WSR-88D Radar Research Articles

An Automated Approach to Estimating Convective Boundary Layer Depth from Dual-Polarization WSR-88D Radar Observations

Abstract Convective boundary layer (CBL) depth can be estimated from dual-polarization WSR-88D radars using the product differential reflectivity ZDR because the CBL top is collocated with a local ZDR minimum produced by Bragg scatter at the interface of the CBL and the free troposphere. Quasi-vertical profiles (QVPs) of ZDR are produced for each radar volume scan and profiles from successive times are stitched together to form a time–height plot of ZDR from sunrise to sunset. QVPs of ZDR often show a low-ZDR channel that starts near the ground and rises during the morning and early afternoon, identifying the CBL top. Unfortunately, results show that this channel within the QVP can occasionally be misleading. This motivated creation of a new variable DVar, which combines ZDR with its azimuthal variance and is particularly helpful at identifying the CBL top during the morning hours. Two methods are developed to track the CBL top from QVPs of ZDR and DVar. Although each method has strengths and weaknesses, the best results are found when the two methods are combined using inverse variance weighting. The ability to detect CBL depth from routine WSR-88D radar scans rather than from rawinsondes or lidar instruments would vastly improve our understanding of CBL depth variations in the daytime by increasing the temporal and spatial frequencies of the observations. Significance Statement The daytime convective boundary layer (CBL) can increase in depth from a few hundred to a few thousand meters between sunrise and sunset and is strongly connected to temperature changes at Earth’s surface. Unfortunately, current observations of CBL depth primarily consist of measurements from twice daily rawinsonde launches at 97 locations across the United States. As a result, CBL depth observations lack fine spatial and temporal resolution and miss the daily cycle of CBL growth. This study seeks to fill the gaps in CBL depth observations by developing an automated method to estimate CBL depth from dual-polarization WSR-88D radar observations with a temporal resolution as fine as 5–10 min. These observations will greatly enhance our ability to observe and monitor CBL depth in real time.

Comparison of Tornado Damage Characteristics to Low-Altitude WSR-88D Radar Observations and Implications for Tornado Intensity Estimation

Abstract Given the obvious difficulties in directly sampling tornadic wind fields, ongoing work continues to improve estimates of near-ground wind speeds in tornadoes. This study builds upon a recently proposed empirical relationship between radar-observed velocities in the lowest 150 m above ground level (AGL) and the theoretical peak 15-m AGL wind speed. We create and analyze a dataset of 194 velocity observations within tornadoes in the lowest 150 m AGL. These observations are drawn from 105 individual tornadoes that occurred across a diverse range of EF-scale ratings (EF0–4), convective modes (discrete supercell and quasi-linear convective system), geographical regions, and housing unit densities (HUDs). Comparing the radar-estimated and damage-estimated tornado wind speeds, and corresponding EF- and F-scale ratings, is the primary focus of the ensuing analysis. Consistent with recent work, damage-estimated tornado wind speeds tend to be lower than radar-estimated near-surface wind speeds, especially for stronger tornadoes. Damage- and radar-estimated wind speed differences are not strongly related to the availability of damage indicators (as measured by HUD). While some relationship exists—particularly underestimates of peak wind speeds for strong–violent tornadoes in low HUD areas—the tendency of radar-based strong/violent tornado intensity estimates to be meaningfully higher than EF-scale-based damage estimates exists across the HUD spectrum. The legacy F-scale wind speed ranges may ultimately provide a better estimate of peak tornado wind speeds at 10–15 m AGL for strong–violent tornadoes and a better damage-based intensity rating for all tornadoes. These results are contextualized with regard to ongoing community efforts to improve tornado intensity estimation. Significance Statement Due to the numerous difficulties in collecting direct observations of tornado wind speeds, we compare methods that are used to estimate near-ground wind speeds using both radar measurements of wind speeds aloft and damage. Our results show that 1) the damage-estimated intensities of stronger tornadoes are more likely to be underestimates of true wind speeds than weaker tornadoes based on radar observations, 2) this bias is present for tornadoes that occur in more built-up areas as well as more sparsely populated ones, and 3) the legacy Fujita scale may provide better wind speed estimates in stronger tornadoes. These findings contribute to community-wide efforts to improve damage-based estimates of peak tornado wind speeds.

