Updraft Helicity Research Articles

Use of a Reflectivity Operator Based on Double-Moment Thompson Microphysics for Direct Assimilation of Radar Reflectivity in GSI-Based Hybrid En3DVar

Abstract The assimilation of reflectivity (Z) within 3DVar or hybrid ensemble-3DVar (En3DVar) requires the adjoint of the Z observation operator. With the 3DVar or En3DVar method, previous studies often use Z operators consistent with a single-moment microphysics scheme even when the forecast model uses a double-moment scheme. As such, only the mixing ratios of hydrometeors are directly updated by the data assimilation (DA) system, leading to inconsistency between the analyzed microphysics state variables and the microphysics scheme in the prediction model. In this study, we formulated a Z operator consistent with the double-moment Thompson microphysics used in the numerical integrations; in the operator the snow and graupel reflectivity components are simplified using functions fitted to T-matrix simulation results. This operator and its adjoint are implemented within the GSI hybrid En3DVar DA system to enable direct assimilation of Z with a consistent operator. The impacts of this new operator on convective storm analysis through DA cycles, and on the ensuing 3-h forecasts are first examined in detail for a tornado outbreak case of 16 May 2017 in Texas and Oklahoma, and then for five additional thunderstorm cases. Forecast reflectivity, hourly precipitation, and updraft helicity tracks are subjectively evaluated, while neighborhood ETSs and performance diagrams are examined for reflectivity and/or precipitation. Compared to experiments using a Z operator consistent with a single-moment microphysics scheme, the Z operator consistent with double-moment Thompson microphysics used in the forecast model produces better forecasts of reflectivity, hourly precipitation, and updraft helicity tracks with smaller biases, and the improvement is somewhat larger for a higher Z threshold.

Exploring the Usefulness of Downscaling Free Forecasts from the Warn-on-Forecast System

Abstract The National Severe Storms Laboratory (NSSL) Warn-on-Forecast System (WoFS) is an experimental real-time rapidly updating convection-allowing ensemble that provides probabilistic short-term thunderstorm forecasts. This study evaluates the impacts of reducing the forecast model horizontal grid spacing Δx from 3 to 1.5 km on the WoFS deterministic and probabilistic forecast skill, using 11 case days selected from the 2020 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiment (SFE). Verification methods include (i) subjective forecaster impressions; (ii) a deterministic object-based technique that identifies forecast reflectivity and rotation track storm objects as contiguous local maxima in the composite reflectivity and updraft helicity fields, respectively, and matches them to observed storm objects; and (iii) a recently developed algorithm that matches observed mesocyclones to mesocyclone probability swath objects constructed from the full ensemble of rotation track objects. Reducing Δx fails to systematically improve deterministic skill in forecasting reflectivity object occurrence, as measured by critical success index (CSIDET), a metric that incorporates both probability of detection (PODDET) and false alarm ratio (FARDET). However, compared to the Δx = 3 km configuration, the Δx = 1.5 km WoFS shows improved midlevel mesocyclone detection, as evidenced by its statistically significant (i) higher CSIDET for deterministic midlevel rotation track objects and (ii) higher normalized area under the performance diagram curve (NAUPDC) score for probability swath objects. Comparison between Δx = 3 km and Δx = 1.5 km reflectivity object properties reveals that the latter have 30% stronger mean updraft speeds, 17% stronger median 80-m winds, 67% larger median hail diameter, and 28% higher median near-storm-maximum 0–3-km storm-relative helicity.

High Resolution Real-data WRF Modeling and Verification of Tropical Cyclone Tornadoes Associated with Hurricane Ivan 2004

This study used high-resolution real-data WRF simulations of Hurricane Ivan (2004) to document the structure of potentially tornadic supercells embedded in tropical cyclone (TC) rainbands. The simulated TC track and intensity matched well with observations post landfall, and the simulated TC structure closely replicated the observed shape and rainbands, as evident by an assessment of observed composite and simulated radar reflectivity. TC tornado surrogates (TCT-Ss) were identified and calibrated using thresholds based on percentile values of maximum updraft helicity (UHmax) and simulated radar reflectivity. Although the magnitude of UHmax generally decreased as the simulated TC moved inland, sensitivity testing revealed that a threshold based on the 99.95% percentile value of UHmax achieved optimal TCT-S coverage and agreement with observed TC tornado (TCT) tracks on domains with 3-km and 1-km grid spacings. Three cells were identified at three different stages of the TC inland evolution and were found to have hook appendages resembling supercells found in midlatitudes. The cells produced storm tops of 13 km, with rotating cores with depths of 4 km above ground level. Although results in this study are unique to Hurricane Ivan, the authors believe the use of convection-allowing models and UH-based surrogate methodologies can be applied to other TC cases.

