Warn-on-Forecast System Research Articles

The 21 August 2021 Catastrophic Flash Flood at Waverly, Tennessee: Harnessing the Warn-on-Forecast System for Confident Prewarning Messaging of Extreme Rainfall

Abstract On the morning of 21 August 2021, extreme rainfall spurred a flood wave on Trace Creek that ravaged Waverly, Tennessee, causing 19 fatalities. Peak 24-h rainfall of 526 mm was recorded just upstream at McEwen, setting the Tennessee 24-h state rainfall record. A Slight Risk of excessive rainfall and a Flash Flood Watch were issued 16 and 8 h, respectively, before rain began; however, predicting mesobeta scale extreme rainfall remains an elusive skill for models and humans alike. Operational convection-allowing models not only suggested pockets of heavy rain but also displayed 1) peak values generally less than half of those observed, 2) widely ranging solutions, and 3) erroneous similarly heavy rain elsewhere. Future use of storm-scale ensembles which use rapid data assimilation promises to help forecasters anticipate extrema that may only be predictable at shorter time scales. This work will examine compelling forecasts from a retrospective run of the experimental Warn-on-Forecast System (WoFS). The authors, who include National Weather Service forecasters who worked the event, discuss how WoFS and its probabilistic framework could influence services during low-predictability, high-impact flash floods.

Using Machine Learning to Predict Convection-Allowing Ensemble Forecast Skill: Evaluation with the NSSL Warn-on-Forecast System

Abstract 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.

Assessing the Fidelity of Landfalling Tropical Cyclone Convective‐Scale Environments in the Warn‐On‐Forecast System Using Radiosondes

AbstractForecasts of tropical cyclone (TC) tornadoes are less skillful than their non‐TC counterparts at all lead times. The development of a convection‐allowing regional ensemble, known as the Warn‐on‐Forecast System (WoFS), may help improve short‐fused TC tornado forecasts. As a first step, this study investigates the fidelity of convective‐scale kinematic and thermodynamic environments to a preliminary set of soundings from WoFS forecasts for comparison with radiosondes for selected 2020 landfalling TCs. Our study shows reasonable agreement between TC convective‐scale kinematic environments in WoFS versus observed soundings at all forecast lead times. Nonetheless, WoFS is biased toward weaker than observed TC‐relative radial winds, and stronger than observed near‐surface tangential winds with weaker winds aloft, during the forecast. Analysis of storm‐relative helicity (SRH) shows that WoFS underestimates extreme observed values. Convective‐scale thermodynamic environments are well simulated for both temperature and dewpoint at all lead times. However, WoFS is biased moister with steeper lapse rates compared to observations during the forecast. Both CAPE and, to a lesser extent, 0–3‐km CAPE distributions are narrower in WoFS than in radiosondes, with an underestimation of higher CAPE values. Together, these results suggest that WoFS may have utility for forecasting convective‐scale environments in landfalling TCs with lead times of several hours.

Machine Learning Estimation of Maximum Vertical Velocity from Radar

Abstract The quantification of storm updrafts remains unavailable for operational forecasting despite their inherent importance to convection and its associated severe weather hazards. Updraft proxies, like overshooting top area from satellite images, have been linked to severe weather hazards but only relate to a limited portion of the total storm updraft. This study investigates if a machine learning model, namely, U-Nets, can skillfully retrieve maximum vertical velocity and its areal extent from three-dimensional gridded radar reflectivity alone. The machine learning model is trained using simulated radar reflectivity and vertical velocity from the National Severe Storm Laboratory’s convection permitting Warn-on-Forecast System (WoFS). A parametric regression technique using the sinh–arcsinh–normal distribution is adapted to run with U-Nets, allowing for both deterministic and probabilistic predictions of maximum vertical velocity. The best models after hyperparameter search provided less than 50% root mean squared error, a coefficient of determination greater than 0.65, and an intersection over union (IoU) of more than 0.45 on the independent test set composed of WoFS data. Beyond the WoFS analysis, a case study was conducted using real radar data and corresponding dual-Doppler analyses of vertical velocity within a supercell. The U-Net consistently underestimates the dual-Doppler updraft speed estimates by 50%. Meanwhile, the area of the 5 and 10 m s−1 updraft cores shows an IoU of 0.25. While the above statistics are not exceptional, the machine learning model enables quick distillation of 3D radar data that is related to the maximum vertical velocity, which could be useful in assessing a storm’s severe potential. Significance Statement All convective storm hazards (tornadoes, hail, heavy rain, straight line winds) can be related to a storm’s updraft. Yet, there is no direct measurement of updraft speed or area available for forecasters to make their warning decisions from. This paper addresses the lack of observational data by providing a machine learning solution that skillfully estimates the maximum updraft speed within storms from only the radar reflectivity 3D structure. After further vetting the machine learning solutions on additional real-world examples, the estimated storm updrafts will hopefully provide forecasters with an added tool to help diagnose a storm’s hazard potential more accurately.

