Nonhydrostatic, mesobeta-scale, real-data simulations with the Penn ...

Meteorol. Atmos. Phys. 49, 209-227 (1992)

Meteorology and Atmospheric Physics 9 Springer-Verlag 1992 Printed in Austria 551.511.3

1Department of Meteorology, The Pennsylvania State University, University Park, PA, U.S.A. 2 Mesoscale and Microscale Meteorology Division, National Center for Atmospheric Research,* Boulder, CO, U.S.A. 3 Earth System Science Center, The Pennsylvania State University, University Park, PA, U.S.A.

Nonhydrostatic, Mesobeta-Scale, Real-Data Simulations with the Penn State University/National Center for Atmospheric Research Mesoscale Model T. T. Warner 1'3, Y.-H. Kuo z, J. D. Doyle 1, J. Dudhia 2, D. R. Stauffer ~, and N. L. Seaman x With 21 Figures Received December 2, 1991 Revised June 16, 1992

Summary The Penn State University/National Center for Atmospheric Research (PSU/NCAR) mesoscale model is a widely used research tool that has been applied in a wide variety of real-data, mesoalpha-scale applications. Recently a nonhydrostatic version of this model has been developed by Dudhia (1993). It is the purpose of this paper to illustrate the capabilities of this modeling system by describing four examples of mesobeta-scale simulations: two of the cases involve maritime processes and two deal with continental weather events. All utilize fully three-dimensional sets of initial conditions that are based on real data, both standard data and from special measurements programs. One case employs the model in a data-assimilation configuration, wherein Newtonian relaxation terms are used in the equations to assimilate data from a variety of platforms. This example ofnonhydrostatic four-dimensional data assimilation (FDDA) is performed for the purpose of generating a dynamically consistent four-dimensional data-set, however the same procedure can be used for model initialization. The first case, described in section 2, involves the simulation of a coastal front that forms offshore near the western edge of the Gulf Stream. In the second case, described in section 3, the model is used in the FDDA mode to define the mesobeta-scale windfield over the complex terrain of the region around Grand Canyon, Arizona. In sections 4 and 5 will be described

* NCAR is sponsored by the National Science Foundation.

the mesobeta-scale structure of cold fronts, one within a marine cyclone, and another near the Rocky Mountains.

1. Introduction Increasing c o m p u t e r p o w e r a n d the availability of d a t a f r o m new m e t e o r o l o g i c a l o b s e r v i n g systems is m a k i n g real-data, m e s o b e t a - s c a l e n u m e r i c a l weather prediction more practical. However, taking full a d v a n t a g e of these technological advances will require i m p r o v e m e n t s in the m o d e l physics as well as in o u r ability to assimilate these fourdimensional operational d a t a sets for model initialization. T h e l o n g - t e r m objective of a n u m b e r of research m o d e l i n g g r o u p s a n d o p e r a t i o n a l - f o r e c a s t i n g centers is the d e v e l o p m e n t of general-purpose, relocatable, m e s o b e t a - a n d m e s o g a m m a - s c a l e m o d e l s and data-assimilations systems that can be applied to a wide range of m e t e o r o l o g i c a l processes over all seasons. These m o d e l s will also, necessarily, need to be n o n h y d r o s t a t i c . Even t h o u g h there are some m e s o b e t a - s c a l e m o d e l s that have been used r o u t i n e l y with real data, m o s t have either 1) n o t been applied to a variety of geographic and climatic regions, 2) not included a c o m p l e t e r e p r e s e n t a t i o n of all physical processes (turbulence, moist convection, radiation), 3) not used fully t h r e e - d i m e n -

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sional sets of initial conditions based on real data, or 4) not employed dynamic initialization or fourdimensional data assimilation to define the initial state. It is the purpose of this paper to describe some initial tests of a model that it is hoped will satisfy the above criteria as a general-purpose nonhydrostatic model for use on the mesobeta and mesogamma scales. The model is based on the general framework of the Penn State University/National Center for Atmospheric Research (PSU/NCAR) mesoscale model, a hydrostatic model with relatively complete physics and efficient numerics that has been applied extensively by a number of research groups, primarily on the mesoalpha scale (Anthes and Warner, 1978; Anthes et al., 1987; Anthes, 1990). Additionally, this model has been used for the production of real-time weather forecasts for teaching, research, and public service applications at Penn State (Warner and Seaman, 1990). Recently this model has been reformulated in a nonhydrostatic framework by Dudhia (1993). In combination with the existing initialization techniques and physics of the current hydrostatic model, this provides a model capable of real-data simulations on any scale. The nonhydrostatic PSU/NCAR model employs reference pressure as the basis for a terrain-following vertical coordinate, and a fully compressible system of equations. The pressure perturbation and temperature are prognostic variables, and the model uses a B-grid staggering (Arakawa and Lamb, 1977), in contrast to most current nonhydrostatic models. A multilayer parameterization of the planetary boundary layer (Zhang and Anthes, 1982) is employed, in conjunction with a surface energy equation for the calculation of the ground temperature. Except when the model is applied on mesoalpha scales, say with grid increments of 20 km or greater, no convective parameterization is employed. Conservation equations are used to predict water vapor, cloud water, and rain water. The vertical coordinate is similar to the conventional terrain-following sigma system, except that the reference-state pressure is used; thus the sigma levels are fixed in space. In the following sections will be described four mesobeta-scale applications of the new nonhydrostatic model: two of the cases involve maritime processes and two deal with continental weather events. All utilize fully three-dimensional sets of

