A Numerical Investigation of Storm Structure and Evolution during the July 1999 Las Vegas Flash Flood

J. Li Department of Hydrology and Water Resources, The University of Arizona, Tucson, Arizona

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R. A. Maddox Department of Hydrology and Water Resources, The University of Arizona, Tucson, Arizona

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X. Gao Department of Hydrology and Water Resources, The University of Arizona, Tucson, Arizona

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S. Sorooshian Department of Hydrology and Water Resources, The University of Arizona, Tucson, Arizona

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K. Hsu Department of Hydrology and Water Resources, The University of Arizona, Tucson, Arizona

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Abstract

Severe flash flood storms that occurred in Las Vegas, Nevada, on 8 July 1999, were unusual for the semiarid southwest United States because of their extreme intensity and the morning occurrence of heavy convective rainfall. This event was simulated using the high-resolution Regional Atmospheric Modeling System (RAMS), and convective rainfall, storm cell processes, and thermodynamics were evaluated using Geostationary Operational Environmental Satellite (GOES) imagery and a variety of other observations. The simulation agreed reasonably well with the observations in a large-scale sense, but errors at small scales were significant. The storm's peak rainfalls were overestimated and had a 3-h timing delay. The primary forcing mechanism for storms in the simulation was clearly daytime surface heating along mountain slopes, and the actual trigger mechanism causing the morning convection, an outflow from nighttime storms to the northeast of Las Vegas, was not captured accurately. All simulated convective cells initiated over and propagated along mountain slopes; however, cloud images and observed rainfall cell tracks showed that several important storm cells developed over low-elevation areas of the Las Vegas valley, where a layer of fairly substantial convective inhibition persisted above the boundary layer in the simulation. The small-scale errors in timing, location, rain amounts, and characteristics of cell propagation would seriously affect the accuracy of streamflow forecasts if the RAMS simulated rainfall were used in hydrologic models. It remains to be seen if explicit storm-scale simulations can be improved to the point where they can drive operationally useful streamflow predictions for the semiarid southwest United States.

Additional affiliation: Department of Atmospheric Science, The University of Arizona, Tucson, Arizona

Corresponding author address: Jialun Li, Department of Hydrology and Water Resources, The University of Arizona, Tucson, AZ 85721. Email: jialun@hwr.arizona.edu

Abstract

Severe flash flood storms that occurred in Las Vegas, Nevada, on 8 July 1999, were unusual for the semiarid southwest United States because of their extreme intensity and the morning occurrence of heavy convective rainfall. This event was simulated using the high-resolution Regional Atmospheric Modeling System (RAMS), and convective rainfall, storm cell processes, and thermodynamics were evaluated using Geostationary Operational Environmental Satellite (GOES) imagery and a variety of other observations. The simulation agreed reasonably well with the observations in a large-scale sense, but errors at small scales were significant. The storm's peak rainfalls were overestimated and had a 3-h timing delay. The primary forcing mechanism for storms in the simulation was clearly daytime surface heating along mountain slopes, and the actual trigger mechanism causing the morning convection, an outflow from nighttime storms to the northeast of Las Vegas, was not captured accurately. All simulated convective cells initiated over and propagated along mountain slopes; however, cloud images and observed rainfall cell tracks showed that several important storm cells developed over low-elevation areas of the Las Vegas valley, where a layer of fairly substantial convective inhibition persisted above the boundary layer in the simulation. The small-scale errors in timing, location, rain amounts, and characteristics of cell propagation would seriously affect the accuracy of streamflow forecasts if the RAMS simulated rainfall were used in hydrologic models. It remains to be seen if explicit storm-scale simulations can be improved to the point where they can drive operationally useful streamflow predictions for the semiarid southwest United States.

Additional affiliation: Department of Atmospheric Science, The University of Arizona, Tucson, Arizona

Corresponding author address: Jialun Li, Department of Hydrology and Water Resources, The University of Arizona, Tucson, AZ 85721. Email: jialun@hwr.arizona.edu

1. Introduction

The geographical and meteorological environments of the semiarid regions of southwestern North America encompass complex topography, including deserts, mountain ranges, and diverse land-surface characteristics; intense solar radiation; and subtropical weather systems that occur during the summer season. Summertime rainfall in this region (see Fig. 1), sometimes accompanied by strong winds, hail, and intense lightning, is usually produced by thunderstorms and is very concentrated and short-lived.

The Las Vegas, Nevada, flash flood storms of 8 July 1999 were unusually severe for this region, and they were focused on the fastest-growing metropolitan area in the United States. During the event, much of the Las Vegas valley had rainfall amounts (Fig. 2) of 35%–70% of the average annual precipitation (which is about 100 mm, or 4 in.) during a brief period of 60–90 min. The torrential rains caused the worst flooding in the Las Vegas area in the twentieth century, resulting in two deaths and $20 million in property damage. Many specific details about the meteorology of this event and its impacts are available in a National Weather Service, Western Region, Technical Attachment by Haro et al. (1999).

The flash flooding caused by this unusual event was very rapid and severe. The photograph in Fig. 3a shows Duck Creek flooding across major roadways. A hydrograph from Duck Creek (Fig. 3b) shows a spectacular flash flood with an extremely rapid rise from no flow to 4300 ft3 S−1 in just a few minutes. The water level in one detention basin increased by 7 ft in height in only 5 min. Peak discharges that occurred during the 2-h period centered on local noontime, including several large flows greatly exceeding previous flows of record (Table 1).

Many studies (e.g., Dunn and Horel 1994a,b; Wallace et al. 1999) have shown that numerical predictions of intense convective events, especially within the weak synoptic flow regimes that characterize the summertime meteorology of this region, are typically not reliable. For example, this important and severe flooding event was not forecasted well by the operational National Centers for Environmental Prediction (NCEP) Eta Model. Instead of predicting major rainfall in the Las Vegas area, the operational Eta Model predicted intense storms over the Four Corners area, that is, the Colorado Plateau. However, numerical models can be used to investigate many characteristics of convective storm events over this region.

The Las Vegas flash flood event has been studied within a numerical modeling framework because of its meteorological and hydrological importance. A version of the Regional Atmospheric Modeling System (RAMS), including sophisticated cloud microphysics schemes (Walko et al. 1995; Meyers et al. 1997) and cloud radiation transfer schemes (Harrington 1997), was used for the simulation [for details about this model refer to the list of references here and to Pielke et al. (1992) and Walko and Tremback (1997)]. The objectives of this study were to 1) develop an overview of the flash flood event, 2) determine whether an accurate, explicit prediction of the flash flood storm could be made using a fine-resolution (2.5 km) version of the RAMS model, 3) investigate positive and negative aspects of the simulation, and 4) evaluate subjectively the model predictions in the context of improving hydrometeorological predictions.