Contextualizing Polarimetric Retrievals of Boundary Layer Height Using State-of-the-Art Boundary Layer Profiling

Abstract Knowledge about the depth of the planetary boundary layer (PBL) is crucial for a variety of applications, but direct observations of PBL depth are spatiotemporally sparse. Recent studies have proposed using operational dual-polarization weather radars to observe the evolution of PBL depth by capitalizing on unique differential reflectivity (ZDR) signatures of Bragg scatter at the top of the PBL. While this approach appears promising and cost-effective, uncertainties remain about the representativeness of these estimates and how its efficacy may vary by geography and climatology. To address these outstanding uncertainties, this study compares collocated observations collected from two WSR-88D radars and two state-of-the-art mobile boundary layer profiling systems and evaluates the proposed methodology over the full diurnal cycle. Results indicate good overall correspondence between the profiling- and radar-based PBL depth estimates, with an abrupt divergence during the early evening transition and large discrepancies overnight. Relatively large root-mean-square-deviations (RMSDs) coupled with small biases match expectations when comparing spatially averaged data with point observations during PBL growth, which capture frequent fluctuations. A qualitative examination of the radar data reveals signatures of elevated residual layers, clouds, and ground clutter, all of which can obfuscate the desired surface-based PBL signal but which may have their own utility. The prominence of the Bragg scatter signal is found to be correlated with the observed moisture gradient at the top of the PBL, reflecting climatological variability that should be considered. These findings motivate further work to improve the automated detection of Bragg scatter layers from polarimetric radar data. Significance Statement Knowledge of the height of the planetary boundary layer matters for weather forecasting, air quality, and renewable energy production. Currently, boundary layer height measurements are taken at select locations twice a day. However, a method to use the existing national network of polarimetric weather radars for this purpose has been proposed. This work evaluates this method against specialized boundary layer measurements. The results show that the method is generally reliable during the daytime and could be used for a variety of applications including climatologies and model evaluation. There remain a number of situational caveats, including residual turbulence, clouds/precipitation, ground clutter, and certain meteorological environments, that may require modification of the approach and need to be considered in future work.

Analysis and Prediction of Summer Rainfall over Southwestern Utah

Abstract While many studies have examined intense rainfall and flash flooding during the North American monsoon (NAM) in Arizona, Nevada, and New Mexico, less attention has focused on the NAM’s extension into southwestern Utah. This study relates flash flood reports and Multi-Radar Multi-Sensor (MRMS) precipitation across southwestern Utah to atmospheric moisture content and instability analyses and forecasts from the High-Resolution Rapid Refresh (HRRR) model during the 2021–23 monsoon seasons. MRMS quantitative precipitation estimates over southwestern Utah during the summer depend largely on the areal coverage from the KICX WSR-88D radar near Cedar City, Utah. Those estimates are generally consistent with the limited number of precipitation gauge reports in the region except at extended distances from the radar. A strong relationship is evident between days with widespread precipitation and afternoons with above-average precipitable water (PWAT) and convective available potential energy (CAPE) estimated from HRRR analyses across the region. Time-lagged ensembles of HRRR forecasts (initialization times from 0300 to 0600 UTC) that are 13–18 h prior to the afternoon period when convection is initiating (1800–2100 UTC) are useful for situational awareness of widespread precipitation events after adjusting for underprediction of afternoon CAPE. Improved skill is possible using random forest classification relying only on PWAT and CAPE to predict days experiencing excessive (upper quartile) precipitation. Such HRRR predictions may be useful for forecasters at the Salt Lake City National Weather Service Forecast Office to assist in issuing flash flood potential statements for visitors to national parks and other recreational areas in the region. Significance Statement Summer flash floods in southwestern Utah are a risk to area residents and millions of visitors annually to the region’s national parks, monuments, and recreational areas. The likelihood of flash floods within the region’s catchments depends on the intense afternoon and early evening convection initiated by lift and instability primarily due to terrain–flow interactions over elevated plateaus and mountains. Forecasts at lead times of 13–18 h of moisture and instability from the operational High-Resolution Rapid Refresh model have the potential to predict summer afternoons that are likely to have increased risks for higher rainfall amounts across southwestern Utah, although they are not expected to predict the likelihood of flash floods in any specific locale.

