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Generated using version 3.2 of the official AMS L A T E X template The dispersion of silver iodide particles from ground-based 1 generators over complex terrain. Part 2: WRF Large-Eddy 2 simulations vs. Observations 3 Lulin Xue * National Center for Atmospheric Research, Boulder, Colorado 4 Xia Chu University of Wyoming, Laramie, Wyoming Roy Rasmussen National Center for Atmospheric Research, Boulder, Colorado Daniel Breed National Center for Atmospheric Research, Boulder, Colorado Bruce Boe Weather Modification, Inc., Fargo, North Dakota Bart Geerts University of Wyoming, Laramie, Wyoming 5 * Corresponding author address: Lulin Xue, current affiliation: National Center for Atmospheric Research, Boulder, CO 80301 E-mail: [email protected] 1
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Page 1: Xia Chu Roy Rasmussen Daniel Breed Bruce Boe Bart Geertsgeerts/bart/xue_dry.pdf · 2013. 10. 4. · Bart Geerts University of Wyoming, Laramie, Wyoming 5 ∗Corresponding author address:

Generated using version 3.2 of the official AMS LATEX template

The dispersion of silver iodide particles from ground-based1

generators over complex terrain. Part 2: WRF Large-Eddy2

simulations vs. Observations3

Lulin Xue ∗

National Center for Atmospheric Research, Boulder, Colorado

4

Xia Chu

University of Wyoming, Laramie, Wyoming

Roy Rasmussen

National Center for Atmospheric Research, Boulder, Colorado

Daniel Breed

National Center for Atmospheric Research, Boulder, Colorado

Bruce Boe

Weather Modification, Inc., Fargo, North Dakota

Bart Geerts

University of Wyoming, Laramie, Wyoming

5

∗Corresponding author address: Lulin Xue, current affiliation: National Center for Atmospheric Research,

Boulder, CO 80301

E-mail: [email protected]

1

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ABSTRACT6

A numerical modeling study has been conducted to explore the ability of the WRF-based7

Large-Eddy Simulation (LES) with 100 m grid spacing to reproduce AgI particle dispersion8

by comparing the model results with measurements made on Feb. 16, 2011 over the Medicine9

Bow Mountains in Wyoming. The recently developed AgI cloud seeding parameterization10

(Xue et al. 2013b,a) was applied in this study to simulate AgI release from ground-based11

generators. Qualitative and quantitative comparisons between the LES results, observed12

soundings, and airborne/ground-based observed AgI concentrations were conducted. Anal-13

yses of TKE features within the planetary boundary layer (PBL) and comparisons between14

the 100 m LES simulation and simulations with 500 m grid spacing were performed as well.15

The results showed that: 1) Despite the moist bias close to the ground and above 4 km AGL,16

the LES simulation with 100 m grid spacing captured the essential environmental conditions17

except for a slightly more stable simulated PBL compared to the observed soundings. 2)18

Wind shear is the dominant TKE production mechanism in wintertime PBL over complex19

terrain and generates a PBL with about 1000 m depth. The terrain-induced turbulent eddies20

are primarily responsible for the vertical dispersion of AgI particles. 3) The LES-simulated21

AgI plumes were shallow and narrow, in agreement with observations. The LES simulation22

overestimated AgI concentrations close to the ground, which is consistent with the higher23

static stability in the model than observed. 4) Non-LES simulations using PBL schemes had24

difficulty capturing the shear-dominant turbulent PBL structure over complex terrain in25

wintertime. Therefore, LES simulations of wintertime orographic clouds with a grid spacing26

close to 500 m or finer are recommended.27

1. Introduction28

Inadequate or uncertain targeting of seedable clouds from silver iodide (AgI) ground-29

based generators has been a complex and a long-standing problem in winter orographic30

1

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cloud seeding programs. The efficacy of the ground-based seeding depends significantly31

on the effective dispersion of the seeding agent in orographic clouds over complex terrain.32

To address how AgI particles released from ground-based generators disperse over complex33

terrain within the Wyoming Weather Modification Pilot Program (WWMPP), which is an34

outcome-focused randomized program (Breed et al. 2011, 2013), a focused field experiment35

was conducted between 9 February and 1 March 2011. Part 1 of this study (Boe et al.36

2013) describes airborne measurements of AgI-based ice nuclei (IN) plumes from ground-37

based generators collected by Weather Modification Inc. (WMI) Piper Cheyenne II research38

aircraft equipped with an updated NCAR acoustic IN counter (Langer et al. 1967; Langer39

1973; Heimbach et al. 1977; Langer et al. 1978; Heimbach et al. 2008; Super et al. 2010). The40

airborne data were collected over the Wyoming Medicine Bow and Sierra Madre mountain41

ranges on nine different days during the field experiment period.42

Previous observational studies have documented the dispersion of ground-released AgI43

plumes over mountainous target regions. An airborne experiment conducted by Super (1974)44

studied the dispersion of an AgI plume over the Bridger Range in Montana using the original45

version of the NCAR acoustic IN counter. The plume width was ∼ 28 degrees and was46

mostly confined to the lowest 500 m above the ridge line. A similar experiment conducted47

by Holroyd et al. (1988) over the Grand Mesa of Colorado showed that the median spread of48

AgI plumes was ∼ 15 degrees and that the median plume height above the crest exceeded49

500 m. They also pointed out that the dispersion efficiency was higher during cloudy days50

than clear days. Measurements of microphysical changes induced by seeding such as high51

concentrations of small ice crystals also indicated that AgI plumes have relatively narrow52

spreads and remain close to the ground (Super and Heimbach 1988; Super and Boe 1988;53

Huggins 2007). More recently, Geerts et al. (2010, 2011) showed, by means of reflectivity data54

from a profiling radar, that the impact of ground-based orographic cloud seeding was confined55

to the planetary boundary layer (PBL), about 1 km deep. Most of the aforementioned studies56

focused on AgI dispersion from a single ground-based generator while the field experiment57

2

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within the WWMPP tried to assess the features of AgI plumes from both single and multiple58

ground-based generators, better representing the real WWMPP seeding experiments.59

Besides physical measurements of AgI plumes from ground-based generators by airborne60

and ground-based instruments, numerical models were used to investigate plume disper-61

sion. In the air quality modeling community, Lagrangian particle trajectories and dispersion62

models are commonly used to simulate pollutant transport and the dispersion of hazardous63

materials. Commonly used models include: the Hybrid Single Particle Lagrangian Inte-64

grated Trajectory (HYSPLIT) model (Draxler and Rolph 2003), the Second-order Closure65

Integrated Puff (SCIPUFF) model (Sykes and Gabruk 1997), and the FLEXTRA and FLEX-66

PART models (Stohl et al. 1995, 2005). These models are usually run in an “offline” mode,67

which means that they are driven by meteorological reanalysis data or meteorological con-68

ditions generated by numerical forecast models. However, for certain types of applications69

such as aerosol-cloud-precipitation interactions and glaciogenic seeding effects on orographic70

clouds, “online” calculations of particle transport and dispersion are needed. The reason71

is that the transport and dispersion of such passive particles will impact the microphysics72

of clouds and precipitation, which also influences the flow dynamics through microphysics-73

dynamics feedbacks. These interactions are not represented in “offline” models.74

Bruintjes et al. (1995) was probably the first study in the weather modification com-75

munity that compared online-calculated gaseous tracer concentrations (SF6), using a three-76

dimensional model, with the airborne in-situ observations. This pioneering work demon-77

strated the capability of numerical models to qualitatively and quantitatively capture the78

dispersion of AgI over complex terrain. The interaction between the airflow and the topog-79

raphy was identified as the dominant factor in determining the dispersion and transport of80

tracer materials. Part of the discrepancy between the simulated and observed results can be81

explained by the relatively large grid spacing (2 km) used in Bruintjes et al. (1995). Schicker82

and Seibert (2009) found that a grid spacing of 2.4 km did not reasonably simulate flows83

over complex terrain while 0.8 km did reproduce characteristic flow features. Weigel et al.84

