Mobile Radar Observations Research Articles

Enhancing Meteorological Mobile Radar Observations Through Radar Location Optimization

Enhancing Meteorological Mobile Radar Observations Through Radar Location Optimization

The strongest winds in tornadoes are very near the ground

Tornadoes contain some of the strongest winds on earth, causing death and damage when impacting man-made and natural objects, such as buildings and trees. Quantifying tornado winds near the surface is critical to characterizing tornado hazards. Direct measurements of tornado winds are rare and are usually obtained at least >100 m above the ground, well above building height, by proximate mobile radars. The representativeness of these mobile radar-obtained measurements to wind speeds closer to the surface is unknown. Here we analyze rare, low-level mobile radar observations of 73 different tornadoes to demonstrate that the strongest winds in tornadoes generally occur very near the ground. Therefore, even proximate radar measurements at >100 m above the ground usually substantially underestimate actual tornado wind intensity.

Additional Evaluation of the Spatiotemporal Evolution of Rotation during Tornadogenesis Using Rapid-Scan Mobile Radar Observations

Abstract This study builds upon recent rapid-scan radar observations of mesocyclonic tornadogenesis in supercells by investigating the formation of seven tornadoes (four from a single cyclic supercell), most of which include samples at heights < 100 m above radar level. The spatiotemporal evolution of the tornadic vortex signatures (TVSs), maximum velocity differentials across the vortex couplet, and pseudovorticity are analyzed. In general, the tornadoes formed following a non-descending pattern of evolution, although one case was descending over time scales O(<60) s and the evolution of another case was dependent upon the criteria used to define a tornado, and may have been associated with a rapidly occurring top-down process. Thus, it was determined that the vertical sense of evolution of a tornado can be sensitive to the criteria employed to define a TVS. Furthermore, multiple instances were found in which TVSs terminated at heights below 1.5 km, although vertical sampling above this height was often limited. Significance Statement It is generally well understood that tornadoes form over short time scales [i.e., O(∼60) s]. Despite this fact, detailed scientific measurements of tornado evolution during and just prior to genesis remains limited, particularly very near the ground and on time and space scales sufficient to observe tornado processes. Multiple recent studies have supported a non-descending evolution of rotation in supercell tornadoes, but the small number of analyzed cases is still insufficient for generalization. This study investigates seven new cases of tornadogenesis using high spatiotemporal resolution radar data that include near-ground level observations to examine the evolution of rotation with time and height. For the time scales observable by the radar platform [i.e., O(∼30) s], genesis occurred predominately following a non-descending manner in five out of the seven tornadoes studied, while the vertical evolution of two tornadoes were sensitive to the criterion used to define a “tornadic” vortex signature.

Mobile Radar Observations of the Evolving Debris Field Compared with a Damage Survey of the Shawnee, Oklahoma, Tornado of 19 May 2013

AbstractA detailed damage survey is combined with high-resolution mobile, rapid-scanning X-band polarimetric radar data collected on the Shawnee, Oklahoma, tornado of 19 May 2013. The focus of this study is the radar data collected during a period when the tornado was producing damage rated EF3. Vertical profiles of mobile radar data, centered on the tornado, revealed that the radar reflectivity was approximately uniform with height and increased in magnitude as more debris was lofted. There was a large decrease in both the cross-correlation coefficient (ρhv) and differential radar reflectivity (ZDR) immediately after the tornado exited the damaged area rated EF3. Low ρhv and ZDR occurred near the surface where debris loading was the greatest. The 10th percentile of ρhv decreased markedly after large amounts of debris were lofted after the tornado leveled a number of structures. Subsequently, ρhv quickly recovered to higher values. This recovery suggests that the largest debris had been centrifuged or fallen out whereas light debris remained or continued to be lofted. Range–height profiles of the dual-Doppler analyses that were azimuthally averaged around the tornado revealed a zone of maximum radial convergence at a smaller radius relative to the leading edge of lofted debris. Low-level inflow into the tornado encountering a positive bias in the tornado-relative radial velocities could explain the existence of the zone. The vertical structure of the convergence zone was shown for the first time.