Meteorological Research Enabled by Rapid-Scan Radar Technology

Abstract The scientific community has long acknowledged the importance of high-temporal-resolution radar observations to advance science research and improve high-impact weather prediction. Development of innovative rapid-scan radar technologies over the past two decades has enabled radar volume scans of 10–60 s compared to 3–5 min with traditional parabolic dish research radars and the WSR-88D radar network. This review examines the impact of rapid-scan radar technology, defined as radars collecting volume scans in 1 min or less, on atmospheric science research spanning different subdisciplines and evaluates the strengths and weaknesses of the use of rapid-scan radars. In particular, a significant body of literature has accumulated for tornado and severe thunderstorm research and forecasting applications, in addition to a growing number of studies of convection. Convection research has benefited substantially from more synchronous vertical views, but could benefit more substantially by leveraging multi-Doppler wind retrievals and complementary in situ and remote sensors. In addition, several years of forecast evaluation studies are synthesized from radar testbed experiments, and the benefits of assimilating rapid-scan radar observations are analyzed. Although the current body of literature reflects the considerable utility of rapid-scan radars to science research, a weakness is that limited advancements in understanding of the physical mechanisms behind observed features have been enabled. There is considerable opportunity to bridge the gap in physical understanding with the current technology using coordinated efforts to include rapid-scan radars in field campaigns and expanding the breadth of meteorological phenomena studied. Significance Statement Recently developed rapid-scan radar technologies, capable of collecting volumetric (i.e., three-dimensional) measurements in 10–60 s, have improved temporal sampling of weather phenomena. This review examines the impact of these radar observations from the past two decades on science research and emerging operational capabilities. Substantial breadth and impact of research is evident for tornado research and forecasting applications, in addition to documentation of other rapidly evolving phenomena associated with deep convection, such as tornadoes, hail, lightning, and tropical cyclones. This review identifies the strengths and weaknesses of how these radars have been used in scientific research to inform future studies, emerging from the increasing availability and capability of rapid-scan radars. In addition, this review synthesizes research that can benefit future operational radar decisions.

A Kinematic Modeling Study of the Reorganization of Snowfall between Cloud-Top Generating Cells and Low-Level Snowbands in Midlatitude Winter Storms

Abstract This paper explores whether particles within uniformly spaced generating cells falling at terminal velocity within observed 2D wind fields and idealized deformation flow beneath cloud top can be reorganized consistent with the presence of single and multibanded structures present on WSR-88D radars. In the first experiment, two-dimensional wind fields, calculated along cross sections normal to the long axis of snowbands observed during three northeast U.S. winter storms, were taken from the initialization of the High-Resolution Rapid Refresh model. This experiment demonstrated that the greater the residence time of the particles in each of the three storms, the greater particle reorganization occurred. For experiments with longer residence times, increases in particle concentrations were nearly or directly collocated with reflectivity bands. For experiments with shorter residence times, particle reorganization still conformed to the band features but with less concentration enhancement. This experiment demonstrates that the combination of long particle residence time and net convergent cross-sectional flow through the cloud depth is sufficient to reorganize particles into locations consistent with precipitation bands. Increased concentrations of ice particles can then contribute, along with any dynamic forcing, to the low-level reflectivity bands seen on WSR-88D radars. In a second experiment, the impact of flow deformation on the reorganization of falling ice particles was investigated using an idealized kinematic model with stretching deformation flow of different depths and magnitudes. These experiments showed that deformation flow provides for little particle reorganization given typical deformation layer depths and magnitudes within the comma head of such storms. Significance Statement Past research with vertically pointing and scanning radars presents two different perspectives regarding snowfall organization in winter storms. Vertically pointing radars often observe cloud-top generating cells with precipitation fallstreaks descending into a broad stratiform echo at lower altitudes. In contrast, scanning radars often observe snowfall organized in quasi-linear bands. This work attempts to provide a connection between these two perspectives by examining how two-dimensional convergent and deformation flow occurring in winter storms can contribute to the reorganization of snowfall between cloud top and the ground.