3

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(2007) performed Large-Eddy Simulations (LES) using a grid spacing of 350 m to investi-85

gate turbulent kinetic energy (TKE) evolution in a steep and narrow Alpine valley. They86

found that wind shear was the dominant production mechanism for TKE over such complex87

terrain. LES simulations at 150 m grid spacing showed that the stable stratification limited88

turbulent stress to the lowest few hundred meters near the surface of the mountain (Chow89

et al. 2006).90

It is promising that high resolution LES simulations can reproduce flow features over91

complex terrain reasonably well. Moreover, high resolution cloud-resolving LES simulations92

capture the interactions between turbulent eddies and cloud microphysics over complex ter-93

rain. There are some observational evidences that PBL turbulence is important in snow94

growth in cold clouds hugging mountains (Geerts et al. 2011). This study does not examine95

cloud microphysical processes (the case study in question is a dry event), but it serves as a96

prerequisite study for follow-up observational and numerical work (currently in progress) into97

the impact of glaciogenic seeding on microphysical processes in orographic clouds. Specif-98

ically, this study examines the capability of LES at 100 m grid spacing using the Weather99

Research and Forecast (WRF) model to capture AgI dispersion over mountainous topogra-100

phy for a case on Feb. 16, 2011 described in Part 1 (Boe et al. 2013). The model results101

were evaluated using airborne and ground-based observations. Comparisons of model results102

between simulations with 500 m grid spacing and that of LES with 100 m grid spacing have103

also been conducted to provide guidance for future simulations. The observational exper-104

iment on Feb. 16 and the numerical experiment are described in section 2 and section 3105

respectively. The model-observation and model-model comparisons are presented in section106

4 followed by discussions in section 5. The main conclusions are summarized in section 6.107

4

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2. Description of the observational experiment108

Nine flights were conducted over the Medicine Bow and Sierra Madre mountains in109

Wyoming between Feb. 9 and Mar. 1, 2011. Operations required that (a) the region110

be devoid of low-level orographic clouds and snowfall in order to allow the aircraft to fly111

under visual meteorological conditions close to the terrain, and (b) substantial flow over the112

mountain. The latter was ascertained by requiring that the Froude number Fr > 1. On one113

of the nine flights, on Feb. 16, 2011 between 2240 UTC and 0216 UTC on Feb. 17, 2011,114

the IN counter registered high IN concentrations on several flight legs, with some well above115

mountain top level (Boe et al. 2013). Hereafter, all times are in UTC. The IN counter at116

the surface site also recorded the passage of AgI plumes (Boe et al. 2013). Given the rich117

observational data, this case was chosen for a LES simulation.118

The synoptic conditions during the observational period are illustrated in Fig. 1 using the119

North American Regional Reanalysis (NARR) data. These data have a spatial resolution of120

32 km and are available every 3 hours. The 700 hPa temperature (color shaded) was chosen121

because it roughly corresponds with the average mountain elevation above sea level. Mod-122

erately strong southwesterly flow persisted over a broad area ahead of a slowly progressing123

trough over California. Local cooling over the Medicine Bow Mountains (within the green124

circle) at about 2 K over 6 hours was due to cold-air advection and diurnal surface cooling.125

Wind speed and direction were fairly steady during this 6 hour period. The Medicine Bow126

Mountains was devoid of low-level clouds, and there was altostratus overhead (Boe et al.127

2013).128

For this case, radiosondes were released from Saratoga, a town upwind of the Medicine129

Bow Mountains (see Figs. 2(a) and (b)), on Feb. 16 at 2200 and on Feb. 17 at 0100.130

The observed soundings along with those from model simulations are shown in Fig. 3.131

The sounding parameters averaged between the surface and the peak of the Medicine Bow132

Mountains are listed in the left column of Table 1. Based on the Saratoga soundings, the133

winds (speed and direction) did not change much and the atmospheric stability decreased134

5

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slightly during a three-hour period. These sounding parameters are broadly consistent with135

the synoptic analyses.136

Five ground-based generators (red triangles in Fig. 2) were operated between 2148, 02-16137

and 0115, 02-17. The aircraft flew 8 legs transecting the plumes (see Fig. 2(c) and Fig. 6 for138

the flight pattern) and took measurements from 2319, 02-16 to 0035, 02-17 corresponding139

to local late afternoon1. The flight legs were upwind of the steep mountain crest around140

Medicine Bow Peak to avoid boundary-layer separation and vertical transport in the lee141

of this sharp crest, something commonly observed over this mountain range (French et al.142

2013). An IN counter, with a larger cloud chamber than the airborne version, was operated143

at the surface site (mountain meadow cabin #9 as shown in Fig. 2) to detect AgI plumes144

in downwind regions. The generator operational times and flight times are listed in Table 2.145

More details about this case can be found in Boe et al. (2013).146

3. Description of the numerical experiment147

The WRF model was run on two nested grids with grid spacings of 2500 m and 500 m148

respectively driven by the North America Regional Reanalysis (NARR) data in a non-LES149

mode initially. Hereafter, these two domains are referred to “coarse-resolution” or “non-150

LES” domains. Since a practical technique that communicates information between the151

outter domain in a non-LES mode and the nested domain in a LES mode (two-way nesting)152

is generally unavailable in WRF, a one-way nesting procedure was applied to drive the 100153

m LES simulation. The simulations on the two non-LES grids were spun up for 21 hours154

from 0000 to 2100 on Feb. 16, 2011. They were then run from 2100, 02-16 to 0300, 02-17155

with an output interval of 20 minutes. Subsequently, these outputs were processed to drive156

the LES simulation over the same period with lateral boundary conditions being updated157

every 20 minutes.158

1Local solar noon is at 1919 UTC, and sunset is at 0040 UTC on this day.

6

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The topographies of the domains with different grid spacings are shown in Fig. 2. The159

2500 m and 500 m domains consist of 320 by 220 grid points while there are 800 by 800 grid160

points for the LES domain. The vertically stretching coordinate as applied in Xue et al.161

(2010, 2012, 2013b,a) was adopted for all three domains. The vertical grid spacing is less162

than 200 m in the lowest 2000 m above ground level (AGL), which makes the grid aspect163

ratio of the LES domain less than 2 in regions experiencing the strongest AgI dispersion.164

For the LES domain, high-resolution elevation data from USGS 2 were used. For the two165

coarse-resolution domains, regular USGS 30-second data were used.166

In this study, the AgI cloud seeding parameterization documented in Xue et al. (2013b,a)167

was applied to simulate the release of AgI particles from ground-based generators. The size168

distribution of AgI particles from the generators is assumed to follow a lognormal distribution169

with a mean diameter of 0.05 µm and a geometric standard deviation of 2. The mean size170

of the AgI particle is slightly larger than that was specified in Xue et al. (2013b,a) due171

to slightly different ingredients of the AgI solution. The vertical mixing of AgI particles172

by the Mellor-Yamada-Janjic (MYJ) and the Yonsei University (YSU) PBL schemes was173

explicitly simulated in the coarse-resolution domains. No PBL scheme was specified for174

the LES simulation since we assumed that most of the terrainr-induced eddies responsible175

for AgI vertical mixing would be appropriately resolved with a 100 m grid spacing. For176

the LES simulation, a 1.5-order Turbulent Kinetic Energy (TKE) closure model was chosen177

(Deardorff 1980; Moeng 1984). A very short time step of 1/15 s was applied to ensure that178

the LES simulation remained stable3. The detailed configurations of the WRF model for all179

domains are listed in Table 3.180

21 arc-second data (about 30 m resolution) over the area of interest was downloaded from the National

Map Viewer and Download Platform for the LES simulation.3The abruptly changing topography introduces vigorous upward and downward motions close to the

ground which easily violates the vertical CFL criterion when a longer time step was applied.