Gap-Filling Mobile Radar Observations of a Snow Squall in the San Luis Valley

Abstract On 24 January 2017, a convective snow squall developed in the San Luis Valley of Colorado. This squall produced rapidly varying winds at San Luis Valley airport in Alamosa, Colorado, with gusts up to 12 m s−1, and an associated visibility drop to 1.4 km from unlimited in less than 10 min. This snow squall was largely undetected by the operational WSR-88D network because of the Sangre de Cristo Range of the Rocky Mountains lying between the valley and the nearest WSR-88D in Pueblo, Colorado. This study presents observations of the snow squall from the X-band NOAA X-Pol radar, which was deployed in the San Luis Valley during the event. These observations document the squall developing from individual convective cells and growing upscale into a linear squall, with peak radial velocities of 15 m s−1. The environment conducive to the development of this snow squall is examined using data from the High-Resolution Rapid Refresh model, which shows an environment unstable to ascending surface-based parcels, with surface-based convective available potential energy (SBCAPE) values up to 600 J kg−1 in the San Luis Valley. The mobile radar data are integrated into the Multi-Radar Multi-Sensor (MRMS) mosaic to illustrate both the large improvement in detectability of this event gained from a gap-filling radar as well as the capability of MRMS to incorporate data from new radars designed to fill gaps in the current radar network.

Comparison of Disdrometer and X-Band Mobile Radar Observations in Convective Precipitation

Abstract Microphysical data from thunderstorms are sparse, yet they are essential to validate microphysical schemes in numerical models. Mobile, dual-polarization, X-band radars are capable of providing a wealth of data that include radar reflectivity, drop shape, and hydrometeor type. However, X-band radars suffer from beam attenuation in heavy rainfall and hail, which can be partially corrected with attenuation correction schemes. In this research, the authors compare surface disdrometer observations to results from a differential phase-based attenuation correction scheme. This scheme is applied to data recorded by the National Oceanic and Atmospheric Administration (NOAA) X-band dual-polarized (NOXP) mobile radar, which was deployed during the second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2). Results are presented from five supercell thunderstorms and one squall line (183 min of data). The median disagreement (radar–disdrometer) in attenuation-corrected reflectivity Z and differential reflectivity ZDR is just 1.0 and 0.19 dB, respectively. However, two data subsets reveal much larger discrepancies in Z (ZDR): 5.8 (1.6) dB in a hailstorm and −13 (−0.61) dB when the radar signal quality index (SQI) is less than 0.8. The discrepancies are much smaller when disdrometer and S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) Z are compared, with differences of −1.5 dB (hailstorm) and −0.66 dB (NOXP SQI < 0.8). A comparison of the hydrometeor type retrieved from disdrometer and NOXP radar data is also presented, in which the same class is assigned 63% of the time.

Comparison between Dual-Doppler and EnKF Storm-Scale Wind Analyses: The 29–30 May 2004 Geary, Oklahoma, Supercell Thunderstorm

Abstract Kinematical analyses of storm-scale mobile radar observations are critical to advancing our understanding of supercell thunderstorms. Maximizing the accuracy of these analyses, and characterizing the uncertainty in ensuing conclusions about storm structure and processes, requires knowledge of the error characteristics of different retrieval techniques under different observational scenarios. Using storm-scale mobile radar observations of a tornadic supercell, this study examines the impacts on ensemble Kalman filter (EnKF) wind analyses of the number of available radars (one versus two), uncertainty in the model-initialization sounding, the sophistication of the microphysical parameterization scheme (double versus single moment), and assimilating reflectivity observations. The relative accuracy of three-dimensional variational data assimilation (3DVAR) dual-Doppler wind retrievals and single- and dual-radar EnKF wind analyses of the supercell is also explored. The results generally reinforce the findings of a previous study that used observing system simulation experiments to explore the same issues. Both studies suggest that single-radar EnKF wind analyses can be very useful once enough data have been assimilated, but that subsequent analyses that operate on the retrieved wind field gradients should be interpreted with caution. In the present study, severe errors appear to occur in computed Lagrangian circulation time series, imperiling interpretation of the underlying dynamics. This result strongly suggests that dual- and multiple-Doppler radar deployment strategies continue to be used in mobile field campaigns.

