Quantitative Analysis of Rapid-Scan Phased Array Weather Radar Benefits and Data Quality Under Various Scan Conditions

Currently, NEXRAD provides weather radar coverage for the contiguous United States. It is believed that a replacement system for NEXRAD will be in place by the year 2040, where a major goal of such a system is to provide improved temporal resolution compared to the 5-10-min updates of NEXRAD. In this dissertation, multiple projects are undertaken to help achieve the goals of improved temporal resolution, and to understand possible scanning strategies and radar designs that can meet the goal of improved temporal resolution while either maintaining (or improving) data quality. Chapter 2 of this dissertation uses a radar simulator to simulate the effect of various scanning strategies on data quality. It is found that while simply reducing the number of pulses per radial decreases data quality, other methods such as beam multiplexing and radar imaging/digital beamforming offer significant promise for improving data quality and/or temporal resolution. Beam multiplexing is found to offer a speedup factor of 1.7-2.9, while transmit beam spoiling by 10 degrees in azimuth can offer speedup factors up to ~4 in some regions. Due to various limitations, it is recommended that these two methods be used judiciously for rapid-scan applications. Chapter 3 attempts to quantify the benefits of a rapid-scan weather radar system for tornado detection. The first goal of Chapter 3 is to track the development of a common tornado signature (tornadic debris signature, or TDS) and relate it to developments in tornado strength. This is the first study to analyze the evolution of common tornado signatures at very high temporal resolution (6 s updates) by using a storm-scale tornado model and a radar emulator. This study finds that the areal extent of the TDS is correlated with both debris availability and with tornado strength. We also find that significant changes in the radar moment variables occur on short (sub-1-min) timescales. Chapter 3 also shows that the calculated improvement in tornado detection latency time (137-207 s) is greater than that provided by theory alone (107 s). Together, the two results from Chapter 3 emphasize the need for sub-1-min updates in some applications such as tornado detection. The ability to achieve these rapid updates in certain situations will likely require a combination of advanced scanning strategies (such as those mentioned in Chapter 2) and adaptive scanning. Chapter 4 creates an optimization-based model to adaptively reallocate radar resources for the purpose of improving data quality. This model is primarily meant as a proof of concept to be expanded to other applications in the future. The result from applying this model to two real-world cases is that data quality is successfully improved in multiple areas of enhanced interest, at the expense of worsening data quality in regions where data quality is not as important. This model shows promise for using adaptive scanning in future radar applications. Together, these results can help the meteorological community understand the needs, challenges, and possible solutions to designing a replacement system for NEXRAD. All of the techniques studied herein either rely upon (or are most easily achieved by) phased array radar (PAR), which further emphasizes the utility of PAR for achieving rapid updates with sufficient data quality. It is hoped that the results in this dissertation will help guide future decisions about requirements and design specifications for the replacement system for NEXRAD

Examining the Predictability of Tornadic and Nontornadic Non-Supercellular MCS Storms using GridRad-Severe Radar Data and Machine Learning Techniques