initial conditions that are based on real data, both standard data and data from special measurements programs. One case employs the model in a dataassimilation configuration, wherein Newtonian relaxation terms are used in the equations to assimilate data from a variety of platforms. This example of nonhydrostatic four-dimensional data assimilation (FDDA) is performed for the purpose of generating a dynamically consistent fourdimensional data-set, however the same procedure can be used for model initialization. The first case, described in section 2, involves simulation of a coastal front that forms offshore near the western edge of the Gulf Stream. In the second case, described in section 3, the model is used in the F D D A mode to define the mesobeta-scale windfield over the complex terrain of the region around Grand Canyon, Arizona. In sections 4 and 5 will be described the mesobeta-scale structure of cold fronts, one within a marine cyclone, and another near the Rocky Mountains. It is worth noting that all of the simulations described here did not necessarily require the use of a nonhydrostatic model. However, the grid increments were small enough that nonhydrostatic effects could have been locally important. Regardless, our primary goal is to demonstrate the overall ability of this general-purpose model to perform reasonable simulations in a variety of meteorological settings.

2. Coastal Front Structure During GALE lOP 2

2.1 Introduction The coastal front, characterized by a large thermal contrast and cyclonic shear, is a shallow (less than 500m deep) mesoscale boundary that separates warm, moist oceanic air from cold, dry continental air. More frequently observed in the winter months, coastal fronts have been observed in many regions including New England (Bosart et al., 1972), the Carolinas (Bosart, 1981), the Gulf Coast of Texas (Bosart, 1984) and the Black Sea Coast (Draghici, 1984), and are frequently associated with major cyclone events along the east coast of the United States (Kocin and Uccellini, 1990). The mesobetascale structure of the coastal front has been studied for a number of cases. However, because of data limitations there have been few investigations of the structure and dynamics of the coastal front when it is located offshore.

Nonhydrostatic, Mesobeta-Scale, Real-Data Simulations with the Penn State University/National Center

Riordan (1990) and Doyle and Warner (1990) used the special data from the Genesis of Atlantic Lows Experiment (GALE) for Intensive Observation Period (IOP) 2 to analyze the mesoscale characteristics of a case of Carolina coastal frontogenesis. They found significant mesobeta-scale variations in the frontal intensity, structure and movement. The objectives of applying the PSU/ NCAR nonhydrostatic mesoscale model to this coastal front during GALE IOP2 are to: (1) determine if a sophisticated nonhydrostatic model is capable of simulating the complex mesobeta-scale frontal structure and (2) use the dynamically consistent model-generated data set to better understand the physical processes. A detailed description of the synoptic-scale and mesoscale conditions associated with the coastal front observed during GALE IOP 2 can be found in Riordan (1990) and Doyle and Warner (1990). Briefly, a large anticyclone developed in central Canada and dominated the eastern one-third of the United States. Air flowing around the southern portion of this anticyclone was modified rapidly by heat and moisture fluxes within the marineatmospheric boundary layer (MABL) as it approached the southeast coast of the United States. As the anticyclone moved eastward, surface winds offshore of the Carolinas began to gradually veer from northerly to easterly after 0000 UTC 25 January 1986 (00 UTC/25). Over the continent, the surface winds remained northeasterly as a sea-level pressure ridge became well-defined. This ridge reflects the wedge of cold air entrenched to the east of the Appalachian Mountains associated with cold-air damming. A strong middle-tropospheric trough remained well to the west of the Carolinas during the study period and did not significantly influence the coastal frontogenesis process. The formation of bands of confluence and diffluence (Riordan, 1990) and the development of a surface pressure trough (Doyle and Warner, 1990) over the warm Gulf Stream preceded the formation of the coastal front on 24 January. Strong cyclonic wind shear at the surface associated with the coastal front became apparent by 00 UTC/25 over the warmest waters of the Gulf Stream, to the southeast of the strong sea-surface temperature (SST) gradient at the western edge of the Gulf Stream. At this time, National Weather Service (NWS) radar composites as well as data from the

