONUS simulation was verified by comparison to SNOTEL data 1 The results of the Laramie Range study are documented in a and showed that the model was able to realistically report to the WWDC submitted by the Desert Research Institute represent the observed precipitation (Liu et al. 2017). (McDonough et al. 2017). The results of the studies for the other Herein, we show that the WRF-CONUS simulation is ranges that NCAR was funded to study (SRR/WYR, WRR, BH, MB, and SM) are presented herein, as well as documented in re- able to adequately represent the amount and spatial ports provided to the WWDC (Tessendorf et al. 2016, 2017a,b) and distribution of precipitation over the mountain ranges the USBR (Tessendorf et al. 2015b). examined in this study using SNOTEL data, and that it

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TABLE 1. WRF-CONUS model simulation physics options.

WRF physics Parameterization schemes References Land surface Noah-MP (multiphysics) land surface model Niu et al. (2011) Microphysics Thompson aerosol-aware mixed-phase scheme Thompson and Eidhammer (2014) Planetary boundary layer Yonsei University (YSU) PBL Hong et al. (2006) Longwave and shortwave radiation RRTMG Iacono et al. (2008) can reasonably simulate the presence of liquid water by (LWP) data at the Cedar Creek site west of the Medicine comparison with radiometer data. Bow Range (Fig. 2c)fortheWWMPP(Breed et al. 2014; Ritzman et al. 2015). These data were compared with the b. SNOTEL data LWP from the model for a 2-month period of overlap SNOTEL observations provide a long-term record of between the period of this study and the radiometer precipitation data from gauges that weigh precipitation dataset. During this period, the observed LWP was en- and snow water content via pressure-sensing snow pillows tirely supercooled (given a surface temperature , 08C) located at numerous sites throughout the western United 84% of the time. This comparison shows that the model States. These sites are owned and operated by the U.S. reasonably simulates the presence of supercooled liquid Department of Agriculture NRCS and are typically lo- water (Fig. 3). In particular, the timing between the model cated at elevations between 2400 and 3600 m above mean and observational data is reasonable (Fig. 3b). The model sea level (MSL). Historical and real-time data are available tends to underpredict small amounts of observed liquid from the NRCS website (http://www.wcc.nrcs.usda.gov/ water and overpredict larger values relative to radiometer snow/). There are known measurement deficiencies of observations. The model captures the occurrences of these gauges (e.g., Serreze et al. 1999, 2001; Johnson and observed LWP, but in some storms the model can over- Marks 2004) such as an undercatch of snowfall in the predict or underpredict the amount of LWP. On average, weighing gauges due to wind (Serreze et al. 2001; Yang the model predicted LWP to within 0.01 mm (standard et al. 1998; Rasmussen et al. 2012). The SNOTEL gauges deviation of 0.08 mm). Since the primary use of the model are often located in a forest clearing where the wind speed for this study is to determine whether or not liquid water 2 is typically less than 2 m s 1, and an undercatch of ap- was present, with less focus on the amount of SLW, we proximately 10%–15% is expected (Yang et al. 1998). assume (as did Ritzman et al. 2015)thatthemodelisable The SNOTEL data resolution is 0.1 in. (2.5 mm), making to adequately detect the presence of liquid water for the it difficult to study precipitation characteristics or verify purposes of this study. model data on a subdaily basis. However, these data are d. Cloud-seeding criteria suitable for use over monthly or longer periods. To evaluate the ability of the model simulation to adequately The hypothesized impacts of precipitation enhance- represent precipitation, as well as to understand the ment via cloud seeding with AgI requires certain at- orographic precipitation behavior, SNOTEL precipita- mospheric conditions to be present. The most basic tion gauge data were analyzed over three regions of the requirements are that (i) the temperature in the cloud is state. Region 1 contains the SRR, WYR, and WRR, amenable to the activation of AgI to form ice crystals; Region 2 includes the BH, and Region 3 includes the MB and (ii) that SLW is present to allow the ice crystals to and SM ranges (Fig. 1). To compare the observations with grow rapidly by riming, diffusion, and aggregation. In the model, precipitation accumulation from the model addition to these basic requirements, another necessary was matched to SNOTEL data by taking the inverse- condition is that the AgI can be dispersed into the cloud distance weighted average of the four model data points that meets these two basic criteria. This latter condition is closest to each SNOTEL site. These results show that the location dependent and specific criteria should be set based model represents the observed precipitation patterns and upon characteristics of the mountain being targeted. amounts well (see section 4 for more details). 1) BASIC SEEDING CRITERIA c. Radiometer data and model comparison In many cases, natural orographic clouds have low 2 Beginning in 2008, a two-channel microwave radi- ice crystal concentrations (,1L1) at temperatures ometer2 collected vertically integrated liquid water path warmer than 148C(DeMott et al. 2010) unless secondary ice processes are active. However, AgI has been shown to activate at temperatures as warm as 268C(DeMott 2 Radiometrics WVR-1100 series. 1995; DeMott 1997), with up to two orders of magnitude