Many studies have aimed to identify novel storm characteristics that are indicative of current or future severe weather potential using a combination of ground-based radar observations and severe reports. However, this is often done on a small scale using limited case studies on the order of tens to hundreds of storms due to how time-intensive this process is. Herein, we introduce the GridRad-Severe dataset, a database including ~100 severe weather days per year and upwards of 1.3 million objectively tracked storms from 2010-2019. Composite radar volumes spanning objectively determined, report-centered domains are created for each selected day using the GridRad compositing technique, with dates objectively determined using report thresholds defined to capture the highest-end severe weather days from each year, evenly distributed across all severe report types (tornadoes, severe hail, and severe wind). Spatiotemporal domain bounds for each event are objectively determined to encompass both the majority of reports as well as the time of convection initiation. Severe weather reports are matched to storms that are objectively tracked using the radar data, so the evolution of the storm cells and their severe weather production can be evaluated. Herein, we apply storm mode (single cell, multicell, or mesoscale convective system) and right-moving supercell classification techniques to the dataset, and revisit various questions about severe storms and their bulk characteristics posed and evaluated in past work. Additional applications of this dataset are reviewed for possible future studies. Given this large dataset of severe storms, questions about storm structure of very specific storm types can be investigated using what is still a large subsample of the total GridRad-Severe dataset. This study compares populations of tornadic non-supercellular MCS storm cells to their nontornadic counterparts, focusing on nontornadic storms that have similar radar characteristics to tornadic storms. Comparison of single-polarization radar variables during storm lifetimes show that median values of low-level, mid-level, and column-maximum azimuthal shear, as well as low-level radial divergence, enable the highest degree of separation between tornadic and nontornadic storms. Focusing on low-level azimuthal shear values, null storms were randomly selected such that the distribution of null low-level azimuthal shear values matches the distribution of tornadic values. After isolating the null cases from the nontornadic population, signatures emerge in single-polarization data that enable discrimination between nontornadic and tornadic storms. In comparison, dual-polarization variables show little deviation between storm types. Tornadic storms both at tornadogenesis and at 20-minute lead time show collocation of the primary storm updraft with enhanced near-surface rotation and convergence, facilitating the non-mesocyclonic tornadogenesis processes. With this additional knowledge about the structure of tornadic vs. nontornadic storms and which radar variables best differentiate the two, machine learning methods can be used to learn the differences between these storm type at various lead times and improve tornado predictability. A convolutional neural network was trained on tornadic and nontornadic data where the nontornadic data were either sampled from storms that have similar radar characteristics to tornadic storms as in the PMM analyses or sampled from the entire population of non-supercellular MCS storms. These models were then tested on independent data from 2020-2021, again either including all tornadic storms and sampling nontornadic cases as in the PMM analyses or including all tornadic and nontornadic storms. Models that were tested on all tornadic and nontornadic storms, whether they were trained and validated on datasets including sampled strong nontornadic storms or a sample of all nontornadic storms, both performed well below the baseline performance metrics from the NWS. However, when the model was trained, validated, and tested using samples of all tornadic storms and only strong nontornadic storms, model test performance far exceeded the baseline NWS metrics. Performance metrics include a probability of detection (POD) of 79%, a false alarm ratio (FAR) of 58%, and a CSI of 0.38. Compared to the NWS metrics of 49%, 75%, and 0.2, respectively, this model shows clear promise as a supplemental forecasting tool for scenarios where a storm is identified as (at least) borderline tornadic. However, further analyses of the model performance scaled to account for the true proportion of tornadic vs. nontornadic storms shows that it was the unnatural ratio of tornadic to nontornadic storms, and not the focus on strong nontornadic storms, that was the cause for the improved model performance. Finally, a brief analysis of the underlying populations and their demographic characteristics in the vicinity of tornadoes are examined. Special attention is given to non-supercellular MCS storms, as well as discrete supercells, whose tornadoes are often a main focus of tornado research in the U.S. Analyses show that groups making up ~3% or less of the CONUS mean population typically have lower relative population densities in the vicinity of storms. The Black or African American Alone demographic has higher relative populations in the vicinity of all tornadoes compared to their CONUS mean population density, as do all Non-Hispanic categories (Not Hispanic, Non-Hispanic White and Non-Hispanic Black). Comparing population densities near specific types of tornadoes (i.e., mode and combination of mode and human impact) to their densities near all tornadoes, the White Alone demographic has population densities near the CONUS mean for supercellular tornadoes, but that density jumps 6-7 percentage points in the vicinity of deadly supercellular tornadoes when examining underlying population density by deadly event and by death, suggesting that the deadliest supercellular tornadoes occur in predominantly White areas. On average, populations in the vicinity of all tornadoes have ~75-80% higher Black or African American Alone and Non-Hispanic Black densities when compared to the CONUS mean, with those demographics' relative densities only increasing when isolating MCS tornadoes and deadly MCS tornadoes, suggesting that the deadliest MCS tornadoes preferentially occur in areas with relatively higher Black or African American Alone and Non-Hispanic Black populations. One particularly striking result is that the mean Social Vulnerability Index (SVI) of populations near all tornadoes is just barely above the CONUS mean (0.52 vs. CONUS mean of 0.51), but is slightly lower for supercellular tornadoes (0.49) and higher for MCS tornadoes (0.57). Therefore, MCS tornadoes tend to occur in areas that are less resilient to natural disasters than both the CONUS mean and areas in the vicinity of supercellular tornadoes. For both MCS and supercellular tornadoes that were associated with deaths or injuries, the local SVI is higher, likely pointing to the applicability of SVI in identifying areas less resilient to natural disasters