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Fig. 1. Surface streamlines (solid) and isotherms (dashed, ~ for eastern North Corolina for 14 UTC/25 (from Riordan, 1990)

Doppler radar unit at Ocracoke, North Carolina (Riordan and Anderson, 1991) indicate a line of echoes with high reflectivity over the Gulf Stream in the vicinity of the coastal front. The northern portion of the coastal front, located to the east of North Carolina at 00 UTC/25, moved slowly westward and was located near Cape Hatteras, North Carolina by 06 UTC/25. The southern portion of the coastal front remained almost stationary near the western edge of the Gulf Stream, extending from the coastal waters of South Carolina to Florida. The westward movement of the northern section of the front continued, and by 12 UTC/25 the surface confluence zone was located just to the west of Pamlico Sound. The surface streamline and isotherm analysis for 14 UTC/25, shown in Fig. 1 (Riordan, 1990), indicates the presence of a weak mesoscale vortex along the front and a discontinuous structure to the frontal confluence zone.

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2.2 Simulations

The PSU-NCAR nonhydrostatic mesoscale model used in this application employed temporally invariant ocean temperatures from the National Oceanic and Atmospheric Administration's 14kin SST analysis, which is derived from satellite radiometer data corrected with available in-situ measurements. The model computations were performed at 33 irregularly spaced vertical levels, with the greatest resolution in the lower troposphere (15 model levels below 850 mb). The horizontal grid contains 126 • 126 points, and has a grid length of 5 km. For display purposes, only a portion of the computational grid will be used to show the simulation results. A 30-h integration from 12 UTC/24 (6-12h prior to coastal frontogenesis) is performed. The initialization data set is interpolated from a 24-h nonhydrostatic simulation of this case that used a 30-km grid increment and 33 vertical layers. The lateral boundary tendencies for the 5-kin grid are obtained from a linear spatial and temporal interpolation of the output from the 30-kin resolution simulation. The nonhydrostatic mesoscale model accurately simulates many of the observed mesobetascale characteristics of the coastal frontogenesis

Fig. 2. Surface-layer ( ~ 2 5 AGL) streamlines for the 15-h simulation time (03 UTC/25). Shading denotes regions where the 900-rob vertical ascent is greater than 0.15 m s-2. The heavier shadings represents stronger ascent. The line A-B shows the orientation of a vertical cross section on which the model fields are displayed in Figs. 3 and 4

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during GALE IOP 2. A series of confluent bands develop near the surface over the warmest portion of the Gulf Stream in a region of banded precipitation, prior to the 9-h simulation time (21 UTC/24). This is in general agreement with the surface and satellite data (Riordan, 1990). A well-defined coastal front is apparent by the 15-h time (03 UTC/25), as indicated by the simulated surfacelayer ( ~ 25 m AGL) streamlines and 900-mb vertical motion shown in Fig. 2. At this time, the coastal front is positioned near the strong SST gradient at the western edge of the Gulf Stream. On the cold side of the coastal front, air rapidly warms and moistens as it flows from the stable boundary layer over the continent, southeastward over the Gulf Stream. Surface sensible and latent heat fluxes are in excess of 300 W m - 2 and 750 W m - 2, respectively, in areas where the air-sea temperature difference is large. The frontal structure separating the two air masses consists of a series of discontinuous confluent bands with embedded regions of strong ascent, as indicated by the shading in Fig. 2. Also, the bands of convergence and regions of ascent are oriented at an angle to the mesoalpha-scale front. These characteristics have also been observed in narrow cold-frontal rainbands associated with synopticscale cold fronts (Hobbs and Persson, 1982). Along the coastal front, copious precipitation exists as convective cells, embedded in a line of less-intense