Relationship of Convection Initiation and Subsequent Storm Strength to Ensemble Simulated Environmental Conditions during IOP3b of VORTEX Southeast 2017

AbstractA 50-member convection-allowing ensemble was used to examine environmental factors influencing afternoon convection initiation (CI) and subsequent severe weather on 5 April 2017 during intensive observing period (IOP) 3b of the Verification of the Origins of Rotation in Tornadoes Experiment in the Southeast (VORTEX-SE). This case produced several weak tornadoes (rated EF1 or less), and numerous reports of significant hail (diameter ≥ 2 in.; ≥~5 cm), ahead of an eastward-moving surface cold front over eastern Alabama and southern Tennessee. Both observed and simulated CI was facilitated by mesoscale lower-tropospheric ascent maximized several tens of kilometers ahead of the cold-frontal position, and the simulated mesoscale ascent was linked to surface frontogenesis in the ensemble mean. Simulated maximum 2–5 km AGL updraft helicity (UHmax) was used as a proxy for severe-weather-producing mesocyclones, and considerable variability in UHmax occurred among the ensemble members. Ensemble members with UHmax > 100 m2 s−2 had stronger mesoscale ascent than in members with UHmax < 75 m2 s−2, which facilitated timelier CI by producing greater adiabatic cooling and moisture increases above the PBL. After CI, storms in the larger UHmax members moved northeastward toward a mesoscale region with larger convective available potential energy (CAPE) than in smaller UHmax members. The CAPE differences among members were influenced by differences in the location of an antecedent mesoscale convective system, which had a thermodynamically stabilizing influence on the environment toward which storms were moving. Despite providing good overall guidance, the model ensemble overpredicted severe weather likelihoods in northeastern Alabama, where comparisons with VORTEX-SE soundings revealed a positive CAPE bias.

A Benchmark to Test Generalization Capabilities of Deep Learning Methods to Classify Severe Convective Storms in a Changing Climate

AbstractThis is a test case study assessing the ability of deep learning methods to generalize to a future climate (end of 21st century) when trained to classify thunderstorms in model output representative of the present‐day climate. A convolutional neural network (CNN) was trained to classify strongly rotating thunderstorms from a current climate created using the Weather Research and Forecasting model at high‐resolution, then evaluated against thunderstorms from a future climate and found to perform with skill and comparatively in both climates. Despite training with labels derived from a threshold value of a severe thunderstorm diagnostic (updraft helicity), which was not used as an input attribute, the CNN learned physical characteristics of organized convection and environments that are not captured by the diagnostic heuristic. Physical features were not prescribed but rather learned from the data, such as the importance of dry air at mid‐levels for intense thunderstorm development when low‐level moisture is present (i.e., convective available potential energy). Explanation techniques also revealed that thunderstorms classified as strongly rotating are associated with learned rotation signatures. Results show that the creation of synthetic data with ground truth is a viable alternative to human‐labeled data and that a CNN is able to generalize a target using learned features that would be difficult to encode due to spatial complexity. Most importantly, results from this study show that deep learning is capable of generalizing to future climate extremes and can exhibit out‐of‐sample robustness with hyperparameter tuning in certain applications.

Anthropogenic Influences on Tornadic Storms

AbstractThe impact of climate change on severe storms and tornadoes remains uncertain, largely owing to inconsistencies in observational data and limitations of climate models. We performed ensembles of convection-permitting climate model simulations to examine how three tornadic storms would change if similar events were to occur in pre-industrial and future climates. The choice of events includes winter, nocturnal, and spring tornadic storms to provide insight into how the timing and seasonality of storms may affect their response to climate change. Updraft helicity (UH), convective available potential energy (CAPE), storm relative helicity (SRH), and convective inhibition (CIN) were used to determine the favorability for the three tornadic storm events in the different climate states. We found that from the pre-industrial to present, the potential for tornadic storms decreased in the winter event and increased in the nocturnal and spring events. With future climate change, the potential for tornadic storms increased in the winter and nocturnal events in association with increased CAPE, and decreased in the spring event despite greater CAPE.

