Numerical Prediction of 8 May 2003 Oklahoma City Supercell Tornado with ARPS and Radar Data Assimilation

Transcription

1 Numerical Prediction of 8 May 2003 Oklahoma City Supercell Tornado with ARPS and Radar Data Assimilation Ming Xue 1,2 and Ming Hu 1 1 Center for Analysis and Prediction of Storms and 2 School of Meteorology University of Oklahoma Norman, Oklahoma 73072, USA Submitted to Geophysical Research Letters Submitted August 2007 * Corresponding Author Address: Dr. Ming Xue, School of Meteorology, University of Oklahoma, NWC Suite David Boren Blvd, Norman OK mxue@ou.edu 1

2 Abstract The 8 May 2003 Oklahoma City tornado and its parental storm are numerically simulated using the ARPS model with 4 one-way nested grids, with up to 50-m horizontal resolution. A 100-m resolution forecast is initialized 30 minutes before the observed tornado touch down time from interpolated 1-km initial condition, which is obtained by assimilating data from a WSR-88D radar at 5 minute intervals for 70 minutes, using the ARPS 3DVAR and cloud analysis procedures. The nested 50-m forecast presented in this paper successfully captures the intensification of two successive tornado vortices that reach F1 to F2 intensity. The first tornado-intensity vortex formed at exactly the time of observed tornado touch down. The predicted tornado tracks are within 8 km of the observed track and have the correct orientations. The predicted tornado vortex structures are shown to be asymmetric but the main features agree well with existing dynamic conceptual models. 2

3 Introduction As the only observational network capable of resolving convective storms, the operational WSR-88D radar network of the United States provides key information for convective-scale data assimilation and model initialization. However, initializing dynamically consistent storms for a model forecast from radar observations is challenging because radars only observe a very limited set of parameters and cover limited spatial area. Efforts to initialize and predict real thunderstorms using Doppler radar observations started in the nineteen nineties (Lilly 1990). Advanced radar data assimilation methods, including the 3DVAR (e.g., Gao et al. 2004), 4DVAR (e.g., Sun and Crook 1997), and ensemble Kalman filter methods (e.g., Snyder and Zhang 2003; Tong and Xue 2005), are necessary to effectively assimilate radar data. Most recently, encouraging results have been obtained by applying the ARPS (Xue et al. 2000) 3DVAR system (Gao et al. 2004) together with a complex cloud analysis procedure (Zhang 1999) to the analysis and prediction of tornadic thunderstorms (Hu et al. 2006; Hu and Xue 2007). In these studies, radar radial velocity data are analyzed together with conventional observations using the ARPS 3DVAR while the complex cloud analysis determines incloud temperature and microphysical fields from radar reflectivity. Frequent, intermittent, assimilation cycles with 5 to 10 minute intervals are employed to build up dynamically consistent storms in the model. Using the above procedure, Hu and Xue (2007) obtained reasonable analysis and prediction of the general structure and evolution of the 8 May 2003 Oklahoma City (OKC) tornadic supercell thunderstorm using a 3-km horizontal resolution. Due to the relatively 3

4 coarse resolution used, the analyzed and predicted supercell storm was rather smooth in structure and the reflectivity fields had only slight indications of hock echo structure at the low levels (see their Fig. 4). The mid-level mesocyclone was captured in the model prediction but its diameter was too large (see their Fig. 5). Of course, it is impossible to tell from the 3-km model prediction whether a tornado would spawn or not. In this study, we extend the above work by performing radar data assimilation cycles on a 1-km horizontal resolution grid (1-km grid hereafter, similar for other resolutions), to better take advantage of the high-resolution radar observations and to better resolve the thunderstorm. We then perform the forecasts at 1-km, 100-m, and 50-m horizontal resolutions with one-way nested grids with the ultimate goal to capture the embedded tornado on the high-resolution grids. Compared to the earlier results of 3-km grid, the 1-km results are clearly improved. The 100- and 50-m horizontal resolutions unprecedented for this type of studies indeed are able to capture the intensification of low-level rotation related to tornadogenesis. The timing of the onset of predicted tornado and the predicted tornado intensity agree well with observations. We believe our results represent the first ever successful numerical model prediction of a tornado embedded within a real thunderstorm that is initialized using real data. Limited by space, we report in this paper the prediction results of the 50-m grid only, with emphasis on aspects related to the tornado. Details on the data assimilation process as well as additional sensitivity experiments that examine, e.g., the additional benefit of assimilating radial velocity data in a 3DVAR framework will be reported in a future paper. 98 4