Nonhydrostatic, Mesobeta-Scale, Real-Data Simulations with the Penn State University/National Center

precipitation oriented parallel to the front, develop in the warm and moist MABL to the east of the front. The simulated six-hourly accumulated precipitation is in excess of 10 cm in the vicinity of several of these cells. The explicitly resolved mesoscale downdrafts associated with the convection contribute to intense mesobeta-scale gradients in the surface heat fluxes. To the east of the coastal front, bands of ascent are embedded in the unstable MABL (Fig. 2) and are oriented nearly normal to the front. The orientation of the bands is in general agreement with echoes from the Doppler radar at 0050 UTC 25 January (Riordan and Anderson, 1991). The vertical structure of the simulated low-level baroclinic zone at the 15-h time (03 UTC/25) is depicted in the vertical cross section of potential temperature shown in Fig. 3. The simulated baroclinic zone extends from the surface to ,-, 500 m, in agreement with the observations of the coastal front when it existed over land (Riordan, 1990; Doyle and Warner, 1990). A deep neutral-stability layer exists to the southeast on the warm side of the front as a result of the destabilizing effects of the large surface sensible heat fluxes. Internal gravity waves and latent heat release associated with the precipitating bands contribute to the fine-scale structure in the potential temperature field above the boundary layer. Bennetts and Hoskins (1979) and Emanuel (1979) have suggested that the formation of precipitation bands, which occur in stable or neutral

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stratification in regions of negative potential vorticity, may be caused by symmetric instability. In the vertical cross section shown in Fig. 3, several regions of negative moist potential vorticity (shaded areas) are present, which suggests that symmetric instability may play a role in the formation of the banded precipitation over the Gulf Stream. One layer of negative moist potential vorticity to the southeast of the frontal zone, which extends from near the top of the MABL to about 600 mb, is collocated with several regions of weak ascent (Fig. 4). A strong updraft of over 2 m s- 1 is associated with a precipitation band at the leading edge of the frontal zone, and is embedded in a second region of negative moist potential vorticity. This convective band is located near the area where undulations in the surfacelayer winds formed along the frontal zone (see Fig. 2). The latent heat release associated with the intense convective bands may be important for the development of the frontal vortices. To the rear of the front, weak sinking motion within the boundary layer is enhanced by the descending branch of the direct thermal coastal front circulation. At the 24-h simulation time (12 UTC/25), the coastal front has become significantly perturbed by the development of a series of mesobeta-scale vortices along the front, as shown in the simulated

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Fig. 5. Surface-layer (~25 AGL) streamlines for the 24-h simulation time (12 UTC/25). Shading denotes regions where the surface-layer cyclonic vorticity exceeds 10-~s -1. The heavier shading represents larger values of cyclonic vorticity

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surface-layer streamline and vorticity fields in Fig. 5. The surface-layer cyclonic vorticity maxima that are in excess of 10 - 4 S-i are shown by the shaded regions: they are located approximately 100-km apart along the frontal zone. These simulated mesobeta-scale vortices have a wavelength of approximately 50 km and a phase speed of 812 m s- 1. The position of the mesovortex near the Pamlico Sound at the 24-h time (12 UTC/25) agrees remarkable well with the circulation analyzed at 14 UTC/25 (Fig. 1). By this time, the simulated coastal front had moved westward to a position near the western portion of the Pamlico Sound, however it remained stationary to the south near the western edge of the Gulf Stream, in agreement with the observations. At the top of the MABL near 800 rob, regions of mesoscale ascent greater than 3 m s-1 are organized in discontinuous bands along the front. Copious precipitation continues at this time along the frontal boundary, with the simulated six-hourly accumulated precipitation maxima exceeding 12 cm in the vicinity of the mesoscale vortices. During the early stage of the coastal front development, between the 12-h and 18-h times, the simulated thermal gradient doubles due to the strong frontogenetical forcing (with contributions from the deformation, tilting and diabatic effects) which is in excess of 10 K (10 km h r - 1). At the 24-h time (12 UTC/25), the simulated mesoscale vortices along the coastal front have attained cyclonic vorticity and convergence maxima at the surface of "-'2 • 10 - 3 S - 1 . The vorticity and convergence maxima are in general agreement with the peak observed values of 3-4 x 10- 3 s- ~ for an intense cold front in the Great Plains (Sanders, 1983), however they are somewhat less intense than the 10-2 s - 1 observed by Carbone (1982) in a narrow cold-frontal rainband. 2.3 Summary It is evident that the nonhydrostatic model is capable of simulating the development and movement of this coastal front with a high degree of accuracy. Many of the mesobeta-scale characteristics of the coastal front, such as the vorticity, divergence and frontogenesis fields, are simulated in a realistic manner and are in general agreement with previous mesoscale observations of other types of fronts.