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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. more effective activation by 288C(DeMott 1997). WWMPP, conducted by Ritzman et al. (2015),utilized As a result, the additional ice created by seeding a the same criteria as defined for the research program. cloud with AgI has the chance to grow and deplete In contrast, many operational orographic cloud-seeding existing supercooled liquid water by diffusion and programs utilize a temperature threshold warmer than that riming. The WWMPP research program required a used in the WWMPP research program in order to opti- 700-hPa temperature of 288C or colder before per- mize the frequency of seeding opportunities. To ensure forming any seeding experiments (Breed et al. 2014), adequate AgI activation efficiency in this analysis, while based on the increased rate of AgI activation shown being consistent with typical operational cloud-seeding to occur at these temperatures (DeMott 1997). An program practices, the temperature threshold for this analysis of how often seeding criteria were met for the study was set at 268C. Given that natural ice nucleation

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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. may become quite active at colder temperatures, the with AgI to work. The presence of SLW is a sign that temperature criterion to determine a seedable cloud was natural precipitation processes are inefficient, and if limited to clouds warmer than 2188C. additional ice crystals are nucleated (via AgI activation) In addition to a proper activation temperature for they could grow at the expense of the SLW and fall out AgI, liquid water needs to be present for cloud seeding on the ground as additional snow. The criteria utilized in this study defined seedable LWC as greater than 2 0.01 g kg 1 within the temperature range from 268 to TABLE 2. Summary of the seeding criteria utilized in this study. 2 8 The primary criteria represent the most basic conditions required 18 C. In summary, both proper temperature and LWC for either ground-based or airborne seeding. The additional criteria criteria need to be met to determine seeding potential apply only to ground-based seeding and are meant to ensure that (Table 2). The WRF-CONUS model output utilized for AgI from ground-based generators could be transported into this analysis were temperature and LWC mixing ratio the cloud. (defined as cloud water mixing ratio). Additional criteria 2) DISPERSION CRITERIA Primary criteria (ground-based seeding only) Temperature between Fr . 0.5 or Fr . 1.0 Seeding with an aircraft allows the AgI to be released 2188 and 268C directly into the cloud and the flight track adjusted based 2 LWC . 0.01 g kg 1 The wind direction for each barrier has upon the wind direction; therefore, only the basic seeding to fall within a given range (see Table 3, criteria need to be met to determine the seeding potential below) chosen so that a significant by aircraft seeding. However, for ground-based seeding, component of the wind is normal to additional variables, such as atmospheric stability and the mountain winds, play a role in determining seeding potential because

