Abstract The deployment of a multistatic radar network in the Oklahoma City metropolitan area in Spring 2024 has allowed for unique observations of several severe weather events. This passive multistatic network operates in conjunction with the operational KTLX WSR‐88D, allowing for synchronous multi‐Doppler analyses. This work presents a case study of a tornadic mesovortex in central Oklahoma. Prior to tornadogenesis, a horizontal rotor circulation is tilted into the mesovortex, providing a pathway for an intense low‐level updraft to develop. The release of horizontal shearing instability beneath this updraft is shown to supply near‐surface vertical vorticity, which is then amplified by vertical stretching, which is followed by tornadogenesis. The ability to derive kinematic fields from a single operational radar represents a major expansion of the current observational capabilities of the WSR‐88D network.

The Warn-on-Forecast System (WoFS) is a regional, rapidly updating, ensemble data assimilation and prediction system designed to provide short-term probabilistic guidance of severe and hazardous weather, including individual thunderstorms. As with most convection-allowing modeling systems, WoFS occasionally produces forecasts of thunderstorms with storm motion biases, which can be caused by multiple sources of error within the data assimilation and forecast system. The storm motion biases lead to storm displacement errors during the forecasts resulting in increasingly worse forecasts. In this study, we investigate storm displacement errors in WoFS forecasts from cases in 2020-23 using an objectbased technique in a novel way to define and match WoFS and Multi-Radar Multi-Sensor (MRMS) reflectivity objects. The storm displacement mean absolute errors and location biases are grouped together by various attributes, including year, lead time, ensemble member, MRMS relative storm age, 850-300-hPa mean wind, and MRMS object mean intensity. Results from this investigation reveal storm displacement errors in WoFS forecasts generally have an eastward bias, grow the fastest within the first hour after forecast initialization, and are the smallest 1-3 h after a thunderstorm has been assimilated. By understanding and characterizing the storm displacement errors, WoFS developers will be able to focus attention on possible error sources and preventative measures to further improve WoFS, and NWS forecasters will be able to mentally account for the storm displacements errors when issuing forecast and warning products.

Forecasts from a 1-km horizontal grid spacing Warn-on-Forecast System (WoFS-1km) prototype are compared to a 3-km WoFS (WoFS-3km) for 23 days when high-impact events occurred in 2022 and 2023. Composite reflectivity forecasts are verified using object-based verification. The probability of detection (POD) is significantly higher in WoFS-1km than in WoFS-3km for observed objects less than 400 km(2) in area, and false alarm ratio (FAR) is significantly lower in WoFS-1km than in WoFS-3km for forecast objects less than 1500 km(2) in area. This higher POD in WoFS-1km is consistent for all lead times while lower FAR is most pronounced in the first 90-120 min of the forecast. It is shown that improvements in WoFS-1km POD relative to WoFS-3km POD is dependent on object shape as well as size, with significant improvement in WoFS-1km forecasts of storms with a minor axis length less than approximately 24 km. WoFS-1km is found to produce improved POD for area bins representing 67% of the total thunderstorm objects in the dataset. A case study example of WoFS-1km and WoFS-3km forecasts demonstrates the ability of WoFS-1km to better resolve a violent tornado-producing supercell occurring on 10 December 2021. The supercell's composite reflectivity object area was less than 400 km(2) for several hours, during which WoFS-1km produced improved predictions of the supercell track and maintenance. Once the storm surpassed this area threshold, WoFS-3km produced similar forecasts to WoFS-1km. The statistical findings and qualitative analysis demonstrate benefits of employing reduced horizontal grid spacing for short-term prediction of thunderstorms with convection-allowing models. Significance Statement: This study concludes that an ensemble data assimilation and modeling system with 1-km versus 3-km horizontal grid spacing better resolves smaller thunderstorms (<400 km(2)) while also reducing false alarms of both small and large thunderstorms (of up to 1500 km(2)). Better resolution of smaller thunderstorms is directly applicable to a high-impact severe event that produced multiple significant tornadoes.