Python-Based Supercell Tracking for Coarse Temporal and Spatial Resolution Numerical Model Simulations

AbstractDeep convective thunderstorm tracking methodologies and software have become useful and necessary tools across many applications, from nowcasting to model verification. Despite many available options, many of these pre-existing methods lack a customizable, fast, and flexible methodology that can track supercell thunderstorms within convective-allowing climate datasets with coarse temporal and spatial resolution. This project serves as one option to solve this issue via an all-in-one tracking methodology, built upon several open-source Python libraries, and designed to work with various temporal resolutions, including hourly. Unique to this approach is accounting for varying data availability of different model variables, while still sufficiently and accurately tracking specific convective features; in this case, supercells were the focus. To help distinguish supercells from ordinary cells, updraft helicity and other three-dimensional atmospheric data were incorporated into the tracking algorithm to confirm its supercellular status. Deviant motion from the mean wind was also used identify supercells. The tracking algorithm was tested and performed on a dynamically-downscaled regional climate model dataset with 4 km horizontal grid spacing. Each supercell was tracked for its entire lifetime over the course of 26 years of model output, resulting in a supercell climatology over the central United States. Due to the tracking configuration and dataset used, the tracking performs most consistently for long-lived and strong supercells compared to weak and short-lived supercells. This tracking methodology allows for customizable open-source tracking of supercells in any downscaled convective-allowing dataset, even with coarse temporal resolution.

A Progress Report on the Development of the High-Resolution Rapid Refresh Ensemble

AbstractThe High-Resolution Rapid Refresh Ensemble (HRRRE) is a 36-member ensemble analysis system with 9 forecast members that utilizes the Advanced Research version of the Weather Research and Forecasting (ARW-WRF) dynamic core and the physics suite from the operational Rapid Refresh/High-Resolution Rapid Refresh deterministic modeling system. A goal of HRRRE development is a system with sufficient spread among members, comparable in magnitude to the random error in the ensemble mean, to represent the range of possible future atmospheric states. HRRRE member diversity has traditionally been obtained by perturbing the initial and lateral boundary conditions of each member, but recent development has focused on implementing stochastic approaches in HRRRE to generate additional spread. These techniques were tested in retrospective experiments and in the May 2019 Hazardous Weather Testbed Spring Experiment (HWT-SE). Results show a 6%–25% increase in the ensemble spread in 2-m temperature, 2-m mixing ratio, and 10-m wind speed when stochastic parameter perturbations are used in HRRRE (HRRRE-SPP). Case studies from HWT-SE demonstrate that HRRRE-SPP performed similar to or better than the operational High-Resolution Ensemble Forecast system, version 2 (HREFv2), and the nonstochastic HRRRE. However, subjective evaluations provided by HWT-SE forecasters indicated that overall, HRRRE-SPP predicted lower probabilities of severe weather (using updraft helicity as a proxy) compared to HREFv2. A statistical analysis of the performance of HRRRE-SPP and HREFv2 from the 2019 summer convective season supports this claim, but also demonstrates that the two systems have similar reliability for prediction of severe weather using updraft helicity.

Influence of a portable near-surface observing network on experimental ensemble forecasts of deep convection hazards during VORTEX-SE

AbstractEnsemble forecasts are generated with and without the assimilation of near-surface observations from a portable, mesoscale network of StickNet platforms during the Verification and Origins of Rotation in Tornadoes EXperiment – Southeast (VORTEX-SE). Four VORTEX-SE intensive observing periods are selected to evaluate the impact of StickNet observations on forecasts and predictability of deep convection within the southeast United States. StickNet observations are assimilated with an experimental version of the HighResolution RapidRefresh Ensemble (HRRRE) in one experiment, and withheld in a control forecast experiment. Overall, StickNet observations are found to effectively reduce mesoscale analysis and forecast errors of temperature and dewpoint. Differences in ensemble analyses between the two parallel experiments are maximized near the StickNet array and then either propagate away with the mean low-level flow through the forecast period or remain quasi-stationary, reducing local analysis biases. Forecast errors of temperature and dewpoint exhibit periods of improvement and degradation relative to the control forecast, and error increases are largely driven on the storm scale. Convection predictability, measured through subjective evaluation and objective verification of forecast updraft helicity, is driven more by when forecasts are initialized (i.e., more data assimilation cycles with conventional observations) rather than the inclusion of StickNet observations in data assimilation. It is hypothesized that the full impact of assimilating these data is not realized in part due to poor sampling of forecast sensitive regions by the StickNet platforms, as identified through ensemble sensitivity analysis.