7

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4. Results181

a. Appropriateness of the LES simulation182

Before any LES results can be compared with the observations, appropriateness of the183

LES simulation must be assessed. The first general and important question about such a184

simulation is how much spin-up time is needed. In this study, the coarse-resolution domains185

were spun up for 21 hours before the generators were turned on. However, the limited186

computational resources prevented us from performing a long LES spin-up simulation. Here,187

we investigate the spin-up time issue by analyzing the kinetic energy power spectrum as188

described in Errico (1985) and Skamarock (2004).189

The instantaneous 1D kinetic energy spectra at 15 min intervals averaged over the entire190

horizontal domain (excluding 10 grid points near boundary in each direction) between the191

surface and 2000 m AGL are plotted in Fig. 4 from 0 to 120 minutes. The height of 2000 m192

AGL was chosen because most of the terrain-induced turbulent eddies are only active below193

this level and the vertical resolution of the model is also appropriate for a LES simulation194

below this altitude, as shown later in this study. The blue line in each panel represents the195

k−5/3 slope, which indicates the inertial subrange of the kinetic energy spectrum. The LES196

domain covers 80 km by 80 km, which means that the largest scale in the energy spectrum197

is still within the mesoscale range; therefore the spectrum follows the inertial slope at most198

scales. The deviation of the spectrum from the inertial subrange at scales less than 1 km199

(> 10−3 m−1 in 1/wavelength space) is due to the numerical dissipation of the integration200

scheme in the model (Skamarock 2004) and to the vertical averaging. The effective resolution201

of this LES simulation is about 1 km (where the deviation of the spectrum begins) which is202

10 times the grid spacing of 100 m. This effective resolution-to-grid spacing ratio is slightly203

greater than that for mesoscale NWP model (typically arond 7, Skamarock 2004).204

Apparently, the initial wind field that was interpolated from the 500 m results did not205

produce the correct spectrum. Fifteen minutes into the LES simulation, the wind field was206

8

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adjusting to the underlying complex terrain. The turbulence at small scales injected energy207

into the wind field at large scales, causing an “overshoot” of the spectrum with respect to208

the inertial slope. Such adjustments and overshooting still existed after 30 minutes into209

the simulation. At 45 minutes, the wind field reached a balanced state and produced a210

spectrum following the inertial subrange at scales greater than 1 km. The spectrum remained211

basically unchanged after 45 minutes. Such a steady state spectrum will not be achieved if212

the synoptic conditions change significantly during this period. Based on the evolution of213

the model kinetic energy power spectrum, we conclude that the LES simulation needs about214

45 minutes to spin up in this specific case. Since the AgI particles were released from the215

generators after 48 minutes in the LES simulation, the dispersion of these particles should216

be properly simulated by the balanced flow field.217

Another important aspect for LES simulations to be verified is whether the subgrid-scale218

TKE is small compared to the total or resolved-scale TKE. LES by definition requires the219

model to resolve eddies at the most energetic scales. The energy at unresolved scales must be220

modeled or parameterized. The profiles of total TKE, subgrid TKE and half of the vertical221

velocity variance (vertical momentum flux) are illustrated in Fig. 5 for both the upwind222

and downwind regions. The upwind region is defined as the 360 by 360 grid points in the223

south-west corner of the domain while the downwind region is the 440 by 440 grid points224

in the north-east corner4. The profiles from 0 to 2000 m AGL represent values of these225

terms temporally averaged between 2145, 02-16 and 0115, 02-17 (operational time of the226

generators) and spatially averaged within the upwind and downwind regions.227

For both the upwind and downwind regions, the subgrid TKE is a small portion of228

the total TKE at each level. The subgrid-scale TKE to total TKE ratio is around 10%229

to 20% in the lowest 200 m and becomes less than 10% above 200 m. The higher ratio230

close to ground agrees with previous LES studies of a buoyancy-driven PBL (Deardorff231

1980; Moeng 1984). Since the atmosphere was stable and windy, the flow in the downwind232

4The peak of the Medicine Bow Mountains is roughly at grid point (360,360).

9

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region was approximately between a resonance state and a boundary layer separation regime233

(1 < Fr < 1.7, Table 1) (Stull 1988). The facts that no terrain-induced gravity waves or234

mountain wave breaking existed in this case and that the interactions between the flow235

and terrain were weaker in the downwind region resulted in a weaker TKE profile in the236

downwind region than in the upwind region. The profiles of vertical velocity variances (Fig.237

5) highlighted the fact that the majority of the TKE resided in the horizontal dimension238

because the wind shear was the dominant TKE production mechanism in this case (see239

detailed discussions in section 5). These profiles also indicated a PBL height of about240

1000 m AGL in this case, which agrees with the observations of Geerts et al. (2010, 2011).241

Such a PBL height suggests that the vertical grid spacing used in the LES simulation was242

appropriate.243

b. Sounding comparisons244

After the appropriateness of the LES simulation is justified, model simulated and observed245

meteorological conditions are compared in this section. Since the NARR data were used to246

drive the simulations, comparisons of synoptic fields between the model and the NARR data247

are inappropriate. We then compare the sounding information at Saratoga in this section.248

The observed and simulated soundings at 2200, 02-16 and 0100, 02-17 are plotted in Fig.249

3. The observations were taken at Saratoga (black curves); simulated soundings from the 500250

m non-LES simulation were also located at Saratoga (red curves); and simulated soundings251

from the 100 m LES simulation were located about 6 km to the east of Saratoga (the star252

symbol in Fig. 2(c)) (blue curves). Both simulations reproduced the observed temperature253

structure above 700 hPa reasonably well. However, they both failed to simulate the moisture254

structure above 4 km AGL (∼ 600hPa). The NARR data apparently diagnosed more mois-255

ture in the free atmosphere than Saratoga sounding indicated, which forced the simulations256

to generate a more humid atmosphere. Close to the ground, both simulations overestimated257

the dewpoint temperature and underestimated the temperature. This is mainly due to the258

10

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incorrect soil moisture initialization in the NARR data5. At both sounding times, the 500259

m simulation slightly outperformed the 100 m LES simulation in terms of temperature and260

dewpoint temperature between the surface and 700 hPa. The 500 m simulation also cap-261

tured more realistic low-level wind directions than the 100 m LES simulation. However, such262

differences might be the result of the slightly different sounding location used in the 100 m263

LES simulation.264

Sounding parameters were calculated between 60 m AGL and the peak of the Medicine265

Bow Mountains (Table 1). Observations below 60 m were not included due to artifacts266

introduced during the sounding launch. The model data below 60 m were removed as well267

due to the unrealistic values caused by the erroneous soil moisture initialization. Similar268

to what was found in Fig. 3, both simulations generated realistic wind direction between269

the surface and the mountain peak. The 500 m run simulated slightly better wind speed270

than the 100 m LES simulation. But the 100 m LES simulation captured wind shear and271

shear direction changing trend better than the 500 m simulation6. Both runs simulated a272

more stable atmosphere than the soundings with the 100 m LES simulation being slightly273

more stable than the 500 m simulation. As a result, both simulations under-predicted Fr274

and over-predicted Ri. Nevertheless, both simulations showed the same downward trend in275

stability as in the observations. Due to the moist nature of the NARR-driven simulations,276

the simulated lifted condensation level (LCL) heights were lower than observed. The LCL277

height was especially low in the 100 m LES simulation at 0100, 02-17. Despite the moist278

bias aloft and close to the ground simulated by the model, both the 500 m non-LES and279

the 100 m LES simulations captured the general features of the flow in the PBL where the280

dispersion of AgI particles occurred as will be revealed in the following sections.281

5Saratoga, as a small urban area which is not resolved by the NARR data, has higher surface temperature

and lower soil water content than its surroundings.6Wind shear turned counter clockwise in both observations and 100 m LES simulation while it turned

clockwise in 500 m simulation.

11

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c. AgI concentration comparisons282

As discussed in Part 1 (Boe et al. 2013), the IN counter has a time-distributed delay283

averaging 80 seconds from the ingestion of atmospheric IN to the detection of them. Thus,284

the measurements in the form of IN count rate (s−1) accounting for the 80 s delay are plotted285

along the flight tracks in Fig. 6. Figure 6(a) reveals that the horizontal spread of the AgI286

plumes was not significant, with the majority of the plumes evident in the immidiate down-287

wind vicinity of the generators. However, since the flight legs were vertically orientated, the288

horizontal spread of the plumes at low levels may have been wider. The temporal smoothing289

of IN measurements (and thus spatial dispersion because of the aircraft movements) was290

not accounted for in Fig. 6. Thus, in reality, the IN plumes may be more narrow than291

suggested in Fig. 6(a) (Boe et al. 2013). Figure 6(b) illustrates the vertical structure of the292

observed AgI plumes. The vertical coordinate is referenced to mean sea level (MSL). The293

highest count rates (> 10 s−1) were observed in the lowest leg and the count rates gener-294

ally decreased with increasing altitude. The features of observed AgI plumes in this case295

agreed with many previous observational studies (Super 1974; Holroyd et al. 1988; Super296

and Heimbach 1988; Super and Boe 1988).297

Figure 7 shows 3D depictions of LES-simulated AgI plumes and wind fields at 2230 on298

02-16, 0000 on 02-17 and 0130 on 02-17. The wind field showed little change during the 3-299

hour period, which is consistent with the analyses presented in previous sections. Spatially,300

the high-level wind showed constant wind speed and almost uniform wind direction while301

the low-level wind was forced by the complex topography and presented higher variability302

than the wind field aloft. The mid-level wind was more variable than the high-level wind303

but less variable than the low-level wind.304

In Fig. 7, the visible blue plumes indicate AgI particles with number concentration305

greater than 100 L−1, which roughly corresponds to one IN count per second in the ob-306

servations (approximately the lowest concentration measurable with the IN counter in its307

configuration used in this experiment, see Part 1 for details). The AgI plumes remained308