3. Windflow in the Region of Grand Canyon, Arizona Defined Using Four-Dimensional Data Assimilation 3.1 Introduction As a first step in testing F D D A procedures in the nonhydrostatic PSU/NCAR mesoscale model, a wintertime complex-terrain case has been chosen for which the hydrostatic version of the PSU-NCAR mesoscale model has been run, both with and without F D D A (Stauffer and Seaman, 1991; hereafter referred to as SS91). The objectives are (1) to verify the performance of the nonhydrostatic model using FDDA over complex terrain for a case where special mesobeta-scale data exist for both verification and assimilation, and (2) to compare these results with those from the hydrostatic model described in SS91. In this case, the FDDA is used to generate dynamically consistent four-dimensional mesobeta-scale meteorological fields that can be used for air-quality applications. Along with emission inventories, these fields are crucial inputs for three-dimensional Eulerian atmospheric chemistry models such as the Regional Acid Deposition Model (Chang et al., 1987). 3.2 Simulations In this investigation, the nonhydrostatic model is configured to be consistent with the hydrostatic model used in SS91. The hydrostatic model was nested, with both meshes containing 61 by 61 grid points in the horizontal, 20 layers in the vertical, a model top at 150 rob, and grid lengths of 30- and 10-kin. Since the nesting capability is not yet fully tested for the nonhydrostatic model, a 30-kin simulation was performed first, and the lateral boundary tendencies for the 10-kin mesh were then interpolated from the 30-km simulation at hourly intervals. Figure 6 shows the area covered by the 10-kin grid, and the terrain surrounding the Grand Canyon, Arizona region; much of the canyon itself is, of course, unresolved at this resolution. In the winter of 1990, a special mesobeta-scale observing system was deployed in the Grand Canyon region, under the sponsorship of Salt River Project, to study the Canyon's highly publicized visibility-impairment problem. The sphcial data included five supplemental rawinsondes (6 hourly), three wind profilers (hourly), four Doppler sodars (hourly) and 13 surface sites (hourly). These data

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were generally concentrated along the Colorado River from southern Utah into the Grand Canyon region. The case study period is from 0000 UTC 18 January to 0000 UTC 20 January 1990. At 00 UTC/18, a weak 1010-mb winter storm was located in southern California near the Nevada-Arizona border, beneath a closed upper-level cyclone over southern California. By 12 UTC/19, the upperlevel cyclone had moved eastward to southeastern Arizona, and the NWS surface analysis (not shown) had the surface low located in Monument Valley, in northeastern Arizona, about 100-kin southwest of Four Corners. Two 48-h experiments were performed with the nonhydrostatic model to simulate the flow over the complex terrain of the 10-kin grid shown in Fig. 6: a control simulation with no FDDA, that is initialized with standard NWS data, and a similarly initialized simt~lation with F D D A in which the model was nudged to the special data to generate a more dynamically consistent four-dimensional analysis. Because many air-quality studies require data sets of several days duration, mesoalpha-scale and mesobeta-scale effects can both be important;

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thus the continuous, multi-scale F D D A strategy described in SS91 is used here. In this approach, the model assimilates the special mesobeta-scale observations on the fine, 10-km mesh by nudging the model solution directly to the observations using the technique described in Stauffer and Seaman (1990), while mesoalpha-scale rawinsonde and surface data are assimilated into the model solution on the coarse, 30-kin mesh by nudging the model solution to an analysis of the observations (Stauffer et al., 1991). Figure 7a shows the surface-layer streamlines at the 36-h simulation time (12 UTC/19) for the control experiment (no FDDA). Figure 8 presents the observed surface winds and sea-level pressure analysis for comparison. This control simulation is too slow in moving the mesoalpha-scale surface low to the east-northeast. The simulated cyclone circulation center/sea-level pressure minimum is located near Winslow, Arizona, about 150km to the southwest of the analyzed position. The simulated surface low is closer to the uppertropospheric circulation center than is the observed low. In addition to the possible effects of uncertainties in the initial and lateral boundary conditions, it appears that the model's cold advec-

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Fig. 7. Surface-layerstreamlines at 36-h (12 UTC/19) on the 10-kin mesh for (a) the control experiment without FDDA and (b) the experiment with FDDA. The heavy solid lines represent the rivers shown in Fig. 6, the L locates the pressure center of the storm, and the shading denotes regions with wind speeds exceeding4 m s- 1

tion in the mountainous terrain of Nevada and western Utah was deflected too much toward the southwest by the Wasatch Mountains, which helped to retard the northeastward movement of the low-level center. As a result, the simulated flow