This study examines the implications of using traditional local storm reports (LSRs) versus radar-derived Multi-Radar Multi-Sensor (MRMS) system maximum estimated size of hail (MESH) as classification target variables for training and evaluating machine learning (ML) models to predict severe hail events. Using input data from the NSSL Warn-on-Forecast System (WoFS), we explore how the LSR and MESH severe hail climatologies compare in WoFS and the variation in model performance with the choices of target variable for training and testing. Regardless of the training target variable, all ML models performed better when evaluated on MESH. The improved performance of the LSRtrained model on MESH was attributed to MESH better capturing nighttime events, which reduced spurious false alarms compared to evaluating LSRs only. However, the best model for a given target variable was the one trained on that target variable. For example, when evaluating LSRs, the LSR-trained model performed best. This has operational significance as MESH-trained models may underperform LSR-trained models if the target variable is LSRs. We attribute the better MESH scores to MESH being more spatially and temporally consistent with WoFS versus LSRs. Nevertheless, whether either approach better predicts severe hail occurrence is still to be determined. Last, combining MESH and LSRs did not significantly improve model performance, which may be attributed to the fact that both datasets have unique error sources that do not cancel out. Ultimately, the main goal of this study is to shed light on the broader implications of data choice in the training and verification of ML models.

The Warn-on-Forecast System (WoFS) is a convection-allowing ensemble that rapidly assimilates high-resolution radar, satellite, and other observational data to enhance forecasting capabilities at 0-6-h lead times. However, data latency and model spinup can reduce WoFS' utility, especially within the first hour after initialization. On the other hand, Probability of Severe (ProbSevere), version 2 (PS2), is a set of statistical models that considers radar, satellite, and numerical weather prediction environment data to provide skillful severe hail, wind, and tornado probabilities at 0-1-h lead times, updated every 2 min. To enable seamless, probabilistic severe weather hazard forecasts leveraging the strengths of both systems, a random forest (RF) algorithm is developed that considers predictors from both WoFS and PS2. Experiments are conducted using different combinations of predictors at various spatial radii and lead times, and predictor importance is assessed. RFs configured using predictors from both WoFS and PS2 are found to outperform RFs configured from only one system, with the greatest gains in forecast skill from the all-predictor RFs coming at lead times less than 90 min. PS2 (WoFS) predictors are identified as more important at earlier (later) lead times. Severe hail forecasts were the most skillful of the three hazards, followed by severe wind and tornadoes. This new algorithm shows how combining complementary datasets using machine learning can improve short-term severe hazard guidance. Upcoming work will report on the results from testing during real-time forecasting experiments. SIGNIFICANCE STATEMENT: A machine learning algorithm is used to combine two complimentary data sources for short-term severe weather forecasting. One is a high-resolution, rapidly updating numerical weather prediction model, while the other is a set of statistical models based on observations. The results show that combining these data sources results in more skillful forecasts than using either source alone. This algorithm will enhance short-term severe weather forecasting capabilities and provide insight into severe weather predictability at short lead times over local regions.

Multistatic weather radar systems have the potential to provide three-dimensional wind information from both operational and research radars, given that each radar provides a distinct Doppler velocity measurement. A prototype multistatic network consisting of two passive receivers and the nearby operational KTLX Weather Surveillance Radar-1988 Doppler (WSR-88D) has been deployed in the Oklahoma City metropolitan area. To achieve sufficiently precise Doppler frequency estimates while reducing system cost, transmitter/receiver synchronization is accomplished through direct-path measurements of the WSR-88D's sidelobe radiation. Several cases of observed severe convection are presented, including instances of quasi-linear convective systems (QLCSs) and supercells. Multi-Doppler retrievals performed with the multistatic data are shown to resolve important structures in the three-dimensional wind fields, including mesocyclones. Quantitative analysis and evaluation of the retrievals are performed by comparison with simultaneous multi-Doppler retrievals done with monostatic radar data only, along with direct numerical comparison to independent radial velocity measurements. For the case analyzed under this framework, the multistatic system yields a mean error of -1.3 m s(-1), compared to -2.8 m s(-1) for the monostatic system. In addition, retrieved vertical velocities from the multistatic system are compared to a locally deployed vertically pointing radar providing additional system validation and error quantification for the vertical velocity field, where the multistatic system provides a stark improvement in retrieved vertical velocity over the monostatic system. This relatively low-cost technology has the potential to significantly expand the observing capabilities of the operational WSR-88D SIGNIFICANCE STATEMENT: This study explores the performance of a novel, low-cost weather radar network in producing three-dimensional winds compared to conventional radar networks. Several cases of observed severe weather show that this novel system performs as well as or better than conventional radar systems. Given these results, it is suggested that this system could be adopted for operational use, as three-dimensional wind information is useful for severe weather forecasting.

