Mobile Radars

It is difficult to capture rapidly evolving severe weather phenomena at low lev- els and high spatial resolution with traditional fixed-site radars due to the need for close proximity. Thus, mobile weather radar systems are used to increase the likelihood of near-storm observations. For a number of years now, it has been common in mid-western towns to see one or more mobile radar systems in- route to a potential high-impact weather event. Some images of typical mobile radars used in field campaigns are given in Figure 1.11. One of the original mo- bile systems was tfhe Center for Severe Weather Research (CSWR) Doppler on Wheels (DOW) (Wurman et al. 1997) followed by the Shared Mobile Atmospheric Research and Teaching Radar, or SMART Radar (Biggerstaff et al. 2005). Addi- tional systems, such as the UMass X-Pol (Venkatesh et al. 2008) and the NSSL

(a) (b)

(c) (d)

Figure 1.11: Images depicting mobile radar systems in use today. (a) The SMART-R systems maintained by the University of Oklahoma (b) Ka-band radars maintained by Texas Tech University (http://- www.depts.ttu.edu/weweb/WindEnergy) (c) MWR-05XP phased array system maintained by the Navy Postgraduate School(http://www.cirpas.org) and (d) RaXpol built by Prosensing and maintained by the Atmospheric Radar Re- search Center (http://arrc.ou.edu). Many of these radars employ both dual- polarization and methods to expedite the scanning process. Additionally, all of these radars have been used in the gathering of severe weather data.

NO-XP (Schwarz and Burgess 2010), improved mobile radar measurements by in- corporating dual-polarization capabilities, which allow for attenuation correction and hydrometeor classification (Ryzhkov and Zrni´c 1994) among other benefits. Other systems advance mobile radar performance by enhancing spatial resolution through the utilization of higher frequencies. The Texas Tech University Ka-band (TTUKa) radar (Weiss et al. 2009) and the UMass W-Band radar (Bluestein et al. 1997; Seliga and Mead 2009) are two such systems. One common feature of all of the systems mentioned, is their use of a traditional parabolic dish. In other words, the systems utilize a pencil beam to gather volumetric storm data. Temporal res- olution is thus limited and has prompted further exploration for technological advancement in the mobile radar community.

As a result of the high spatial resolution gained by the use of mobile radars, temporal resolution can be increased to observe small-scale phenomena as they form. A relationship between the necessary temporal resolution for a given spatial resolution can be derived from the spatial decorrelation time of a specific weather event, as shown by Bodine et al. (2009) for a simulated tornado case. Results of the analysis indicate that the spatial structure of the vortex (sampled at 25, 50 and 100 m) decorrelates in approximately two seconds. A more qualitative study provided by Wurman (2005) illustrates the relationship between spatial and tem- poral resolution as well as indicating the ability of a variety of systems to detect quickly evolving phenomena and an illustration is presented in Figure 1.12. In general, spatial resolution is proportional to the temporal scale, meaning, smaller features of a phenomena require less time between samples to be detected and analyzed. For instance, multiple vortices are indicated by the black box outlined in the lower left corner of Figure 1.12, requiring 50 m resolution with 20 s samples. None of the radars indicated in the figure are able to achieve this temporal or spatial scale.

Figure 1.12: An illustration from Wurman (2005) indicating the relationship be- tween spatial and temporal resolution. Additionally, several radar systems, both fixed and mobile, are denoted, indicating the ability to detect various meteoro- logical phenomena. The Atmospheric Imaging Radar (discussed later) would be located in the lower left corner, exceeding the 12 s minimum time scale shown in the figure.

To improve the update time of volumetric storm data, mobile radars have employed various techniques. Electronic beam steering and frequency hopping are two, somewhat unrelated techniques that aim to improve temporal resolu- tion. Electronic steering can be accomplished in a fraction of the time it takes to mechanically steer the dish of the radar. The CSWR Rapid Scan DOW (Wurman and Randall 2001) makes use of multiple frequencies to generate several beams fixed in elevation while mechanically scanning in azimuth. The Rapid Scanning X-band Polarimetric Radar (RaXpol), employs a frequency hopping technique to allow for high-speed antenna rotation. Similar to the beam multiplexing idea put forth by Yu et al. (2007), data accuracy is determined by the number of in- dependent samples acquired. Utilizing multiple, independent frequencies nearly simultaneously, the radar is able to increase its rotation rate because it is col- lecting multiple independent samples where a conventional radar collects one, thus reducing the time required to collect volumetric storm data (Pazmany and Bluestein 2009). The Meteorological Weather Radar 2005 X-band (MWR-05XP) uses both phase and frequency scanning to steer the beam in elevation and az- imuth, but also relies on mechanical steering to a large extent (Bluestein et al. 2010).

Many significant experiments have been completed with these systems, in- cluding the Verifications of the Origins of Rotation in Tornadoes Experiment 2 (VORTEX2) project (Bluestein et al. 2009), where a fleet of radars, including those mentioned above, were deployed over several weeks and two spring seasons primarily in search of tornado events. Data collected during these experiments have improved the understanding of tornado life cycle and helped to enhance radar tornado detection algorithms. Some cases could potentially lead to a reduction in lives lost to severe weather through the construction of structures better able to withstand the force of tornadic winds (Metzger et al. 2011).

While the aforementioned techniques are valid in the attempt to improve temporal and spatial resolution of Doppler weather radars, additional approaches exist. As an example, this work presents the Atmospheric Imaging Radar (AIR), developed by the Atmospheric Radar Research Center (ARRC) at the University of Oklahoma (OU), which employs imaging techniques to simultaneously gather volumetric data on a mobile platform.