2. Overview of event

A detailed map of southern Nevada, with terrain, is shown in Fig. 4. The thunderstorms that produced the flash flooding occurred just ahead of an inverted trough in the middle troposphere that was moving slowly westward (Fig. 5). This inverted trough in the middle levels was the only synoptic feature of interest on this day. There were no fronts or jet streaks or significant large-scale vertical motion fields associated with it. However, a large region characterized by high moisture contents was moving westward with this feature. The inverted trough had been associated with strong thunderstorms and locally heavy rains over south-central Arizona on the previous afternoon and evening. Synoptic patterns in the Southwest are so weak during the summer that translating features, such as the inverted trough in this case, tend to moisten or dry the large-scale environment, modulating the moisture and convective instability available for convective development. Smaller-scale vertical motions, associated with diurnal circulations driven by complex terrain, are routinely present and once storms develop the convergence zones along convective outflows tend to determine the evolution of storms (see, e.g., McCollum et al. 1995).

This general synoptic pattern, with an inverted trough moving westward across the southwestern United States, has been documented by Maddox et al. (1980) as one often associated with significant summertime flash floods in this region. Precipitable moisture amounts were quite high ahead of this feature, and convective available potential energy (CAPE) was large. The 1200 UTC upper-air sounding taken at Desert Rock, to the northwest of Las Vegas, indicates (Fig. 6) that precipitable water in the area exceeded 40 mm and that CAPE exceeded 1000 J kg−1. These values are quite high for this semiarid region, as per Haro et al. (1999). Winds aloft were light and variable, indicating that storms would be very slow moving. During this particular event, there was little inhibition to convection, and storms developed during the morning hours, which is very unusual. Storms in the Las Vegas valley during the summer typically occur during the late afternoon, more in phase with the diurnal heating cycle.

An overview of the event from 1400 through 1900 UTC (0600–1100 LST) is presented in the composite radar maps shown in Fig. 7. Very heavy storms occurred throughout this period both ahead of and behind the 700-mb inverted trough. Intense storms and flash flooding were reported in west-central Arizona and southwest California in addition to the very severe events in the vicinity of Las Vegas. Two clusters of strong storms northeast of Las Vegas decayed after 1500 UTC, and Haro et al. (1999) hypothesized that outflow from these storms pushed southwestward during the morning, leading to the development of new storm cells when the outflows reached the eastern flanks of Charleston Peak (see Fig. 4). Surface observations from McCarran International Airport (shown later in section 4b) indicated a wind shift to the northeast at 1500 UTC, supporting this hypothesis.

The storms that eventually produced the flash floods first developed around 1700 UTC (0900 LST) along the southeastern flanks of Charleston Peak. These storm cells moved slowly southward until around 1830 UTC, when new cells began developing toward the east. These new storms were very intense and had affected most of Las Vegas by 2000 UTC. The movement of heavy rain cores across the metropolitan area is shown in Fig. 8 (rainfall data from a network of recording rain gauges at 15-min time resolution were provided by T. Sutko of the Clark County Nevada Flood Control District). Notice that, during the heavy rains over Las Vegas, cell propagation directions were toward the southeast, northeast, east, and even north. Apparently, the cells were developing and moving along outflow boundaries and local convergence zones. It is also apparent that many cells were developing in a downstream direction along the major washes that generally flow toward the east and southeast, eventually into Lake Mead (see Fig. 4). This “downwash” movement undoubtedly contributed to the high discharge rates that were measured. The rain rates with the storms were very high, with amounts of around 25 mm measured at many gauges at 15-min intervals. Maximum rainfall amounts exceeded 75 mm, during only 3 h, at two gauges in the Las Vegas metropolitan area. The high rain rates were supported by the very moist environment (refer back to the sounding in Fig. 6) and by unusually low cloud bases.

3. Numerical simulations

In recent years, advances in cloud-resolving microphysics (explicit) schemes have been incorporated into mesoscale models (Simpson and Tao 1993; Walko et al. 1995; Reisner et al. 1998), providing more comprehensive numerical forecasts. These advances have helped to improve understanding of physical mechanisms of precipitation formation and radiative transfer processes within mesoscale convective systems (MCSs). Model results can be compared with observational data to help explain the physical processes and the behavior of MCSs. A great amount of research has focused on studies of various MCS events using both advanced cloud and mesoscale models. Liu et al. (1997) studied a high-resolution (6 km) fully explicit simulation for Hurricane Andrew. Fovell and Tan (1998) displayed the temporal behavior of multicell-type thunderstorms. Kulie and Lin (1998) simulated a supercell thunderstorm. Tucker and Crook (1999) analyzed the generation of a mountain MCS. Nachamkin and Cotton (2000) investigated the propagation, evolution, and structure of a Colorado MCS and the interactions between the MCS and its environment. Ovtchinnikov and Kogan (2000) developed a new cloud model and investigated ice-production mechanisms within MCSs. Currently, advanced microphysics schemes have been incorporated into most mesoscale models and have become a routine component, accompanying cumulus parameterization (implicit) schemes for predicting convective rainfall.

Limitations and areas requiring improvement within cloud models have been reviewed and assessed (Arakawa and Chen 1987; McCumber et al. 1991; Molinari and Dudek 1992; Emanuel 1994; Khain et al. 2000). In summary, the limitations originate from the computational difficulty required to predict MCSs at convective resolutions of approximately 100 m, the gaps and uncertainties in our knowledge of relevant physical processes, and unrealistic assumptions and simplifications made in model parameterizations. The areas requiring improvement include initiation of models and the adjustment of model processes and parameters for different types of convection. Even though there are many limitations associated with mesoscale modeling, detailed simulations of the Las Vegas flash flood event might provide valuable insights into the storm behaviors that produced this intense rain episode.

a. Model

The RAMS version 4.2 model (Pielke et al. 1992; Walko et al. 1995, 2000) used in this study is based on the nonhydrostatic, compressible equations using a terrain-following (σ) coordinate system. Version 4.2 includes several improvements in physical processes: the two-stream radiation parameterization (Harrington 1997), the Land Ecosystem Atmosphere Feedback (LEAF-2) land surface scheme (Walko et al. 2000), and single-moment and two-moment microphysics parameterizations (Walko et al. 1995; Meyers et al. 1997). Both the topography and vegetation are represented at about 1-km grid lengths.

Four nested grids were used in the simulation with two-way interaction on the boundary between grids. The grid domains are shown in Fig. 1: grid 1 covers the western United States and northern Mexico at a grid spacing of 30 km and is nested within the Eta Model; grid 2 covers southern California, southern Nevada, southern Utah, western New Mexico, and Arizona at a grid spacing of 15 km; grid 3 covers southern California, southern Nevada, and northwest Arizona at a grid spacing of 7.5 km; and grid 4 is centered on the Las Vegas area at a grid spacing of 2.5 km. Figure 4 shows the topography of the grid 4 area. Vertical grid spacing in this simulation started at 100 m and stretched to 1 km at upper levels to the model top of 19.5 km.