WoFS and the Wisdom of the Crowd: The Impact of the Warn-on-Forecast System on Hourly Forecasts during the 2021 NOAA Hazardous Weather Testbed Spring Forecasting Experiment

Abstract During the 2021 Spring Forecasting Experiment (SFE), the usefulness of the experimental Warn-on-Forecast System (WoFS) ensemble guidance was tested with the issuance of short-term probabilistic hazard forecasts. One group of participants used the WoFS guidance, while another group did not. Individual forecasts issued by two NWS participants in each group were evaluated alongside a consensus forecast from the remaining participants. Participant forecasts of tornadoes, hail, and wind at lead times of ∼2–3 h and valid at 2200–2300, 2300–0000, and 0000–0100 UTC were evaluated subjectively during the SFE by participants the day after issuance, and objectively after the SFE concluded. These forecasts exist between the watch and the warning time frame, where WoFS is anticipated to be particularly impactful. The hourly probabilistic forecasts were skillful according to objective metrics like the fractions skill score. While the tornado forecasts were more reliable than the other hazards, there was no clear indication of any one hazard scoring highest across all metrics. WoFS availability improved the hourly probabilistic forecasts as measured by the subjective ratings and several objective metrics, including increased POD and decreased FAR at high probability thresholds. Generally, expert forecasts performed better than consensus forecasts, though expert forecasts overforecasted. Finally, this work explored the appropriate construction of practically perfect fields used during subjective verification, which participants frequently found to be too small and precise. Using a Gaussian smoother with σ = 70 km is recommended to create hourly practically perfect fields in future experiments. Significance Statement This work explores the impact of cutting-edge numerical weather prediction ensemble guidance (the Warn-on-Forecast System) on severe thunderstorm hazard outlooks at watch-to-warning time scales, typically between 1 and 6 h of lead time. Real-time forecast products in this time frame are currently provided on an as-needed basis, and the transition to continuous probabilistic forecast products across scales requires targeted research. Results showed that hourly probabilistic participant forecasts were skillful subjectively and statistically, and that the experimental guidance improved the forecasts. These results are promising for the implementation and value of the Warn-on-Forecast System to provide improved hazard timing and location guidance within severe weather watches. Suggestions are made to aid future subjective evaluations of watch-to-warning-scale probabilistic forecasts.

Collaborative Exploration of Storm-Scale Probabilistic Guidance for NWS Forecast Operations

Abstract 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.

Testing Stochastic and Perturbed Parameter Methods in an Experimental 1-km Warn-on-Forecast System Using NSSL’s Phased-Array Radar Observations