5 The 8 May 2003 Oklahoma City Tornado At about 2210 UTC (1610 LST) on 8 May 2003, Moore, a suburb city about 15 km south of the OKC, Oklahoma, was struck by a major tornado. The tornado tracked east-northeast for about 30 km on the ground, from Moore to Choctaw, Oklahoma, and dissipated at 2238 UTC (Fig. 1c). This tornado caused large areas of F2 to F4 damages worthy of $370 million in property and more than 100 injuries. It is named the OKC tornado by the National Weather Service as it struck the general OKC area. The evolution of the parental supercell storm and the associated mesoscale and synoptic-scale settings can be found in Hu and Xue (2007) Model configuration and experiment setup Data from the OKC WSR-88D radar (KTLX) before the OKC tornado outbreak were assimilated on a 1-km grid to initialize the parental supercell storm. The assimilation using the ARPS 3DVAR and cloud analysis started at 2030 UTC and ended at 2140 UTC with 5 minute analysis cycles, using a 9-km grid ARPS forecast valid at 2030 UTC as the first guess. The latter is the same as that of Hu and Xue (2007). The 1- km forecast was then run for 2 hours which captured well the evolution of the supercell. One-way nested within the 1-km forecast and starting from the interpolated 1-km assimilation results at 2140 UTC, the 100-m grid was run for 1 h to capture the OKC tornado. Within the 100-m grid, a 50-m grid was further nested to capture the finer-scale structures and to hopefully improve the tornado intensity forecast. The 50-m grid was initialized from the interpolated 100-m forecast at 2200 UTC and was run for 40 minutes. Both the 100-m and 50-m forecasts are long enough to cover the observed period of OKC 5

6 tornado outbreak. The 100-m grid started 30 minutes before the initial tornado touch down, giving sufficient lead time for the model to spin up from the 1-km initial condition. The 100-m and 50-m grids are 160 km 120 km and 80 km 60 km in size, respectively, and both are approximately centered on the OKC tornado. The same vertical coordinate is used by all grids, which stretches from 20 m at the surface to 770 m at the model top of 21.1 km height. The same full set of model physics (Lin 3-ice microphysics, long and short-wave radiation, two-layer soil model, stability dependent surface fluxes, 1.5-order TKE-based subgrid-scale turbulence. See Xue et al for details) is used on all grids, except for the subgrid-scale turbulence scheme which is a full 3-D formulation on the 100-m and 50-m grids but is a simplified vertical-only formulation on the 1-km grid. Worth noting here is the use of stability-dependent surface fluxes that are fully coupled with the land-surface/soil model. The formulation of the surface momentum flux/drag is an outstanding issue with idealized tornado simulations using a single sounding and strong sensitivities to the drag coefficient had been found (Wicker and Wilhelmson 1995; Adlerman and Droegemeier 2002). In our case, the regular drag formulation does not seem to hinder the development of tornado Mesocyclone and tornado forecast on 50-m grid The time series of maximum vertical vorticity, maximum wind speed, and minimum perturbation pressure at the surface during the 40 minutes of forecast of the 50- m grid are plotted in Fig. 1a,b. Two periods of tornado intensification are signified by a rapid drop in the perturbation pressure (p') along with a rapid increase in the vertical vorticity (ζ) and horizontal wind speed (V). The life spans of the two predicted tornadoes 6

7 having intensities of over F1 in terms of the maximum surface V (32-50 m s -1 according to the Fujita scale) are about 5 minute long, from 2210 to 2215 UTC and from 2218 to 2223 UTC, respectively. An examination of the surface fields shows that these extrema are associated with a small region of strong circulation (c.f., Fig. 3) that we call tornado. In fact, the first predicted tornado reaches the intensity of F2 (50-70 m s -1 ) from 2213 to UTC with a peak maximum V of 67.6 m s -1, peak maximum surface ζ of 1.31 s -1, and minimum p' of hpa, while the second tornado reaches the F2 intensity from 2219 to UTC with a maximum V of 62.4 m s -1, a maximum ζ of 1.35 s -1, and a minimum p' of hpa. The intensity and time span of these predicted circulations, in terms of the above measures, qualify them as tornadoes. The large pressure deficit found at the center of tornadoes is required by an approximately cyclostrophic balance. The paths of the predicted tornado circulation centers are plotted in Fig. 1c along with the observed tornado damage track. Unlike the real OKC tornado that produced a continuous 30 km long F2 to F4 damage track from 2210 to 2238 UTC, the forecast produces two successive shorter-lived tracks of about 4 and 6 km long, between 2210 and 2215 UTC and between 2218 and 2223 UTC, respectively. Their tracks are located approximately 8 km north of the observed track but have correct orientations. The timing of the tornado onset at 2210 UTC matches exactly the initial touch down of observed tornado. The weaker intensity and short lives of predicted tornadoes may be due to the still insufficient spatial resolution, although errors in the initial condition and model physics can also contribute to the discrepancies. In an attempt to directly compare the model prediction with observations at the tornadic circulation scale, the predicted reflectivity (Z) and radial velocity (V r ) at