Controls of Quasi-Linear Convective System Tornado Intensity

AbstractAlthough tornadoes produced by quasi-linear convective systems (QLCSs) generally are weak and short lived, they have high societal impact due to their proclivity to develop over short time scales, within the cool season, and during nighttime hours. Precisely why they are weak and short lived is not well understood, although recent work suggests that QLCS updraft width may act as a limitation to tornado intensity. Herein, idealized simulations of tornadic QLCSs are performed with variations in hodograph shape and length as well as initiation mechanism to determine the controls of tornado intensity. Generally, the addition of hodograph curvature in these experiments results in stronger, longer-lived tornadic-like vortices (TLVs). A strong correlation between low-level mesocyclone width and TLV intensity is identified (R2 = 0.61), with a weaker correlation in the low-level updraft intensity (R2 = 0.41). The tilt and depth of the updraft are found to have little correlation to tornado intensity. Comparing QLCS and isolated supercell updrafts within these simulations, the QLCS updrafts are less persistent, with the standard deviations of low-level vertical velocity and updraft helicity approximately 48% and 117% greater, respectively. A forcing decomposition reveals that the QLCS cold pool plays a direct role in the development of the low-level updraft, providing the benefit of additional forcing for ascent while also having potentially deleterious effects on both the low-level updraft and near-surface rotation. The negative impact of the cold pool ultimately serves to limit the persistence of rotating updraft cores within the QLCS.

The Impact of Assimilating ZDR Observations on Storm-Scale Ensemble Forecasts of the 31 May 2013 Oklahoma Storm Event

AbstractAssimilation of dual-polarization (dual-pol) observations provides more accurate storm-scale analyses to initialize forecasts of severe convective thunderstorms. This study investigates the impact assimilating experimental sector-scan dual-pol observations has on storm-scale ensemble forecasts and how this impact changes over different data assimilation (DA) windows using the ensemble Kalman filter (EnKF). Ensemble forecasts are initialized after 30, 45, and 60 minutes of DA for two sets of experiments that assimilate either reflectivity and radial velocity only (EXPZ) or reflectivity and radial velocity plus differential reflectivity (EXPZZDR). This study uses the 31 May 2013 Oklahoma event which included multiple storms that produced tornadoes and severe hail, with focus placed on two storms that impacted El Reno and Stillwater during the event.The earliest initialized forecast of EXPZZDR better predicts the evolution of the El Reno storm than EXPZ, but the two sets of experiments become similar at subsequent forecast times. However, the later EXPZZDR forecasts of the Stillwater storm, which organized towards the end of the DA window, produce improved results compared to EXPZ, in which the storm is less intense and weakens. Evaluation of forecast products for supercell mesocyclones (updraft helicity [UH]) and hail show similar results with earlier EXPZZDR forecasts better predicting the UH swaths of the El Reno storm and later forecasts producing improved UH and hail swaths for the Stillwater storm. The results indicate that the assimilation of ZDR over fewer DA cycles can produce improved forecasts when DA windows sufficiently cover storms during their initial development and organization.

Use of Power Transform Mixing Ratios as Hydrometeor Control Variables for Direct Assimilation of Radar Reflectivity in GSI En3DVar and Tests with Five Convective Storm Cases