The model integration was initialized at 0000 UTC 8 July 1999, more than 12 h before the onset of the intense storms. The Eta analysis, upper-air soundings, and surface observations at 0000 UTC were processed using the four-dimensional data assimilation (4DDA) objective analysis program for RAMS model initiation (Walko et al. 1995). The Eta analysis data provide updated conditions at the upper and lateral boundaries of grid 1 every 12 h. The Mellor–Yamada parameterization scheme was used for diffusion, and the modified Kuo convective parameterization scheme, in combination with the microphysics scheme, was used for convective processes within grids 1 and 2, but only the microphysics scheme was applied on grids 3 and 4.

b. Cloud microphysics scheme in RAMS

The cloud microphysics scheme in RAMS version 4.2 is a bulk parameterization (Cotton et al. 1986) that includes a single-moment scheme (Walko et al. 1995) and a two-moment scheme (Meyers et al. 1997). In the model, water in the atmosphere is categorized into eight forms: vapor, cloud water, rainwater, pristine ice, snow, aggregates, graupel, and hail. Cloud water and rainwater are liquid (phase) only; pristine ice, aggregates, and snow are ice only, and graupel and hail are ice–liquid mixtures. The hydrometeors in each category are assumed to conform to a generalized gamma distribution (bulk parameterization). After prescribing the mean particle size and number concentration, the mass concentration, or mixing ratio (one moment), of the species is predicted through mass balance, which includes sink and source processes of interform transitions and advection.

The main interform transition processes between the hydrometeors include nucleation to generate cloud water and pristine ice from vapor, condensation, and deposition to grow the particles into large-size hydrometeors, for example, cloud water to rain, and pristine ice to snow and/or aggregates. (Evaporation and sublimation cause the inverse hydrometeor transitions.) Collection (collision and coalescence) converts pristine ice, snow, and small aggregates to large aggregates; riming and partial melting converts pristine ice, snow, and aggregates to grauple; and partial or complete melting converts graupel and/or aggregates to hail or rain.

4. Simulation results

a. Convection and rainfall

To illustrate convective cell development and propagation predicted by RAMS, contours of column maximum upward velocity on grid 4 from 0800 UTC 8 July to 0100 UTC 9 July are plotted in Fig. 9 (overlain on model topography). Convection was underway northeast of Las Vegas at 0800 UTC and continued as distinct and strong elements through 1300 UTC and then weakened very rapidly. Comparison with Fig. 7 indicates that the location of these storms was well predicted by the model, but that the dissipation may have occurred a bit sooner than the satellite imagery indicated. It is the outflow from these cells that Haro et al. (1999) hypothesized triggered the early development of new storms on the flanks of Charleston Peak later in the morning. Thus, the evolution of any model-predicted outflow from the early storms northeast of Las Vegas would likely be related to the accuracy of modeled convective developments later in the day. This issue is examined in detail later in this section.

By 1900 UTC, two new convective cells, A and B, developed in the model simulation over the northern slope of Charleston Peak, northwest of Las Vegas (the actual storms in this area had developed about 2 h earlier than the modeled storms). Their bases merged at 2000 UTC. Cell A then gradually diminished at about 2030 UTC and cell B intensified. New cells C, D, and E (1930, 2100, and 2100 UTC, respectively) also developed. Cells C and D were in the northern high-terrain areas, and cell E was located just southeast of cell B on the flanks of Charleston Peak. After 2130 UTC, cell E intensified while cell B started declining. At 2300 UTC, cells F and G appeared. From 2300 to 0000 UTC, the parallel growth and southward movement of cells E and G were the most intense storms simulated over the Las Vegas area. Cells H and I developed around 0030 UTC. Cell E finally merged into cell H near the California–Nevada border, and cell G diminished. At 0100 UTC, only cells H and I remained active in the simulation and, within 2 h, both of them dissipated. The trajectories of these convective cells are plotted in Fig. 10. These tracks indicate that, within the RAMS simulation, the convective storms tended to form along substantial terrain elevation gradients and then to move slowly southward along the terrain. Thus, the convection behaves differently in the model than do the actual convective cells. Figure 8 illustrates that the observed convective cells redeveloped eastward and even northward into the lower-elevation regions of Las Vegas. The model was not able to develop intense storms over the low-elevation regions, which is a serious limitation of the simulation.

In Fig. 11, the isotherms of cloud (infrared) brightness temperature from Geostationary Operational Environmental Satellite (GOES) during the event (1600–2130 UTC) are illustrated at 30-min intervals. The development of cold-topped cloud cells was relatively well correlated to that of simulated convection cells (Figs. 9 and 10); however, the simulated convection essentially exhibited a 2–3-h delay relative to the cloud images.

Cloud cells a, b, c, and f in Fig. 11 (corresponding to convection cells A, B, C, and F) appeared at 1600 UTC; then cells a and b merged together, and cell e occurred southeast of cell b. At 1700 UTC, cells e and f continued growing, and cells h and g appeared in the southern areas. After 1730 UTC, the cloud tops of all of these cells merged into a single, large, cold (<230 K) cloud. It was then difficult to identify any distinct, cold-topped cloud cells. This large anvil cloud continued expanding until 2000 UTC, then diminished. The satellite data also indicate that several cells developed west and south of Charleston Peak that were not present in the simulation.

Shown in Figs. 12a and 12b, the 24-h accumulations of rainfall predicted on RAMS grid 1 from 1200 UTC July 8 to 1200 UTC July 9, compared with Eta forecast data and combined gauge/satellite-retrieved rainfall data. The 24-h gauge data were obtained from the Colorado River Forecast Center. There are about 95 gauge stations in the grid 4 covered area and more than 130 gauge stations over the area shown in Fig. 12a. For areas where gauge data were not available, satellite rainfall-estimation data [i.e., Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN); Sorooshian et al. 2000] were used. The operational Eta forecast initial time was 0000 UTC 8 July 1999 (the same as the RAMS simulation). The operational Eta Model did not predict heavy rainfall over the study area. On the other hand, compared with observed data, RAMS simulated the rainfall much more accurately, both in pattern and in amounts. There are many reasons that probably lead to a better coarse-grid simulation from RAMS compared with that from the Eta Model. These reasons include, but are not necessarily limited to, horizontal resolution differences (30 versus 40 km); different convective parameterizations (Eta uses the Betts–Miller–Janjic convective scheme, while a modified Kuo convective parameterization scheme is used in RAMS); that RAMS has two-way communication among the nested grids; and other differences in model physics, vertical coordinate systems, etc.

The modeled rainfall on grid 4 (Fig. 12d) indicated six concentrated rainfall areas. The two of these most closely related to the actual flood-producing storms were located along the east slopes of Charleston Peak northwest of Las Vegas and also along rising terrain south of Las Vegas. These areas were presented on both the grid 1 and grid 4 simulations. However, the predicted peak amounts of rainfall in these areas were much higher for grid 4 and also greater than the observations. Possible reasons for this will be discussed later.