Joint Collaboration on Comparing NOAA’s Ground-Based Weather Radar and NASA–JAXA’s Spaceborne Radar

Abstract The National Aeronautics and Space Administration (NASA) and National Oceanic and Atmospheric Administration (NOAA) have a long and successful history of weather radar research. The NOAA ground-based radars—WSR-88D network—provide nationwide precipitation observations and estimates with advanced polarimetric capability. As a counterpart, the NASA–JAXA spaceborne radar—the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM DPR)—has global coverage and higher vertical resolution than ground-based radars. While significant advances from both NOAA’s WSR-88D network and NASA–JAXA’s spaceborne radar DPR have been made, no systematic comparisons between the WSR-88D network and the DPR have been done. This study for the first time generates nationwide comprehensive comparisons at 136 WSR-88D radar sites from 2014 to 2020. Systematic differences in reflectivity are found, with ground radar reflectivity on average 2.4 dB smaller than that of the DPR (DPR version 6). This research found the discrepancies between WSR-88D and DPR arise from different calibration standards, signal attenuation correction, and differences in the ground and spaceborne scattering volumes. The recently updated DPR version 7 product improves rain detection and attenuation corrections, effectively reducing the overall average WSR-88D and DPR reflectivity differences to 1.0 dB. The goal of this study is to examine the systematic differences of radar reflectivity between the NOAA WSR-88D network and the NASA–JAXA DPR and to draw attention to radar-application users in recognizing their differences. Further investigation into understanding and alleviating the systematic bias between the two platforms is needed.

The 2021 Hazardous Weather Testbed Experimental Warning Program Radar Convective Applications Experiment: A Forecaster Evaluation of the Tornado Probability Algorithm and the New Mesocyclone Detection Algorithm

Abstract Developed as part of a larger effort by the National Weather Service (NWS) Radar Operations Center to modernize their suite of single-radar severe weather algorithms for the WSR-88D network, the Tornado Probability Algorithm (TORP) and the New Mesocyclone Detection Algorithm (NMDA) were evaluated by operational forecasters during the 2021 National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT) Experimental Warning Program Radar Convective Applications experiment. Both TORP and NMDA leverage new products and advances in radar technology to create rotation-based objects that interrogate single-radar data, providing important summary and trend information that aids forecasters in issuing time-critical and potentially life-saving weather products. Utilizing virtual resources like Google Workspace and cloud instances on Amazon Web Services, 18 forecasters from the NOAA/NWS and the U.S. Air Force participated remotely over three weeks during the spring of 2021, providing valuable feedback on the efficacy of the algorithms and their display in an operational warning environment, serving as a critical step in the research-to-operations process for the development of TORP and NMDA. This article will discuss the details of the virtual HWT experiment and the results of each algorithm’s evaluation during the testbed. Significance Statement Before transitioning newly developed radar-based severe weather applications to forecasting operations, an experiment simulating the use of these tools by end users issuing severe weather warnings is helpful to identify both how they are best utilized and address any needed improvements to increase their operational readiness. Conducted in 2021, this study describes the forecaster evaluation of the single-radar Tornado Probability Algorithm (TORP) and the New Mesocyclone Detection Algorithm (NMDA) in one of the first completely virtual Hazardous Weather Testbed (HWT) experiments. Participants stated both TORP and NMDA offered marked improvement over the currently available algorithms by helping the operational forecaster build their confidence when issuing severe weather warnings and increasing their overall situational awareness of storms within their domain.

Using Ground-Based Radar Observations to Evaluate Asymmetric Convection and Eyewall Dynamics during the Landfall of Hurricane Harvey (2017)

Abstract Asymmetric dynamics in tropical cyclones (TCs) are vital to understanding intensity change and convective distribution at landfall. The growth of barotropic–convective instability (e.g., mesovortices), vortical hot towers, and vortex Rossby waves (VRWs) have been considered through numerical modeling studies, often by mean–eddy partitioning of the tangential wind tendency. Unfortunately, few observational datasets exist that are sufficient for such study. A University of Oklahoma Shared Mobile Atmospheric Research and Teaching radar observed major Hurricane Harvey (2017) as it intensified just before landfall near Port Aransas, Texas. Combined with a coastal WSR-88D radar, dual-Doppler derived kinematic analyses were constructed every ∼6 min at 1-km spatial resolution during Hurricane Harvey’s landfall. In this study, observations of asymmetric mesovortices on the interior edge of Harvey’s eyewall are documented. The asymmetries promoted a dual exchange of vorticity in the TC eyewall and represent an example of an eddy mechanism of intensity change on various time scales. Considering the combined effects of resolvable asymmetries, we examine the change in the tangential wind as a function of mean–eddy kinematics before and after landfall. Before landfall, the low-level eddy contribution was positive to the low-level tangential wind tendency. Following landfall, the contribution from the low-level eddy became weakly positive to weakly negative. Finally, the evolution of some asymmetric features in Harvey’s eyewall are shown to manifest in a VRW-like response that initiates rainbands just outside of Harvey’s eyewall.