AbstractWhen directly assimilating radar data within a variational framework using hydrometeor mixing ratios (q) as control variables (CVq), the gradient of the cost function becomes extremely large when background mixing ratio is close to zero. This significantly slows down minimization convergence and makes the assimilation of radial velocity and other observations ineffective because of the dominance of the reflectivity observation term in the cost function gradient. Using logarithmic hydrometeor mixing ratios as control variables (CV logq) can alleviate the problem but the high nonlinearity of logarithmic transformation can introduce spurious analysis increments into mixing ratios. In this study, power transform of hydrometeors is proposed to form new control variables (CVpq) where the nonlinearity of transformation can be adjusted by a tuning exponent or power parameter p. The performance of assimilating radar data using CVpq is compared with those using CVq and CV logq for the analyses and forecasts of five convective storm cases from the spring of 2017. Results show that CVpq with p = 0.4 (CVpq0.4) gives the best reflectivity forecasts in terms of root-mean-square error and equitable threat score. Furthermore, CVpq0.4 has faster convergence of cost function minimization than CVq and produces less spurious analysis increment than CV logq. Compared to CVq and CV logq, CVpq0.4 has better skills of 0–3-h composite reflectivity forecasts, and the updraft helicity tracks for the 16 May 2017 Texas and Oklahoma tornado outbreak case are more consistent with observations when using CVpq0.4.

Toward the Improvement of High-Impact Probabilistic Forecasts with a Sensitivity-Based Convective-Scale Ensemble Subsetting Technique

AbstractEnsemble sensitivity analysis (ESA) is a useful and computationally inexpensive tool for analyzing how features in the flow at early forecast times affect different relevant forecast features later in the forecast. Given the frequency of observations measured between model initialization times that remain unused, ensemble sensitivity may be used to increase predictability and forecast accuracy through an objective ensemble subsetting technique. This technique identifies ensemble members with the smallest errors in regions of high sensitivity to produce a smaller, more accurate ensemble subset. Ensemble subsets can significantly reduce synoptic-scale forecast errors, but applying this strategy to convective-scale forecasts presents additional challenges. Objective verification of the sensitivity-based ensemble subsetting technique is conducted for ensemble forecasts of 2–5-km updraft helicity (UH) and simulated reflectivity. Many degrees of freedom are varied to identify the lead times, subset sizes, forecast thresholds, and atmospheric predictors that provide most forecast benefit. Subsets vastly reduce error of UH forecasts in an idealized framework but tend to degrade fractions skill scores and reliability in a real-world framework. Results reveal this discrepancy is a result of verifying probabilistic UH forecasts with storm-report-based observations, which effectively dampens technique performance. The potential of ensemble subsetting and likely other postprocessing techniques is limited by tuning UH forecasts to predict severe reports. Additional diagnostic ideas to improve postprocessing tool optimization for convection-allowing models are discussed.

Exploring Convection-Allowing Model Evaluation Strategies for Severe Local Storms Using the Finite-Volume Cubed-Sphere (FV3) Model Core

AbstractVerification methods for convection-allowing models (CAMs) should consider the finescale spatial and temporal detail provided by CAMs, and including both neighborhood and object-based methods can account for displaced features that may still provide useful information. This work explores both contingency table–based verification techniques and object-based verification techniques as they relate to forecasts of severe convection. Two key fields in severe weather forecasting are investigated: updraft helicity (UH) and simulated composite reflectivity. UH is used to generate severe weather probabilities called surrogate severe fields, which have two tunable parameters: the UH threshold and the smoothing level. Probabilities computed using the UH threshold and smoothing level that give the best area under the receiver operating curve result in very high probabilities, while optimizing the parameters based on the Brier score reliability component results in much lower probabilities. Subjective ratings from participants in the 2018 NOAA Hazardous Weather Testbed Spring Forecasting Experiment (SFE) provide a complementary evaluation source. This work compares the verification methodologies in the context of three CAMs using the Finite-Volume Cubed-Sphere Dynamical Core (FV3), which will be the foundation of the U.S. Unified Forecast System (UFS). Three agencies ran FV3-based CAMs during the five-week 2018 SFE. These FV3-based CAMs are verified alongside a current operational CAM, the High-Resolution Rapid Refresh version 3 (HRRRv3). The HRRR is planned to eventually use the FV3 dynamical core as part of the UFS; as such evaluations relative to current HRRR configurations are imperative to maintaining high forecast quality and informing future implementation decisions.

What Does a Convection-Allowing Ensemble of Opportunity Buy Us in Forecasting Thunderstorms?

