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Tornado Detection Using Dual Polarization Radar

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Words: 4172 |

Pages: 9|

21 min read

Published: Aug 31, 2023

Words: 4172|Pages: 9|21 min read

Published: Aug 31, 2023

Table of contents

  1. Abstract
  2. Introduction to Dual-Polarization Radar 
  3. Co-polar Correlation Coefficient
    Differential Reflectivity
  4. Dual Polarization Tornado Detection
  5. History of TDS
    Specific Values of the TDS Parameters
    Deviations in TDS Parameters
  6. Influences on the TDS
  7. Tornado Scale Influences
    Radar Influences
    Radar Wavelength Impacts
  8. Other Influences
  9. Applications
  10. Case Study
  11. Objectives
  12. Methodology
  13. Environmental Setup
  14. Case 1: Franklin/Colbert County Alabama Tornado
  15. Case 2: Marion/Sequatchie County Tennessee Tornado
  16. Comparison
  17. Conclusions
  18. References

Abstract

With the advent of dual-polarization radar, the ability to detect tornadoes has increased. Tornadoes produce different radar signatures than meteorological targets due to the miscellany in the size, shape, and cantering angle of debris. One of these signatures is the tornadic debris signature (TDS). Numerous studies have examined the TDS and have developed slightly different criteria for tornadic debris detection; however, they have consistently agreed upon a general reduction in the co-polar correlation coefficient and differential reflectivity fields. The ability to detect a TDS varies on a case-by-case basis depending on the intensity of the tornado, the distance from the nearest radar, as well as certain storm-scale features. The examination of the TDS has proved beneficial to the operational field through its application to verify tornado warnings and pinpoint the location of an ongoing tornado.

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A miniature case study is performed to evaluate the TDS for two tornadoes that occurred in a tornado outbreak across the Southeast on 29/30 November 2016. The duration of the TDS’s and the spatial (horizontal and vertical) extent are compared and contrasted. Additionally, the TDS is compared to the tornado intensity and the damage survey. It was found that: 1) the TDS lasted longer for the Alabama tornado; 2) the vertical and horizontal extent of the TDS was greater for the Tennessee tornado; and 3) the DPI was higher for the Tennessee tornado.

Introduction to Dual-Polarization Radar 

Dual-polarization, also referred to as polarimetric, radars transmit two pulses of electromagnetic energy, one in the horizontal direction (like the movement of a snake) and one in the vertical direction (like an ocean wave) (Rauber and Nesbitt 2018). This provides more information about the shape, size, homogeneity, and orientation of the targets in the radar volume because information about the horizontal and vertical dimensions of the target is collected (Rauber and Nesbitt 2018). With two planes of polarization, additional radar variables can be derived. Among these variables are co-polar correlation coefficient and differential reflectivity (Rauber and Nesbitt 2018).

Co-polar Correlation Coefficient

Co-polar correlation coefficient (CC) is a statistical variable that measures the similarity in the properties of the wave energy returned in the horizontal polarization versus the vertical polarization (Rauber and Nesbitt 2018). The values range from zero (low correlation) to one (perfect correlation). Correlation coefficient is high when targets are relatively homogeneous in both composition and distribution, such as in instances of only rain or only snow. When there is diversity among the targets, there will be differences in the return power of the horizontal and vertical polarizations, which will reduce the correlation (Rauber and Nesbitt 2018). Correlation coefficient is reduced in cases of mixed phase precipitation, targets with varying orientations, irregularly shaped particles, biological targets, and other nonmeteorological targets (Rauber and Nesbitt 2018).

Differential Reflectivity

Differential reflectivity (ZDR) compares the return power (reflectivity) from the horizontal pulse versus the vertical pulse (Rauber and Nesbitt 2018). Since it compares horizontal and vertical returns, it can provide information about the shape of the radar target (Rauber and Nesbitt 2018). Positive ZDR implies the horizontal axis is larger than the vertical axis (i.e. the object is oblate) (Rauber and Nesbitt 2018). ZDR near zero indicates that the returns in the horizontal and vertical are approximately equal (i.e. the object is spherical) (Rauber and Nesbitt 2018). Values near zero are also possible given the presence of randomly oriented targets (Ryzhkov et al. 2005). Negative ZDR means the vertical axis is larger than the horizontal axis (i.e the object is prolate) (Rauber and Nesbitt 2018).