Unauthenticated | Downloaded 10/04/21 01:43 AM UTC JULY 2020 T E S S E N D O R F E T A L . 1223 they impact how effectively AgI can be transported into WYR, and WRR) and Region 2 (BH) had similar an- the seedable cloud from ground-based generators. In nual precipitation accumulation (615–630 mm), while addition, assessing wind direction is important for de- the most annual precipitation accumulation (; 850 mm) termining locations to site ground-based generators. was observed in Region 3 (SM, MB). These data also Herein, we investigate common wind regimes to deter- indicate that wintertime (November–April) precipita- mine wind directions for use in the ground-seeding dis- tion typically accounts for roughly 60% or more of the persion criteria as well as we assess the likelihood for annual precipitation observed at SNOTEL gauge sites seeding material released from a ground generator to (Fig. 4). The exception to this is the BH in Region 2, flow into clouds over the mountain using the Froude where only 46% of the average annual (;615 mm) number as an indicator. The additional criteria based precipitation fell at SNOTEL sites during the winter- upon wind direction and stability for assessing ground- time months. The red curve in each plot shows that the based seeding are listed in Table 2. WRF-CONUS simulation represents the average an- The Froude number (Fr) expresses the ability of air- nual precipitation reasonably well in all regions, with flow to go over a mountain barrier (Smolarkiewicz et al. the wintertime precipitation accumulation within the 1988; Rasmussen et al. 1989). The flow is mostly blocked observed annual range. The interannual model vari- by the barrier when Fr , 0.5. while the airflow will freely ability (as represented by the error bars) was well within move over the barrier (unblocked) when Fr . 1 the observed interannual variability for all ranges in (Smolarkiewicz and Rotunno 1989). The Froude num- the winter. ber used in this study is defined as The model simulation performance by individual SNOTEL site in each of these regions shows some U/h Fr 5 , scatter about, but generally remained close to, the 1:1 N line (Fig. 5). In Region 1, however, there was one outlier 21 site in the SRR that had a systematic low bias in the where U is the average wind speed (m s ) perpendic- model for all eight years studied (Fig. 6), which led to a ular to the mountain barrier orientation over a depth of deviation from the 1:1 line in Fig. 5. This site, Willow h (m), and N is an average of the Brunt–Väisälä fre- Creek, was evaluated closely for instrumentation/site quency between the same depths following maintenance issues, but none were found. Based on g ›u 1/2 discussions with the NRCS and local area water man- N 5 , agers, the site is known for having higher snowfall than u ›z neighboring areas. We speculate that the site experi- 2 where g is the gravitational acceleration (m s 1) and u is ences large precipitation amounts due to impacts of the the ambient potential temperature (K). local terrain that are not resolved by the 4-km grid To calculate Fr, the wind speed component perpen- spacing in the model. Aside from that one notable out- dicular to each mountain range at each grid point lower lier in Region 1, the comparison between model and than the peak of the range was used. The height h was SNOTEL is very good for nearly all of the SNOTEL calculated as the difference between the peak height of a locations. range and the local height at each grid point. The local In examining the spatial distribution of observed and Brunt–Väisälä frequency N was then used to calculate simulated precipitation in Region 1, it is seen that the the local Fr. Using this method, a 3D field of local Fr was precipitation from both the model and SNOTEL increases generated. with increasing elevation, with maximum precipitation at For this analysis, Fr was used as a criterion to deter- the highest elevation (Fig. 6). This is a commonly observed mine the potential for ground-based seeding material to pattern in mountainous areas (e.g., Henry 1919; Spreen be entrained into the clouds over the mountain barrier. 1947; Daly et al. 1994; Rasmussen et al. 2011). The SRR Assuming that flow is completely blocked when Fr , 0.5, a exhibits more precipitation than the adjacent and par- minimum requirement for ground-based seeding would be allel WYR, yet the WRR receives the most precipitation that Fr . 0.5. Since flow may still be partially blocked for overall according to the model simulation. In the WRR, Fr between 0.5 to 1, we also investigated situations when the model simulated some locations with up to 1300 mm Fr . 1(Table 2). of average annual accumulation. However, the WRR is lacking SNOTEL gauge sites in the highest elevation areas where the model simulated the greatest precipita- 4. Precipitation analysis tion accumulation, both in winter and on the annual basis. The 8-yr average SNOTEL measurements shown in This does not allow for the model-simulated values to Fig. 4 for each region indicate that both Region 1 (SRR, be confirmed by these observations, yet it also means

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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 61 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. that estimates of precipitation in the WRR based upon the BH the location of the maximum precipitation ac- SNOTEL data alone may be underestimated. cumulation is not restricted to the highest-elevation In Region 2, the model-simulated precipitation at terrain, as might be expected and observed in Region 1. each site compares fairly well to that at the individual There is a simulated maximum around the highest sites (Fig. 7), as indicated also in (Fig. 5). However, in elevations in the Cloud Peak area with roughly 1000 mm

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.