AbstractThe High Resolution Ensemble Forecast v2.1 (HREFv2.1), an operational convection-allowing model (CAM) ensemble, is an “ensemble of opportunity” wherein forecasts from several independently designed deterministic CAMs are aggregated and postprocessed together. Multiple dimensions of diversity in the HREFv2.1 ensemble membership contribute to ensemble spread, including model core, physics parameterization schemes, initial conditions (ICs), and time lagging. In this study, HREFv2.1 forecasts are compared against the High Resolution Rapid Refresh Ensemble (HRRRE) and the Multiscale data Assimilation and Predictability (MAP) ensemble, two experimental CAM ensembles that ran during the 5-week Spring Forecasting Experiment (SFE) in spring 2018. The HRRRE and MAP are formally designed ensembles with spread achieved primarily through perturbed ICs. Verification in this study focuses on composite radar reflectivity and updraft helicity to assess ensemble performance in forecasting convective storms. The HREFv2.1 shows the highest overall skill for these forecasts, matching subjective real-time impressions from SFE participants. Analysis of the skill and variance of ensemble member forecasts suggests that the HREFv2.1 exhibits greater spread and more effectively samples model uncertainty than the HRRRE or MAP. These results imply that to optimize skill in forecasting convective storms at 1–2-day lead times, future CAM ensembles should employ either diverse membership designs or sophisticated perturbation schemes capable of representing model uncertainty with comparable efficacy.

Comparisons of Hybrid En3DVar with 3DVar and EnKF for Radar Data Assimilation: Tests with the 10 May 2010 Oklahoma Tornado Outbreak

AbstractIn this study, a hybrid En3DVar data assimilation (DA) scheme is compared with 3DVar, EnKF, and pure En3DVar for the assimilation of radar data in a real tornadic storm case. Results using hydrometeor mixing ratios (CVq) or logarithmic mixing ratios (CVlogq) as the control variables are compared in the variational DA framework. To address the lack of radial velocity impact issues when using CVq, a procedure that assimilates reflectivity and radial velocity data in two separate analysis passes is adopted. Comparisons are made in terms of the root-mean-square innovations (RMSIs) as well as the intensity and structure of the analyzed and forecast storms. For pure En3DVar that uses 100% ensemble covariance, CVlogq and CVq have similar RMSIs in the velocity analyses, but errors grow faster during forecasts when using CVlogq. Introducing static background error covariance at 5% in hybrid En3DVar (with CVlogq) significantly reduces the forecast error growth. Pure En3DVar produces more intense reflectivity analyses than EnKF that more closely match the observations. Hybrid En3DVar with 50% outperforms other weights in terms of the RMSIs and forecasts of updraft helicity and is thus used in the final comparison with 3DVar and EnKF. The hybrid En3DVar is found to outperform EnKF in better capturing the intensity and structure of the analyzed and forecast storms and outperform 3DVAR in better capturing the intensity and evolution of the rotating updraft.

The tornadoes of 30–31 May 2019 in south-Central Chile: Sensitivity to topography and SST

Tornadoes were reported in south-central Chile on 30 and 31 May 2019. To better understand the mechanisms that supported the organization and severity of the storms that generated them, a series of high-resolution sensitivity simulations were conducted using the Weather Research and Forecast (WRF) model. In addition to a control simulation, three simulations were run that successively reduced the topography to 50%, 25%, and 0% of original height; three more simulations were run that increased SST by 2 K everywhere, decreased it by 2 K everywhere, and set all SSTs greater than 16 °C to exactly 16 °C; finally, two more simulations were run to explore the combined effects of SST changes and lower topography.Results indicate that as topography was lowered, a strong northerly low-level jet along the upslope of the Andes weakened and broadened in both vertical and horizontal extent. Values of 0–6-km bulk shear and 0–3-km storm-relative helicity (SRH) over the regions where the tornadoes occurred diminished with successive reduction of the topography. As a result, simulated brightness temperatures were warmer, and swaths of updraft helicity were fewer and shorter. These results indicate that on 30–31 May 2019, flow blocking by the Andes topography generated mesoscale wind shear conditions that favored tornadoes. When SSTs were increased, instability (quantified by convective available potential energy, CAPE) also increased, although primarily offshore, and updraft helicity swath length increased, indicating a positive impact on simulated storm intensity. When SSTs were decreased, CAPE also decreased along with a decline in magnitude of updraft helicity. These results suggest that SSTs also impact storm severity via an influence on atmospheric instability. When the two factors were combined, warmer SSTs were not able to overcome a reduction in wind shear associated with a decrease in the topography. This suggests that in south-central Chile, wind shear is more important than CAPE for tornadic thunderstorms, as has been found for other regions around the world).

