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Analysis of Rear-Flank Downdrafts Observed

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Analysis of Rear-Flank Downdrafts Observed P11.1 THERMODYNAMIC CHARACTERIZATION OF SUPERCELL REAR-FLANK DOWNDRAFTS IN PROJECT ANSWERS 2003 Matthew L. Grzych, Bruce D. Lee and Catherine A. Finley WindLogics, Inc. Grand Rapids, Minnesota John L. Schroeder Texas Tech University Lubbock, Texas 1. INTRODUCTION Project ANSWERS was conducted on the Great Plains and in the Midwest during May and June of 2003 and Recently, much attention has been given to the consisted of 4 mobile mesonet stations (MM, Straka et rear-flank downdraft (RFD) and its association with al. 1996) collecting data every two seconds within tornadogenesis and tornadogenesis failure (Markowski tornadic and nontornadic supercell RFDs. Twelve et al. 2002, hereafter MSR2002; Markowski 2002). second averaged data was used in most of the mesonet Research results revealed compelling evidence analysis. The mesonet dataset was bias supporting the conclusion that RFD thermodynamic checked/adjusted and quality controlled in a manner characteristics were a significant factor contributing to similar to MSR2002. The need for a flux gate compass tornadogenesis and tornadogenesis failure. MSR2002 (and its post-event quality control corrections) was found that tornado likelihood, intensity, and longevity removed by implementing field procedures that allowed increased as the near-surface equivalent potential the GPS to be used for vehicle direction at all times. temperature (θe), virtual potential temperature (θv), and Thermodynamic variables were calculated in a manner convective available potential energy (CAPE) increased similar to MSR2002 with the exception of θv. Since most and convective inhibition (CIN) decreased within the ANSWERS data was collected on storms at a significant RFD. distance from the nearest WSR-88D site, we had little Although the analyzed mobile mesonet dataset of confidence in estimating the liquid water mixing ratio RFD events presented by MSR2002 is considerable, it (used for more accurately calculating θv) from the radar is not exhaustive given the variety of potential scenarios reflectivity (e.g., Rutledge and Hobbs 1984). The radar leading to tornadogenesis and the depth of the beam, at its lowest elevation angle of 0.5 degrees, parameter space. Given this consideration, a primary samples the storm at a significant distance aloft (i.e., > 1 objective of this research was to add to this km for storms ~200 km from the radar). This resulted in observational RFD database through the analysis of the the radar beam scanning echoes aloft that frequently near-surface thermodynamic characteristics of 4 were not reaching the ground (due to a strong updraft or tornadic and 6 nontornadic RFDs. These datasets were evaporation) leading to unrepresentative surface radar collected during a field experiment conducted during reflectivity estimation. May and June 2003. The following hypotheses, guided CAPE and CIN were calculated utilizing the by the research of MSR2002, will be tested herein: Rawinsonde Observation Program (RAOB). The atmospheric soundings analyzed were either obtained • RFDs characterized by small θe and θv deficits nearest to the storm event, both spatially and (as compared to inflow values), the presence temporally, or most representative of the pre-storm of CAPE, and small CIN values are necessary environment. Due to the porous atmospheric sounding for tornadogenesis observational network, representing a storm’s actual • RFDs characterized by large θe and θv deficits, environment is difficult. Attempts have been made to the lack of CAPE, and significant CIN values alter the soundings by making them more representative are conditions conducive to tornadogenesis of each event’s pre-storm environment. Rapid Update failure. Cycle model analysis data at 700, 850, and 925 mb (if applicable) were subjectively analyzed and placed into 2. METHODS each sounding. Surface data within each sounding were represented by averaged MM fast temperature Data for this work was collected during Project and dew point temperature observations for each RFD ANSWERS (Analysis of the Near-Surface Wind and quadrant. Where surface elevations between the Environment Along the Rear Flank of Supercells). sounding site and the storm environment differed, _______________________ surface elevations for the storm sounding were adjusted accordingly. Corresponding author address: Matthew L. Grzych To obtain the perturbation equivalent potential ’ WindLogics, Inc. – Itasca Technology Center temperature (θe ) and perturbation virtual potential ’ Grand Rapids, MN 55744 temperature (θv ), base states were calculated by email: [email protected] linearly interpolating 10-minute average inflow observations that were sporadically taken by the MM. the thermodynamic variability within an RFD over a This method has proven to be the most accurate in small spatial scale (Fig. 2). KABR WSR-88D data at determining base states for Project ANSWERS data. 0054 UTC was used as a reflectivity structural reference Thermodynamic calculations derived from