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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).

of average annual accumulation. The SNOTEL data also accumulation values of 1100–1300 mm. These maps in- show a local maximum in this area, from the Cloud Peak dicate that the SM Range receives more annual and gauge site (albeit the gauge indicated only ;800 mm). wintertime precipitation than the MB. Yet, there is a second maximum in the northern end of the mountains in the model-simulated precipitation. 5. Analysis of seeding potential Several of the northernmost SNOTEL sites (Bone Springs, Sucker Creek, and Bald Mountain) indicate generally The frequency of instances when seeding conditions higher annual precipitation, of similar amounts as ob- occurred over the target areas were determined by served at Cloud Peak, however there are no SNOTEL analyzing the defined criteria needed for clouds to be sites north of Bald Mountain to corroborate the greatest seedable (recall section 3d). The analysis focused on two precipitation values shown in the model simulation. layers of the atmosphere: 1) the layer 0–1 km above Also, it should be reiterated that the majority of pre- ground level (AGL) was analyzed for ground-based cipitation in this region falls outside of the wintertime seeding by averaging each criterion over that layer for months, as illustrated by the low values of precipitation each model grid point (4-km spacing) and output time for both the model and SNOTEL in the wintertime (3 hourly), and 2) a 1-km-thick layer (Table 3) was an- panel of Fig. 7. alyzed for airborne seeding by averaging each criterion The spatial distribution of the model-simulated pre- over that vertical layer as was done for the ground- cipitation again mimics the topography in Region 3, seeding layer. It should be noted that for an aircraft to with the greatest precipitation falling at the highest el- realistically fly in cloud near a mountain range, flight evations (Fig. 8). Unlike other regions, SNOTEL gauge level limitations need to be considered (i.e., minimum sites are available in these higher elevation areas to safe altitudes). Given that the WRR and BH are the confirm the model-simulated maximum precipitation highest elevation mountains in this study, airborne

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FIG.7.AsinFig. 6, but for Region 2. seeding in clouds near them require flying at higher most frequently met shows that the mountainous re- altitudes than other nearby mountain ranges. Therefore, gions of northwestern Wyoming, including the Absaroka the airborne-seeding layer was tailored to each moun- Range, WRR, SRR, and WYR, as well as the Park tain range assessed based upon instrument flight rules Range in north-central Colorado (south of Region 3), all (IFR) Enroute Aeronautical Charts (https://www.faa.gov/ meet the basic seeding criteria greater than 30% of the air_traffic/flight_info/aeronav/digital_products/ifr/;see time in the wintertime months (Fig. 9). The MB and SM Table 3). These will be referred to as ‘‘realistic airborne Ranges (Region 3), the Uinta Mountains in Utah, and seeding layers.’’ However, for the purpose of some of the very highest elevations of the BH (Region 2) also the analysis requiring a common level be examined (i.e., meet these conditions over 20% of the time. The loca- mapping), the 3–4 km MSL layer was used. tions with the highest frequency of supercooled LWC at Using the 8-yr average model simulation output, the temperatures meeting the seeding criteria occur at the spatial distribution of where basic seeding criteria are highest elevations.