There are three important issues related to the RAMS high-resolution grid (grid 4) simulation of the convective event. First, in comparison to the observations, the modeled convection was delayed by about 3 h. This is a common error for explicit convection models. Molinari and Dudek (1992) pointed out that any unrealistically slow development of convective instability within models can produce this error. Additionally, the 2.5-km horizontal resolution is still large for the fully explicit approach. Molinari and Dudek (1992) and Lilly (1990) concluded that “reasonable” grid spacing for explicit simulation of convective storms should be less than 1 km. However, since the atmosphere (i.e., sounding shown in Fig. 6) was already quite unstable early in the morning, the delayed onset of convection west of Las Vegas may also relate to the model-predicted evolution of outflows from the early storms northeast of Las Vegas. Second, all simulated convective cells initiated over mountain slopes; however, the cloud images (Fig. 11) and the rainfall cell tracks (Fig. 8) showed that several important storm cells developed over low-elevation areas of the Las Vegas valley. Reasons that the model was unable to predict convective development over lower elevations are examined later in this section. Third, the model also overpredicted rainfall to the north and northeast of Las Vegas, especially over distinct mountains; this was possibly the result of the former two reasons. These are not surprising discrepancies, given that the model was initiated at 0000 UTC the evening prior to the storms, but they seriously impact the small-scale accuracy of the simulation.

b. Storm cell processes

The evolution of three-dimensional winds and equivalent potential temperature during the development of convection is displayed using several vertical cross sections. Figure 13a is a south–north cross section along longitude 115.5°W, along which convective cells D, F, E, and H occurred (Fig. 10). Remember that convection was delayed by about 3 h in the model. In order to enhance the vertical depiction of the wind fields, the scale used for the vertical wind component is 10 times smaller than that used for the horizontal component.

At 2030 UTC, the model atmosphere was relatively “calm,” with slight mountain–valley circulations in the lower boundary layer resulting from surface heating. During 2100–2200 UTC, an updraft corresponding to cell E developed rapidly. The vertical velocity in the simulated convection exceeded 13 m s−1, and the updraft reached above 10 km at 2200 UTC. A low-level southerly wind flowed along the southern mountain flanks to support the upward motion. At 2200 UTC, the updraft grew so strong that a northerly inflow at about the 4-km level entered the convection column and generated a downdraft in the lower part of the storm cell. The downdraft flowed both northward, apparently helping to trigger cell D, and southward. From 2200–2300 UTC, the downdraft expanded and finally spread beneath the entire base of the convection column, causing the updraft to decay rapidly. From 2300–0000 UTC, cell E moved southeastward (Fig. 9) and finally out of the cross section, while weak, short-lived cell H formed over terrain south of the original strong storms.

Variations of predicted equivalent potential temperature (θe) are presented along the same south–north cross section (Fig. 13b). Before 2000 UTC (1200 LST), no convection had developed in the cross section (refer to Fig. 9), and the low-level atmosphere over the two mountains became warm and unstable because of surface heating. A thick, potentially cold, midlevel layer of the atmosphere (3–6-km height) above the boundary layer allowed rapid development of convective instability. At 2100 UTC (1300 LST), deep convection was present over Charleston Peak. This intense cell was short-lived, and weaker cells developed to the north and south. At 2200 UTC, a downdraft had developed, entraining a large amount of cold air from the midlayer into the column. By 0100 UTC, there was little convective instability remaining along the cross section.

Figure 13c is an east–west cross section at latitude 36.08°N that passes directly through the model grid point located closest to McCarran International Airport, as well as the southern flank of Charleston Peak; cell E occurred along this cross section. At 2230 UTC, cell E has just entered into the cross section (refer to Fig. 9), and after 2300 UTC a distinct and strong, but shallow, outflow from cell E moved rapidly eastward across the Las Vegas valley. By 2330 UTC, downdraft outflow from cell E was approaching McCarran International Airport. Vertical motion along the leading edge of this outflow did not force development of new convective cells over the valley. Rather, weak and elevated updrafts continued along the west side of the valley as cell E propagated rapidly southward (again, refer to Fig. 9).

The evolution of convective outflows during the entire period of the event has been analyzed over grid 4 through examination of the lowest-level (48 m above the model terrain) wind vectors. Hourly plots of these fields, with subjectively analyzed outflow positions, are presented in Fig. 14 for the period 0800–0100 UTC. These charts provide considerable insight into the behavior of the model-predicted convection. From 0800 through 1100 UTC, a distinct outflow was moving southwestward away from the active convection over the northeast corner of the grid. However, this outflow weakened and became indistinct within a general downslope flow regime from 1200 through 1500 UTC. Thus, it appears that the important outflow that was actually observed to cross the Las Vegas valley (Haro et al. 1999) could not persist within the simulation, even though the early storms northeast of Las Vegas were accurately simulated. The temperature gradients along the outflow weakened, leading to its demise, during the hours before sunrise, perhaps because of the surface energy budget in the model. The model therefore cannot accurately predict the further evolution of observed mesoscale and convective-scale features since the critical outflow boundary that initiated the flash flood storms does not persist and impinges upon the slopes of Charleston Peak.

By 1700–1800 UTC, strong, upslope flows were being generated by the higher terrain in the model, and the first convective outflow from new, model-predicted convection on the north slopes of Charleston Peak appeared by 2100 UTC. This outflow initially spread rapidly northward away from Charleston Peak. However, after 2200 UTC, the outflow expanded and spread quickly south- and eastward, passing McCarran International Airport at about 2330 UTC (as per Fig. 13b). An important question is why this strong outflow pushing rapidly across the lower elevations of the Las Vegas valley did not initiate new convective cells within the simulation.

Figures 15 and 16 compare time series plots of temperature, dewpoint temperature, and wind direction and speed for both the surface observations taken at McCarran International Airport and for the RAMS grid point most nearly collocated with the airport. The observed data (Fig. 15) show a distinct wind shift to the northeast and an increase in speed at about 1330 UTC, several hours before the rain and storms began at the airport. The temperature remained very cool at the airport throughout the entire event. The diurnal temperature increase that would be expected after sunrise (and which was forecasted in the model run) did not occur because of the passage of the outflow. This time series supports the hypothesis advanced by Haro et al. (1999) that outflow winds from the late night and early morning storms northeast of Las Vegas played a key role in the morning development of the flash flood storms along the flanks of Charleston Peak.

In contrast, the model-predicted fields (Fig. 16) differ substantially from the observations. The predicted temperature at 48 m reaches 35°C at 2100 UTC, about 6°C warmer than the highest observed surface temperatures, which occurred between 1400 and 1500 UTC (0600–0700 LST). Model-predicted dewpoints were about 4°C lower than those observed prior to the storms. The first convective outflow in the model occurs from the west at about 2330 UTC, more than 8 h later than the first observed outflow that moved across the airport from the northeast. The model was unable to predict small-scale details of convectively generated outflows, particularly those from the early-morning storms to the northeast, seriously limiting the accuracy of the RAMS forecast directly over the lower elevations of the Las Vegas metropolitan area.

c. Model thermodynamics

The question of why outflows simulated by RAMS did not force new storms to develop over the lower-elevation regions of the Las Vegas valley has been examined further. In Fig. 17a, a model grid-predicted sounding (A) on the flanks of Charleston Peak (exact location is indicated on Fig. 10) valid at 1800 UTC is shown. There is no inhibition to convection (as indicated by the lifted parcel at the top of the boundary layer), and cell A developed within the simulation at this location. Over Las Vegas (B in Fig. 10), the model-predicted boundary layer (Fig. 17b) also had substantial conditional instability. However, in contrast to the mountain sounding, there was a layer of fairly substantial convective inhibition (shaded area) above the boundary layer. The outflows predicted in the simulation were apparently not strong or deep enough to lift boundary layer air through the layer of inhibition. This hypothesis is verified by the model simulation soundings at B from 1800 through 0300 UTC (not shown). Through this entire period, a layer of convective inhibition remained present in the model atmosphere over grid point B. No air parcel was ever lifted to saturation at or above a level of free convection; thus, the model predicted no convective cell developments over the lowest elevations.