the nearest to overlay the time-to-space converted MM ASOS or AWOS data typically differed by several observations. At this time, the echo was approximately degrees from MM data. A possible reason for this 200 km from the radar contributing to the distortion and difference is that ASOS and AWOS temperatures and horizontal placement relative to the MM. The supercell dew points were rounded to the nearest whole number at analysis time was transitioning from classic to high- resulting in up to several degree errors in derived precipitation (HP) structural modes as hydrometeor thermodynamic values. Another reason is that concentration became greater behind and around the mesoscale thermodynamic gradients and fluctuations mesocyclone. At bottom left in Fig. 2, the photograph smaller than the observing network were present. shows storm structure at approximate analysis time with For each RFD event analyzed, a mesocyclone the tail cloud, funnel cloud, and rain curtains visible. centroid was identified. When possible, the positions This has been classified as a tornadogenesis failure were found using WSR-88D velocity data. In cases event as rapid low-level rotation and a funnel cloud when the mesocyclone was not detectible by radar, low- evolved with no visual evidence of a tornadic circulation level mesocyclone positions were estimated using a near the ground; however, DOW scans of the event at triangulation technique, that was performed by this approximate time indicated low-end tornadic analyzing photographs and video taken of tornadoes strength winds near the surface (Josh Wurman, and wall clouds from multiple angles and positions. The personal communication). Thus, in some cases, there tornadoes and wall clouds were assumed to be is uncertainty how events such at this one get positioned in the low-level mesocyclone center. All categorized. To underline the complex nature of the analysis times were chosen to be within 10 minutes of thermodynamic signal, the warmest θe conditions are tornadogenesis and tornadogenesis failure. Data points actually just west and southwest of the low-level were plotted relative to WSR-88D radar data using a mesocyclone centroid. Perhaps this is the signal of a time-to-space conversion method described by more undiluted RFD. The larger θe’ deficits are south of MSR2002. The supercell radar echoes were assumed the mesocyclone where evaporation of hydrometeors to be in steady state for 5 minutes, but allowed to falling into this air mass and deep mixing near the RFD translate horizontally. Essentially, this process put the boundary may be responsible for the cooler observational MM data into the storm’s positional frame thermodynamic signal. At least in this case, if a mesonet of reference. only sampled that portion of the RFD 2-4 km south of Due to logistical limitations, the area sampled in the mesocyclone centroid, the potentially important each RFD varied between events. Therefore, attempts “warm” RFD thermodynamic signal, would be missed. have been made to qualify the density of observations in each event. The RFDs were broken down into four quadrants enclosed in a 4 km radius circle centered on the axis of rotation (Fig. 1, which was adopted from MSR2002). The line that separates quadrants I and IV from II and III passes through the low-level mesocyclone circulation center and is parallel to the neck of the hook echo. A 1 km buffer was then placed around each two- second data point for each event in an attempt to estimate the total percentage (to the nearest 10%) of each quadrant sampled within 1 km by the MM. Table 1 presents the percentages of each quadrant sampled for each event as well as each event’s time, location, and characteristic. As may be seen in Table 1, quadrant III was well sampled in a large majority of events with poorer sampling for quadrants II and IV. Due to logistics and safety considerations, quadrant I was rarely sampled. 3. SELECTED EVENT ANALYSIS A detailed examination of the RFDs analyzed reveals rather complex and intricate features associated with both tornadic and nontornadic events. In some cases analyzed, the RFDs were not thermodynamically and kinematically homogenous, but rather, the thermodynamic and/or kinematic fields varied dramatically over a distance of 1-2 km (see also P11.2, P11.3). Event 6 has been highlighted, which displays Figure 1. RFD quadrants. Adopted from MSR2002. Table 1. RFD events in chronological order. Dates are relative to local time. Radar scan times (UTC) used for analysis are listed. Abbreviations for tornadic events are (TOR) and nontornadic events (NON). Event Type Date Location Time F-rating % of Quadrant Sampled I II III IV 1 (TOR) 6/9/03 Springview, NE 2300 F0 20 40 30 0 2 (NON) 6/9/03 Newport, NE 2340 30 80 70 0 3 (TOR) 6/9/03 Oneill, NE 0054 F3 0 0 30 50 4 (NON) 6/11/03 Vivian, SD 2358 0 70 80 20 5 (NON) 6/11/03 Presho, SD 0034 0 0 80 80 6 (NON) 6/11/03 Kennebec, SD 0054 0 0 90 30 7 (NON) 6/24/03 Artesian, SD 2353 0 0 80 0 8 (NON) 6/24/03 Cavour, SD 0013 0 0 70 30 9 (TOR) 6/24/03 Manchester, SD 0042 F4 0 0 70 0 10 (TOR) 6/24/03 Spirit Lake, SD 0127 F1 0 10 80 0 θe’ Figure 2.
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