Extreme Convective Rainfall and Flooding from Winter Season Extratropical Cyclones in the Mid-Atlantic Region of the United States

Abstract Extreme rainfall from extratropical cyclones and the distinctive hydrology of the winter season both contribute to flood extremes in the Mid-Atlantic region. In this study, we examine extreme rainfall and flooding from a winter season extratropical cyclone that passed through the eastern United States on 24/25 February 2016. Extreme rainfall rates during the 24/25 February 2016 time period were produced by supercell thunderstorms; we identify supercells through local maxima in azimuthal shear fields computed from Doppler velocity measurements from WSR-88D radars. Rainfall rates approaching 250 mm h−1 from a long-lived supercell in New Jersey were measured by a Parsivel disdrometer. A distinctive element of the storm environment for the 24/25 February 2016 storm was elevated values of convective available potential energy (CAPE). We also examine the climatology of atmospheric rivers (ARs)—like the February 2016 storm—based on an identification and tracking algorithm that uses Twentieth Century Reanalysis fields for the 66-yr period from 1950 to 2015. Climatological analyses suggest that AR frequency is increasing over the Mid-Atlantic region. An increase in AR frequency, combined with increasing frequency of elevated CAPE during the winter season over the Mid-Atlantic region, could result in striking changes to the climatology of extreme floods.

The Tornado Probability Algorithm: A Probabilistic Machine Learning Tornadic Circulation Detection Algorithm

Abstract A new probabilistic tornado detection algorithm was developed to potentially replace the operational tornado detection algorithm (TDA) for the WSR-88D radar network. The tornado probability algorithm (TORP) uses a random forest machine learning technique to estimate a probability of tornado occurrence based on single-radar data, and is trained on 166 145 data points derived from 0.5°-tilt radar data and storm reports from 2011 to 2016, of which 10.4% are tornadic. A variety of performance evaluation metrics show a generally good model performance for discriminating between tornadic and nontornadic points. When using a 50% probability threshold to decide whether the model is predicting a tornado or not, the probability of detection and false alarm ratio are 57% and 50%, respectively, showing high skill by several metrics and vastly outperforming the TDA. The model weaknesses include false alarms associated with poor-quality radial velocity data and greatly reduced performance when used in the western United States. Overall, TORP can provide real-time guidance for tornado warning decisions, which can increase forecaster confidence and encourage swift decision-making. It has the ability to condense a multitude of radar data into a concise object-based information readout that can be displayed in visualization software used by the National Weather Service, core partners, and researchers. Significance Statement This study describes the tornado probability algorithm (TORP) and its performance. Operational forecasters can use TORP as real-time guidance when issuing tornado warnings, causing increased confidence in warning decisions, which in turn can extend tornado warning lead times.

Simultaneous Assimilation of Planetary Boundary Layer Observations from Radar and All-Sky Satellite Observations to Improve Forecasts of Convection Initiation

Abstract Accurate predictions of the location and timing of convection initiation (CI) remain a challenge, even in high-resolution convection allowing models (CAMs). Many of the processes necessary for daytime CI are rooted in the planetary boundary layer (PBL), which numerical models struggle to accurately predict. To improve ensemble forecasts of the PBL and subsequent CI forecasts in CAM ensembles, we explore the use of underused data from both the GOES-16 satellite and the national network of WSR-88D radars. The GOES-16 satellite provides observations of brightness temperature (BT) to better analyze cloud structures, while the WSR-88D radars provide PBL height estimates and clear-air radial wind velocity observations to better analyze PBL structures. The CAM uses the Advanced Research Weather Research and Forecasting (WRF-ARW) model at 3-km horizontal grid spacing. The ensemble consists of 40 members and observations are assimilated using the Gridpoint Statistical Interpolation (GSI) Ensemble Kalman Filter (EnKF) system. To evaluate the influence of each observation type on CI, conventional, WSR-88D, and GOES-16 observations are assimilated separately and jointly over a 4-h period and the resulting ensemble analyses and forecasts are compared with available observations for a CI event on 18 May 2018. Results show that the addition of the WSR-88D and GOES-16 observations improves the CI forecasts out several hours in terms of timing and location for this case.