Model-predicted vertical motions at 800 mb (not shown but examined hour by hour) show that the Las Vegas valley was characterized by very weak subsidence (vertical motions of 0 to −5 cm s−1). After 1900 UTC, the downward motion intensifies, reaching values as strong as −50 cm s−1 when the intense upward motions are occurring in the model-predicted convective cells to the west. Thus, it appears that compensating subsidence occurs over the low elevations and prevents the elimination of the layer of convective inhibition.

5. Discussion and conclusions

Using a fine-resolution version of RAMS nested within the Eta Model, the Las Vegas severe flash flood storm of 8 July 1999 was simulated. The results were similar in many ways to the actual convective storm events that occurred over southern Nevada, but there were also substantial errors that would have serious impacts on any hydrologic forecasts of runoff. The predicted storms occurred 3 h later than those that were observed. The simulated storms had higher rainfall peak intensity. These errors, although common in numerical rainfall prediction using microphysics (explicit) schemes (Molinari and Dudek 1992), indicate significant model deficiencies when explicit prediction is used to predict flows in the washes. The trigger mechanism for the modeled storms was clearly daytime surface heating along the mountain slopes, which explains why the occurrence of the storms was delayed from morning to afternoon in the simulation. This delay occurs because the real forcing mechanism for the convection, the cool outflow from the northeast, was not captured accurately in the simulation. The model was also not able to predict the propagation of storm cells into the lowest-elevation regions of the Las Vegas valley. This serious discrepancy in the model forecast was caused by unrealistically high convective inhibition present over low elevations in the simulation. Compensating subsidence over the valley to the east of the model-predicted storms on the mountain slopes apparently helped suppress convective development over the low elevations.

In summary, the RAMS simulation was quite good when considered from a broad-scale perspective, considering that it was initialized more than 12 h prior to the convective storms. Events predicted within the model sometimes had obvious counterparts in the observations. The RAMS simulation, in contrast to the benign operational Eta simulation, clearly indicated that significant convective rains were likely to occur over southern Nevada. However, the small-scale discrepancies in timing, location, rain amounts, and characteristics of cell propagation indicate that serious errors in prediction of flow in the washes would result if the RAMS simulation results were used within hydrologic models. It remains to be seen if explicit, storm-scale simulations can be improved to the point at which they can drive useful, short-term streamflow predictions for the semiarid southwest United States.

Acknowledgments

Primary support for this research was provided under the NASA EOS Interdisciplinary Research Program (NAG5-11044), the NOAA GAPP program (NA16GP1605), and the NSF STC Program (Agreement EAR-9876800). The authors would like to thank Dr. David Bright, NWS Tucson, Arizona; Jesus A. Haro and Kim J. Runk, NWS Las Vegas, Nevada; and Tim Sutko, Clark County Regional Flood Control District, Nevada, for providing valuable suggestions, discussions, and data during the preparation of this paper. The first author also would like to thank Dr. Jaime E. Combariza and other staff at the Computer Center and Information Technology, University of Arizona, for their help and support. The comments and suggestions provided by two anonymous reviewers were extremely useful and helped the authors to improve this paper.

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  • Arakawa, A., and J-M. Chen, 1987: Closure assumptions in the cumulus parameterization problem. Short and Medium Range Numerical Weather Prediction, T. Matsuno, Ed., Universal Academy Press, 107–131.

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    • Export Citation
  • Cotton, W. R., G. J. Tripoli, R. M. Rauber, and E. A. Mulvihill, 1986: Numerical simulation of the effects of varying ice crystal nucleation rates and aggregation processes on orographic snowfall. J. Climate Appl. Meteor., 25 , 16581680.

    • Search Google Scholar
    • Export Citation
  • Dunn, L. B., and J. D. Horel, 1994a: Prediction of central Arizona convection. Part I: Evaluation of the NGM and Eta model precipitation forecasts. Wea. Forecasting, 9 , 495507.

    • Search Google Scholar
    • Export Citation
  • Dunn, L. B., and J. D. Horel, 1994b: Prediction of central Arizona convection. Part II: Further examination of the Eta model forecasting. Wea. Forecasting, 9 , 508521.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 1994: Atmospheric Convection. Oxford University Press, 580 pp.

  • Fovell, R. G., and P-H. Tan, 1998: The temporal behavior of numerically simulated multicell-type storms. Part II: The convective cell life and cycle and cell regeneration. Mon. Wea. Rev., 126 , 551577.

    • Search Google Scholar
    • Export Citation
  • Haro, J. A., H. R. Daley, and K. J. Runk, cited 1999: The Las Vegas flash floods of 8 July 1999. [Available online at http://www.wrh.noaa.gov/wrhq/99TAs/9926/index.html.].

    • Search Google Scholar
    • Export Citation
  • Harrington, J. Y., 1997: The effect of radiative and microphysical processes on simulated warm and transition season arctic stratus. Ph.D. dissertation, Colorado State University, 289 pp.

    • Search Google Scholar
    • Export Citation
  • Khain, A., M. Ovtchinnikov, M. Pinsky, A. Pokrovsky, and H. Krugliak, 2000: Notes on the state-of-the-art numerical modeling of cloud microphysics. Atmos. Res., 55 , 159224.

    • Search Google Scholar
    • Export Citation
  • Kulie, M. S., and Y-L. Lin, 1998: The structure and evolution of a numerically simulated high-precipitation supercell thunderstorm. Mon. Wea. Rev., 126 , 20902116.

    • Search Google Scholar
    • Export Citation
  • Lilly, D. K., 1990: Numerical prediction of thunderstorms—Has its time come? Quart. J. Roy. Meteor. Soc., 116 , 779798.

  • Liu, Y., D-L. Zhang, and M. K. Yau, 1997: A multiscale numerical study of Hurricane Andrew (1992). Part I: Model description and verification. Mon. Wea. Rev., 125 , 30733093.

    • Search Google Scholar
    • Export Citation
  • Maddox, R. A., F. Canova, and L. R. Hoxit, 1980: Meteorological characteristics of flash flood events over the western United States. Mon. Wea. Rev., 108 , 18661877.

    • Search Google Scholar
    • Export Citation
  • McCollum, D. M., R. A. Maddox, and K. W. Howard, 1995: Case study of a severe mesoscale convective system in central Arizona. Wea. Forecasting, 10 , 643665.

    • Search Google Scholar
    • Export Citation
  • McCumber, M., W-K. Tao, J. Simpson, R. Penc, and S-T. Soong, 1991: Comparison of ice-phase microphysical parameterization schemes using numerical simulations of tropical convection. J. Appl. Meteor., 30 , 9851004.

    • Search Google Scholar
    • Export Citation
  • Meyers, M. P., R. L. Walko, J. Y. Harrington, and W. R. Cotton, 1997: New RAMS cloud microphysics parameterization. Part II: The two-moment scheme. Atmos. Res., 45 , 339.