in northeastern Arizona is easterly rather than northwesterly. More important for this air-quality study, the winds in the region of the Grand Canyon and the Kaibab Plateau (Fig. 6) are, erroneously, northeasterly and generally greater than 5 m s-1, as indicated by the shading. This is in contrast with the special mesoscale observations included in Fig. 8, which show mostly northwesterly winds ranging from 1-5 m s- 1 in this region9 Despite these problems, the control simulation successfully represented m a n y aspects of the flow including northwesterly flow to the east of the Kaibab Plateau and at Cedar Ridge (near the juncture of the Colorado and Little Colorado Rivers). Also successfully simulated were m a n y of the general characteristics of the synoptic pattern, including the east-northeastward motion of the low center and the spread of west-southwesterly winds across the Mogollon Plateau (west of Winslow) behind the storm. The 36-h surface-layer streamlines for the F D D A experiment are shown in Fig. 7b. The winds near the G r a n d Canyon were reduced in speed and their orientation was shifted to the northwest, as observed. The winds near Page, Arizona and northward along the Colorado River Valley are also much improved compared to th? control (Fig. 7a). The multi-scale F D D A strategy also simulated the storm center near its analyzed position, and caused winds in northeastern Arizona

Nonhydrostatic, Mesobeta-Scale, Real-Data Simulations with the Penn State University/National Center

to have a westerly component rather than an easterly one. These larger-scale improvements outside the special observation network resulted from the mesoalpha-scale FDDA on the 30-kin mesh, which improved the lateral boundary conditions supplied to the 10-kin mesh. The terrain also modifies the mesoalpha- and mesobeta-scale flow in regions away from the special observation network. For example, the northwesterly flow in Utah is deflected around the southern end of the Wasatch Mountains, and on the eastern side of the Wasatch the flow is locally confluent along the Colorado River Valley. Drainage flows can also be seen in several valleys in western Colorado. These realistic mesobeta-scale flow patterns, which were produced by the model in data-void regions, illustrate the advantage of combining a dynamic model solution and data through FDDA. All of these results are very similar to those obtained with the hydrostatic model (compare Figs. 7a and 7b with Figs. 4 and 5, respectively, in SS91). This was expected because nonhydrostatic effects should be small for this wintertime continental case simulated on a 10-kin grid. Figure 9 shows the observed and simulated winds in the lowest 1 km for 19 January at the Page, Arizona profiler site. The observed timeheight series (a) shows three different wind regimes: 1) from 24h to about 31h, large-scale northeasterly flow associated with the mesoalpha-scale flow pattern; 2) from 32 h to about 39 h, a mesobeta-scale nocturnal drainage flow from the Wasatch Mountains to the northwest of Page; and 3) from 40 h to 48 h, a return to large-scale flow conditions with weak westerlies gradually increasing in speed with time. In the control simulation (b), mesoalpha-scale phase errors and channeling of the erroneous wind by the terrain produced strong, persistent east-northeasterly winds at Page from 24 h through 36 h. The northwesterly drainage winds never developed, and the westerly components from 40h to 48 h were much too weak. The simulation with F D D A (c) shows all three wind regimes to be in generally good agreement with the observed profiler winds even though the surface layer winds tend to be too slow at this site from 33 h to 45 h. As shown in SS9I, mesoalphascale effects were important in this case, and the multi-scale F D D A approach was needed to accurately simulate the mesobeta-scale wind patterns.

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Figures 10 shows a statistical summary of the model winds in the lowest 500 m (lowest four model layers) at the special mesobe~a-scale observation sites. The root-mean-square vector-wind-difference errors were computed for 3-hourly time windows throughout the 48-h period. The control and FDDA hydrostatic experimental results from SS91 (CNTLH and FDDAH, respectively) are also plotted in Fig. 10 for comparison. The nonhydrostatic results are consistent with those from the hydrostatic simulation. Some small differences are apparent, and are probably due to the hourly updating of lateral boundary tendencies on the 10-km mesh in the nonhydrostatic model compared to the updating every timestep in the nested hydrostatic model. The larger errors between 30-h and 42-h in both control simulations reflect the phase lag in the mesoalpha-scale circulation (Fig. 7a) and the failure to produce northwesterly flow over the verification region (Fig. 9). The 3.3ms -1 error at t = Oh (00 UTC/18) in Fig. 10 represents the "analysis error" (i.e., how closely the mesoalpha-scale analysis is able to "fit" the special data). On average for the two-day period, the wind errors (at the special observation sites) for the two F D D A experiments were about 50 percent less than in the control simulations; these values are comparable to the analysis error.