This study compares real-time forecasts produced by the Warn-on-Forecast System (WoFS) and a hybrid ensemble and variational data assimilation and prediction system (WoF-Hybrid) for 31 events during 2021. Object-based verification is used to quantify and compare strengths and weaknesses of WoFS ensemble forecasts with 3-km horizontal grid spacing and WoF-Hybrid deterministic forecasts with 1.5-km horizontal grid spacing. The goal of such comparison is to provide evidence as to whether WoF-Hybrid has performance characteristics that complement or improve upon those of WoFS. Results indicate that both systems provide similar accuracy for timing and placement of thunderstorm objects identified using simulated reflectivity. WoF-Hybrid provides more accurate forecasts of updraft helicity tracks. Differences in forecast quality are case dependent; the largest difference in accuracy favoring WoF-Hybrid occurs in eight cases identified as "high-impact" by the quantity of National Weather Service Local Storm Reports, while WoFS performance is favored at short lead times for 10 "moderate-" and 13 "low-impact" events. WoF-Hybrid reflectivity objects are closer in size and location to observed objects. However, a higher thunderstorm overprediction bias is identified in WoF-Hybrid, particularly early in the forecast. Two severe weather events are selected for detailed investigation. In the case of 26 May, both systems had similar skill; however, for 10 December, WoF-Hybrid forecasts significantly outperformed WoFS forecasts. These results show improved performance for WoF-Hybrid over WoFS under certain regimes that warrants further investigation. To understand reasons for these differences will help incorporate higher-resolution modeling into Warn-on-Forecast systems.

The 2024 NOAA Hazardous Weather Testbed Spring Forecasting Experiment What: Over 160 forecasters and researchers convened in-person and virtually to engage in real-time severe weather forecasting and evaluation activities aimed at accelerating research-to-operations and informing NOAA's Unified Forecast System. Major emphases of SFE 2024 included 1) deterministic and ensemble components of the Rapid Refresh Forecast System, 2) the Model for Prediction Across Scales, 3) global artificial intelligence (AI)-based NWP emulators, 4) the Warn-on-Forecast System, and 5) innovative AI-based postprocessing strategies. When: 29 April-31 May 2024 Where: Norman, OK, and Online

In 2009, advancements in NWP and computing power inspired a vision to advance hazardous weather warnings from a warn-on-detection to a warn-on-forecast paradigm. This vision would require not only the prediction of individual thunderstorms and their attributes but the likelihood of their occurrence in time and space. During the last decade, the warn-on-forecast research team at the NOAA National Severe Storms Laboratory met this challenge through the research and development of 1) an ensemble of high-resolution convection-allowing models; 2) ensemble- and variational-based assimilation of weather radar, satellite, and conventional observations; and 3) unique postprocessing and verification techniques, culminating in the experimental Warn-on-Forecast System (WoFS). Since 2017, we have directly engaged users in the testing, evaluation, and visualization of this system to ensure that WoFS guidance is usable and useful to operational forecasters at NOAA national centers and local offices responsible for forecasting severe weather, tornadoes, and flash floods across the watch-to-warning continuum. Although an experimental WoFS is now a reality, we close by discussing many of the exciting opportunities remaining, including folding this system into the Unified Forecast System, transitioning WoFS into NWS operations, and pursuing next-decade science goals for further advancing storm-scale prediction. Significance Statement The purpose of this research is to develop an experimental prediction system that forecasts the probability for severe weather hazards associated with individual thunderstorms up to 6 h in advance. This capability is important because some people and organizations, like those living in mobile homes, caring for patients in hospitals, or managing large outdoor events, require extended lead time to protect themselves and others from potential severe weather hazards. Our results demonstrate a prediction system that enables forecasters, for the first time, to message probabilistic hazard information associated with individual severe storms between the watch-to-warning time frame within the United States.