    • Search Google Scholar
    • Export Citation
  • Molinari, J., and M. Dudek, 1992: Parameterization of convective precipitation in mesoscale numerical models: A critical review. Mon. Wea. Rev., 120 , 326344.

    • Search Google Scholar
    • Export Citation
  • Nachamkin, J. E., and W. R. Cotton, 2000: Interactions between a developing mesoscale convective system and its environment. Part II: Numerical simulation. Mon. Wea. Rev., 128 , 12251244.

    • Search Google Scholar
    • Export Citation
  • Ovtchinnikov, M., and Y. L. Kogan, 2000: An investigation of ice production mechanisms in small cumuliform clouds using a 3D model with explicit microphysics. Part I: Model description. J. Atmos. Sci., 57 , 29893003.

    • Search Google Scholar
    • Export Citation
  • Pielke, R. A., and Coauthors. 1992: A comprehensive meteorological modeling system—RAMS. Meteor. Atmos. Phys., 49 , 6991.

  • Reisner, J., R. M. Rasmussen, and R. T. Bruintjes, 1998: Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model. Quart. J. Roy. Meteor. Soc., 124 , 10711079.

    • Search Google Scholar
    • Export Citation
  • Simpson, J., and W. K. Tao, 1993: Goddard cumulus ensemble model. Part II: Applications for studying cloud precipitating process and for NASA TRMM. Terr. Atmos. Oceanic Sci., 4 , 73116.

    • Search Google Scholar
    • Export Citation
  • Sorooshian, S., K. L. Hsu, X. Gao, H. V. Gupta, B. Imam, and D. Braithwaite, 2000: Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull. Amer. Meteor. Soc., 81 , 20352046.

    • Search Google Scholar
    • Export Citation
  • Tucker, D. F., and N. A. Crook, 1999: The generation of a mesoscale convective system from mountain convection. Mon. Wea. Rev., 127 , 12591273.

    • Search Google Scholar
    • Export Citation
  • Walko, R. L., and C. J. Tremback, cited 1997: Regional Atmospheric Modeling System. [Available online at http://www.atmet.com.].

  • Walko, R. L., W. R. Cotton, M. P. Meyers, and J. Y. Harrington, 1995: New RAMS cloud microphysics parameterization. Part 1: The single-moment scheme. Atmos. Res., 38 , 2962.

    • Search Google Scholar
    • Export Citation
  • Walko, R. L., and Coauthors. 2000: Coupled atmosphere–biophysics–hydrology models for environmental modeling. J. Appl. Meteor., 39 , 931944.

    • Search Google Scholar
    • Export Citation
  • Wallace, C. E., R. A. Maddox, and K. W. Howard, 1999: Summertime convective storm environments in central Arizona: Local observations. Wea. Forecasting, 14 , 9941006.

    • Search Google Scholar
    • Export Citation

Fig. 1.
Fig. 1.

Study area and RAMS grid domains

Citation: Monthly Weather Review 131, 9; 10.1175/1520-0493(2003)131<2038:ANIOSS>2.0.CO;2

Fig. 2.
Fig. 2.

Rainfall amounts (in.) in the Las Vegas metropolitan area on 8 Jul 1999 from Haro et al. (1999). Airport is the McCarran International Airport

Citation: Monthly Weather Review 131, 9; 10.1175/1520-0493(2003)131<2038:ANIOSS>2.0.CO;2

Fig. 3.
Fig. 3.

(a) Flooding along Duck Creek at Boulder Hwy, and (b) hydrograph of Duck Creek at Eastern Ave. (photo and hydrograph provided by T. Sutko, Clark County Nevada Flood Control District)

Citation: Monthly Weather Review 131, 9; 10.1175/1520-0493(2003)131<2038:ANIOSS>2.0.CO;2

Fig. 4.
Fig. 4.

Model topography of southern Nevada (grid 4) with terrain elevation in m. The light gray lines at 35.08°N, −115.5°W show cross sections used in analyses of the model results. Dashed box is the area for which rainfall is shown in Fig. 2. “A” is the location of the Desert Rock sounding, and “B” is the location of the model grid point nearest McCarran International Airport

Citation: Monthly Weather Review 131, 9; 10.1175/1520-0493(2003)131<2038:ANIOSS>2.0.CO;2

Fig. 5.
Fig. 5.

A 700-mb chart for 1200 UTC 8 Jul 1999. Heights are in dam; temperatures and dewpoint depressions are in °C; wind barbs are in m s−1, with full barb equal to 5 m s−1; 12-h height changes are in dam. Regions with dewpoint depressions of 6°C or less are highlighted by dash–dot line. Bold line indicates the position of the inverted trough

Citation: Monthly Weather Review 131, 9; 10.1175/1520-0493(2003)131<2038:ANIOSS>2.0.CO;2

Fig. 6.
Fig. 6.

Upper-air sounding at 1200 UTC 8 Jul 1999 from Desert Rock, NV. Wind vectors are shown with half barb equal to 2.5 m s−1. Lifted parcel shown from 700 mb

Citation: Monthly Weather Review 131, 9; 10.1175/1520-0493(2003)131<2038:ANIOSS>2.0.CO;2

Fig. 7.
Fig. 7.

Composite radar maps for periods of interest with times indicated in UTC. Radar data are from the National Climate Data Center. Red colors indicate high-radar reflectivities of more than 50 dBZ

Citation: Monthly Weather Review 131, 9; 10.1175/1520-0493(2003)131<2038:ANIOSS>2.0.CO;2

Fig. 8.
Fig. 8.

Analysis of the tracks of major rain cores (1632–1931 UTC) derived from 15-min-interval rainfall measurements. Airport is the McCarran International Airport.

Citation: Monthly Weather Review 131, 9; 10.1175/1520-0493(2003)131<2038:ANIOSS>2.0.CO;2

Fig. 9.
Fig. 9.

Convective cell processes represented by column max upward vertical velocity. The contours are 1.0, 5.0, 9.0, 13.0, and 17.0 m s−1. Convective cells are identified by letters “A” through “H.” N–S and E–W lines show the cross sections, and the dark circle indicates the location of the model grid point nearest McCarran International Airport. Model terrain elevation is shown by the grayscale

Citation: Monthly Weather Review 131, 9; 10.1175/1520-0493(2003)131<2038:ANIOSS>2.0.CO;2

Fig. 9.
Fig. 10.
Fig. 10.

Trajectories of modeled convective cells that occurred after 1800 UTC. The letter and number indicate the starting point and time of the trajectory (interval is 30 min). The circles labeled by “A” and “B” are locations for which model-predicted soundings are shown, and “B” is the model grid point nearest McCarran International Airport. The small square is the area shown in Figs. 2 and 8.

Citation: Monthly Weather Review 131, 9; 10.1175/1520-0493(2003)131<2038:ANIOSS>2.0.CO;2

Fig. 11.
Fig. 11.

Isotherms of cloud-top brightness temperature (K) from GOES infrared imagery. Contour interval is 5 K.

Citation: Monthly Weather Review 131, 9; 10.1175/1520-0493(2003)131<2038:ANIOSS>2.0.CO;2

Fig. 12.
Fig. 12.