Abstract The success of the National Severe Storms Laboratory’s (NSSL) experimental Warn-on-Forecast System (WoFS) to provide useful probabilistic guidance of severe and hazardous weather is mostly due to the frequent assimilation of observations, especially radar observations. Phased-array radar (PAR) technology, which is a potential candidate to replace the current U.S. operational radar network, would allow for even more rapid assimilation of radar observations by providing full-volumetric scans of the atmosphere every ∼1 min. Based on previous studies, more frequent PAR data assimilation can lead to improved forecasts, but it can also lead to ensemble underdispersion and suboptimal observation assimilation. The use of stochastic and perturbed parameter methods to increase ensemble spread is a potential solution to this problem. In this study, four stochastic and perturbed parameter methods are assessed using a 1-km-scale version of the WoFS and include the stochastic kinetic energy backscatter (SKEB) scheme, the physically based stochastic perturbation (PSP) scheme, a fixed perturbed parameters (FPP) method, and a novel surface-model scheme blending (SMSB) method. Using NSSL PAR observations from the 9 May 2016 tornado outbreak, experiments are conducted to assess the impact of the methods individually, in different combinations, and with different cycling intervals. The results from these experiments reveal the potential benefits of stochastic and perturbed parameter methods for future versions of the WoFS. Stochastic and perturbed parameter methods can lead to more skillful forecasts during periods of storm development. Moreover, a combination of multiple methods can result in more skillful forecasts than using a single method. Significance Statement Phased-array radar technology allows for more frequent assimilation of radar observations into ensemble forecast systems like the experimental Warn-on-Forecast System. However, more frequent radar data assimilation can eventually cause issues for prediction systems due to the lack of ensemble spread. Thus, the purpose of this study is to explore the use of four stochastic and perturbed parameter methods in a next-generation Warn-on-Forecast System to generate ensemble spread and help prevent the issues from frequent radar data assimilation. Results from this study indicate the stochastic and perturbed parameter methods can improve forecasts of storms, especially during storm development.

Verification of Quasi-Linear Convective Systems Predicted by the Warn-on-Forecast System (WoFS)

Abstract 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, probabilistic forecasting guidance for severe convective events. Examination of WoFS’s capability to predict QLCSs has yet to be systematically 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 observations. 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 displacements between observed and predicted convective lines. While no systematic propagation biases are found, WoFS typically 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.

Warn-on-Forecast System: From Vision to Reality

Abstract 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.

Convection Initiation Forecasting Using Synthetic Satellite Imagery from the Warn-on-Forecast System

Forecasting convection initiation (CI) has advanced greatly during the past decade through the use of high-resolution satellite observations and model output. One of the primary CI products used in forecast operations is based on GOES-16 visible and infrared imagery along with GLM lightning flash detections to determine the location of growing ice-containing cumulus clouds that are the precursor to developing thunderstorms. Another approach to CI forecasting that has recently become available is high frequency output from numerical weather prediction (NWP) models such as the Warn-on-Forecast System (WoFS). NWP model simulated composite reflectivity forecasts are one method used to determine when and where severe thunderstorms might develop. However, waiting for high reflectivity (> 40 dBZ) to be created within the NWP model limits the potential lead time available to forecasters when using WoFS output to anticipate areas where convection might form.Also, forecast reflectivity alone does not always give an indication of whether or not the precipitation developed by the NWP model is convective in nature. To address these limitations, this work applies a CI forecasting methodology developed for GOES satellite data on synthetic satellite imagery produced from WoFS output. Forecast cloud objects are tracked over a 10-min interval and CI forecasting parameters are applied to determine whether or not these cloud objects will continue to develop into organized thunderstorms.

Interpreting Warn-on-Forecast System Guidance, Part I: Review of Probabilistic Guidance Concepts, Product Design, and Best Practices

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.

The NOAA Weather Prediction Center’s Use and Evaluation of Experimental Warn-on-Forecast System Guidance

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).

Assimilating All-Sky Infrared Radiance Observations to Improve Ensemble Analyses and Short-Term Predictions of Thunderstorms

The experimental rapid-cycling Ensemble Kalman Filter (EnKF) in the convection-allowing ensemble-based Warn-on-Forecast System (WoFS) at the National Severe Storms Laboratory (NSSL) is used to assimilate all-sky infrared radiance observations from the GOES-16 7.3 μm water vapor channel in combination with radar wind and reflectivity observations to improve the analysis and subsequent forecast of severe thunderstorms (which occurred in Oklahoma on 2 May 2018). The method for radiance data assimilation is based primarily on the version used in WoFS. In addition, the methods for adaptive observation error inflation and background error inflation and the method of time-expanded sampling are also implemented in two groups of experiments to test their effectiveness and examine the impacts of radar observations and all-sky radiance observations on ensemble analyses and predictions of severe thunderstorms. Radar reflectivity observations and brightness temperature observations from the GOES-16 6.9 μm mid-level troposphere water vapor channel and 11.2 μm longwave window channel are used to evaluate the assimilation statistics and verify the forecasts in each experiment. The primary findings from the two groups of experiments are summarized: (i) Assimilating radar observations improves the overall (heavy) precipitation forecast up to 5 (4) h, according to the improved composite reflectivity forecast skill scores. (ii) Assimilating all-sky water vapor infrared radiance observations from GOES-16 in addition to radar observations improves the brightness temperature assimilation statistics and subsequent cloud cover forecast up to 6 h, but the improvements are not significantly affected by the adaptive observation and background error inflations. (iii) Time-expanded sampling can not only reduce the computational cost substantially but also slightly improve the forecast.