The Tornado Warning Process: A Review of Current Research, Challenges, and Opportunities

With the unusually violent tornado season of 2011, there has been a renewed national interest, through such programs as NOAA\u27s Weather Ready Nation initiative, to reevaluate and improve our tornado warning process. This literature review provides an interdisciplinary, end-to-end examination of the tornado warning process. Following the steps outlined by the Integrated Warning System, current research in tornado prediction and detection, the warning decision process, warning dissemination, and public response are reviewed, and some of the major challenges for improving each stage are highlighted. The progress and challenges in multi-day to short-term tornado prediction are discussed, followed by an examination of tornado detection, focused primarily upon the contributions made by weather radar and storm spotters. Next is a review of the warning decision process and the challenges associated with dissemination of the warning, followed by a discussion of the complexities associated with understanding public response. Finally, several research opportunities are considered, with emphases on understanding acceptable risk, greater community and personal preparation, and personalization of the hazard risk

OSCER state of the Center

Biography Dr. Henry Neeman is the Director of the OU Supercomputing Center for Education & Research, Assistant Vice President Information Techology – Research Strategy Advisor, Associate Professor in the College of Engineering and Adjunct Associate Professor in the School of Computer Science at the University of Oklahoma. He received his BS in computer science and his BA in statistics with a minor in mathematics from the State University of New York at Buffalo in 1987, his MS in CS from the University of Illinois at Urbana-Champaign in 1990 and his PhD in CS from UIUC in 1996. Prior to coming to OU, Dr. Neeman was a postdoctoral research associate at the National Center for Supercomputing Applications at UIUC, and before that served as a graduate research assistant both at NCSA and at the Center for Supercomputing Research & Development. In addition to his own teaching and research, Dr. Neeman collaborates with dozens of research groups, applying High Performance Computing techniques in fields such as numerical weather prediction, bioinformatics and genomics, data mining, high energy physics, astronomy, nanotechnology, petroleum reservoir management, river basin modeling and engineering optimization. He serves as an ad hoc advisor to student researchers in many of these fields. Dr. Neeman's research interests include high performance computing, scientific computing, parallel and distributed computing and computer science education.Presented at the Oklahoma Supercomputing Symposium 2013, October 2, 2013.The OU Supercomputing Center for Education & Research (OSCER) celebrates its 11th anniversary on August 31 2013. In this report, we examine what OSCER is, what OSCER does, what OSCER has accomplished in its 11 years, and where OSCER is going.The University of Oklahoma The University of Oklahoma Supercomputing Center for Education and Resesarch OSCER IT Information Technology The University of Oklahoma's Department of Information TechnologyN

Measurement and magnitude-based system identification of tornado-associated infrasound

Recent evidence indicates that acoustic waves at frequencies below human hearing (infrasound) are produced during tornadogenesis and continue through the life of a tornado. The currently available tornadic infrasound data remains sparse, which has prevented the identification of the fluid mechanism responsible for its production. To increase the probability of detection, this thesis presents an adaptation of fixed infrasound sensing technology to equip storm chasers that are in regular proximity to tornadoes with mobile infrasound measurement capabilities. This approach has and continues to increase the quantity of samples while also reducing the long-range propagation-related uncertainties in the measurement analysis. This thesis describes the design and deployment of the Ground-based Local INfrasound Data Acquisition (GLINDA) system - a system which includes a specialized infrasound microphone, GPS receiver, and an IMU package for data remote collection. This thesis additionally presents analyses of measurements during an EFU tornado and a significant hail event as collected by the GLINDA system. The measured signal from the EFU tornado event notably produced an elevated broadband signal between 10 and 15 Hz consistent with past observations from small tornadoes. Frequency peak identification approaches are discussed in context of the EFU tornado. Utilizing the tornado event's frequency response magnitude as the output and a selection of wind noise pressure and energy models considered for acoustic response forcing inputs, frequency response system identification approaches are utilized to develop a set of transfer function models for the tornado acoustic production mechanism