Forecasters routinely calibrate their confidence in model forecasts. Ensembles inherently estimate forecast confidence but are often underdispersive, and ensemble spread does not strongly correlate with ensemble-mean error. The misalignment between ensemble spread and skill motivates new methods for "forecasting forecast skill" so that forecasters can better utilize ensemble guidance. We have trained logistic regression and random forest models to predict the skill of composite reflectivity forecasts from the NSSL Warn-on-Forecast System (WoFS), a 3-km ensemble that generates rapidly updating forecast guidance for 0-6-h lead times. The forecast skill predictions are valid at 1-, 2-, or 3-h lead times within localized regions determined by the observed storm locations at analysis time. We use WoFS analysis and forecast output and NSSL Multi-Radar/Multi-Sensor composite reflectivity for 106 cases from the 2017 to 2021 NOAA Hazardous Weather Testbed Spring Forecasting Experiments. We frame the prediction task as a multiclassification problem, where the forecast skill labels are determined by averaging the extended fraction skill scores (eFSSs) for several reflectivity thresholds and verification neighborhoods and then converting to one of three classes based on where the average eFSS ranks within the entire dataset: POOR (bottom 20%), FAIR (middle 60%), or GOOD (top 20%). Initial machine learning (ML) models are trained on 323 predictors; reducing to 10 or 15 predictors in the final models only modestly reduces skill. The final models substantially outperform carefully developed persistence-and spread-based models and are reasonably explainable. The results suggest that ML can be a valuable tool for guiding user confidence in convection-allowing (and larger-scale) ensemble forecasts. SIGNIFICANCE STATEMENT: Some numerical weather prediction (NWP) forecasts are more likely to verify than others. Forecasters often recognize situations where NWP output should be trusted more or less than usual, but objective methods for "forecasting forecast skill" are notably lacking for thunderstorm-scale models. Better estimates of forecast skill can benefit society through more accurate forecasts of high-impact weather. Machine learning (ML) provides a powerful framework for relating forecast skill to the characteristics of model forecasts and available observations over many previous cases. ML models can leverage these relationships to predict forecast skill for new cases in real time. We demonstrate the effectiveness of this approach to forecasting forecast skill using a cutting-edge thunderstorm prediction system and logistic regression and random forest models. Based on this success, we recommend the adoption of similar ML-based methods for other prediction models.

Quasi-linear convective systems (QLCSs) can produce multiple hazards (e.g., straight-line winds, flash flooding, and mesovortex tornadoes) that pose a significant threat to life and property, and are often difficult to accurately forecast. The NSSL Warn-on-Forecast System (WoFS) is a convection-allowing ensemble system developed to provide short-term, probabil-istic forecasting guidance for severe convective events. Examination of WoFS's capability to predict QLCSs has yet to be sys-tematically assessed across a large number of cases for 0-6-h forecast times. In this study, the quality of WoFS QLCS forecasts for 50 QLCS days occurring between 2017 and 2020 is evaluated using object-based verification techniques. First, a storm mode identification and classification algorithm is tuned to identify high-reflectivity, linear convective structures. The algorithm is used to identify convective line objects in WoFS forecasts and Multi-Radar Multi-Sensor system (MRMS) gridded observa-tions. WoFS QLCS objects are matched with MRMS observed objects to generate bulk verification statistics. Results suggest WoFS's QLCS forecasts are skillful with the 3-and 6-h forecasts having similar probability of detection and false alarm ratio values near 0.59 and 0.34, respectively. The WoFS objects are larger, more intense, and less eccentric than those in MRMS. A novel centerline analysis is performed to evaluate orientation, length, and tortuosity (i.e., curvature) differences, and spatial dis-placements between observed and predicted convective lines. While no systematic propagation biases are found, WoFS typi-cally has centerlines that are more tortuous and displaced to the northwest of MRMS centerlines, suggesting WoFS may be overforecasting the intensity of the QLCS's rear-inflow jet and northern bookend vortex. SIGNIFICANCE STATEMENT: Quasi-linear convective systems (QLCSs), also known as squall lines, can be very destructive to life and property as they produce multiple hazards such as hail, severe straight-line winds, flash flooding, and tornadoes that typically form quickly and may be difficult to observe on radar. These storms can occur year-round and have the propensity to develop overnight or into the early morning hours, potentially catching the public off-guard. An ensemble prediction system called the Warn-on-Forecast System (WoFS), created by the National Severe Storms Laboratory, has shown promise in accurately forecasting a variety of severe weather events. This research evaluates the quality of the WoFS's QLCS forecasts. Results show WoFS can accurately predict these systems for forecast times out to 6 h.

The increasing frequency of high-impact wildfires has led to an emphasis on improving forecasts of the conditions that are favorable for wildfire initiation and rapid spread. The key atmospheric forecast products currently used are derived from low-level humidity and wind speed. One product that has seen widespread use by the National Weather Service (NWS) in the southern plains is known as the red flag threat index (RFTI). RFTI represents an index ranging from 0 to 10 where larger values indicate a more critical threat for favorable wildfire conditions. The current RFTI is based on 2-m humidity and 6-m wind speed climatologies from surface measurement sites located within a NWS forecast area resulting in a product that differs somewhat from office to office. RFTI forecasts are also only available from existing numerical weather prediction systems such as the Texas Tech modeling system that do not output forecasts with the temporal resolution and latency to forecast rapidly evolving environmental conditions. To address these limitations, this work describes the creation of a grid-based RFTI using a 5-yr, high-resolution (3-km, hourly) reanalysis product known as the Real-Time Mesoscale Analysis (RTMA). Using RTMA data allows for continuous wind and humidity climatology fields to be developed for a larger W-CONUS domain, potentially expanding the use of RFTI. We combine these new climatologies with forecast output from the Warn-on-Forecast System (WoFS) which provides short-term (0-6 h) probabilistic forecasts of high-impact weather over a regional domain. RFTI forecasts from examples occurring in 2022 and 2024 will be discussed as well as its evaluation in the new Fire Weather Testbed.