8 minutes of the 50-m grid (valid at about 2214 UTC) are mapped to the 1.45 elevation of the KTLX radar and compared to corresponding radar observations at the nearest time (2216 UTC, Fig. 2). At this time, the predicted tornado is at its strongest stage in terms of the surface pressure deficit and shows a pronounced hook echo at the southwest end of the precipitation region (Fig. 2a), while the observed Z approximately 2 minutes later (Fig. 2b) also had a similar hook echo at the same relative position of the main supercell. Associated with this hook echo is a strong V r couplet in both predicted and observed V r fields (Fig. 2c and Fig. 2d). The outbound and inbound V r are both about 24 ms -1 in magnitude, in both prediction and observation. At this distance of about 20 km from the radar and at the 1.45 elevation, the data shown are at about 0.5 km above ground; the couplet therefore indicates the presence of a strong mesocyclone above the ground. As will be seen next, the winds at the surface are stronger, due to the presence of low-level tornado circulation. The predicted mesocyclone is located about 8 km off to the north of the observed one; the position error contributes to some of the differences seen in the radial velocity fields. The near-surface (at the first model level or 10 m above ground) wind, Ζ, and p' fields at the time of Fig. 2 are plotted in Fig. 3a for a 2 km 2 km square domain indicated in Fig. 2. The vertical velocity (w) and ζ fields in a north-south cross section along line AB in Fig. 3a are plotted in Fig. 3b. In Fig. 3a, a strong circulation with a core radius (radius of strongest tangential velocity) of less than 200 m is found. The maximum surface winds are found on the south side of the circulation, having a maximum east-west velocity of 57 ms -1. The p' at the vortex center is hpa (not shown). Such an intensity qualifies the circulation as F2 tornado. There exists clear asymmetry with the circulation, 8

9 with the tangential flow being broader and stronger on the south side, where a broad region of generally westerly flow is present (Fig. 3a). The latter flow is mostly originated from the downdraft region located to the northwest (not shown) of tornado. Occluding gust fronts are drawn in Fig. 3a on the north side, which results from the rapid advancement of the rear flank gust front helped by the strong cyclonic circulation and the merging of the two fronts near the occlusion point. Therefore, at the low levels, the tornado circulation is found behind the rear flank gust front, in the cold air region. The proximity of the tornado to the occlusion point and the tornado positioning related to the gust fronts are consistent with the classic conceptual models of, e.g., Lemon and Doswell (1979). Outside the core radius, there exists clear radial convergence, in particular on the north and northwest side, with the convergent radial flow stopping at the core radius. This radius also roughly corresponds to a ring of vertical vorticity maximum, which is consistent with the tangential flow acceleration due to angular moment conservation as air converges toward this ring. Within the core radius, there exists weak divergent flows at the surface (seen more clearly in a zoomed-in version of Fig. 3a, not shown) corresponding to the descending motion at the center of the circulation (Fig. 3b), which further enhances the radial convergence at the core radius, producing ascending motion at the location (Fig. 3a) and inevitable vertical stretching that acts to enhance ζ there. The surface convergence is the strongest on the north side, so are w and ζ. The vortex ring is strongly axisasymmetric, with a maximum ζ of 1.1 s -1 at the north side at this particular time. This 9