Storm-scale Ensemble-based Severe Weather Guidance: Development of an Object-based Verification Framework and Applications of Machine Learning

A goal of the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast (WoF) project is to provide rapidly updating probabilistic guidance to human forecasters for short-term (e.g., 0-3 h) severe weather forecasts. Several case studies have shown that experimental WoF systems (WoFS) can produce accurate short-term probabilistic guidance for hazards such as tornadoes, hail, and heavy rainfall. However, without an appropriate probabilistic verification method for WoFS-style forecasts (which provide guidance for individual thunderstorms), a robust evaluation of WoFS performance has been lacking. In this dissertation, I develop a novel object-based verification method for short-term, storm-scale probabilistic forecasts and apply it to WoFS probabilistic mesocyclone guidance and further adapted to evaluate machine learning-based calibrations of WoFS severe weather probabilistic guidance. The probabilistic mesocyclone guidance was generated by calculating grid-scale ensemble probabilities from WoFS forecasts of updraft helicity (UH) in layers 2—5 km (mid-level) and 0—2 km (low-level) above ground level (AGL) aggregated over 60-min periods. The resulting ensemble probability swaths are associated with individual thunderstorms and treated as objects. Each ensemble track object is assigned a single representative probability value. A mesocyclone probability object, conceptually, is a region bounded by the ensemble forecast envelope of a mesocyclone track for a thunderstorm over 1 hour. The mesocyclone probability objects were matched against rotation track objects in Multi-Radar Multi-Sensor data using the total interest score, but with the maximum displacement varied between 0, 9, 15, and 30 km. Forecast accuracy and reliability were assessed at four different forecast lead time periods: 0-60 min, 30-90 min, 60-120 min, and 90-150 min. In the 0-60 minute forecast period, the low-level UH probabilistic forecasts had a POD, FAR, and CSI of 0.46, 0.45, and 0.31, respectively, with a probability threshold of 22.2% (the threshold of maximum CSI). In the 90-150 minute forecast period, the POD and CSI dropped to 0.39 and 0.27 while FAR remained relatively unchanged. Forecast probabilities >60% over-predicted the likelihood of observed mesocyclones in the 0-60 min period; however, reliability improved when allowing larger maximum displacements for object matching and at longer lead times. To evaluate the ability of machine learning (ML) models to calibrate WoFS severe weather guidance, the probability object-based method was generalized for identifying any ensemble storm track (based on individual ensemble updraft tracks rather than mesocyclone tracks). Using these ensemble storm tracks, three sets of predictors were extracted from the WoFS forecasts: intra-storm state variables, near-storm environment variables, and morphological attributes of the ensemble storm tracks. Random forests, gradient-boosted trees, and logistic regression algorithms were then trained to predict which WoFS 30-min ensemble storm tracks will produce a tornado, severe hail, and/or severe wind report. To provide a baseline against which to test the ML models’ performance, I extracted the probability of mid-level UH exceeding a threshold (tuned per severe weather hazard) from each ensemble storm track. The three ML algorithms discriminated well for all three hazards and produced far more reliable probabilities than the UH-based predictions. Using state-of-the-art ML interpretability methods, I found that the ML models learned sound physical relationships and the appropriate responses to the ensemble statistics. Intra-storm predictors were found to be more important than environmental predictors for all three ML models, but environmental predictors made positive contributions to severe weather likelihood in situations where the WoFS fails to analyze ongoing convection. Overall, the results suggest that ML-based calibrations of dynamical ensemble output can improve short term, storm-scale severe weather probabilistic guidance