Dual Polarization Tornado Detection

It is evident that dual-polarization radar has become an asset in discriminating between meteorological and nonmeteorolgical targets (Ryzhkov et al. 2005). Tornadic debris is composed of irregularly shaped, randomly oriented particles that produced different signatures than hydrometeors (Ryzhkov et al. 2005). One of these signatures is the tornadic debris signature (TDS).

Tornadoes sometimes loft nonmeteorological debris to a height where it is detected by a radar beam, producing unique characteristics (Van Den Broeke and Jauernic 2014), hence a tornadic debris signature. The TDS is often comprised of high values in the horizontal reflectivity factor, low differential reflectivity, and low co-polar correlation coefficient values, collocated with a tornadic vortex signature in the radial velocity field (Bodine et al. 2013) (Fig. 1). A benefit of the TDS is that is not dependent upon the viewpoint of the radar (Ryzhkov et al. 2005).

History of TDS

The first TDS was documented by Ryzhkov et al. 2002a using the National Severe Storms Laboratory Cimarron dual-polarized radar (KOUN). The TDS was associated with the 3 May 1999 Oklahoma tornado outbreak. Ryzhkov et al. 2002a stated the signature was located at the tip of the hook echo, had differential reflectivity values near zero, and correlation coefficient values below 0.5. The ability for tornado detection using polarimetric radar was further examined in the Joint Polarization Experiment (JPOLE) (Ryzhkov et al. 2005). This tested the engineering principles of the KOUN Weather Surveillance Radar-1988 Doppler (WSR-88D), as well as the quality of the radar data, and revealed the data products to the operational field (Ryzhkov et al. 2005). Through the analysis of other tornado events, such as two tornadoes that were captured by the KOUN radar in May 2003, the link between the observed signatures and tornadic debris became evident.

Specific Values of the TDS Parameters

Ryzhkov et al. 2005 developed a list of five criteria for dual-polarization tornado detection: 1) the presence of a hook echo; 2) CC less than 0.8; 3) a well-defined vortex signature in the Doppler velocity field; 4) ZDR less than 0.5 dB; 5) Z greater than 45 dBZ. However, different standards have been used in other studies.

Deviations in TDS Parameters

Horizontal reflectivity has been found ranging from 51 to 72 dBZ (Bunkers and Baxter 2011) and much lower values have also been observed. As previously mentioned, differential reflectivity values can either be positive or negative, depending on size and cantering angle of the object (Ryzhkov et al. 2005). Other possible explanations for negative ZDR include the characteristics of Mie scattering and some degree of common alignment among the targets (this is more likely with smaller particles) (Ryzhkov et al. 2005). Schultz et al. 2012a defined a TDS as having horizontal reflectivity values greater than 30 dBZ and CC less than 0.7. Furthermore, the Warning Decision Training Branch stated that CC values up to 0.95 and horizontal reflectivity values exceeding 20 dBZ may be sufficient for a TDS, given the presence of a strong vortex (Van Den Broeke and Jauernic 2014). They then used ZDR for extra verification. Correlation coefficient is most useful for identifying potential tornadic debris because, unlike ZDR, it is unaffected by radar miscalibration, precipitation attenuation, and partial beam blockage (Ryzhkov et al. 2005). However, anomalously low CC values are sometimes evident with extremely large hail and can be mistaken for tornadic debris (Van Den Broeke and Jauernic 2014).

Influences on the TDS

Tornado Scale Influences

Storm scale factors can impact the tornadic debris signature. Precipitation entrainment may cause positive ZDR values and differential attenuation can cause negative ZDR. It has been observed that CC increases in the TDS after precipitation was ingested (Bodine et al. 2011). This increase would occur if the debris concentration remained the same, but the concentration of raindrops increased. Additionally, tornadoes surrounded by a downdraft have a reduced number of debris that can be transported vertically because a downdraft enhances debris fallout, which correlates to a reduced TDS height. Similarly, the TDS is dependent upon the strength of the storm’s updraft and whether or not the updraft can support lofted debris (Bodine et al. 2013). Centrifuged debris may be recycled by the storm’s updraft and centrifuging results in a TDS that is broader than the damage path (Bodine et al. 2013).