12

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narrow during this period. The plumes from generators MP, BR and BCH (see Fig. 2 for309

locations) merged into one large plume at 0000 on 02-17, but the plumes from generators310

FCO and RR2 stayed separate throughout the simulation. The general morphology of these311

plumes in the horizontal compared qualitatively well with the observations (see Fig. 6(a)).312

The vertical extent of the simulated plumes are also illustrated in Figs. 7(b1)-(b3). Note313

that the plume from RR2 (the southeastern generator) had more vertical dispersion than314

the others during this period. Most of the time, the plumes draped over the mountain with315

high concentrations of AgI particles close to the ground.316

To better compare the vertical structure of the AgI plumes, the AgI concentrations are317

plotted along two cross sections (as indicated by two black dashed lines in Fig. 2(c)) in Fig.318

8 for the same times as shown in Fig. 7. The two cross sections, NW to SE orientated and319

about 6 km apart, were chosen to cover the flight legs 3 to 8. Also plotted are the TKE isoline320

of 1 m2s−2 (red lines) and cloud water mixing ratio (black lines). Similar to Fig. 6(b1), the321

plume from RR2 (the southernmost plume) reached a higher altitude than the others at322

02-16 22:30 (Figs. 8(a1) and (b1)). The plumes from FCO (the center plume) and RR2 had323

AgI concentrations greater than 105 m−3 or 100 L−1 (colors that are warmer than yellow in324

the color bar) above the maximum observation altitude of 3800 m MSL (indicated by black325

dashed lines) at this time (Figs. 8(a1) and (b1)). The envelope of the plumes’ upper edges326

is co-located with the active turbulence region (red outlines), which implies that turbulent327

mixing is the primary mechanism for AgI vertical dispersion. Since the instantaneous plume328

morphology is an integrated reflection of the turbulent eddy history, occasional mismatches329

between the plumes’ upper edges and the instantaneous TKE isoline are reasonable.330

Figures 8(a2) and (b2) show that at 0000 on 02-17, the turbulence was not as active331

in the plume locations as 1.5 hours ago. Correspondingly, the plumes stayed close to the332

ground. Only a small portion of the plume from the FCO generator reached 3800 m MSL333

with a AgI concentration greater than 10 L−1 (see yellow areas in Fig. 8(b2)). This match334

between low turbulent activity and weak AgI vertical extent confirms that turbulent eddies335

13

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are primarily responsible for the vertical dispersion of AgI particles. Also at this time, clouds336

started to form above 4000 m MSL, consistent with the sounding analyses in section 4b. At337

the later time (0130 on 02-17), the turbulence became strong again over the plume regions.338

As a result, the AgI plumes reached higher than 1.5 hours earlier. The clouds grew into a339

deck locating just above the plumes. Visual observations showed that there was a cloud deck340

close to this altitude when the airborne observations were taken (see Fig. 6 in Boe et al.341

(2013)).342

It is noticed from Fig. 8 that the turbulence was more active over the southern part of343

the domain. Indeed, the southern portions of these cross sections are part of the upwind344

region and the northern parts are associated with the downwind region due to the southerly345

component of the prevailing low level wind. Based on the analyses showed in section 4a,346

the TKE is stronger in upwind region than in downwind region (see Fig. 5). The isoline347

of TKE=1 m2 s−2 is almost exactly the cut off value for downwind TKE (see Fig. 5).348

Therefore, less TKE active region in the northern part of the cross sections showed up in349

Fig. 8. The TKE budget terms in this case will be analyzed in detail in section 5.350

The qualitative comparisons between the observations and LES-simulated AgI dispersion351

presented so far indicate that the horizontal spread of the simulated AgI plumes was similar to352

what was observed and the simulated plumes extended to the highest observational altitude353

with comparable AgI concentrations. Quantitative comparisons are needed to further assess354

the usefulness of the LES simulation. Therefore, we plot the contour-frequency by altitude355

diagrams (CFAD) of airborne measurements (all the data from flight legs 1 to 8) and LES356

results in Fig. 9. In each panel, the X-axis represents the IN count rate in s−1 (the AgI357

count rate converted from the concentration for model results), the Y-axis is the altitude (m358

AGL), and the frequency (ratio of AgI-altitude data points over total number of smaples) is359

color shaded. The observations numbered 2918 along the flight legs, and are plotted in the360

CFAD format in Fig. 9(a). To make the comparisons as consistent as possible, we identified361

the 2918 grid points corresponding to the locations of all the measurements in space. Since362

14

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the LES results were output every 5 mins (see Table 3), it is impossible to match the exact363

timing of each measurement. Therefore, the average value of each grid point between 2315,364

02-16 and 0035, 02-17 (the observational period as listed in Table 2) was plotted in Fig.365

9(b). Notice that the observed AgI concentrations as a whole is a subset of the entire AgI366

concentration space (a continuous temporal-spatial manifold), the observations should be367

bounded by the maximum possible values in the entire concentration space. We thus plotted368

the maximum AgI count rate from each of the 2918 grid points between 2315, 02-16 and369

0035, 02-17 in Fig. 9(c).370

Figure 9 shows that low values of both observed and simulated AgI count rate (< 2 s−1)371

dominated the appearance frequency at all altitudes (blue to green or even red colors). The372

CFAD of the average simulated AgI count rate resembled the observed CFAD below about373

400 m AGL, but it deviated in the AgI count rate comparison between 400 and 1000 m374

AGL. In this layer, the LES-averaged CFAD overestimated the frequency of small values375

(low AgI concentration) and underestimated the large values (high AgI concentration). As376

discussed previously, the vertical dispersion of AgI particles was largely determined by the377

turbulent eddies which are highly intermittent in space and time. The LES-averaged CFAD378

(over 1 hour and 20 mins) significantly smoothed out the intermittency and underestimated379

AgI concentration than the observed CFAD. In contrast, the maximum LES-simulated AgI380

concentrations resulting from the strongest turbulent eddy during the measurement period381

should encompass the observed values. Figure 9(c) shows that the maximum LES-simulated382

CFAD overestimated the frequencies of large values at almost all altitudes. Quantitatively,383

the observations lie between the two LES-simulated CFADs. Considering the uncertainties384

associated with the observations and the LES simulation, the CFAD comparisons show good385

agreements between the observed and LES-simulated AgI concentrations.386

Measurements of IN concentrations at the surface site (mountain meadow cabin #9)387

were also collected in the downwind region of the airborne measurements in this case (see388

Fig. 2(c) for the location of this site). Figure 10 shows time series of 15-min averaged AgI389

15

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concentration between 2200, 02-16 and 0300, 02-17 for observations at the surface site (panel390

(a)), results of the LES simulation at the closest grid point to the site (panel (b)) and results391

of the LES simulation averaged over the 9 by 9 closest grid points to the site (panel (c)).392

The model values were taken from the first layer which is about 7 m AGL at the surface393

site.394

The LES simulation generally over-predicted AgI concentrations at the surface site but395

remained within one order of magnitude. The over-predicted AgI concentrations maybe396

caused by 1) the inefficient vertical dispersion by the turbulent eddies, and 2) errors in the397

simulated wind direction. Relative to point 1), the LES simulation showed more stable398

conditions than observed (see Table 1). Therefore, the simulated eddies might be weaker399

than the actual conditions, particularly at the surface site (downwind region), which would400

leads to higher concentrations of AgI close to the ground. As for point 2), the LES-simulated401

wind direction was more southerly than the observed wind in the first half of the simulation.402

Thus, more AgI particles were advected from the RR2 generator to the surface site in the403

model. Although the model overestimated the AgI concentrations at the surface site, it404

captured the trends quite well. A weak plume of AgI particles was observed to arrive at the405

site at 2245 on 02-16 and last for about 45 minutes. The LES simulation showed that the406

plume arrived about 15 minutes earlier than observed and had elevated concentrations for407

about an hour. Observations also showed that a second strong plume swept over the surface408

site from 0045, 02-17 to 0200, 02-17 while the model predicted a second wave of elevated409

concentrations between 0130 and 0200 on 02-17.410

d. LES and non-LES comparisons411

The 100 m LES simulation has been validated using the airborne and ground-based412

measurements of AgI number concentration in the previous section. The LES simulation413

with 100 m grid spacing was shown to reasonably capture the AgI dispersion characteristics414

over the Medicine Bow Mountains in the Feb. 16, 2011 case. If we assume the LES simulation415