The 24-h rainfall accumulation from 1200 UTC 8 Jul to 1200 UTC 9 Jul: (a) Eta forecast, (b) RAMS grid 1 simulation, and (c) gauge/satellite data. Contours are 1, 5, 10, 15, 20, 30, 50, 80, and 120 mm. (d) Grid 4 24-h rainfall accumulation from 1200 UTC 8 Jul to 1200 UTC 9 Jul. The contours are 1, 5, 10, 15, 30, 50, 80, 120, and 150 mm. Model terrain elevation is shown by the grayscale.

Citation: Monthly Weather Review 131, 9; 10.1175/1520-0493(2003)131<2038:ANIOSS>2.0.CO;2

Fig. 13
Fig. 13

a. Evolution of wind components with time on the south–north cross section along 115.5°W

Citation: Monthly Weather Review 131, 9; 10.1175/1520-0493(2003)131<2038:ANIOSS>2.0.CO;2

Fig. 13
Fig. 13

b. Evolution of equivalent potential temperature (K) with time on the south–north cross section (115.5°W)

Citation: Monthly Weather Review 131, 9; 10.1175/1520-0493(2003)131<2038:ANIOSS>2.0.CO;2

Fig. 13
Fig. 13

c. The same as Fig. 13a but on the east–west cross section along 36.08°N; “B” is the location of the model grid point nearest McCarran International Airport

Citation: Monthly Weather Review 131, 9; 10.1175/1520-0493(2003)131<2038:ANIOSS>2.0.CO;2

Fig. 14.
Fig. 14.

Grid 4 wind vectors at lowest layer (48 m above ground level). Contour lines are topography at 400-m intervals. Solid lines are convectively generated outflow boundaries, dashed lines are decaying outflow boundaries, and “B” is the location of the model grid point nearest McCarran International Airport

Citation: Monthly Weather Review 131, 9; 10.1175/1520-0493(2003)131<2038:ANIOSS>2.0.CO;2

Fig. 14.
Fig. 15.
Fig. 15.

Time series of surface (2 m) observations from McCarran International Airport: (a) wind speed (m s−1), (b) wind direction (degrees), and (c) temperature and dewpoint temperature (°C) on 8 and 9 Jul 1999.

Citation: Monthly Weather Review 131, 9; 10.1175/1520-0493(2003)131<2038:ANIOSS>2.0.CO;2

Fig. 16.
Fig. 16.

The same as Fig. 15 but predicted by RAMS at 48 m above the surface, at the grid point closest to McCarran International Airport.

Citation: Monthly Weather Review 131, 9; 10.1175/1520-0493(2003)131<2038:ANIOSS>2.0.CO;2

Fig. 17.
Fig. 17.

Model-predicted upper-air soundings at 1800 UTC 8 Jul 1999 for (a) position “A” on the north slope of Charleston Peak (see Fig. 10), and (b) position “B” at the grid point closest to McCarran International Airport.

Citation: Monthly Weather Review 131, 9; 10.1175/1520-0493(2003)131<2038:ANIOSS>2.0.CO;2

Table 1.

Peak discharges on Las Vegas valley washes during the 8 Jul 1999 flash flood and previous maximum flows (from Haro et al. 1999)

Table 1.
Save
  • Arakawa, A., and J-M. Chen, 1987: Closure assumptions in the cumulus parameterization problem. Short and Medium Range Numerical Weather Prediction, T. Matsuno, Ed., Universal Academy Press, 107–131.

    • Search Google Scholar
    • Export Citation
  • Cotton, W. R., G. J. Tripoli, R. M. Rauber, and E. A. Mulvihill, 1986: Numerical simulation of the effects of varying ice crystal nucleation rates and aggregation processes on orographic snowfall. J. Climate Appl. Meteor., 25 , 16581680.

    • Search Google Scholar
    • Export Citation
  • Dunn, L. B., and J. D. Horel, 1994a: Prediction of central Arizona convection. Part I: Evaluation of the NGM and Eta model precipitation forecasts. Wea. Forecasting, 9 , 495507.

    • Search Google Scholar
    • Export Citation
  • Dunn, L. B., and J. D. Horel, 1994b: Prediction of central Arizona convection. Part II: Further examination of the Eta model forecasting. Wea. Forecasting, 9 , 508521.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 1994: Atmospheric Convection. Oxford University Press, 580 pp.

  • Fovell, R. G., and P-H. Tan, 1998: The temporal behavior of numerically simulated multicell-type storms. Part II: The convective cell life and cycle and cell regeneration. Mon. Wea. Rev., 126 , 551577.

    • Search Google Scholar
    • Export Citation
  • Haro, J. A., H. R. Daley, and K. J. Runk, cited 1999: The Las Vegas flash floods of 8 July 1999. [Available online at http://www.wrh.noaa.gov/wrhq/99TAs/9926/index.html.].

    • Search Google Scholar
    • Export Citation
  • Harrington, J. Y., 1997: The effect of radiative and microphysical processes on simulated warm and transition season arctic stratus. Ph.D. dissertation, Colorado State University, 289 pp.

    • Search Google Scholar
    • Export Citation
  • Khain, A., M. Ovtchinnikov, M. Pinsky, A. Pokrovsky, and H. Krugliak, 2000: Notes on the state-of-the-art numerical modeling of cloud microphysics. Atmos. Res., 55 , 159224.

    • Search Google Scholar
    • Export Citation
  • Kulie, M. S., and Y-L. Lin, 1998: The structure and evolution of a numerically simulated high-precipitation supercell thunderstorm. Mon. Wea. Rev., 126 , 20902116.

    • Search Google Scholar
    • Export Citation
  • Lilly, D. K., 1990: Numerical prediction of thunderstorms—Has its time come? Quart. J. Roy. Meteor. Soc., 116 , 779798.

  • Liu, Y., D-L. Zhang, and M. K. Yau, 1997: A multiscale numerical study of Hurricane Andrew (1992). Part I: Model description and verification. Mon. Wea. Rev., 125 , 30733093.

    • Search Google Scholar
    • Export Citation
  • Maddox, R. A., F. Canova, and L. R. Hoxit, 1980: Meteorological characteristics of flash flood events over the western United States. Mon. Wea. Rev., 108 , 18661877.

    • Search Google Scholar
    • Export Citation
  • McCollum, D. M., R. A. Maddox, and K. W. Howard, 1995: Case study of a severe mesoscale convective system in central Arizona. Wea. Forecasting, 10 , 643665.

    • Search Google Scholar
    • Export Citation
  • McCumber, M., W-K. Tao, J. Simpson, R. Penc, and S-T. Soong, 1991: Comparison of ice-phase microphysical parameterization schemes using numerical simulations of tropical convection. J. Appl. Meteor., 30 , 9851004.

    • Search Google Scholar
    • Export Citation
  • Meyers, M. P., R. L. Walko, J. Y. Harrington, and W. R. Cotton, 1997: New RAMS cloud microphysics parameterization. Part II: The two-moment scheme. Atmos. Res., 45 , 339.

    • Search Google Scholar
    • Export Citation
  • Molinari, J., and M. Dudek, 1992: Parameterization of convective precipitation in mesoscale numerical models: A critical review. Mon. Wea. Rev., 120 , 326344.