Radar Influences

The ability to detect a TDS is also related to the size and intensity of the tornado, as well as the location of the nearest radars (Brown et al. 1978; Brown and Wood 2015). When radars emit beams of electromagnetic energy, the beamwidth increases as it propagates away from the radar (Rauber and Nesbitt 2018). Furthermore, because the radar beam is directed at some elevation angle away from the horizontal, the Earth’s surface falls out from below as the beam travels away from the radar (Rauber and Nesbitt 2018). These have implications on tornado detection. As the beamwidth increases, the ability to detect a TDS decreases due to decreased resolution (Brown and Wood 2015). Because tornadoes occur at low levels and radar beam height increases with distance, in regions with poor radar coverage, the radars closest to the storms are sometimes overshooting the tornadic circulation and it goes undetected (Markowski and Richardson 2010).

Due to these radar characteristics, the tornadic debris signature is rarely observed at distances greater than 120 km from the radar where the radar beam height is around 3 km (Schultz et al. 2012a). More specifically, TDS’s associated with EF1 tornadoes can be observed out to approximately 74 km, whereas a TDS with EF3/4 tornadoes can be observed out to 111 km. The greatest debris concentration is near the surface so lofted debris reaches the lowest tilts faster than the higher tilts (Bodine et al. 2013). Therefore, the lowest tilts may contain most pertinent information about lofted debris and the severity of damage (Bodine et al. 2013). The time it takes to loft debris to the height of different elevation angles varies on updraft strength and fall speeds. Thus, there is a delay between the time damage occurs and when changes in TDS parameters are perceptible, with the lowest elevation angle having the shortest delay (Bodine et al. 2013). The height debris is lofted generally increases with increasing tornado intensity and has been reported as high as 12.5 km (Van Den Broeke and Jauernic 2014). Moreover, it takes some time for lofted debris to descend to the ground (Magsig and Snow 1998) so the TDS can persist after the tornado dissipates.

Radar Wavelength Impacts

According to a study by Bodine et al. 2014, the wavelength of the radar observing the TDS can impact the values of the TDS parameters. They found that the reflectivity factor values for S band radars exceeded the values for C band radars, with the median reflectivity factor being 4.8 to 7.2 dB higher than C band. Bodine et al. 2014 stated this may be related to resonance effects. They also found the median of the correlation coefficient in S band radars is 0.14 to 0.15 higher than C band radars (Bodine et al. 2014). It was deduced that this may be caused by differences in non-Rayleigh scattering characteristics, with large particles exhibiting non-Rayleigh scattering and smaller particles scattering in the Rayleigh regime. Because non-Rayleigh scattering occurs for larger debris sizes at the S band, this essentially results in less debris scattering in the non-Rayleigh regime for S band radars compared to C band. Lastly, Bodine et al. 2014 concluded ZDR exhibits variability at both S and C bands and errors in ZDR increase as CC decreases.

Other Influences

Other factors have also been found to influence the TDS. Tornadic debris signatures are most commonly observed with supercell tornadoes, but can also be produced by mesoscale convective vortex and quasi-linear convective system tornadoes (Van Den Broeke and Jauernic 2014). A study by Van Den Broeke and Jauernic 2014 examined tornadoes from 1 January 2012 to 1 June 2013 and found only 16% of tornadoes were associated with a TDS. However, their study utilized WSR-88D data, which is known to have aforementioned shortcomings in tornado detection. TDS’s are not as common in tornadoes rated below EF3, but have been observed in tornadoes rated as low as EF0 (Van Den Broeke and Jauernic 2014). It has been speculated that weaker tornadoes do not loft debris in an ample quantity to produce a TDS (Kumjian and Ryzhkov 2008).

Additionally, tornadoes with a longer pathlength are more likely to affect an area that promotes lofted debris (Van Den Broeke and Jauernic 2014). For example, Van Den Broeke and Jauernic 2014 found that tornadoes with pathlengths exceeding 4.8 km were more than twice as likely to be affiliated with a TDS than tornadoes with shorter pathlengths. Most tornadoes with a pathlength of 32.2 km or greater had a TDS (Van Den Broeke and Jauernic 2014). Likewise, tornadoes with greater path widths are thought to exhibit TDS’s more readily due their propensity to be affiliated with stronger wind speeds (Van Den Broeke and Jauernic 2014). Most tornadoes with a path width of approximately 915 m had a TDS present (Van Den Broeke and Jauernic 2014).