16

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to be a reasonable representation of the reality, we can use the high resolution LES results416

to validate other simulations with lower resolutions. Here, we provide an example of such417

validations using simulated CFADs of AgI concentration and profiles of total AgI number.418

To conduct such validations, three extra simulations over the domain with 500 m grid419

spacing were performed. Each run was a one-way nesting simulation driven by the outputs420

of 2500 m simulation. The output frequency was set to 5 minutes to match that of the421

100 m LES simulation. One of the runs was the LES simulation, one a non-LES simulation422

using the MYJ PBL scheme and one a non-LES simulation using the YSU PBL scheme.423

The CFADs of AgI concentration over the 100 m LES domain (for 500 m simulations, it is424

a subset of the domain as indicated by the black box in Fig. 2(b)) during the AgI release425

period (2145 on 02-16 to 0115 on 02-17) are plotted in Fig. 11 for the 100 m LES simulation426

and the other three 500 m simulations. The X-axis is model simulated AgI concentration427

from 10−3 to 109 m−3 in a logarithmic scale. The Y-axis and color shaded areas have the428

same meanings as the Fig. 9. The data sample included all the model data below 1800429

m AGL over the indicated domain and from all output records. The large data population430

resulted in much smoother CFADs compared to those in Fig. 9.431

The CFAD of the 500 m LES simulation looks very similar to that of the 100 m LES432

simulation. Both CFADs showed high frequencies of data that are confined in concentra-433

tion between 104 and 107 m−3 and in altitude between 0 and 300 m AGL. For the lower434

concentration range, the 500 m LES simulation also predicted similar pattern to the 100435

m LES simulation (blue belt from 10−3 to 103 m−3 and from 300 to 1000 m AGL). Such436

features were also captured by the 500 m MYJ simulation. But it totally missed the high437

concentration features as in both LES simulations. Basically, the 500 m MYJ simulation438

predicted a much shallower mixed layer than LES simulations. Similarly, the 500 m YSU439

simulation also simulated a very shallow mixed layer in which very high concentration of440

AgI stayed close to the ground. Some local maxima close to 103 m−3 at about 200 m AGL441

were also predicted by the 500 m YSU simulation.442

17

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The different properties of AgI vertical dispersion simulated by these simulations can be443

found in the profiles of total AgI number as well (Fig. 12). As showed in Fig. 12, the 500 m444

LES simulation predicted a similar profile to the 100 m LES simulation when the generators445

were turned off. The AgI number is greatest in the 100 m LES simulation between 150446

and 1700 m AGL. The 500 m MYJ simulation reproduced the trend of the LES simulations447

but under predicted the number more than an order of magnitude between 200 and 1500 m448

AGL. The 500 m YSU simulation completely missed the main features produced by the LES449

simulations and simulated a very inactive PBL with mininum vertical dispersion. Both LES450

simulations predicted fewer AgI number close to ground compared to non-LES simulations,451

which indicates the low-level vertical dispersion is stronger in LES mode.452

Based on the CFAD and AgI number profile comparisons, the LES simulation with453

500 m grid spacing mostly reproduced the “real” AgI dispersion characteristics. Non-LES454

simulations using PBL schemes had difficulty capturing the shear-dominant turbulent PBL455

structure over complex terrain in wintertime. More analyses on flow features, turbulent456

properties and surface fluxes need be done in the future to better understand why PBL457

schemes failed in this case. In this study, we tentatively suggest that LES simulations458

should be performed for wintertime orographic clouds with a grid spacing close to 500 m459

or finer. In a coarse grid, the MYJ PBL scheme should be used to simulate the glaciogenic460

seeding of wintertime orographic clouds.461

5. Discussions462

The wintertime PBL over complex terrain is not consistent with the traditional buoyancy-463

driven mixed layer concept. This statement can be confirmed by analyzing the profiles of464

TKE budget terms in the TKE tendency equation7. A symbolic form of the TKE tendency465

equation can be written as:466

7The complete equation can be found in Stull (1988).

18

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467

∂TKE

∂t= BUOY + SHEA + ADV T + TUTR + PRAD + DISS (1)

where ∂TKE∂t

is the TKE tendency or storage term (Stull 1988), BUOY is the buoyant produc-468

tion term, SHEA is the shear or mechanical production term, ADVT is the TKE advection469

term, TUTR is the turbulent transport term, PRAD is the pressure adjustment of corre-470

lation term, and DISS is the dissipation term. Figure 13 illustrates the profiles from 0 to471

2000 m AGL of the BUOY, SHEA, ADVT, TUTR and PRAD terms over the upwind region472

(same as in Fig. 5) during the AgI release time (2145 on 02-16 to 0115 on 02-17). The473

storage term and dissipation term are ignored to make the plot more readable.474

Since this experiment was carried out in late afternoon on an overcast winter day, the475

buoyancy played an insignificant role in TKE production. Actually, it worked as a destruction476

term since the atmosphere was stably stratified8. Wind shear, on the other hand, dominated477

the TKE production between 100 and 600 m AGL. Such a deep shear layer consistently478

generating turbulent eddies does not exist in a traditional convective PBL over flat terrain.479

The breakdown of the shear term indicates that the negative values below 100 m AGL are480

associated with the persistent positive vertical kinematic eddy fluxes (u′w′ > 0 and v′w′ > 0)481

even if the shears are positive (du/dz > 0 and dv/dz > 0). Such features are suspected to482

relate to the interactions between the fine-scale terrain properties and the low level wind483

field. Further work should be done to decode this phenomenon. Both buoyancy-driven484

and shear-driven turbulent eddies are anisotropic. Buoyant turbulence is more vertically485

oriented while shear-induced turbulence is strongest in the horizontal. The shear dominant486

TKE production showed in Fig. 13 explains the small fraction of the vertical component to487

the total TKE in the PBL as shown in Fig. 5.488

The turbulent advection, transport and pressure adjustment terms showed similar ver-489

tical patterns with the destruction of turbulence at low levels and the production at high490

8Under such stable conditions, an air parcel displaced vertically by turbulent eddies will experience a

buoyancy force pushing it back to its original place (Stull 1988).

19

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levels. However, the interception of the curve and the zero line increases from 400 m AGL491

for advection, to about 600 m AGL for transport and to about 900 m AGL for pressure ad-492

justment term. All these three terms might work as production or destruction terms locally.493

But integrated over the horizontal and the vertical, they effectively become zero. These494

terms only adjust or redistribute turbulent energy generated by buoyancy and shear. Figure495

13 shows that these mechanisms moved the turbulent energy generated in the wind shear496

layer to higher levels. The pressure adjustment term was responsible for the nonnegligible497

TKE above 1000 m AGL as shown in Fig. 5.498

The discussions on the TKE budgets indicate that the wintertime PBL over complex499

terrain is very different from the summer time convective PBL over flat terrain. In this case,500

the wind shear induced by the rough terrain existing in a relatively deep layer dominates501

the TKE production that is horizontally orientated. Such a shear-driven turbulent layer is502

not only responsible for AgI dispersion but also believed to enhance orographic precipitation503

by increasing riming efficiency as proposed in Houze and Medina (2005). In this case, the504

shear-driven turbulent layer is entirely within the PBL, as evident from the TKE profile, in505

contrast to the elevated shear zone in Houze and Medina (2005). Clearly, PBL turbulence506

is ubiquitous over complex terrain (where winter storms are usually accompanied by strong507

winds), thus orographic precipitation may rather generally be enhanced by PBL turbulence,508

as suggested by Geerts et al. (2011). It is worth mentioning that the longwave radiation509

cooling of the cloud deck could invigorate the underlying turbulence, which enhances the510

scalar dispersion and snow growth. Further discussion of this topic is the subject of a future511

paper.512

AgI contamination over target areas from the previous seeding operations is a practical513

problem for many randomized wintertime orographic cloud seeding programs. Usually, the514

mean wind speed and the target scale are used to calculate the average AgI dissipation515

time. However, the interactions between the topography and the low level wind have been516

shown to be very complex in this study. The actual AgI dissipation time is believed to be517

20

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different than the simple calculated value. This problem is hard to address by the physical518

experiments performed by WMI but it is relatively easy to answer by the LES simulation.519

Based on this LES simulation, it took about 1.5 hours for AgI plumes to transport and520

dissipate away the target region after seeding ceased. This time is significantly longer than521

the simple calculated time of 50 mins.522

6. Conclusions523

A numerical modeling study has been conducted to explore the ability of the WRF-based524