    • Search Google Scholar
    • Export Citation
  • Nachamkin, J. E., and W. R. Cotton, 2000: Interactions between a developing mesoscale convective system and its environment. Part II: Numerical simulation. Mon. Wea. Rev., 128 , 12251244.

    • Search Google Scholar
    • Export Citation
  • Ovtchinnikov, M., and Y. L. Kogan, 2000: An investigation of ice production mechanisms in small cumuliform clouds using a 3D model with explicit microphysics. Part I: Model description. J. Atmos. Sci., 57 , 29893003.

    • Search Google Scholar
    • Export Citation
  • Pielke, R. A., and Coauthors. 1992: A comprehensive meteorological modeling system—RAMS. Meteor. Atmos. Phys., 49 , 6991.

  • Reisner, J., R. M. Rasmussen, and R. T. Bruintjes, 1998: Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model. Quart. J. Roy. Meteor. Soc., 124 , 10711079.

    • Search Google Scholar
    • Export Citation
  • Simpson, J., and W. K. Tao, 1993: Goddard cumulus ensemble model. Part II: Applications for studying cloud precipitating process and for NASA TRMM. Terr. Atmos. Oceanic Sci., 4 , 73116.

    • Search Google Scholar
    • Export Citation
  • Sorooshian, S., K. L. Hsu, X. Gao, H. V. Gupta, B. Imam, and D. Braithwaite, 2000: Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull. Amer. Meteor. Soc., 81 , 20352046.

    • Search Google Scholar
    • Export Citation
  • Tucker, D. F., and N. A. Crook, 1999: The generation of a mesoscale convective system from mountain convection. Mon. Wea. Rev., 127 , 12591273.

    • Search Google Scholar
    • Export Citation
  • Walko, R. L., and C. J. Tremback, cited 1997: Regional Atmospheric Modeling System. [Available online at http://www.atmet.com.].

  • Walko, R. L., W. R. Cotton, M. P. Meyers, and J. Y. Harrington, 1995: New RAMS cloud microphysics parameterization. Part 1: The single-moment scheme. Atmos. Res., 38 , 2962.

    • Search Google Scholar
    • Export Citation
  • Walko, R. L., and Coauthors. 2000: Coupled atmosphere–biophysics–hydrology models for environmental modeling. J. Appl. Meteor., 39 , 931944.

    • Search Google Scholar
    • Export Citation
  • Wallace, C. E., R. A. Maddox, and K. W. Howard, 1999: Summertime convective storm environments in central Arizona: Local observations. Wea. Forecasting, 14 , 9941006.

    • Search Google Scholar
    • Export Citation
  • Fig. 1.

    Study area and RAMS grid domains

  • Fig. 2.

    Rainfall amounts (in.) in the Las Vegas metropolitan area on 8 Jul 1999 from Haro et al. (1999). Airport is the McCarran International Airport

  • Fig. 3.

    (a) Flooding along Duck Creek at Boulder Hwy, and (b) hydrograph of Duck Creek at Eastern Ave. (photo and hydrograph provided by T. Sutko, Clark County Nevada Flood Control District)

  • Fig. 4.

    Model topography of southern Nevada (grid 4) with terrain elevation in m. The light gray lines at 35.08°N, −115.5°W show cross sections used in analyses of the model results. Dashed box is the area for which rainfall is shown in Fig. 2. “A” is the location of the Desert Rock sounding, and “B” is the location of the model grid point nearest McCarran International Airport

  • Fig. 5.

    A 700-mb chart for 1200 UTC 8 Jul 1999. Heights are in dam; temperatures and dewpoint depressions are in °C; wind barbs are in m s−1, with full barb equal to 5 m s−1; 12-h height changes are in dam. Regions with dewpoint depressions of 6°C or less are highlighted by dash–dot line. Bold line indicates the position of the inverted trough

  • Fig. 6.

    Upper-air sounding at 1200 UTC 8 Jul 1999 from Desert Rock, NV. Wind vectors are shown with half barb equal to 2.5 m s−1. Lifted parcel shown from 700 mb

  • Fig. 7.

    Composite radar maps for periods of interest with times indicated in UTC. Radar data are from the National Climate Data Center. Red colors indicate high-radar reflectivities of more than 50 dBZ

  • Fig. 8.

    Analysis of the tracks of major rain cores (1632–1931 UTC) derived from 15-min-interval rainfall measurements. Airport is the McCarran International Airport.

  • Fig. 9.

    Convective cell processes represented by column max upward vertical velocity. The contours are 1.0, 5.0, 9.0, 13.0, and 17.0 m s−1. Convective cells are identified by letters “A” through “H.” N–S and E–W lines show the cross sections, and the dark circle indicates the location of the model grid point nearest McCarran International Airport. Model terrain elevation is shown by the grayscale

  • Fig. 9.

    (Continued)

  • Fig. 10.

    Trajectories of modeled convective cells that occurred after 1800 UTC. The letter and number indicate the starting point and time of the trajectory (interval is 30 min). The circles labeled by “A” and “B” are locations for which model-predicted soundings are shown, and “B” is the model grid point nearest McCarran International Airport. The small square is the area shown in Figs. 2 and 8.

  • Fig. 11.

    Isotherms of cloud-top brightness temperature (K) from GOES infrared imagery. Contour interval is 5 K.

  • Fig. 12.

    The 24-h rainfall accumulation from 1200 UTC 8 Jul to 1200 UTC 9 Jul: (a) Eta forecast, (b) RAMS grid 1 simulation, and (c) gauge/satellite data. Contours are 1, 5, 10, 15, 20, 30, 50, 80, and 120 mm. (d) Grid 4 24-h rainfall accumulation from 1200 UTC 8 Jul to 1200 UTC 9 Jul. The contours are 1, 5, 10, 15, 30, 50, 80, 120, and 150 mm. Model terrain elevation is shown by the grayscale.

  • Fig. 13

    a. Evolution of wind components with time on the south–north cross section along 115.5°W

  • Fig. 13

    b. Evolution of equivalent potential temperature (K) with time on the south–north cross section (115.5°W)

  • Fig. 13

    c. The same as Fig. 13a but on the east–west cross section along 36.08°N; “B” is the location of the model grid point nearest McCarran International Airport

  • Fig. 14.

    Grid 4 wind vectors at lowest layer (48 m above ground level). Contour lines are topography at 400-m intervals. Solid lines are convectively generated outflow boundaries, dashed lines are decaying outflow boundaries, and “B” is the location of the model grid point nearest McCarran International Airport

  • Fig. 14.

    (Continued)

  • Fig. 15.

    Time series of surface (2 m) observations from McCarran International Airport: (a) wind speed (m s−1), (b) wind direction (degrees), and (c) temperature and dewpoint temperature (°C) on 8 and 9 Jul 1999.

  • Fig. 16.

    The same as Fig. 15 but predicted by RAMS at 48 m above the surface, at the grid point closest to McCarran International Airport.

  • Fig. 17.

    Model-predicted upper-air soundings at 1800 UTC 8 Jul 1999 for (a) position “A” on the north slope of Charleston Peak (see Fig. 10), and (b) position “B” at the grid point closest to McCarran International Airport.

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