10 maximum is found to rotate around the vortex center along the ring (not shown), maintaining asymmetry of the vortex. In addition to the expected suction effect from the overlying mesocyclone (not shown) and from buoyancy lifting of the main updraft, the surface friction is believed to have played a role in enhancing the low-level convergence because the friction breaks the cyclostrophic balance and causes the flow to turn inward toward the low-pressure center. Within the north-south vertical cross section shown in Fig. 3b, a roughly 1 km deep downdraft is found at the vortex center which is surrounded by updrafts that are roughly located at and outside the ring of maximum horizontal circulation (and vorticity ring) discussed earlier. On the north side along the boundary of the downdraft and updraft is a column of strong ζ extending from the surface to nearly 1 km above ground. The largest vorticity in the column is near the surface, where the vertical stretching tends to be the largest. These structures are consistent with Davies-Jones conceptual model of tornado inner core region for high swirl ratio cases (see Fig. 5.29e of Davies-Jones et al. 2001), although his conceptual model assumes axisymmetry. Limited by space, we will not perform or present detailed analyses on the dynamics of the simulated tornadogenesis here; it will be a topic for future study Summary and discussion In this paper, the prediction of the 8 May 2003 OKC tornado on a 50-m grid is presented. During the 40-minute forecast, two successive tornadoes of F1-F2 intensity with life spans of about 5 minutes each are produced within the period of the actual 10

11 tornado outbreak and the predicted tornadoes travel along a path about 8 km north of the observed damage track with correct orientations. The predicted tornadoes are clearly identified in the time series of maximum vertical vorticity and wind speed, and minimum perturbation pressure at the surface. Their presence is also indicated by the strong hook-echo and mesocyclone in the model reflectivity and radial velocity fields, which match observations well. The near-surface fields in a small domain covering the predicted tornado at its most intense stage shows that the forecast is able to capture many core vortex features that are consistent with conceptual models derived from observations and theories. A half-hour forecast lead time from the onset of tornado is achieved by the 100-m and the nested 50-m runs. In fact, the predictions of the 100-m and 50-m grids are similar, with the main differences being with the fine-scale details. The long enough lead time and similar behaviors on the two grids suggest that the timing of the predicted tornadogenesis is not strongly dependent on the resolution refinement. In reality, a mid-level mesocyclone within the parental storm as observed by radar is captured in the initial condition through data assimilation on the 1-km grid (not shown). The mesocyclone is evident in the subsequent 1-km prediction and is believed to have played an essential role in the tornadogenesis on the high-resolution grids. When no radar data is used on the 1-km grid, the model fails to initialize any storm during the assimilation or subsequent forecast period, let alone tornadic features. This paper demonstrates, for the first time, that the prediction of tornado, up to 30 minutes ahead of time, is possible using a storm-scale numerical weather prediction model with proper assimilation of radar data. Such results give us hope for numerically 11

12 predicting tornadoes with much longer lead times than current extrapolation-based nowcasting techniques can offer. Detailed analysis of the model results on the dynamical processes with verifications against available observation data can help us improve the understanding of tornadogenesis as well as the dynamics of its evolution and decay. Further sensitivity experiments can improve our understanding on tornado predictability. Even higher horizontal resolutions may future improve the tornado intensity and structure forecast. These are topics for future studies and some require more computational resources than currently available. For this study, a supercomputer with 1-Ghz Alpha processors was used. Using 1600 processors, the 100-m and 50-m forecasts took 1 and 3 days, respectively, including a significant amount of I/O. Clearly, significant increase in computational power is necessary for realtime forecast of tornadoes to be possible. This study serves to demonstrate the scientific feasibility, which is very important Acknowledgments. This work was mainly supported by DOT-FAA grant NA17RJ1227 and NSF grant ATM Xue was also supported by NSF grants ATM , ATM , ATM and EEC Supercomputing resources at the Pittsburgh Supercomputing Center were used for the experiments. 12

13 References Adlerman, E. J. and K. K. Droegemeier, 2002: The sensitivity of numerically simulated cyclic mesocyclongenesis to variations in model physical and computational parameters. Mon. Wea. Rev., 130, Davies-Jones, R., R. J. Trapp, and H. B. Bluestein, 2001: Tornadoes and tornadic storms. In Severe Convective Storms, C. A. Dowswell, III, Ed., Amer. Meteor. Soc., Gao, J.-D., M. Xue, K. Brewster, and K. K. Droegemeier, 2004: A three-dimensional variational data analysis method with recursive filter for Doppler radars. J. Atmos. Ocean. Tech., 21, Hu, M. and M. Xue, 2007: Impact of configurations of rapid intermittent assimilation of WSR-88D radar data for the 8 May 2003 Oklahoma City tornadic thunderstorm case. Mon. Wea. Rev., 135, Hu, M., M. Xue, J. Gao, and K. Brewster, 2006: 3DVAR and cloud analysis with WSR- 88D level-ii data for the prediction of Fort Worth tornadic thunderstorms. Part II: Impact of radial velocity analysis via 3DVAR. Mon. Wea. Rev., 134, Lemon, L. R. and C. A. Doswell, 1979: Severe thunderstorm evolution and mesocyclone structure as related to tornadogenesis. Mon. Wea. Rev., 107, Lilly, D. K., 1990: Numerical prediction of thunderstorms - Has its time come? Quart. J. Roy. Meteor. Soc., 116, Snyder, C. and F. Zhang, 2003: Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 131,