LES model to reproduce AgI particle dispersion by comparing the model results with mea-525

surements made on Feb. 16, 2011 over the Medicine Bow Mountains in Wyoming. The526

recently developed AgI cloud seeding parameterization (Xue et al. 2013b,a) was applied in527

this study to simulate AgI release from ground-based generators. Qualitative and quan-528

titative comparisons between LES results and observed soundings, airborne/ground-based529

observations were conducted. Analyses on TKE features within the PBL and comparisons530

between 100 m LES simulation and simulations with 500 m grid spacing have been performed531

as well. The main conclusions of this study are:532

1 Despite the moist bias close to the ground and above 4 km AGL, the LES simulation533

with 100 m grid spacing captured the essential environmental conditions and simulated534

a slightly more stable PBL compared to the observed soundings at Saratoga.535

2 Wind shear is the dominant TKE production mechanism in wintertime PBL over536

complex terrain and generates a PBL with about 1000 m depth. The terrain-induced537

turbulent eddies are responsible for the vertical dispersion of AgI particles.538

3 The LES simulated AgI plumes were shallow and narrow, in agreement with inherently539

limited observations. The LES simulation overestimated AgI concentrations close to540

the ground due to more stable simulated condition than the real atmosphere.541

21

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4 Non-LES simulations using PBL schemes had difficulty capturing the shear-dominant542

turbulent PBL structure over complex terrain in wintertime. LES simulations should543

be performed for wintertime orographic clouds with a grid spacing close to 500 m or544

finer.545

The LES simulation was shown to reasonably simulate the AgI dispersion over the com-546

plex terrain in this study. Using the AgI cloud seeding parameterization, more LES simula-547

tions will be performed in the near future to investigate the glaciogenic cloud seeding effect548

of the wintertime orographic clouds.549

Acknowledgments.550

This study was partly supported by the NCAR Advanced Study Program and the551

Wyoming Weather Modification Pilot Program. X. Chu and B. Geerts are greatful for the552

support of the AgI Seeding Cloud Impact Investigation project (NSF AGS-1058426). All553

rights to the underlying data collected and/or generated with funding from the Wyoming554

Water Development Office (WWDO) from which this report was created, remain with the555

WWDO. This report does not constitute the opinions of the State of Wyoming, the Wyoming556

Water Development Commission or the Wyoming Water Development Office.557

22

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558

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energy in a steep and narrow Apline valley. Bound.-Layer Meteor., 123, 177 – 199.637

Xue, L., A. Teller, R. M. Rasmussen, I. Geresdi, and Z. Pan, 2010: Effects of Aerosol Solu-638

bility and Regeneration on Warm-phase Orographic Clouds and Precipitation Simulated639

by a Detailed Bin Microphysical Scheme. J. Atmos. Sci., 67, 3336 – 3354.640

Xue, L., A. Teller, R. M. Rasmussen, I. Geresdi, Z. Pan, and X. Liu, 2012: Effects of641

Aerosol Solubility and Regeneration on Mixed-phase Orographic Clouds and Precipitation.642

J. Atmos. Sci., 69, 1994 – 2010.643

Xue, L., S. Tessendorf, E. Nelson, R. Rasmussen, D. Breed, S. Parkinson, P. Holbrook, and644

D. Blestrud, 2013a: AgI cloud seeding effects as seen in WRF simulations. Part II: 3D645

real case simulations and sensitivity tests. J. Appl. Meteor. Climatol., 52, 1458 – 1476.646

Xue, L., et al., 2013b: AgI cloud seeding effects as seen in WRF simulations. Part I: Model647

description and idealized 2D sensitivity tests. J. Appl. Meteor. Climatol., 52, 1433 – 1457.648

26

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List of Tables649

1 Summary of the sounding parameters. 28650

2 Generator and flight operational time. 29651

3 Model configurations. 30652

27

Page 29: Xia Chu Roy Rasmussen Daniel Breed Bruce Boe Bart Geertsgeerts/bart/xue_dry.pdf · 2013. 10. 4. · Bart Geerts University of Wyoming, Laramie, Wyoming 5 ∗Corresponding author address:

Table 1. Summary of the sounding parameters.

Parameters1\Cases2 Observations 500 m non-LES 100 m LES2200 UTC on 2011-02-16

U (ms−1) 22 19 17U dir (o) 227 225 221S (10−2 s−1) 1.25 0.70 1.21S dir (o) 250 265 272N (10−2 s−1) 0.77 1.01 1.10Fr (-) 1.87 1.22 0.97Ri (-) 0.38 2.07 0.83HLCL (m AGL) 2280 1730 1327

0100 UTC on 2011-02-17U (ms−1) 23 21 19U dir (o) 219 222 220S (10−2 s−1) 1.35 1.20 1.51S dir (o) 242 286 265N (10−2 s−1) 0.64 0.77 1.00Fr (-) 2.32 1.78 1.23Ri (-) 0.23 0.41 0.44HLCL (m AGL) 1642 1320 9481 All parameters were calculated between 60 m AGL and the peak

of the Medicine Bow Mountains. U is the mean wind speed; Udir is the direction of the mean wind; S is the bulk wind shear be-tween 60 m AGL and the peak of the Medicine Bow Mountains; Sdir is the direction of the wind shear; N is the Brunt-Vaisala fre-quency (average of the dry and the moist values); Fr = U/(NH)is the local bulk Froude number, where H is the height of be-tween 60 m AGL and the peak of the Medicine Bow Mountains;Ri = N2/S2 is the bulk Richardson number; and HLCL is theheight of the lifted condensation level (LCL) above the ground.

2 For the observations and the 500 m non-LES simulation, the pa-rameters were calculated over Saratoga (see Figs. 2(a) and (b)).For the 100 m LES simulation, the parameters were calculatedover the starred location shown in Fig. 2(c).

28

Page 30: Xia Chu Roy Rasmussen Daniel Breed Bruce Boe Bart Geertsgeerts/bart/xue_dry.pdf · 2013. 10. 4. · Bart Geerts University of Wyoming, Laramie, Wyoming 5 ∗Corresponding author address:

Table 2. Generator and flight operational time.

Generators Operational timeMullison Park (MP) 2148, 02-16 to 0104, 02-17Barrett Ridge (BR) 2149, 02-16 to 0106, 02-17French Creek Overlook (FCO) 2150, 02-16 to 0111, 02-17Rob Roy 2 (RR2) 2151, 02-16 to 0113, 02-17Beaver Creek Hills (BCH) 2152, 02-16 to 0115, 02-17Flight leg Observational timeLeg 1 2319, 02-16 to 2323, 02-16Leg 2 2326, 02-16 to 2333, 02-16Leg 3 2335, 02-16 to 2342, 02-16Leg 4 2345, 02-16 to 2353, 02-16Leg 5 2355, 02-16 to 0004, 02-17Leg 6 0007, 02-17 to 0014, 02-17Leg 7 0017, 02-17 to 0025, 02-17Leg 8 0027, 02-17 to 0035, 02-17

29

Page 31: Xia Chu Roy Rasmussen Daniel Breed Bruce Boe Bart Geertsgeerts/bart/xue_dry.pdf · 2013. 10. 4. · Bart Geerts University of Wyoming, Laramie, Wyoming 5 ∗Corresponding author address:

Table 3. Model configurations.

