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12.9 Experimental High-Resolution Wsr-88D Measurements in Severe Storms

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12.9 Experimental High-Resolution Wsr-88D Measurements in Severe Storms 12.9 EXPERIMENTAL HIGH-RESOLUTION WSR-88D MEASUREMENTS IN SEVERE STORMS Rodger Brown1, Bradley Flickinger1,2, Eddie Forren1,2, David Schultz1,2, Phillip Spencer1,2, Vincent Wood1, and Conrad Ziegler1 1 NOAA, National Severe Storms Laboratory, Norman, OK 2 Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, OK 1. INTRODUCTION resolution to current-resolution measurements in mesocyclones, we present some of the simulation Through use of Doppler velocity simulations, results of Wood et al. (2001). A comparison of Wood et al. (2001) and Brown et al. (2002) have simulated Doppler radar scans across a shown that stronger Doppler velocity signatures of mesocyclone for 1.0o and 0.5o azimuthal sampling mesocyclones and tornadoes, respectively, can is shown in Fig. 1. The curves with dots along be obtained when WSR-88D (Weather Surveil- them represent the curves along which Doppler lance Radar–1988 Doppler) measurements are velocity measurements are made. There are two made at azimuthal sampling intervals of 0.5o factors illustrated in Fig. 1 that contribute to instead of the current intervals of 1.0o. In addition, stronger mesocyclone signatures with 0.5o for signatures exceeding a particular Doppler azimuthal sampling. First, the peaks of the meas- velocity threshold, the signatures are detectable urement curve in Fig. 1b are closer to the pointed 50% farther in range using 0.5o azimuthal peaks of the “true” curve being sampled by the sampling. radar than in Fig. 1a. The peaks are closer To help substantiate the simulation results, because the radar beam has to scan only half the the National Severe Storms Laboratory’s (NSSL) distance in azimuth for 0.5o sampling and testbed WSR-88D radar (KOUN) collected high- therefore there is less smearing of the true curve. resolution data in severe thunderstorms during Second, with 0.5o azimuthal sampling, there are Spring 2003. The procedure was to record base twice as many data points along the measurement data (reflectivity, mean Doppler velocity, spectrum curve and therefore there is a greater probability width) at 0.5o azimuthal resolution and at full that a data point will fall close to the peaks of the range resolution of 0.25 km. Then a recombina- curve. For the example in Fig. 1, the mean tion algorithm (e.g., Curtis et al. 2003) was used rotational velocity (average of the two extreme to produce the lower-resolution data (1.0o, 1 km Doppler velocity values) is 16.2 m s-1 for 1.0o for reflectivity and 1.0o, 0.25 km for Doppler sampling, whereas the mean velocity is 19.0 m s-1 velocity and spectrum width) that are currently for 0.5o sampling—an increase of 17%. recorded and displayed by WSR-88Ds. The positions of data points along each In this paper, we present comparisons of measurement curve in Fig. 1 represent just one of Doppler velocity signatures and reflectivity fields many possible placements of data points along for high-resolution and current-resolution data the curve. Each possible placement would fields. The fields were displayed using NSSL’s produce a different value for the mean rotational Warning Decision Support System II (WDSS II) velocity. In order to approximate all of these display system. Results show that high- possibilities, Wood et al. (2001) divided the 1.0o resolution data produce mesocyclone signatures and 0.5o azimuthal intervals into 51 equally- that typically are up to 15–20% stronger and spaced intervals for which mean rotational reflectivity signatures that reveal otherwise poorly velocities were computed. To produce realistic resolved or unresolved details. computations, random noise was added to individual data points before extreme values were 2. MESOCYCLONE SIMULATIONS selected for the mean rotational velocity computations. As an introduction to comparisons of high- It was important to add random noise ________________________________________ because there is an inherent uncertainty in Doppler velocity measurements owing to a finite Corresponding author address: Dr. Rodger A. Brown, number of samples being used to compute mean National Severe Storms Laboratory, 1313 Halley Circle, Doppler velocity values. In order to collect 0.5o Norman, OK 73069; e-mail [email protected] azimuthal data, it was assumed that the antenna was rotating at the same speed as for 1.0o data as indicated by the Wood et al. (2001) collection. With half the number of samples being simulations, they continue to exceed the threshold collected for 0.5o data, random noise increases by up to 50% farther in range. the square root of 2. Therefore, the standard deviation of random noise added to the simulated 4. REFLECTIVITY COMPARISONS data point was increased by 1.414 (from 0.7 to 1.0 m s-1) for 0.5o data collection. Reflectivity signatures