Van Den Broeke and Jauernic 2014 also investigated the seasonality of debris signatures. It was found that the presence of TDS’s peaked in the spring and fall seasons, specifically in the months of March and October. While EF-scale tornado ratings were not as high in the fall, it was speculated that the peak may be due to the abundance of natural debris (i.e. fallen leaves) that can readily be lofted to a detectable height (Van Den Broeke and Jauernic 2014). Consistent with this hypothesis was their finding that TDS’s were observed two minutes, on average, after tornadogenesis during the fall. However, in winter, it took around 6.5 minutes before a TDS was evident, likely due to the lack of vegetation (Van Den Broeke and Jauernic 2014).

Applications

The spatial extent of the tornadic debris signature can provide information on the amount of damage occurring (Bodine et al. 2013). Additionally, the TDS is used operationally to verify tornado warnings and to determine the location of a tornado, especially if the tornado is not visually observable, such as in instances where they become rain wrapped (Kumjian and Ryzhkov 2008). They can also be used to confirm tornado damage and can be helpful in issuing accurate updates (Ryzhkov et al. 2005). However, Van Den Broeke and Jauernic 2014 stated that a TDS should never be used for issuing a warning, unless a warning is not already in place, due to limited confidence beyond a certain distance from the radar.

Polarimetric tornado detection has proven to be an effective tool in evaluating the occurrence and location of a tornado. While there are caveats in the ability of radars to detect tornadoes, the additional parameters and increased detectability through the implementation of dual-polarization radars is an improvement over single polarization radars. Through further examination of radar features, such as the tornadic debris signature, hopefully scientists will gain more information about tornado severity and predictability.

Case Study

To examine the tornadic debris signature firsthand, a miniature case study was performed. A tornado outbreak that occurred across the southeastern United States on 29/30 November 2016 was selected for analysis. This case was selected because it was previously analyzed for my thesis, meaning the radar data needed to examine the event was already obtained, and a few TDS’s evident in the correlation coefficient field were noted. Two tornadoes were compared and contrasted: 1) the EF2 Franklin/Colbert County Alabama tornado and 2) the EF2 Marion/Sequatchie County Tennessee tornado.

Objectives

The goal was to examine the duration the TDS was evident, as well as the spatial (horizontal and vertical) extent, and to compare this information with the tornado intensity and damage surveys.

Methodology

Information about the state, county, EF-scale tornado rating, start and end times (local and UTC), start and end latitude/longitude was recorded using tornado reports from the Storm Prediction Center (SPC) severe-weather database. This information made it easier to pinpoint the tornadic storms in the radar data. Additionally, the tornado destruction potential index (DPI) was calculated as a better measure of tornado intensity because it factors in tornado pathlength and path width. DPI was calculated using the following equation: DPI = (EF scale + 1) x path length (km) x path width (km)

WSR-88D radar data were then downloaded from an Amazon Web Services site (https://s3.amazonaws.com/noaa-nexrad-level2/index.html). For this specific case study, radar data from the Columbus Airforce Base WSR-88D (KGWX) in Mississippi and the Huntsville WSR-88D (KHTX) in Alabama were examined. The radar data were analyzed using Gibson Ridge Level-II Analyst (GR2 Analyst®) software. This was done to assess correlation coefficient and differential reflectivity values, as well as their horizontal and vertical extent. It should be noted that the lowest pixel value for each of these parameters was documented.

Environmental Setup

On the evening of 29 November 2016, a closed upper-level low was present in the eastern Dakotas and western Minnesota (Fig. 2a). The area of the severe weather event was located in warm sector. Weak surface cyclogenesis was taking place in Louisiana and there was a 40-50 kt low-level jet present. Based upon SPC mesoscale discussions, there was 1000 J kg-1 to 1500 J kg-1 of convective available potential energy for evening convection in the Southeast (this was also evidenced in a 0000 UTC sounding from KJAN in Mississippi) (Fig. 2b). Lifting condensation level heights were between 500 m AGL and 1000 m AGL (Fig. 2c). The 0–1-km storm relative helicity (SRH) values exceeded 250 m2 s-2 and the 0–3-km SRH was greater than 300 m2 s-2. The 0–6-km shear was west-southwesterly at 60+ kt across the region (Fig. 2d). The low-level 0–1-km shear was predominantly southerly at 20+ kt. The SPC had ”Moderate Risk” issued for northern Mississippi and small portions of Alabama and Tennessee.