Comparison between Dual-Doppler and EnKF Storm-Scale Wind Analyses: Observing System Simulation Experiments with a Supercell Thunderstorm

AbstractKinematical analyses of mobile radar observations are critical to advancing the understanding of supercell thunderstorms. Maximizing the accuracy of these and subsequent dynamical analyses, and appropriately characterizing the uncertainty in ensuing conclusions about storm structure and processes, requires thorough knowledge of the typical errors obtained using different retrieval techniques. This study adopts an observing system simulation experiment (OSSE) framework to explore the errors obtained from ensemble Kalman filter (EnKF) assimilation versus dual-Doppler analysis (DDA) of storm-scale mobile radar data. The radar characteristics and EnKF model errors are varied to explore a range of plausible scenarios.When dual-radar data are assimilated, the EnKF produces substantially better wind retrievals at higher altitudes, where DDAs are more sensitive to unaccounted flow evolution, and in data-sparse regions such as the storm inflow sector. Near the ground, however, the EnKF analyses are comparable to the DDAs when the radar cross-beam angles (CBAs) are poor, and slightly worse than the DDAs when the CBAs are optimal. In the single-radar case, the wind analyses benefit substantially from using finer grid spacing than in the dual-radar case for the objective analysis of radar observations. The analyses generally degrade when only single-radar data are assimilated, particularly when microphysical parameterization or low-level environmental wind errors are introduced. In some instances, this leads to large errors in low-level vorticity stretching and Lagrangian circulation calculations. Nevertheless, the results show that while multiradar observations of supercells are always preferable, judicious use of single-radar EnKF assimilation can yield useful analyses.

An analysis of the axisymmetric three‐dimensional low level wind field in a tornado using mobile radar observations

The purpose of this letter is to present an analysis of the three‐dimensional wind field retrieved from mobile Doppler radar data of the 12 May 2004 Harper, KS tornadoes. The derived three‐dimensional vortex structure and its surrounding flow show marked temporal and spatial variability. This has significant implications on the numerical modeling of tornado‐scale flow.

Observations of Vertical Wind Shear Heterogeneity in Convective Boundary Layers

AbstractDual-Doppler wind syntheses from mobile radar observations obtained during the International H2O Project document some of the spatial variability of vertical wind profiles in convective boundary layers. Much of the variability of popular forecasting parameters such as vertical wind shear magnitude and storm-relative helicity is thought to result from pressure and temperature gradients associated with mesoscale boundaries (e.g., drylines, outflow boundaries, fronts). These analyses also reveal substantial heterogeneity even in the absence of obvious mesoscale wind shifts—in regions many might have classified as “horizontally homogeneous” with respect to these parameters in the past. This heterogeneity is closely linked to kinematic perturbations associated with boundary layer convection. When a mean wind is present, the large spatial variability implies significant temporal variability in the vertical wind profiles observed at fixed locations, with the temporal variability increasing with mean wind speed. Significant differences also can arise between true hodographs and “pseudohodographs” obtained from rawinsondes that are advected horizontally as they ascend. Some possible implications of the observed heterogeneity with respect to forecasting and simulating convective storms also are discussed.

Mobile Radar Observations of Severe Convective Storms

Fixed-site ground-based radars seldom are able to collect data with sufficient resolution for research purposes owing to the small-scale nature, rapid translation, and infrequency of severe convective storms and the small-scale phenomena they spawn. Accordingly, in recent years, radars mounted on mobile1 platforms have played an important role in advancing our understanding of the behavior of these phenomena. Ground-based mobile platforms such as vans and trucks, and airborne mobile platforms such as aircraft, have been used to transport various types of radars close to storms as they evolve. In addition to increasing substantially the likelihood of collecting datasets in which convective phenomena are sampled adequately, it is possible to document simultaneously the evolution of severe storm features photographically. Satellites have also been used to carry radars that can view a wide region of the globe.