Horus—A Fully Digital Polarimetric Phased Array Radar for Next-Generation Weather Observations

Weather radars have become indispensable to meteorologists and the general public for both understanding and awareness of high-impact weather events and as part of the operational warning infrastructure. In the U.S., the operational weather radar network is composed of approximately 160 WSR-88D radars, which are S-band, dish-based, polarimetric Doppler radars. This work reports on the development of a fully digital phased array weather radar that is being used to assess the potential of such technology as a replacement for the WSR-88D radars. The “Horus” radar is a truck-based, S-band, fully digital polarimetric phased array radar. Fully digital systems hold promise for meeting some of the greatest technical challenges facing the meteorological community, such as the effective integration of dual-polarization capability with phased array beam agility. This paper describes the fully digital Horus phased array weather radar that was recently completed by the Advanced Radar Research Center (ARRC) at the University of Oklahoma (OU). An overview of the advantages and challenges facing fully digital arrays for weather observations is provided along with potential mitigation strategies. Initial weather observations with Horus are given with the goal of assessing the radar scanning capabilities and most importantly the polarimetric quality. Finally, a vision for the future of next-generation weather radar operations is given with an eye toward leveraging the scalable design of Horus for high-resolution weather observations.

Range Dependence of Polarimetric Radar Estimates for Extreme Flood-Producing Rainfall in Urban Watersheds

Abstract We examine polarimetric rainfall estimates of extreme rainfall through intercomparisons of radar rainfall estimates with rainfall observations from a dense network of rain gauges in Kansas City. The setting provides unique capabilities for examining range dependence in polarimetric rainfall estimates due to the overlapping coverage of the Kansas City, Missouri, and Topeka, Kansas, WSR-88D radars. We focus on polarimetric measurements of specific differential phase shift, KDP, for estimating extreme rainfall. Gauge–radar intercomparisons from the “close-range” Kansas City radar and from the “far-range” Topeka radar show that KDP can provide major improvements in estimating extreme rainfall, but the advantages of KDP rainfall estimates diminish with range. Storm-to-storm variability of multiplicative bias remains an important issue for polarimetric rainfall estimates; variability in bias is comparable at both close and far range from the radar. “Conditional bias,” in which peak radar rainfall estimates are lower than rain gauge observations, is a systematic feature of polarimetric rainfall estimates, but is more severe at far range. The Kansas City region has experienced record flooding in urban watersheds since the polarimetric upgrade of the Kansas City and Topeka radars in 2012. Polarimetric rainfall estimates from the far-range Topeka radar provide useful quantitative information on basin-average rainfall, but the ability to resolve spatial variation of the most extreme rain rates diminishes significantly with range from the radar.

An Analysis of 2016–18 Tornadoes and National Weather Service Tornado Warnings across the Contiguous United States

AbstractPrevious work has considered tornado occurrence with respect to radar data, both WSR-88D and mobile research radars, and a few studies have examined techniques to potentially improve tornado warning performance. To date, though, there has been little work focusing on systematic, large-sample evaluation of National Weather Service (NWS) tornado warnings with respect to radar-observable quantities and the near-storm environment. In this work, three full years (2016–18) of NWS tornado warnings across the contiguous United States were examined, in conjunction with supporting data in the few minutes preceding warning issuance, or tornado formation in the case of missed events. The investigation herein examines WSR-88D and Storm Prediction Center (SPC) mesoanalysis data associated with these tornado warnings with comparisons made to the current Warning Decision Training Division (WDTD) guidance. Combining low-level rotational velocity and the significant tornado parameter (STP), as used in prior work, shows promise as a means to estimate tornado warning performance, as well as relative changes in performance as criteria thresholds vary. For example, low-level rotational velocity peaking in excess of 30 kt (15 m s−1), in a near-storm environment, which is not prohibitive for tornadoes (STP > 0), results in an increased probability of detection and reduced false alarms compared to observed NWS tornado warning metrics. Tornado warning false alarms can also be reduced through limiting warnings with weak (<30 kt), broad (>1 n mi; 1 n mi = 1.852 km) circulations in a poor (STP = 0) environment, careful elimination of velocity data artifacts like sidelobe contamination, and through greater scrutiny of human-based tornado reports in otherwise questionable scenarios.