Configurations\Domains 2500 m non-LES 500 m non-LES 100 m LESSimulation time 0000, 02-16 to 0300, 02-17 2100, 02-16 to 0300, 02-17Time step 15 s 5 s 1/15 sOutput frequency 1 h 20 mins 5 minsRadiation CAM short wave and long wave schemesPBL Mellor-Yamada-Janjic (MYJ) scheme N/ASurface Noah land surface schemeMicrophysics Thompson scheme with AgI cloud seeding parameterizationTurbulence Horizontal Smagorinsky first order closure 1.5-order TKE closure

30

Page 32: Xia Chu Roy Rasmussen Daniel Breed Bruce Boe Bart Geertsgeerts/bart/xue_dry.pdf · 2013. 10. 4. · Bart Geerts University of Wyoming, Laramie, Wyoming 5 ∗Corresponding author address:

List of Figures653

1 Maps of NARR 700 hPa height (thick black contours, 30 m interval), temper-654

ature in Celsius (color shaded), wind barbs (full barbs equals 5 m s−1) and655

300 hPa height (thick white contours, 60 m interval). Green circles represent656

target area. (a) valid 2100, 2011-02-16, (b) valid 0000, 2011-02-17, (c) valid657

0300, 2011-02-17. 34658

2 Topography of (a) 2500 m grid spacing domain, (b) 500 m grid spacing domain659

and (c) 100 m grid spacing LES domain. Red triangles indicate the locations of660

five generators operated on Feb. 16, 2011. The black square symbol represents661

the surface site – mountain meadow cabin #9. The black cross in (a) and (b)662

indicates the town of Saratoga where the radiosondes were released. The large663

black boxes in (a) and (b) represent the domains in (b) and (c) respectively. In664

(c), the abbreviated name of each generator (listed in Table 2) is listed below665

the corresponding red triangle symbol. The flight pattern is illustrated by the666

blue line. The black dashed lines show the locations of cross-sections depicted667

in Fig. 8. The star symbol close to the western border of (c) represents the668

location of the LES-simulated (blue) soundings shown in Fig. 3. 35669

3 Soundings from observations (black), 500 m non-LES simulation (red) at670

Saratoga and from 100 m LES simulation (blue) at starred location in Fig.671

2(c) for (a) 2200, 2011-02-16 and (b) 0100, 2011-02-17. Temperature (oC) is672

the solid line and dewpoint temperature (oC) is the dashed line. Wind barbs673

are in m s−1 (full barb equals 5 m s−1). 36674

4 Average kinetic energy power spectra (energy density in m3 s−2) over the LES675

domain below 2000 m AGL. Each panel illustrates the spectrum at a moment676

from 0 to 120 minutes in 15 min invervals. The blue line indicates the k−5/3677

slope. 37678

31

Page 33: Xia Chu Roy Rasmussen Daniel Breed Bruce Boe Bart Geertsgeerts/bart/xue_dry.pdf · 2013. 10. 4. · Bart Geerts University of Wyoming, Laramie, Wyoming 5 ∗Corresponding author address:

5 Average profiles of the total TKE (solid lines), the subgrid-scale TKE (dashed679

lines) and half of the vertical velocity variance (dotted lines) over the upwind680

region (red) and the downwind region (green) between 02-16 21:45 and 02-17681

01:15. All terms have units of m2 s−2 38682

6 The plan view (a) and the cross-section viewed from west (b) of the in-situ683

measurements of AgI IN along the flight legs. The IN count rate (s−1) from684

the NCAR acoustic IN counter is color coded. In (a), the terrain is contoured685

every 500 m from 2000 to 3500 m MSL. Generators and the surface site are686

indicated by red triangles and the black square respectively. In (b), the flight687

legs are labeled from 1 to 8. 39688

7 3D depictions of the topography, AgI number concentration (greater than689

100 L−1 for visible plumes) and wind vectors (∼ 2800 m – yellow, ∼ 3600 m –690

blue and ∼ 4400 m – purple MSL). Panels (a) are bird’s-eye view perspective691

from the south, and panels (b) are side views from the southeast. Three692

snapshots are shown at times (1) 2230 on 02-16, (2) 0000 on 02-17 and (3)693

0130 on 02-17. 40694

8 Cross sections of AgI concentration (in logarithmic scale and color shaded in695

log(m−3)), isoline of TKE=1 m2 s−2 (red outlines) and cloud water mixing696

ratio (black contours in intervals of 0.1 g kg−1). Panels (a) are the western697

cross section and panels (b) are eastern cross section in Fig. 2(c). Valid at698

(1) 2230 on 02-16, (2) 0000 on 02-17 and (3) 0130 on 02-17. The black dashed699

lines indicate the highest level at which the in-situ measurements were taken700

(around 3800 m MSL). 41701

9 CFADs of (a) observed IN count rate (s−1), (b) average LES-simulated AgI702

count rate (s−1) between 2315, 02-16 and 0035, 02-17, and (c) maximum LES-703

simulated AgI count rate (s−1) between 2315, 02-16 and 0035, 02-17. All data704

are along the flight legs 1 to 8. Frequencies (0 – 0.03) are color coded. 42705

32

Page 34: Xia Chu Roy Rasmussen Daniel Breed Bruce Boe Bart Geertsgeerts/bart/xue_dry.pdf · 2013. 10. 4. · Bart Geerts University of Wyoming, Laramie, Wyoming 5 ∗Corresponding author address:

10 Time series of AgI concentration (L−1) at the surface site (mountain meadow706

cabin #9) for (a) observations, (b) 100 m LES results at the closest grid point707

and (c) 100 m LES results averaged over the 9 × 9 closest grid points. 43708

11 CFADs of AgI concentration over the 100 m LES domain for the (a) 100 m709

LES simulation, (b) 500 m LES simulation, (c) 500 m MYJ simulation and710

(d) 500 m YSU simulation, between 2145, 02-16 and 0115, 02-17. Frequencies711

(0 – 0.0012) are color coded. 44712

12 Profiles of AgI total number over the 100 m LES domain for the 100 m LES713

simulation (black), the 500 m LES simulation (blue), the 500 m MYJ simula-714

tion (green) and the 500 m YSU simulation (red) at 0115 on 02-17. Only the715

data between 0 and 2000 m AGL are shown. 45716

13 Average profiles of TKE budget terms (10−4 m2 s−3) over the upwind region717

between 2145, 02-16 and 0115, 02-17. The red line represents the buoyancy718

term, the black is the shear term, the green indicates the advection term,719

the blue line is the turbulent transport term and the orange line shows the720

pressure correlation term. 46721

33

Page 35: Xia Chu Roy Rasmussen Daniel Breed Bruce Boe Bart Geertsgeerts/bart/xue_dry.pdf · 2013. 10. 4. · Bart Geerts University of Wyoming, Laramie, Wyoming 5 ∗Corresponding author address:

2820

2850

2880

2910

2940

2970

3000

3030

3060

3090

8640

8700

8760

882088808940

9000

9060

9120

9180

92409300

9360

9420

2820

2850

2880

2910

2940

2970

3000

30303060

8640

8700

87608820

88808940

9000

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28202820

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3000 3030

3060

8640

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876088208880

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9300

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9420

a)

b)

c)

-10 -8 -6 -4 -2 0 2 4 6 8

2011

-02-1

6 2

1:0

02011

-02-1

7 0

0:0

02011

-02-1

7 0

3:0

0

Fig. 1. Maps of NARR 700 hPa height (thick black contours, 30 m interval), temperaturein Celsius (color shaded), wind barbs (full barbs equals 5 m s−1) and 300 hPa height (thickwhite contours, 60 m interval). Green circles represent target area. (a) valid 2100, 2011-02-16, (b) valid 0000, 2011-02-17, (c) valid 0300, 2011-02-17.

34

Page 36: Xia Chu Roy Rasmussen Daniel Breed Bruce Boe Bart Geertsgeerts/bart/xue_dry.pdf · 2013. 10. 4. · Bart Geerts University of Wyoming, Laramie, Wyoming 5 ∗Corresponding author address:

a)

b)

c)

MP

BR

BCHFCO

RR2

Fig. 2. Topography of (a) 2500 m grid spacing domain, (b) 500 m grid spacing domain and(c) 100 m grid spacing LES domain. Red triangles indicate the locations of five generatorsoperated on Feb. 16, 2011. The black square symbol represents the surface site – mountainmeadow cabin #9. The black cross in (a) and (b) indicates the town of Saratoga where theradiosondes were released. The large black boxes in (a) and (b) represent the domains in(b) and (c) respectively. In (c), the abbreviated name of each generator (listed in Table 2) islisted below the corresponding red triangle symbol. The flight pattern is illustrated by theblue line. The black dashed lines show the locations of cross-sections depicted in Fig. 8. Thestar symbol close to the western border of (c) represents the location of the LES-simulated(blue) soundings shown in Fig. 3.

35

Page 37: Xia Chu Roy Rasmussen Daniel Breed Bruce Boe Bart Geertsgeerts/bart/xue_dry.pdf · 2013. 10. 4. · Bart Geerts University of Wyoming, Laramie, Wyoming 5 ∗Corresponding author address:

Fig. 3. Soundings from observations (black), 500 m non-LES simulation (red) at Saratogaand from 100 m LES simulation (blue) at starred location in Fig. 2(c) for (a) 2200, 2011-02-16 and (b) 0100, 2011-02-17. Temperature (oC) is the solid line and dewpoint temperature(oC) is the dashed line. Wind barbs are in m s−1 (full barb equals 5 m s−1).