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This study illustrates that the use of a multiscale F D D A technique previously employed successfully on the mesoalpha-scale for data-set generation and model initialization can be effective for data-set generation on the mesobeta scale. In this case, special data from a variety of mesoscale measurements platforms were assimilated into the model solution so that the resulting four-dimensional data set represented a dynamically consistent blend of large-scale influences, mesobeta-scale local forcing, and the assimilated data. Future F D D A work with this nonhydrostatic model will involve the use of this F D D A technique with interactive nested grids for model initialization. 4. A Cold Front Within an Explosive Marine Cyclone 4,1 Introduction

The nonhydrostatic version of the PSU/NCAR mesoscale model has been used to simulate a cold

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Nonhydrostatic, Mesobeta-Scale,Real-Data Simulations with the Penn State University/NationalCenter front using a grid resolution of 6.67 kin. The case studied was the Ocean Ranger Storm (13-14 February 1982), an explosively deepening North Atlantic cyclone simulated successfully with the hydrostatic version of the P S U / N C A R mesoscale model at resolutions down to 20 km by Kuo et al. (1992). The analyzed central pressure of this storm dropped 4 4 m b in 24h from 12 UTC/13 to 12 UTC/14.

4.2 Simulations 4.2.1 Cyclone Simulation The nonhydrostatic model was first used with a grid increment of 20 km to verify that the nonhydrostatic model could reproduce the hydrostatic results. This version of the model has 15 levels, 5 of which are in the lowest kilometer, and covers a region of 1800 x 2400km. It is initialized with fields interpolated from a 40-kin-resolution hydrostatic simulation (Kuo et al., 1992). Boundary conditions are supplied hourly by this large-domain simulation and interpolated in time. The moisture scheme with cloud/rain microphysics was used in conjunction with the Grell (1992) cumulus parameterization, but the parameterized rain comprised only about two per cent of the total rainfall. Figure 11 shows the simulated sea-level pressure (Fig. 11a), the corresponding hydrostatic model field (Fig. 1 lb), and the manual analysis (Fig. 1 lc, Prof. R.J. Reed, personal communication) for 12 UTC/14. The agreement with the observations and with the hydrostatic model is close and remains good throughout the 24 h simulation. The similarity with the hydrostatic model is as expected on this scale where nonhydrostatic effects should be small. Figures 12a and b show the temperature and vertical velocity, respectively, at approximately 1 km above the surface at the 12 h simulation time. The temperature field displays the occluded front, with a strong temperature gradient to the northwest of the low pressure center (marked L). The occluded front secludes a pocket of warm air to the south of Newfoundland. The seclusion process is discussed in more detail by Kuo et al. (1992). Another c o m m o n signature of marine cyclones is that the cold front is marked by a discontinuity in temperature gradient rather than in temperature, and there is, in fact, a strong moisture gradient at theft'ont. The vertical motion shows evidence of elongated structures along the cold front,

219

with magnitudes of 0.5-1 m s- ~. Also, behind the southern end of the front is shallow cellular motion that results from the sea-air temperature contrast of up to 10 ~ but this convection is not well represented on a 20 km grid. 4.2.2 Cold Front Simulation The above simulation results were interpolated to a 6.67 km mesh of 101 x 121 x 15 points centered on the cold front region of the cyclone depicted by the box in Fig. 12a. The small domain covers an area of 667 x 800 kin. With hourly data from the 20 km simulation supplying the lateral boundary conditions, the high-resolution simulation

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was run for 8 h from 05- t3 UTC/14. The physicalprocess parameterization were the same as in the coarser mesh model, but the cumulus parameterization was not used. Tests with ice microphysics included showed no major differences in the results. The full effects of the larger scales are thus supplied at the lateral boundaries, and a realistic cold-frontal structure develops within 2 h from the initial, relatively smooth fields supplied by the 20-km resolution model. The results show several interesting features, including the development of a pre-frontal cloud band with a line of deep convective updraft cores and a pre-frontal southerly jet. The surface front itself was marked by narrow linear updrafts organized in elements of about