14 Sun, J. and N. A. Crook, 1997: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiments. J. Atmos. Sci., 54, Tong, M. and M. Xue, 2005: Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSS Experiments. Mon. Wea. Rev., 133, Wicker, L. J. and R. B. Wilhelmson, 1995: Simulation and analysis of tornado development and decay within a three-dimensional supercell thunderstorm. J. Atmos. Sci., 52, Xue, M., K. K. Droegemeier, and V. Wong, 2000: The Advanced Regional Prediction System (ARPS) - A multiscale nonhydrostatic atmospheric simulation and prediction tool. Part I: Model dynamics and verification. Meteor. Atmos. Physics, 75, Xue, M., K. K. Droegemeier, V. Wong, A. Shapiro, K. Brewster, F. Carr, D. Weber, Y. Liu, and D.-H. Wang, 2001: The Advanced Regional Prediction System (ARPS) - A multiscale nonhydrostatic atmospheric simulation and prediction tool. Part II: Model physics and applications. Meteor. Atmos. Phy., 76, Zhang, J., 1999: Moisture and Diabatic Initialization Based on Radar and Satellite Observation, Ph.D., School of Meteorology, University of Oklahoma, 194 pp

15 Figure Captions Fig. 1. (a) The time series of maximum surface vertical vorticity (solid line) and maximum surface wind speed (dashed line), and (b) of minimum perturbed surface pressure in the 50-m forecast. The two periods of tornado intensification are marked by two thick lines in the time abscissa. (c) The observed damage path of the OKC tornado and the path of the modeled tornado represented by the central location of the tornado circulation. The domain shown represents a portion of the 50-m model grid. Fig. 2. Predicted (a) reflectivity and (c) radial velocity fields at minutes of the 50-m forecast valid at 2213:45 UTC mapped into at the 1.45 elevation of the KTLX radar and the corresponding observations from KTLX at 2216 UTC. Arrows in (a) and (b) indicate the location of hook echo and in (c) and (d) indicate the directions of peak radial velocities. Fig. 3. (a) Predicted vertical vorticity (color shaded), wind vectors, perturbation pressure (contours) fields at the surface from the minutes of 50-m forecast valid at 2213:45 UTC, and (b) north-south cross section of vertical vorticity (color shaded) and vertical velocity (contours) at the same time along the line shown in (a). The domain is 2 km by 2 km and covers area indicated by the square in Fig. 2a. The occluding gust fronts are drawn using standard front symbols in (a). 15

16 pressure (hpa) vertical vorticity(1/s) (a) vertical vorticity wind speed wind speed (m/s) UTC (b) UTC (km) (c) 2210Z Modeled Oklahoma City 2210Z 2210Z 2210Z 2210Z Tinker AFB Moore Choctaw 2238Z Observed Oklahoma County Cleveland County (km) Fig

17 a Model forecast valied at 22:13:45 UTC Radar observation at 22:16 UTC Reflectivity Reflectivity b c Radial Veolcity 16.0 KTLX KTLX 15. d 16.0 Radial Veolcity KTLX KTLX Fig

18 a 10 FIRST LEVEL ABOVE GROUND (SURFACE) B (km) :13:45 A (km) Vort (1/s, SHADED) MIN=-.29 MAX=1.1 U-V (m/s, VECTOR) Umin=-37 Umax=57 Vmin=-55 Vmax=46 pprt (hpa, CONTOUR) MIN=-35.7 MAX=0.12 inc=4. 2 b -4.0 Y-Z PLANE AT X=21.7 KM (km) A B :13: (km) Vort (1/s, SHADED) MIN=-.47 MAX=1.1 w (m/s, CONTOUR) MIN=-16.4 MAX=36.0 inc=2. Fig