Applying Time-Expended Sampling to Ensemble Assimilation of Remote-Sensing Data for Short-Term Predictions of Thunderstorms

By sampling perturbed state vectors from each ensemble forecast at additional time levels shifted by ±τ (where τ is a selected time interval) from the analysis time, time-expanded sampling (TES) can not only sample timing errors (or phase errors) but also triple the analysis ensemble size for covariance construction without increasing the forecast ensemble size. In this study, TES was applied to the convection-allowing ensemble-based warn-on-forecast system (WoFS), for four severe storm events, to reduce the computational costs that constrain real-time applications in the assimilation of remote-sensing data from radars and the geostationary satellite GOES-16. For each event, TES was implemented against a 36-member control experiment (E36) by reducing the forecast ensemble size to 12 but tripling the analysis ensemble size to 12 × 3 = 36 with τ = 2.5 min, 5 min and 7.5 min in three TES experiments, named E12×3τ2.5, E12×3τ5 and E12×3τ7.5, respectively. A 0–6-h forecast was created hourly after the second hour during the assimilation in each experiment. The assimilation statistics were evaluated for each experiment applied to each event and were found to be little affected by the TES, while reducing the computational cost. The forecasts produced in each experiment were verified against multi-sensor observed/estimated rainfall, reported tornadoes and damaging winds for each event. The verifications indicated that the forecasts produced in the three TES experiments had about the same capability and quality as that in the E36 for predicting hourly rainfall and the probabilities of tornadoes and damaging winds; in addition, the predictive capability and quality were not sensitive to τ, although they were slightly enhanced by selecting τ = 7.5 min. These results suggest that TES is attractive and useful for cost-saving real-time applications of WoFS in the assimilation of remote-sensing data and the generation of short-term severe-weather forecasts.

An Efficient Ensemble Technique for Hydrologic Forecasting Driven by Quantitative Precipitation Forecasts

Abstract Uncertainty in quantitative precipitation forecasts (QPFs) from numerical weather prediction (NWP) models manifests in errors in the amounts of rainfall, storm structure, storm location, and timing, among other precipitation characteristics. In flash flood forecasting applications, errors in the QPFs can translate into significant uncertainty in forecasts of surface water flows and their impacts. In particular, the QPF errors in location and structure result in errors on flow paths, which can be highly detrimental in identifying locations susceptible to flash flood impacts. To account for this type of uncertainty, the neighboring pixel ensemble technique (NPET) was devised and implemented as a postprocessing algorithm of deterministic or ensemble outputs from a distributed hydrologic model. The aim of the technique is to address displaced hydrologic responses resulting from location biases in QPFs using a probabilistic approach. NPET identifies a sampling region surrounding each forecast pixel and builds an ensemble of surface water flow values considering the pixel’s physiographic similarities. The probabilistic information produced with NPET can be calibrated through a set of tunable parameters that are adjusted to account for NWP-specific QPF error characteristics. The utility of NPET is demonstrated for the Ellicott City flash flood event on 27 May 2018, using products and tools routinely used in the U.S. National Weather Service for warning operations. Results from this case demonstrate that NPET effectively conveys uncertainty information about QPF precipitation location in a hydrologic context. Significance Statement This study introduces a new method suitable for operational use called the neighboring pixel ensemble technique (NPET). NPET is an algorithm that generates ensemble-based streamflow forecasts accounting for the location uncertainties in quantitative precipitation forecasts (QPFs) without the requirement of multiple hydrologic model runs. NPET is capable of this feat through probabilistic assimilation of a priori QPF displacement information and its uncertainty. The application of NPET with the Flooded Locations and Simulated Hydrographs (FLASH) project shows the technique could be beneficial for flash flood warning operations in the U.S. National Weather Service (NWS). It is envisioned that the application of NPET with Warn-on-Forecast System (WoFS)-forced FLASH outputs will further enhance the quality of flash flood forecasts that support NWS warning operations.