150 km in length, and separated by small gaps. This is similar to structures that are often observed in oceanic cold fronts (e.g., James and Browning, 1979). Figure 13a shows the vertical velocity at about 1 km above the surface at 4 h, corresponding to the time of Fig. 12b. Updrafts of 1-2 m s-1 form sharp cloud lines that have some resemblance to observed rope clouds (Shapiro and Keyser, 1990). This simulated feature is shallow, occupying only the lowest 1-1.5 km. Above, the linearity gives way to a more wavy structure with wellresolved horizontal eddies that are apparently mixing post-frontal and pre-frontal air. This can be seen more clearly from the equivalent potential temperature (Fig. 13b) near 775 rob, where a broad mixing zone appears to have developed. It appears that the transport by organized frontal updrafts, of air with low along-front momentum (from the surface friction layer up to about 3 km), produces regions of strong vertical vorticity bounding the updraft outflows because the ambient along-front momentum at the upper level is stronger. This may lead to the production of eddies through shearing instability or even inertial instability. Also, this mixing appears responsible for the increased suppression of deep convection as one goes farther south along the front, approaching the pointed tail of the upper comma cloud. The mixing of detrained updraft air moistens the environment and renders mixing downstream (farther north) less effective at suppressing the updrafts. By 6 h there is a pre-frontal line of convective clouds 150km ahead of the surface front. In Fig. 14a is seen a broad cloud band between these lines associated with general ascent in the warm sector air. Figure 14b shows that the rain water at about 1 km altitude is comprised of a line of cells ahead of the surface frontal line. The pre-frontal band appears to result from the release of conditional instability by the slantwiserising "warm conveyor belt"; soundings from the model show this air to have a deep near-neutral lapse rate with respect to moist ascent. Farther ahead, a weak low-level cap may be sufficient to suppress convection. Thus, the mechanism for the pre-frontal band appears not to depend upon conditional symmetric instability nor upon gravity waves in this case. The low-level temperature gradient is also weakening with time due to the strong post-frontal surface heat flux. The upper frontal jet also weakens gradually. This may suggest that

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increment, nonhydrostatic simulation of the frontal evolution. 5.2 Simulations

4.3 Conclusions

These simulations have shown the capability of a nonhydrostatic version of the PSU/NCAR mesoscale model to simulate smaller scales within a synoptic-scale cyclone. The results are promising in that features commonly associated with midlatitude oceanic cold fronts appear to be resolved to a reasonable degree. This will allow further studies on the dynamics of cold fronts and their evolution as they interact with the larger scale cyclone development. In the above case, observations were limited to routine weather data so that only the gross features of the cyclone could be verified. Comparisons of simulated cases with radar images, surface observations and soundings taken near fronts would be desirable in verifying the finer model-generated structures.

There was good agreement between the hydrostatic and nonhydrostatic 20-kin, 24-h simulations starting at 12 UTC/12. Figure 17 presents the 20-kin nonhydrostatic model's lowest layer temperature at 12 and 24 hours (00 and 12 UTC/13), and shows the extent of the computational domain. At 00 U T C it can be seen that the cold front is not well-defined, but during the following 12h it sharpened considerably. The front extending east from the Rocky Mountains is evident, with a 10-

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The model has been applied to a study of cold air damming to the east of the Rocky Mountains. The case chosen for this study is that of 12-14 February 1990, "The Valentine's Day Storm". This event was marked by a frontal passage in the Winter Icing Storm Program (WISP) network centered near Denver, Colorado resulting in a 20-25~ drop in a 12 h period starting ground 21 UTC/12. This lead to low clouds and icing conditions after 09 UTC/13. A72-h hydrostatic simulation with 40 km resolution, starting at 00 UTC/12 and using standard NWS upper-air observations for initialization, provided the initial and boundary conditions for 20kin grid increment simulations with both the hydrostatic and nonhydrostatic models. The 20 km simulation has 23 levels with a high-resolution boundary layer, a single-cloud type cumulus parameterization (Grell, 1992), an explicit moisture scheme including ice and snow (Dudhia, 1989) and a land-surface energy budget. The 20-kin, nonhydrostatic simulation was then used to define initial and boundary conditions for a 5-km grid

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12 ~ C / 4 0 k m temperature gradient. The location agrees reasonably well with observations, but there is a time lag discussed later. There was a large change in relative humidity but almost none in specific humidity with this frontal passage. The nonhydrostatic 20 k m data were then interpolated to a 5-km grid, and provided initial and lateral b o u n d a r y conditions for a 12 h simulation, starting at 00 UTC/13, over a 5 0 0 k m square d o m a i n (Fig. 17b) centered on the W I S P network. The 5-kin grid simulation presented here was initialized with a terrain resolution consistent with the grid resolution (Fig. 18c), and thus incorporates a more detailed representation of the Rocky M o u n t a i n topography than did the 20-km simulation. In the 5-kin simulation (Fig. 18), the details of the frontal collapse and its interaction with the Cheyenne Ridge, the Palmer Divide and the Rocky Mountains are simulated. Figure 18a can be compared with Fig. 17b to show that, at 12 UTC/13, the location of the front is similar at the two resolutions, but even on the 5-km grid the temperature gradient is concentrated at the shortest resolvable scales. Figure 18b shows the strong wind shift at the front and Fig. 18c shows the topography. It is useful to trace the development of the equivalent potential temperature at the lowest model level (40 m) in Figs. 19a-d. The frontal collapse starts in ;everal locations, leading to a series

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