in severe storms are The resulting computations are presented in more difficult to quantify than Doppler velocity Fig. 2 as frequency distributions at three different signatures, so advantages of 0.5o/0.25 km data ranges. The right (left) distribution in each panel collection over 1.0o/1.0 km data collection are is the distribution of mean rotational velocities illustrated by comparing images on reflectivity computed from 0.5o (1.0o) data. It is curious that displays. Since high-resolution reflectivity data the 0.5o distributions are narrower than the 1.0o have eight times the resolution of current WSR- distributions at all ranges. With appreciably more 88D reflectivity data, dramatic improvements can random noise being added to the 0.5o data points, be anticipated. one would expect the 0.5o distributions to be significantly broader. Evidently, the reduced a. Surface boundaries smearing and more closely spaced mean Doppler velocity values associated with 0.5o sampling Figure 4 shows a cold front that has passed more than offset effects of greater random noise. southeastward over KOUN and a ridge to the It is obvious in Fig. 2 that, on average, data southwest of the radar. As the front moved over collected at 0.5o azimuthal intervals produce the ridge, a wave developed on it and a hailstorm stronger mean rotational velocities for meso- formed at the occluded apex. The high-resolution cyclones. There is a slight overlap of the two image on the right shows more continuity in the distributions, indicating that on rare occasions the enhanced radar return along the cold front, 1.0o data will produce stronger rotational especially in the vicinity of the growing storm. velocities. During the next 70 min, the storm split into a left-moving storm and a right-moving storm. The 3. MESOCYCLONE COMPARISONS gust front associated with the right-moving storm is shown in Fig. 5. While the current-resolution During Spring 2003, KOUN collected high- display contains the suggestion of a possible gust resolution data (0.5o, 0.25 km) in Oklahoma front, the high-resolution display shows it very mesocyclones on six days during April and May. clearly. The high-resolution gust front position is These data were then recombined into current confirmed by high-resolution Doppler velocity WSR-88D resolution data (1.0o, 0.25/1.0 km) convergence. Generally, boundaries indicated by using the Curtis et al. (2003) approach. Doppler high-resolution data (both reflectivity and Doppler velocity displays of these data sets were visually velocity) have greater temporal continuity than inspected for mesocyclone signatures and provided by current-resolution data. This signature characteristics were tabulated. About suggests that the combination of high-resolution 600 pairs of high- and current-resolution signa- reflectivity and Doppler velocity data can be used tures were found at various elevation angles on to improve automated boundary detection those six days. algorithms. Plotted in Fig. 3 are ratios of mean rotation velocity from the 0.5o data to mean velocity from b. Bounded weak echo regions the 1.0o data. As expected based on Fig. 2, the vast majority of 0.5o mesocyclone signatures were A bounded weak echo region (BWER) is a stronger than 1.0o signatures, with most being up midaltitude reflectivity minimum that indicates the to 15-20% stronger. As also expected, some presence of a strong updraft within a severe (about 10%) of the 1.0o signatures were stronger. thunderstorm. The BWER is caused by new The data in Fig. 3, reinforced by the hydrometeors that are carried rapidly aloft by the simulation results in Fig. 2, indicate that higher- updraft and do not have time to grow to radar- resolution 0.5o data collection would benefit the detectable sizes until they are in the upper issuing of severe storm warnings. On average, portions of the storm (e.g., Glickman 2000). mesocyclone signatures are stronger with 0.5o Figures 6–10 show BWERs in different data collection. Being stronger, they exceed a storms at progressively farther ranges (45 to 195 given threshold value earlier in their lifetime and, km) from KOUN. The current-resolution display in Fig. 6 does not obviously indicate the presence of These findings hold great promise for the BWER that is readily evident in the southwest helping to improve severe storm warning portion of the storm in the high-resolution display. capabilities for National Weather Service forecast The storm at 80-km range in Fig. 7 reveals the offices. It appears that high-resolution data may presence of a BWER at both resolutions, but the be available in a couple of years. Initially, high- minimum is more pronounced in the high- resolution data probably would be used only for resolution display. Beyond 100 km from the the displays, with current-resolution data being radar, the BWER is only detected in the high- used for the various meteorological algorithms. resolution display (Figs.
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