Lagrangian Integro-Difference Equation Model for Precipitation Nowcasting

Abstract Delivering reliable nowcasts (short-range forecasts) of severe rainfall and the resulting flash floods is important in densely populated urban areas. The conventional method is advection-based extrapolation of radar echoes. However, during rapidly evolving convective rainfall this so-called Lagrangian persistence (LP) approach is limited to deterministic and very short-range nowcasts. To address these limitations in the 1-h time range, a novel extension of LP, called Lagrangian Integro-Difference equation model with Autoregression (LINDA), is proposed. The model consists of five components: 1) identification of rain cells, 2) advection, 3) autoregressive process describing growth and decay of the cells, 4) convolution describing loss of predictability at small scales, and 5) stochastic perturbations to simulate forecast uncertainty. Advection is separated from the other components that are applied in the Lagrangian coordinates. The reliability of LINDA is evaluated using the NEXRAD WSR-88D radar that covers the Dallas–Fort Worth metropolitan area, as well as the NEXRAD mosaic covering the continental United States. This is done with two different configurations: LINDA-D for deterministic and LINDA-P for probabilistic nowcasts. The validation dataset consists of 11 rainfall events during 2018–20. For predicting moderate to heavy rainfall (5–20 mm h−1), LINDA outperforms the previously proposed LP-based approaches. The most significant improvement is seen for the ETS and POD statistics with the 5 mm h−1 threshold. For 30-min nowcasts, they show 15% and 16% increases, respectively, to the second-best method and 48% and 34% increases compared to LP. For the 5 mm h−1 threshold, the increase in the relative operating characteristic (ROC) skill score of 30-min nowcasts from the second-best method is 10%. Significance Statement Delivering reliable forecasts of severe rainfall for the next few hours has a major societal importance. This is particularly true for densely populated urban areas, where flash floods can cause property damage and loss of lives. Such forecasts are conventionally produced by direct extrapolation of weather radar measurements. However, for intense localized rainfall this approach has low prediction ability beyond 30 min. To extend this limit, we propose a novel method that combines machine vision with a statistical model for growth and decay of rainfall. The method is designed for predicting highly localized rain cells and bands. In addition, a stochastic extension for producing probabilistic forecasts is developed. Using several verification metrics, we demonstrate that for predicting moderate to heavy rainfall (5–20 mm h−1), the proposed method has significantly improved forecast skill compared to the reference methods. The evaluation is done by using the NEXRAD WSR-88D that covers the Dallas–Fort Worth urban metroplex with a population of over 7 million. Demonstration of the applicability of LINDA in a larger domain is done by using the NEXRAD radar network that covers the continental United States.

Flood Impacts on Critical Infrastructure in a Coastal Floodplain in Western Puerto Rico during Hurricane María

Flooding during extreme weather events damages critical infrastructure, property, and threatens lives. Hurricane María devastated Puerto Rico (PR) on 20 September 2017. Sixty-four deaths were directly attributable to the flooding. This paper describes the development of a hydrologic model using the Gridded Surface Subsurface Hydrologic Analysis (GSSHA), capable of simulating flood depth and extent for the Añasco coastal flood plain in Western PR. The purpose of the study was to develop a numerical model to simulate flooding from extreme weather events and to evaluate the impacts on critical infrastructure and communities; Hurricane María is used as a case study. GSSHA was calibrated for Irma, a Category 3 hurricane, which struck the northeastern corner of the island on 7 September 2017, two weeks before Hurricane María. The upper Añasco watershed was calibrated using United States Geological Survey (USGS) stream discharge data. The model was validated using a storm of similar magnitude on 11–13 December 2007. Owing to the damage sustained by PR’s WSR-88D weather radar during Hurricane María, rainfall was estimated in this study using the Weather Research Forecast (WRF) model. Flooding in the coastal floodplain during Hurricane María was simulated using three methods: (1) Use of observed discharge hydrograph from the upper watershed as an inflow boundary condition for the coastal floodplain area, along with the WRF rainfall in the coastal flood plain; (2) Use of WRF rainfall to simulate runoff in the upper watershed and coastal flood plain; and (3) Similar to approach (2), except the use of bias-corrected WRF rainfall. Flooding results were compared with forty-two values of flood depth obtained during face-to-face interviews with residents of the affected communities. Impacts on critical infrastructure (water, electric, and public schools) were evaluated, assuming any structure exposed to 20 cm or more of flooding would sustain damage. Calibration equations were also used to improve flood depth estimates. Our model included the influence of storm surge, which we found to have a minimal effect on flood depths within the study area. Water infrastructure was more severely impacted by flooding than electrical infrastructure. From these findings, we conclude that the model developed in this study can be used with sufficient accuracy to identify infrastructure affected by future flooding events.