The operational utility of the NOAA National Severe Storm Laboratory's storm -scale probabilistic Warn -on -Forecast System (WoFS) was examined across the watch -to -warning time frame in a virtual NOAA Hazardous Weather Testbed (HWT) experiment. Over four weeks, 16 NWS forecasters from local Weather Forecast Offices, the Storm Prediction Center, and the Weather Prediction Center participated in simulated forecasting tasks and focus groups. Bringing together multiple NWS entities to explore new guidance impacts on the broader forecast process is atypical of prior NOAA HWT experiments. This study therefore provides a framework for designing such a testbed experiment, including methodological and logistical considerations necessary to meet the needs of both local office and national center NWS participants. Furthermore, this study investigated two research questions: 1) How do forecasters envision WoFS guidance fitting into their existing forecast process? and 2) How could WoFS guidance be used most effectively across the current watch -to -warning forecast process? Content and thematic analyses were completed on flowcharts of operational workflows, real-time simulation interactions, and focus group activities and discussions. Participants reported numerous potential applications of WoFS, including improved coordination and consistency between local offices and national centers, enhanced hazard messaging, and improved operations planning. Challenges were also reported, including the knowledge and training required to incorporate WoFS guidance effectively and forecasters' trust in new guidance and openness to change. The solutions identified to these challenges will take WoFS one step closer to transition, and in the meantime, improve the capabilities of WoFS for experimental use within the operational community. SIGNIFICANCE STATEMENT: A first -of -its -kind experiment brought together forecasters from local weather forecast offices and national centers to examine the experimental Warn -on -Forecast System's (WoFS's) potential applications across watch -to -warning scales. This experiment demonstrated that WoFS can provide great benefit to forecasters, though a few challenges remain. Benefits provided by WoFS frequently overlap roles and responsibilities at local and national scales, suggesting the potential for enhanced cross -office collaboration. The challenges anticipated for WoFS operational use are far fewer than the benefits, and some solutions to these challenges are now being implemented. Finally, the mixed -methods experimental framework described herein also provides guidance for future collaborative experiments in testbed research that examine impacts of new technologies across NWS entities.

The streamwise vorticity current (SVC) has been shown in recent research to have a role in the tornadogenesis process in some supercells. Although field experiments have succeeded in observing the feature using mobile radars, limited knowledge exists on how/if SVCs can be observed using operational radars. To explore this possibility, simple radar emulation software was used to create simulated Weather Surveillance Radar 1988 Doppler (WSR-88D) radar images of an SVC in a high-resolution numerical simulation of a supercell thunderstorm. This proof-of-concept approach shows that SVCs likely can be observed in an operational setting under idealized circumstances. Two analysis times from the simulation are used to compare the signatures of both a weak and a strong SVC. This study suggests that results are highly contingent on radar viewing angle, and quality observations of the SVC may only be possible when a storm is within 45 km of the radar site. In Plan Position Indicator (PPI) scans, SVCs are found to be associated with inflow winds that penetrate more rearward with height downstream of the mesocyclone. Range Height Indicator (RHI) reconstructions show a reflectivity billow that resembles a density current head containing a Kelvin-Helmholtz (KH) billow, as well as a radial velocity couplet co-located with the SVC. A vertical shear product is introduced to visualize low-level vertical wind shear in the vicinity of the SVC to facilitate identification of the feature without interrogation of multiple elevation angles of radial velocity data.