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FIG.8.AsinFig. 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. a. Wind regimes each major mountain barrier when precipitation occurred over the mountains (Fig. 10). For the SRR/WYR, Understanding wind regimes is important to deter- the 700-hPa wind direction during precipitating events is mining which side of the mountain should be targeted predominantly westerly and west-northwesterly. While less for seeding, especially for placing ground-based gener- frequent, southwesterly winds also bring large amounts of ators. The 700-hPa wind direction corresponds to the precipitation to the region. There is a lack of any easterly near or just below crest-level height of these mountain component in the 700-hPa winds during the time period ranges and was analyzed when the temperature and SLW when precipitation impacted these mountain ranges. seedingcriteriaweremetusingoutputfromtheWRF- CONUS simulation at individually selected grid points surrounding the mountain ranges in each region (Fig. 2). TABLE 3. Summary of area-speciﬁc criteria used in this analysis, To normalize the results by when precipitation occurred, a including the wind direction criteria for the ground-seeding layers and the vertical layer analyzed for airborne seeding in each area’s ‘‘representative SNOTEL site’’ for the given wind site was western slope. Wind direction ranges were selected to capture re- selected (Fig. 2). Part of this selection required the model alistic upslope conditions that could carry seeding material into precipitation at the SNOTEL site to have a reasonable clouds over the mountain barrier. This was determined based upon comparison with the SNOTEL data (based upon the the orientation and shape of each mountain barrier, as well as being SNOTEL model evaluation performed as part of the informed by the wind regime analysis. precipitation analysis presented in section 4), which re- Ground-seeding wind Airborne-seeding sulted in some site pairings having closer proximity than Area direction range (8) layer (MSL) others. For each representative SNOTEL site selected, SRR 220–320 3–4 km the model grid point nearest that SNOTEL site was used WRR 180–290 4–5 km to determine the amount of precipitation that occurred. BH 225–330 3.5–4.5 km For Region 1, the 700-hPa wind direction from the SM 210–315 3–4 km model simulation was examined to the west and east of MB 210–315 3–4 km

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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.

For the WRR, the wind direction on the western slope This was especially true at Sheridan when precipitation of the barrier (i.e., Pinedale) is predominantly west- was simulated at the Sucker Creek SNOTEL site in the northwesterly when precipitation occurred on the west northeastern area of the mountains. Northerly and slope. However, given the orientation of the WRR, northeasterly winds do bring precipitation to some areas southwesterly flow provides a stronger upslope flow, and of the BH, especially in the north and eastern sites (i.e., occurs in conjunction with large precipitation amounts. 20 Mile Creek, Sheridan, and especially Kaycee); how- In contrast, while there are some north-northwesterly ever, these occur at such low frequencies that it is barely winds during precipitation events, these typically con- perceptible in Fig. 11. Nonetheless, similar to the WRR, tribute very little precipitation. these northerly and northeasterly events, when they At Lander, on the east side of the WRR, there is more occur, tend to produce a large amount of precipitation. variability in the wind direction during precipitating Precipitation in the SM Range in Region 3 occurs events. Due westerly and some southwesterly winds nearly equally under southwesterly through northwesterly bring precipitation to the eastern slope, but this is likely wind flow, with no particular dominant direction (Fig. 12). spillover precipitation from those events. There is also In contrast, precipitation in the MB Range occurs pre- a north and north-northeasterly wind component that dominantly under westerly winds, with southwesterly occurs less frequently but contributes to large precipi- being only slightly less frequent. North and northeast- tation amounts. Because of the orientation of this erly winds bring precipitation to these mountain range mountain barrier, this leads to upslope conditions that very infrequently, even on the eastern slopes of these brings precipitation to the eastern slope of the barrier, in barriers (i.e., Riverside and Centennial). contrast to negligible upslope conditions for the east b. Frequency of seeding conditions slope of the WYR. Interestingly, in Region 2 (Bighorn Mountains) the Within each of the three regions of study, an analysis predominant 700-hPa wind direction on all sides of was performed to assess the frequency of seeding condi- the mountain barrier when precipitation occurs in the tions on each slope of the mountain barrier. Target areas mountains was consistently from the northwest (Fig. 11). were defined for investigation (cross-hatched areas