Effect of Precipitation Sampling Error on Flash Flood Monitoring and Prediction: Anticipating Operational Rapid-Update Polarimetric Weather Radars

AbstractQuantitative precipitation estimates (QPE) at high spatiotemporal resolution are essential for flash flood forecasting, especially in urban environments and headwater areas. An accurate quantification of precipitation is directly related to the temporal and spatial sampling of the precipitation system. The advent of phased array radar (PAR) technology, a potential next-generation weather radar, can provide updates that are at least 4-5 times faster than the conventional WSR-88D scanning rate. In this study, data collected by the KOUN WSR-88D radar with ~1 minute temporal resolution is used as an approximation of data that a future PAR system could provide to force the Ensemble Framework for Flash Flood Forecasting (EF5) hydrologic model. To assess the effect of errors resulting from temporal and spatial sampling of precipitation on flash flood warnings, KOUN precipitation data (1-km/1-min) is used to generate precipitation products at other spatial/temporal resolutions commonly used in hydrologic models, such as those provided by conventional WSR-88D radar (1-km/5-min), spaced-based observations (10-km/30-min), and hourly rainfall products (1-km/60-min). The effect of precipitation sampling errors on flash flood warnings are then examined and quantified by using discharge simulated from KOUN (1-km/1-min) as truth to assess simulations conducted using other generated coarser spatial/temporal resolutions of other precipitation products. Our results show that: 1) observations with coarse spatial and temporal sampling can cause large errors in quantification of the amount, intensity, and distribution of precipitation, 2) time series of precipitation products show that QPE peak values decrease as the temporal resolution gets coarser, and 3) the effect of precipitation sampling error on flash flood forecasting is large in headwater areas and decrease quickly as drainage area increases.

PyDDA: A Pythonic Direct Data Assimilation Framework for Wind Retrievals

This software assimilates data from an arbitrary number of weather radars together with other spatial wind fields (eg numerical weather forecasting model data) in order to retrieve high resolution three dimensional wind fields. PyDDA uses NumPy and SciPy’s optimization techniques combined with the Python Atmospheric Radiation Measurement (ARM) Radar Toolkit (Py-ART) in order to create wind fields using the 3D variational technique (3DVAR). PyDDA is hosted and distributed on GitHub at https://github.com/openradar/PyDDA . PyDDA has the potential to be used by the atmospheric science community to develop high resolution wind retrievals from radar networks. These retrievals can be used for the evaluation of numerical weather forecasting models and plume modelling. This paper shows how wind fields from 2 NEXt generation RADar (NEXRAD) WSR-88D radars and the High Resolution Rapid Refresh can be assimilated together using PyDDA to create a high resolution wind field inside Hurricane Florence. Funding statement: The development of this software is supported by the Climate Model Development and Validation (CMDV) activity which is funded by the Office of Biological and Environmental Research in the US Department of Energy Office of Science.

Infrasound characteristics of nontornadic and tornadic thunderstorms using high-resolution simulations

There has been increased interest in improving severe weather detection by supplementing NOAA's WSR-88D radar network with an infrasound observation network, which may be able to detect distinct sub-audible signatures from tornadic thunderstorms. While there is evidence that tornadic thunderstorms exhibit observable infrasound signals, what is not well-understood is whether these infrasound signals are unique to tornadic storms compared to nontornadic storms or whether there is any useful signal prior to tornadogenesis (which would be most relevant to NWS forecasters). In this presentation, we will present the results of high-resolution simulations of severe thunderstorms, specifically tailored to represent acoustic wave propagation with frequencies of 0.1 to 1 Hz. The spectral analysis of pressure perturbations generated by a nontornadic and tornadic supercell thunderstorm will be compared against each other. Two additional sensitivity tests that employ different microphysics schemes (i.e., the representation of precipitation within the storm) will be discussed, since prior work has indicated that the melting of precipitation is a dominant contributor to infrasound generation in thunderstorms. So far, preliminary results suggest that there are no discernible differences in infrasound observed in the vicinity of tornadic versus nontornadic supercell thunderstorms prior to tornadogenesis.