Direct Assimilation of Radar Data With Ensemble Kalman Filter and Hybrid Ensemble‐Variational Method in the National Weather Service Operational Data Assimilation System GSI for the Stand‐Alone Regional FV3 Model at a Convection‐Allowing Resolution

AbstractCapabilities to directly assimilate radar radial velocity (Vr) and reflectivity (Z) data are implemented within the operational GSI data assimilation (DA) framework and coupled with the new stand‐alone regional (SAR) FV3 model. The effectiveness and performance of 3DVar, EnKF, and hybrid En3DVar methods are evaluated with a storm cluster over the U.S. Central Plains at 3‐km grid spacing. During the DA cycles, 3DVar analyses show better fit to Z observations but fastest error growth, while EnKF and pure En3DVar lead to smaller forecast errors. For Vr, EnKF outperforms other methods in both analysis and forecast. Good correspondence with tornado reports is obtained by most experiments for probabilistic forecast of updraft helicity (UH), except for 3DVar which shows insufficient confidence in certain regions. Overall, EnKF and hybrid En3DVar show best forecast skills in terms of composite reflectivity and UH. Tests with more cases are needed to draw more general conclusions, however.

A Comparison of Neural-Network and Surrogate-Severe Probabilistic Convective Hazard Guidance Derived from a Convection-Allowing Model

AbstractA feed-forward neural network (NN) was trained to produce gridded probabilistic convective hazard predictions over the contiguous United States. Input fields to the NN included 174 predictors, derived from 38 variables output by 497 convection-allowing model forecasts, with observed severe storm reports used for training and verification. These NN probability forecasts (NNPFs) were compared to surrogate-severe probability forecasts (SSPFs), generated by smoothing a field of surrogate reports derived with updraft helicity (UH). NNPFs and SSPFs were produced each forecast hour on an 80-km grid, with forecasts valid for the occurrence of any severe weather report within 40 or 120 km, and 2 h, of each 80-km grid box. NNPFs were superior to SSPFs, producing statistically significant improvements in forecast reliability and resolution. Additionally, NNPFs retained more large magnitude probabilities (>50%) compared to SSPFs since NNPFs did not use spatial smoothing, improving forecast sharpness. NNPFs were most skillful relative to SSPFs when predicting hazards on larger scales (e.g., 120 vs 40 km) and in situations where using UH was detrimental to forecast skill. These included model spinup, nocturnal periods, and regions and environments where supercells were less common, such as the western and eastern United States and high-shear, low-CAPE regimes. NNPFs trained with fewer predictors were more skillful than SSPFs, but not as skillful as the full-predictor NNPFs, with predictor importance being a function of forecast lead time. Placing NNPF skill in the context of existing baselines is a first step toward integrating machine learning–based forecasts into the operational forecasting process.

Impact of Assimilating Future Clear-Air Radial Velocity Observations from Phased-Array Radar on a Supercell Thunderstorm Forecast: An Observing System Simulation Experiment Study

Abstract Phased-array radar (PAR) technology offers the flexibility of sampling the storm and clear-air regions with different update times. As such, the radial velocity from clear-air regions, typically with a lower signal-to-noise ratio, can be measured more accurately. In this work, observing system simulation experiments are conducted to explore the potential value of assimilating clear-air radial velocity observations to improve numerical prediction of supercell thunderstorms. Synthetic PAR observations of a splitting supercell are assimilated at different life cycle stages using an ensemble Kalman filter. Results show that assimilating environmental clear-air radial velocity can reduce wind errors in the near-storm environment and within the precipitation region. Improvements in the forecast are seen at different stages, especially for the forecast after 30 min. After assimilating clear-air radial velocity observations, the probabilities of updraft helicity and precipitation within the corresponding swaths of the truth simulation increase up to 30%–40%. Additional diagnostics suggest that the more accurate track forecast, stronger vertical motion, and better-maintained supercell can be attributed to the better analysis and prediction of the mean environmental winds and linear and nonlinear dynamic forces. Consequently, assimilating clear-air radial velocity produces accurate storm structure (rotating updrafts), updraft size, and storm track, and improves the surface accumulated precipitation forecast. The performance of forecasts with a higher frequency of assimilating clear-air radial velocity does not show systematic improvement. These results highlight the potential of assimilating clear-air radial velocity observations to improve numerical weather prediction forecasts of supercell thunderstorms.