Case 1: Franklin/Colbert County Alabama Tornado

The Franklin/Colbert County Alabama tornado had a listed start time of 0105 UTC and end time of 0123 UTC. However, a TDS was evident at 0103 UTC using the 0.5° elevation angle, with CC below 0.75 (Fig. 3). At 0105 UTC (the reported start time), a TDS was evident up through the 0.9° elevation angle, with reduced CC and a slight reduction in ZDR. When examining the spatial dimensions, a vertical cross section revealed the TDS extended up to around 5000 feet and it was around 1.6 km wide in the horizontal. By 0111 UTC, the TDS was evident up to the 1.3° elevation angle, which translated to a height of around 7000 feet. The lowest CC value was below 0.3 and the ZDR was near zero (Fig. 4). At this time, the horizontal extent of the TDS was around 3.2 km. The TDS appeared to weaken by 0117 UTC as CC values started to increase, with the lowest pixel being around 0.5, and ZDR values that were mostly positive. However, the TDS was still evident up to the 1.3° elevation angle. It was no longer visible by 0126 UTC. This tornado was rated EF2 on the EF-scale. With a pathlength of 10.87 miles and a path width 100 yards, the intensity rating translated to a DPI value of 4.80.

Case 2: Marion/Sequatchie County Tennessee Tornado

The Marion/Sequatchie County Tennessee tornado began at 0503 UTC and dissipated at 0512 UTC. The TDS was evident at 0503 UTC at the 0.5° elevation angle, below 5000 feet, with CC below 0.80 and ZDR below 0 (Fig. 5). It was around 3 km in width. However, it was not yet evident at higher elevation angles. At 0509 UTC, the TDS was evident up to the 1.4° elevation angle, and was below 10000 feet in the vertical and around 8.7 km in the horizontal. At this time, CC was below 0.7 and ZDR was near 0. At higher elevation angles, the signal got lost in the melting layer. By 0511 UTC, the TDS was evident up to the 1.4° elevation angle and CC values were below 0.55 with ZDR near 0 (Fig. 6). The signature appeared to be about 10 km wide. The TDS diminishes around 0520 UTC. This tornado was rated an EF2. The pathlength was 8.15 miles and the path width was 350 yards, resulting in a DPI of 12.59.

Comparison

The Franklin/Colbert County Alabama tornado (case 1) was rated EF2 and had a 4.80 DPI. The maximum wind speeds were around 115 mph. A TDS was evident for more than 20 minutes and the lowest CC was below 0.3. The maximum height of the TDS extended to around 7000 km and it reached approximately 3.4 km in width. The tornado snapped hardwood and softwood trees, power poles, and destroyed a roof and walls. The Marion/Sequatchie County Tennessee tornado (case 2) was rated EF2 and had a DPI of 12.59, with peak winds speeds around 130 mph. The TDS lasted just under 20 minutes and the lowest CC was below 0.55. The TDS did not extend past 10000 feet in the vertical and 10 km in the horizontal. This tornado destroyed a double-wide mobile home, damaged the roof of a church, snapped a grove of trees nearly halfway up the trunk, and removed a roof and a few exterior walls from a two-story house.

Both tornadoes were rated EF2. However, the Tennessee tornado had a larger DPI value. This can be contributed to the greater width (350 yards compared to 100 yards in the Alabama tornado). The Tennessee tornado also had stronger wind speeds (130 mph versus 115 mph in the Alabama tornado). The TDS in the Tennessee tornado was greater in both the vertical and horizontal dimensions; however, in both cases, the TDS did not extend past 10000 feet. There are several plausible reasons why the TDS’s between the two cases varied, but it appears the Tennessee tornado tracked through an area with more manmade structures, which increased the amount of debris available to be lofted. Additionally, the path width of the Tennessee tornado was nearly three times greater than that of the Alabama tornado and this was reflected in the horizontal dimension of the TDS. Interestingly, the Alabama tornado had a lower correlation coefficient value and the TDS lasted longer than the Tennessee tornado.

Conclusions

Meteorological advances have led to an increase in tornado detection capabilities. With the implementation of polarimetric radar, meteorologists can now gain more information about what kind of objects a radar volume is composed of. It is natural to assume that tornadic debris would produce different signatures than hydrometeors. Indeed, this is the case and a handful of tornado detection signatures have been developed. The focus of this paper was the tornadic debris signature. The TDS is represented by an increase in reflectivity factor, a marked decrease in co-polar correlation coefficient, as well as differential reflectivity values near zero, all collocated with a strong rotational couplet in the radial velocity field.