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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. shown in Fig. 2) and the area-averaged values over the time in the ground-seeding layer (0–1 km AGL) for each target area for each seeding criterion (for either any given month between November–March (Fig. 13a), ground-based- or airborne-seeding layers, see Tables with the most frequent opportunities between December– 2 and 3) were produced at every model output time February. The exception to this is the BH, which exhibited (3-hourly). Then, the frequency of time (over a given lower frequencies in every month compared to the other month or winter season) that the area-averaged condi- regions and had its peak monthly frequencies (of near tions met the thresholds deﬁned in Table 2 was deter- 20%) occur later in winter during February and March mined. These frequencies were also normalized by when (Fig. 13a). precipitation occurred, which was determined using When dispersion criteria for ground-based seeding model-simulated precipitation from a grid point nearest are considered (wind direction, which varies depending the ‘‘representative SNOTEL site’’ (that had a good on the orientation of the mountain barrier as listed in comparison between model and SNOTEL observations) Table 3, and Fr . 0.5 or . 1.0), these monthly fre- within the given area. quencies are reduced (Figs. 13b,c). In the WRR and BH, Here, only the western slope regions are presented, the monthly frequency of occurrence was reduced sub- since the frequency of easterly upslope conditions is stantially. For example, the peak frequency in the month much less frequent, if not negligible, in all study regions of December for the WRR was reduced from near 30% as shown by the wind regime analysis (section 5a). to less than 20%, and a similar reduction occurred in the However, it should be noted that in the regions where other months. For the BH, the peak frequency in the easterly upslope conditions are not negligible, such as month of February was reduced from just over 20% to the WRR and BH, operational cloud seeding to target 10% using Fr . 0.5 and to less than 5% using Fr . 1.0 these conditions could be pursued. (Fig. 13). In fact, the monthly frequency for ground- In the west slope areas in all ranges, basic seeding based seeding in the BH is less than 10% of any given criteria (temperature and LWC) are met 15%–30% of wintertime month using a Fr . 0.5 threshold, and well

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FIG. 11. As in Fig. 10, for Region 2. less than 5% in any wintertime month using a Fr . 1.0 Bighorns Mountains have higher peak elevations than threshold. The ground-seeding potential in these two the other ranges included in this study. As such, the mountain ranges is reduced due to frequent blocking sit- airborne-seeding layer that could realistically be flown uations that would prevent the ground-released AgI to be by an aircraft for cloud-seeding operations is 4–5 and transported over the mountain barrier, which was espe- 3.5–4.5 km MSL, respectively, for those ranges; higher cially apparent in the BH (Fig. 14), and due to the fre- than the layer at 3–4 km MSL assessed in all other ranges quency of wind conditions that are not perpendicular to (Table 3). It is clear that the frequency for seeding the mountain barrier (Figs. 10,11). In fact, 53% of the time conditions in those higher airborne layers for the WRR when precipitation occurred over the BH, Fr was less than and BH are reduced compared to the conditions in the 0.5 indicating low-level flow blocking, while Fr , 0.5 oc- other ranges for the layer at 3–4 km MSL (Fig. 15). curred less than 20% of the time when precipitation oc- For the WRR and BH, most winter months have close curred in the other mountain ranges (Fig. 14). Moreover, to 10% of the month meeting seeding conditions in the the dominant wind direction along the west slope of the realistic flight layer, whereas it increases to 15%–20% by WRR is northwesterly (Fig. 10b), which is nearly parallel March and April, respectively (Fig. 15). The peak in to the WRR. For the remaining areas, the reduction due to March and April is interesting in that this period is when the additional ground-seeding dispersion criteria was not ground-based seeding criteria are met less frequently, as dramatic, but a reduction was observed. Nonetheless, and therefore suggests airborne seeding could extend after all criteria for ground-based seeding are considered, the overall period of time that seeding could take all areas except the BH have monthly frequencies for place targeting these ranges. For the other ranges with ground-based cloud seeding between 15% and 25% be- realistic airborne layers of 3–4 km MSL, the SM and tween the peak months of December–March. MB have fairly consistent monthly frequencies for The frequency of airborne-seeding conditions is airborne seeding of around 15%–20%, while the SRR variable depending on what vertical layer of the at- exhibits a peak season between December–March of up mosphere is being assessed. Recall that the WRR and to 25% (Fig. 15).

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FIG. 12. As in Fig. 10, for Region 3.