Multiple Cyclic Tornado Production Modes in the 5 May 2007 Greensburg, Kansas Supercell Storm

Long-track, violent tornadoes are rare events, but are responsible for a disproportionate majority of tornado fatalities, injuries, and property damage. It has been observed that such tornadoes are often generated as part of a series produced by one supercell, and preceded by one or more smaller tornadoes. At some point, a transition in the tornado production mode occurs, from short-track, cyclic tornado production (mode I), to long-track, single (plus satellite) tornado production (mode II). This transition has been documented only a few times at close range by Doppler weather radars.A cyclic, tornadic supercell ("the Greensburg storm") generated at least 22 tornadoes in southwest Kansas on 5 May 2007. One of these was the first documented EF-5 tornado ("the Greensburg tornado"), which destroyed 95% of the buildings in Greensburg, Kansas and caused 11 fatalities. The University of Massachusetts X-band, polarimetric, mobile Doppler radar (UMass X-Pol), which was operating in the area as part of a severe storms research project, collected data in the Greensburg storm for over an hour, including its transition from tornado production mode I to mode II. The first 10 tornadoes produced by the Greensburg storm can be seen in this UMass X-Pol data set.In this study, the UMass X-Pol data (as well as contemporaneous data from the WSR-88D at Dodge City, Kansas, or KDDC) are analyzed with the aim of diagnosing whether this transition occurred as a result of changes in the environmental wind profile, interaction of tornadoes with the storm's cold pool, or a combination of the two. These efforts met with limited success, largely because of the relative scarcity of observations of low-level flow in the inflow sector of the Greensburg storm. However, in the process, features of the Greensburg storm related to tornado production (such as vortices, updrafts, and polarimetric signatures) are documented, and relationships among them before, during, and after this transition are diagnosed. In particular, it is found that:*The horizontal motions of the earlier tornadoes (mode I) tracked to the left with respect to the updraft motion, while the motion of the Greensburg tornado and its satellites (mode II) more closely matched that of the updraft.*The vortex signatures in the UMass X-Pol data matched with the surveyed damage tracks. In addition, several non-tornadic circulations were documented.*A forward surge and retreat of a RFGF was documented a few minutes before the development of the Greensburg tornado.*At least two cyclonic-anticyclonic pairs of satellite tornadoes (of the Greensburg tornado) occurred, possibly indicating the upward arching of low-level horizontal vortex lines over bulges in the RFGF.*Weak-echo holes are documented in several tornadoes, and found to be consistently collocated with corresponding vortex signatures in azimuth but biased slightly far from the radar in range.*A polarimetric tornadic debris signature is found near the surface in the mature Greensburg tornado. In addition, a ZDR arc is documented whose presence corroborates increasing low-level vertical wind shear in the inflow sector. Other polarimetric supercell features are consistent with those found in previous studies.In an attempt to retrieve in-storm variables not observed by radar, KDDC and UMass X-Pol radar data were assimilated into a numerical weather prediction model using the ensemble Kalman filter (EnKF) technique. Two sets of experiments were performed, one in which UMass X-Pol data were either included or withheld from assimilation with KDDC data, and another in which the 0 - 3 km AGL initial environmental wind profile was modified to include a low-level jet, or not.Assimilation of UMass X-Pol data results in more pronounced changes to the analyses than the addition of a low-level jet, although both changes result in near-surface vortices that are stronger, deeper, and longer-lived than in experiments without. When UMass X-Pol data are assimilated, vortices appear in the analyses that correspond to mode I tornadoes, and the southward-spreading, surface cold pool from the Greensburg storm (which likely results from the use of a relatively simple microphysical parameterization scheme) deflects around the assimilated observations of southerly flow at the UMass X-Pol deployment site. Neither of these features appear when UMass X-Pol data are withheld.I close by discussing the implications of these results for future avenues of research involving analysis and assimilation of data from mobile Doppler radars, including storm-scale prediction