Through further examination of the TDS, numerous factors have been shown to impact the TDS parameters, such as the CC values, ZDR values, and detectability. Perhaps the most obvious effects would be those related to available debris, tornado intensity, and the distance the tornado is from the nearest radar. The radar wavelength and scattering properties can also contribute to deviations. Storm scale factors also have an influence. For example, the amount of precipitation present can impact CC values and the existence of a nearby downdraft suppresses the vertical extent of the signature. Pathlength, path width, and seasonality have even been found to have an influence on the tornadic debris signature. Putting all the limitations and variability aside, the TDS has proved to be a beneficial tool in verifying tornado warnings, determining the location of a tornado, and estimating the amount of damage occurring.

From the 29/30 November 2016 case study, the variability between TDS’s was evident. Both tornadoes examined were rated EF2, but produced TDS’s with differing durations, horizontal and vertical extents, and values of CC and ZDR. The main conclusions were:

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  1. The TDS lasted slightly longer for the Alabama tornado (while this tornado also lasted longer, both tornadoes had TDS durations near 20 min);
  2. The vertical and horizontal extent of the TDS was greater for the Tennessee tornado;
  3. The CC was lower for the Alabama tornado;
  4. The DPI was higher for the Tennessee tornado (due to a significantly greater path width).

The work of future studies could provide more information about tornadic signatures and lead to even better tornado prediction and detection.

References

  1. Bodine, D., M. R. Kumjian, R. D. Palmer, P. L. Heinselman, and A. Ryzhkov, 2013: Tornado damage estimation using polarimetric radar. Wea. Forecasting, 28, 139–158.
  2. R. D. Palmer, and G. Zhang, 2014: Dual-wavelength polarimetric radar analyses of tornadic debris signatures. J. Appl. Meteor. Climatol., 53, 242–261.
  3. Brown, R. A., L. R. Lemon, and D. W. Burgess, 1978: Tornado detection by pulsed Doppler radar. Mon. Wea. Rev., 106, 29-39.
  4. V. T. Wood, 2015: Detection of the presence of tornadoes at the center of mesocyclones using simulated doppler velocity measurements. Wea. Forecasting, 30, 957–963.
  5. Bunkers, M. J., and M.A. Baxter, 2011: Radar tornadic debris signatures on 27 April 2011. Electron. J. Oper. Meteor., 12(7), 1–6.
  6. Kumjian, M. R., and A. B. Ryzhkov, 2008: Polarimetric signatures in supercell thunderstorms. J. Appl. Meteor. Climatol., 47, 1940-1961.
  7. Magsig, M. A., and J. T. Snow, 1998: Long-distance debris transport by tornadic thunderstorms. Part I: The 7 May 1995 supercell thunderstorm. Mon. Wea. Rev., 126, 1430–1449.
  8. Markowski, P. M., and Y. P. Richardson, 2010: Mesoscale meteorology in midlatitudes. Wiley-Blackwell.
  9. Rauber, R. M., and S. W. Nesbitt, 2018: Radar meteorology: A first course. John Wiley & Sons.
  10. Ryzhkov, A. V., D. W. Burgess, D. S. Zrnic, T. Smith, and S. E. Giangrande. 2002a. Polarimetric analysis of a 3 May 1999 tornado. Preprints, 21st Conf. on Severe Local Storms, San Antonio, TX, Amer. Meteor. Soc., 515–518. 
  11. Ryzhkov, A., T. Schuur, D. W. Burgess, and D. S. Zrnić, 2005: Polarimetric tornado detection. J. Appl. Meteor., 44, 557–570. 
  12. Schultz, C. J., and Coauthors, 2012a: Dual-polarization tornadic debris signatures part I: Examples and utility in an operational setting. Electron. J. Oper. Meteor., 13, 120–137.
  13. Van Den Broeke, M. S., 2015: Polarimetric tornadic debris signature variability and debris fallout signatures. J. Appl. Meteor. Climatol., 54, 2389–2405.
  14. S. T. Jauernic, 2014: Spatial and temporal characteristics of polarimetric tornadic debris signatures. J. Appl. Meteor. Climatol., 53, 2217–2231. 
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Tornado Detection Using Dual Polarization Radar. (2023, August 31). GradesFixer. Retrieved June 19, 2024, from https://gradesfixer.com/free-essay-examples/tornado-detection-using-dual-polarization-radar/
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