There is some variability in the frequency that ground determine how much of the precipitation that falls or airborne-seeding conditions are met from year to year in a given winter could be impacted by cloud seeding in a given region during the wintertime months of (Ritzman et al. 2015). The total (liquid equivalent) November–April (Fig. 16). This is most notable for precipitation that was simulated at the representative airborne-seeding criteria, especially in the MB area, SNOTEL sites between November–April is illustrated which exhibited a change from near 13%–23% in in Fig. 17. The portion of this precipitation that occurred 2001–02 to the next winter in 2002–03. The frequency during conditions suitable for ground (Fig. 17a) or air- for ground-based seeding in the BH is far reduced borne (Fig. 17b) seeding are colored (unshaded) on each compared to the other areas analyzed. On average, bar and is labeled as a fraction of total precipitation atop ground-based-seeding criteria are met approximately each bar. On average, over the eight years studied, 15% of the wintertime months in the western slopes generally less than 50% of the wintertime precipitation of the mountain regions analyzed, except for the BH in any given area fell when ground-based seeding cri- where it is less than 5%. Airborne-seeding criteria were teria were met, and was as low as only 7% in the BH met, on average, close to 20% of the wintertime months (using the Fr . 1 criterion; for Fr . 0.5 it was 19% for for the SRR, SM, and MB Ranges, while they were met the BH while all other ranges did not change by more approximately 13% of the wintertime on average for the than 4 percentage points, not shown). This means that WRR and BH. As described above, one reason these two less than half of the precipitation that fell could be im- areas have less frequent airborne-seeding opportunities pacted by ground-based seeding. is the higher altitude that an aircraft must ﬂy in cloud In the SM and MB Ranges, where Ritzman et al. near these mountains. (2015) had examined this question using criteria set forth by the WWMPP research program, the 8-yr aver- c. Fraction of precipitation that could be seeded age results herein indicate that 35% of wintertime pre- The above discussion focused on how frequently cipitation fell under conditions that could be targeted by conditions for seeding are met. It is also important to ground-based seeding (Fig. 17a). This result is slightly

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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.

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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. 6–8).

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 FIG. 14. Cumulative distributions of Froude number for each of that exhibit potential for cloud seeding as well (recall the five western regions of each mountain range (see Fig. 2) for all times (solid) and only those times when precipitation occurred over Fig. 9) that could be further investigated for a possible the mountain range (dotted). 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 higher than the estimates of 27%–30% found by Ritzman (calculated at every grid point) can be used to illustrate et al. (2015), as would be expected given the more lenient the locations with the maximum potential for precipi- operational seeding criteria employed herein (notably tation enhancement from AgI cloud seeding. The mean the warmer temperature threshold utilized in this study). SLW compared to precipitation that occurs can provide On the other hand, 40%–65% of precipitation generally an estimate of how efficient precipitation is produced falls when airborne-seeding criteria are met, with the in a given region, where more SLW relative to pre- most in the SRR, SM, and MB Ranges (Fig. 17b). cipitation might indicate less efficient storms and Airborne seeding in the BH has the potential to impact thereby more potential for enhancement by cloud only 39% of what falls in the winter on 8-yr average, seeding. This analysis highlights areas that may hold the compared to 65% of precipitation in the SRR. It should most potential for precipitation enhancement from

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.

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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. cloud seeding, but it should be noted that this is based frequency of meeting seeding criteria (Fig. 9), both have upon the model estimates of SLW, which as shown in quite low mean SLWP in the model simulation, indi- section 3c, can be overpredicted (or underpredicted) cating they may have less potential yield from cloud in some storms. seeding. Interestingly, the Uinta Mountains have con- The column-integrated SLW path (SLWP) at each siderable wintertime precipitation, but less mean SLWP, grid point in each 3-hourly output of the simulation suggesting that the precipitation efficiency in this region between November–April were averaged and then is fairly good. This is in agreement with results presented the 8-yr average SLWP mapped in Fig. 18. The regions in Eidhammer et al. (2018). On the other hand, the BH in Wyoming with the most mean SLWP over the has lower overall wintertime precipitation and low mean wintertime period are the mountainous regions in SLWP. For any areas that have high mean SLWP that west-central Wyoming, including the SRR, WRR, and have not already undergone a cloud-seeding feasibility the Teton Range, as well as the SM and MB Ranges in study, such as the Park Range, the Teton Range, Absaroka south-central Wyoming (Fig. 18). Most of these re- Range, and area west of the Teton Range, feasibility gions also coincide with areas of greater wintertime studies would need to be conducted to determine the precipitation, except the SRR, which has less precip- actual potential of cloud-seeding technology for enhancing itation compared to the other areas with greater mean precipitation in those regions. SLWP (Fig. 18). This suggests that the SRR may be an area with lower natural precipitation efficiency, and therefore 7. Summary and discussion particularly amenable to precipitation enhancement by cloud seeding. While the BH and the Uinta Mountains In this study, the precipitation patterns and seedability of Utah (far lower left in map) both had a relatively high of orographic clouds in Wyoming were evaluated using

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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.