36

Page 38: Xia Chu Roy Rasmussen Daniel Breed Bruce Boe Bart Geertsgeerts/bart/xue_dry.pdf · 2013. 10. 4. · Bart Geerts University of Wyoming, Laramie, Wyoming 5 ∗Corresponding author address:

0 min 15 mins 30 mins

45 mins 60 mins 75 mins

90 mins 105 mins 120 mins

E(k

) (m

-3 s

-2)

E(k

) (m

-3 s

-2)

E(k

) (m

-3 s

-2)

1/wavelength (m-1) 1/wavelength (m-1) 1/wavelength (m-1)

Fig. 4. Average kinetic energy power spectra (energy density in m3 s−2) over the LESdomain below 2000 m AGL. Each panel illustrates the spectrum at a moment from 0 to 120minutes in 15 min invervals. The blue line indicates the k−5/3 slope.

37

Page 39: Xia Chu Roy Rasmussen Daniel Breed Bruce Boe Bart Geertsgeerts/bart/xue_dry.pdf · 2013. 10. 4. · Bart Geerts University of Wyoming, Laramie, Wyoming 5 ∗Corresponding author address:

0

500

1000

1500

2000

0 0.5 1 1.5 2 2.5 3

He

igh

t (m

AG

L)

TKE (m2 s-2)

Total TKE upwind

Subgrid TKE upwind

1/2 W'W' upwind

Total TKE downwind

Subgrid TKE downwind

1/2 W'W' downwind

Fig. 5. Average profiles of the total TKE (solid lines), the subgrid-scale TKE (dashedlines) and half of the vertical velocity variance (dotted lines) over the upwind region (red)and the downwind region (green) between 02-16 21:45 and 02-17 01:15. All terms have unitsof m2 s−2

38

Page 40: Xia Chu Roy Rasmussen Daniel Breed Bruce Boe Bart Geertsgeerts/bart/xue_dry.pdf · 2013. 10. 4. · Bart Geerts University of Wyoming, Laramie, Wyoming 5 ∗Corresponding author address:

Longitude

La

titu

de

a)

Latitude

He

igh

t (m

AM

SL

)

b)

1

2

34

5 6

7

8

0 1 2 3 4 5 6 7 8 9 10 11

IN count rate (s-1)

Fig. 6. The plan view (a) and the cross-section viewed from west (b) of the in-situ mea-surements of AgI IN along the flight legs. The IN count rate (s−1) from the NCAR acousticIN counter is color coded. In (a), the terrain is contoured every 500 m from 2000 to 3500m MSL. Generators and the surface site are indicated by red triangles and the black squarerespectively. In (b), the flight legs are labeled from 1 to 8.

39

Page 41: Xia Chu Roy Rasmussen Daniel Breed Bruce Boe Bart Geertsgeerts/bart/xue_dry.pdf · 2013. 10. 4. · Bart Geerts University of Wyoming, Laramie, Wyoming 5 ∗Corresponding author address:

a1)

a2)

a3)

b1)

b2)

b3)

2011-0

2-1

7 0

0:0

02011-0

2-1

6 2

2:3

02011-0

2-1

7 0

1:3

0

Fig. 7. 3D depictions of the topography, AgI number concentration (greater than 100 L−1

for visible plumes) and wind vectors (∼ 2800 m – yellow, ∼ 3600 m – blue and ∼ 4400 m– purple MSL). Panels (a) are bird’s-eye view perspective from the south, and panels (b) areside views from the southeast. Three snapshots are shown at times (1) 2230 on 02-16, (2)0000 on 02-17 and (3) 0130 on 02-17.

40

Page 42: Xia Chu Roy Rasmussen Daniel Breed Bruce Boe Bart Geertsgeerts/bart/xue_dry.pdf · 2013. 10. 4. · Bart Geerts University of Wyoming, Laramie, Wyoming 5 ∗Corresponding author address:

a1)

a2)

a3)

b1)

b2)

b3)

Latitude Latitude

AgI conc. in log scale [log(m-3)]

2011

-02-1

7 0

0:0

02011

-02-1

6 2

2:3

02011

-02-1

7 0

1:3

0

Western cross section Eastern cross section

Fig. 8. Cross sections of AgI concentration (in logarithmic scale and color shaded inlog(m−3)), isoline of TKE=1 m2 s−2 (red outlines) and cloud water mixing ratio (blackcontours in intervals of 0.1 g kg−1). Panels (a) are the western cross section and panels (b)are eastern cross section in Fig. 2(c). Valid at (1) 2230 on 02-16, (2) 0000 on 02-17 and(3) 0130 on 02-17. The black dashed lines indicate the highest level at which the in-situmeasurements were taken (around 3800 m MSL).

41

Page 43: Xia Chu Roy Rasmussen Daniel Breed Bruce Boe Bart Geertsgeerts/bart/xue_dry.pdf · 2013. 10. 4. · Bart Geerts University of Wyoming, Laramie, Wyoming 5 ∗Corresponding author address:

AgI count rate (s-1)

a) b)

c)

AgI count rate (s-1)IN count rate (s-1)

Fig. 9. CFADs of (a) observed IN count rate (s−1), (b) average LES-simulated AgI countrate (s−1) between 2315, 02-16 and 0035, 02-17, and (c) maximum LES-simulated AgI countrate (s−1) between 2315, 02-16 and 0035, 02-17. All data are along the flight legs 1 to 8.Frequencies (0 – 0.03) are color coded.

42

Page 44: Xia Chu Roy Rasmussen Daniel Breed Bruce Boe Bart Geertsgeerts/bart/xue_dry.pdf · 2013. 10. 4. · Bart Geerts University of Wyoming, Laramie, Wyoming 5 ∗Corresponding author address:

0

50

100

150

200

250

300

350

400

22h 23h 00h 01h 02h 03h

0

50

100

150

200

250

300

350

400

22h 23h 00h 01h 02h 03h

0

50

100

150

200

250

300

350

400

22h 23h 00h 01h 02h 03h

a) b) c)

Fig. 10. Time series of AgI concentration (L−1) at the surface site (mountain meadow cabin#9) for (a) observations, (b) 100 m LES results at the closest grid point and (c) 100 m LESresults averaged over the 9 × 9 closest grid points.

43

Page 45: Xia Chu Roy Rasmussen Daniel Breed Bruce Boe Bart Geertsgeerts/bart/xue_dry.pdf · 2013. 10. 4. · Bart Geerts University of Wyoming, Laramie, Wyoming 5 ∗Corresponding author address:

AgI concentration (m-3) AgI concentration (m-3)

b) 500 m LES

c) 500 m MYJ d) 500 m YSU

a) 100 m LES

Fig. 11. CFADs of AgI concentration over the 100 m LES domain for the (a) 100 m LESsimulation, (b) 500 m LES simulation, (c) 500 m MYJ simulation and (d) 500 m YSUsimulation, between 2145, 02-16 and 0115, 02-17. Frequencies (0 – 0.0012) are color coded.

44

Page 46: Xia Chu Roy Rasmussen Daniel Breed Bruce Boe Bart Geertsgeerts/bart/xue_dry.pdf · 2013. 10. 4. · Bart Geerts University of Wyoming, Laramie, Wyoming 5 ∗Corresponding author address:

0

500

1000

1500

2000

1.00E+05 1.00E+08 1.00E+11 1.00E+14 1.00E+17 1.00E+20

He

igh

t (m

AG

L)

Total AgI number

100 m LES

500 m LES

500 m MYJ

500 m YSU

Fig. 12. Profiles of AgI total number over the 100 m LES domain for the 100 m LESsimulation (black), the 500 m LES simulation (blue), the 500 m MYJ simulation (green) andthe 500 m YSU simulation (red) at 0115 on 02-17. Only the data between 0 and 2000 mAGL are shown.

45

Page 47: Xia Chu Roy Rasmussen Daniel Breed Bruce Boe Bart Geertsgeerts/bart/xue_dry.pdf · 2013. 10. 4. · Bart Geerts University of Wyoming, Laramie, Wyoming 5 ∗Corresponding author address:

0

500

1000

1500

2000

-50 -40 -30 -20 -10 0 10 20 30 40 50

He

igh

t (m

AG

L)

TKE Budgets (10-4 m2 s-3)

Buoyancy

Shear

Advec!on

Transport

Pressure

Fig. 13. Average profiles of TKE budget terms (10−4 m2 s−3) over the upwind regionbetween 2145, 02-16 and 0115, 02-17. The red line represents the buoyancy term, the blackis the shear term, the green indicates the advection term, the blue line is the turbulenttransport term and the orange line shows the pressure correlation term.

46


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