This study examines use of experimental Warn-on-Forecast System (WoFS) guidance for short-term flash flood prediction at the NOAA Weather Prediction Center's Meteorological Watch (Metwatch) desk. The WoFS guidance provides storm-scale ensemble forecasts for individual thunderstorms out to six hours and has previously shown great promise in its predictive skill for heavy rainfall events. Its operational utility was examined during 2019 and 2020 in a formal collaboration between Warn-on-Forecast scientists and Metwatch meteorologists. During that time, Metwatch meteorologists integrated real-time WoFS guidance into their Mesoscale Precipitation Discussion forecast processes and provided evaluations via a post-event survey. The survey queried impacts of WoFS guidance on their situational awareness, workload, and confidence, and Metwatch meteorologists also reported subjective assessments of model performance. Survey results highlighted the importance of viewing consistency in WoFS guidance across runs and agreement between WoFS guidance with conceptual models, other numerical weather prediction guidance, and observations. The use of WoFS tended to either maintain or slightly increase Metwatch meteorologists' workload, while also increasing their confidence (notably for events perceived as better predicted). Of the different forecast attributes evaluated, Metwatch meteorologists reported convective mode as the attribute best predicted by WoFS. Use of WoFS guidance supported Mesoscale Precipitation Discussion decision making, including the placement and spatial extent of the product and the level of specificity provided about the related flash flood threat(s).

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Corresponding author: Stephanie Avey, stephanie.avey@noaa.gov Publisher's Note: This article was modified on 11 July 2023 to correct the affiliation for authors Heather Reeves and Patrick Skinner.

The Warn-on-Forecast System (WoFS) is a convection-allowing ensemble prediction system designed to primarily provide guidance on thunderstorm hazards from the meso-beta to storm-scale in space and from several hours to less than one hour in time. This article describes unique aspects of WoFS guidance product design and application to short-term severe weather forecasting. General probabilistic forecasting concepts for convection allowing ensembles, including the use of neighborhood, probability of exceedance, percentile, and paintball products, are reviewed, and the design of real-time WoFS guidance products is described. Recommendations for effectively using WoFS guidance for severe weather prediction include evaluation of the quality of WoFS storm-scale analyses, interrogating multiple probabilistic guidance products to efficiently span the envelope of guidance provided by ensemble members, and application of conceptual models of convective storm dynamics and interaction with the broader mesoscale environment. Part II of this study provides specific examples where WoFS guidance can provide useful or potentially misleading guidance on convective storm likelihood and evolution.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Corresponding author: Adam J. Clark, adam.clark@noaa.gov

Multiple high-impact wildfire episodes on the southern Great Plains in 2021/22 provided unique opportunities to demonstrate the emerging utility of Convection-allowing Models (CAMs) in fire-weather forecasting. This short contribution article will present preliminary analyses of the deterministic Texas Tech Real Time Weather Prediction System’s Red Flag Threat Index (RFTI) compared to wildfire activity observed via the Geostationary Operational Environmental Satellite-16 during four southern Great Plains wildfire outbreaks. Visual side-by-side comparisons of model-predicted RFTI and satellite-detected wildfires will be shown in static and animated displays that demonstrate the model’s prognostic signal in depicting fire-outbreak evolution. The data analyses are supplemented with preliminary information from state forestry agencies that provide context to predicted RFTI relative to size-based categorization of observed wildfires and human casualties. In addition, use of the National Severe Storm Laboratory’s Warn-on-Forecast System to provide short-term updates on the evolution of fire-effective atmospheric features that promote new fire ignition, problematic spread, and extreme fire behavior is also demonstrated. The examples presented here suggest that CAMs serve an important role in the mesoscale prediction of dangerous wildfire conditions. With this novel use of CAMs in fire meteorology, the authors advocate for expanded availability of fire weather-specific fields and parameters in high-resolution numerical weather prediction systems that would improve wildfire forecasts and associated impact-based decision support.

Adam J. Clark2,4, Israel L. Jirak1, Burkely T. Gallo1,3, Kent H. Knopfmeier2,3, Brett Roberts1,2,3, Makenzie Krocak1,3,5, Jake Vancil1,3, Kimberly A. Hoogewind2,3, Nathan A. Dahl1,3, Eric D. Loken2,3,4, David Jahn1,3, David Harrison1,3, David Imy2, Patrick Burke2, Louis J. Wicker2,4, Patrick S. Skinner2,3, Pamela L. Heinselman2,4, Patrick Marsh1, Katie A. Wilson2,3, Andrew R. Dean1, Gerald J. Creager2,3, Thomas A. Jones2,3, Jidong Gao2, Yunheng Wang2,3, Montgomery Flora2,3, Corey K. Potvin2,4, Christopher A. Kerr2,3, Nusrat Yussouf2,3,4, Joshua Martin2,3, Jorge Guerra2,3, Brian C. Matilla2,3, and Thomas J. Galarneau2,3,4