SNOTEL precipitation data and the WRF-CONUS model Some mountain ranges exhibit more frequent SLW simulation (Liu et al. 2017) over an 8-yr period. The than others, which impacts the frequency that seedable analysis revealed that high-resolution model simulations conditions are encountered in a given location. The can adequately simulate the precipitation and SLW in seedability of a given mountain range depends on the regions of complex terrain as compared to available orientation and shape of the mountain relative to observations. This supports the findings of Ikeda et al. the predominant wind flow and atmospheric stability. (2010), Rasmussen et al. (2011), Ritzman et al. (2015), When low-level flow blocking occurs, it prevents AgI and Liu et al. (2017). As a result, high-resolution model released from ground-based generators from reach- simulations are very useful tools for studying patterns of ing the clouds, and in the case of severely blocked flow, it orographic precipitation and establishing the seedability also minimizes the frequency of SLW clouds forming on of clouds in regions of complex terrain where observa- the mountain, as in the case of the BH. In locations where tions of key atmospheric variables, such as SLW, are low-level flow blocking is common, airborne seeding may limited at best. be more suitable given the aircraft releases AgI directly

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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. into the cloud. It is important to note, however, that not all in the model simulation over the wintertime period. airborne ‘‘seedable’’ hours can feasibly be seeded, espe- Feasibility studies to investigate how to target these cially with a single aircraft operation that has limited flight areas would need to be conducted to help water man- times. Further analysis is needed to assess the duration agers assess the viability of cloud seeding in those areas. of each seedable period to determine how many of the These techniques utilizing new high-resolution model- seedable hours could effectively be seeded by an aircraft. ing capabilities are invaluable to assessing the feasibil- In most mountain ranges, the greatest precipitation ity of cloud seeding before starting a new cloud-seeding often falls at the highest elevations. The BH mountains program. are an exception, however, as the WRF-CONUS model simulation indicated that the location of maximum Acknowledgments. This material is based upon work precipitation accumulation is not solely coincident supported by the National Center for Atmospheric with the highest elevation terrain. Rather, the model Research, which is a major facility sponsored by the indicated a precipitation maximum at two locations in National Science Foundation under Cooperative Agreement the BH: at the highest elevation (Cloud Peak) and a 1852977. This work does not constitute the opinions of second maximum at lower elevations in the northern the State of Wyoming, the Wyoming Water Development end of the mountains. No SNOTEL sites exist in the far Commission, or the Wyoming Water Development northern end of the BH to corroborate this model result. Office. This study was funded by the Wyoming Water Moreover, in the WRR, SNOTEL gauges are not lo- Development Commission under Contracts RN052914F cated at the highest elevations where the WRF-CONUS and O5SC0296312 and the U.S. Bureau of Reclamation model indicated most of the precipitation falls. As a under Contract R11AC80816. result, SNOTEL analysis alone may give an incomplete picture of precipitation accumulation totals and spatial REFERENCES distribution in some of these regions. This finding sup- Auer, A. H., Jr., and D. L. Veal, 1970: An investigation of liquid ports the notion put forward in Lundquist et al. (2019) water-ice content budgets within orographic cap clouds. that we may now be in an era where high-resolution J. Atmos. Res., 4, 59–64. models are surpassing the skill of our observational Breed, D., R. Rasmussen, C. Weeks, B. Boe, and T. Deshler, 2014: networks. Evaluating winter orographic cloud seeding: Design of the While this analysis focused on five mountain ranges Wyoming Weather Modification Pilot Project (WWMPP). J. Appl. Meteor. Climatol., 53, 282–299, https://doi.org/10.1175/ in Wyoming, other areas across the broader region of JAMC-D-13-0128.1. study may have potential for cloud seeding to enhance Daly, C., R. P. Neilson, and D. L. Phillips, 1994: A statistical- precipitation, based upon the average amount of SLWP topographic model for mapping climatological precipitation over

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