A Path Toward Short-Term Probabilistic Flash Flood Prediction

Abstract There are ongoing efforts to move beyond the current paradigm of using deterministic products driven by observation-only data to make binary warning decisions. Recent works have focused on severe thunderstorm hazards, such as hail, lightning, and tornadoes. This study discusses one of the first steps toward having probabilistic information combined with convective-scale short-term precipitation forecasts available for the prediction and warning of flash flooding. Participants in the Hydrometeorology Testbed–MRMS Hydrology (HMT-Hydro) experiment evaluated several probabilistic-based hydrologic model output from the probabilistic Flooded Locations and Simulated Hydrographs (PRO-FLASH) system during experimental real-time warning operations. Evaluation of flash flood warning performance combined with product surveys highlighted how forecasters perceived biases within the probabilistic information and how the different probabilistic approaches influenced warnings that were verified versus those that were unverified. The incorporation of the Warn-on-Forecast System (WoFS) ensemble precipitation forecasts into the PRO-FLASH product generation provided an opportunity to evaluate the first coupling of subhourly convective-scale ensemble precipitation forecasts with probabilistic hydrologic modeling at the flash flood warning time scale through archived case simulations. The addition of WoFS precipitation forecasts resulted in an increase in warning lead time, including four events with ≥29 min of additional lead time but with increased probabilities of false alarms. Additional feedback from participants provided insights into the application of WoFS forecasts into warning decisions, including how flash flood expectations and confidence evolved for verified flash flood events and how forecast probabilistic products can positively influence the communications of the potential for flash flooding.

Results from a Pseudo-Real-Time Next-Generation 1-km Warn-on-Forecast System Prototype

Abstract Since 2017, the Warn-on-Forecast System (WoFS) has been tested and evaluated during the Hazardous Weather Testbed Spring Forecasting Experiment (SFE) and summer convective seasons. The system has shown promise in predicting high temporal and spatial specificity of individual evolving thunderstorms. However, this baseline version of the WoFS has a 3-km horizontal grid spacing and cannot resolve some convective processes. Efforts are under way to develop a WoFS prototype at a 1-km grid spacing (WoFS-1km) with the hope to improve forecast accuracy. This requires extensive changes to data assimilation specifications and observation processing parameters. A preliminary version of WoFS-1km nested within WoFS at 3 km (WoFS-3km) was developed, tested, and run during the 2021 SFE in pseudo–real time. Ten case studies were successfully completed and provided simulations of a variety of convective modes. The reflectivity and rotation storm objects from WoFS-1km are verified against both WoFS-3km and 1-km forecasts initialized from downscaled WoFS-3km analyses using both neighborhood- and object-based techniques. Neighborhood-based verification suggests WoFS-1km improves reflectivity bias but not spatial placement. The WoFS-1km object-based reflectivity forecast accuracy is higher in most cases, leading to a net improvement. Both the WoFS-1km and downscaled forecasts have ideal reflectivity object frequency biases while the WoFS-3km overpredicts the number of reflectivity objects. The rotation object verification is ambiguous as many cases are negatively impacted by 1-km data assimilation. This initial evaluation of a WoFS-1km prototype is a solid foundation for further development and future testing. Significance Statement This study investigates the impacts of performing data assimilation directly on a 1-km WoFS model grid. Most previous studies have only initialized 1-km WoFS forecasts from coarser analyses. The results demonstrate some improvements to reflectivity forecasts through data assimilation on a 1-km model grid although finer resolution data assimilation did not improve rotation forecasts.

A Method for Assimilating Pseudo Dewpoint Temperature as a Function of GLM Flash Extent Density in GSI‐Based EnKF Data Assimilation System—A Proof of Concept Study

AbstractIn this study, a new lightning data assimilation (LDA) scheme using Geostationary Lightning Mapper (GLM) flash extent density (FED) is developed and implemented in the National Severe Storms Laboratory Warn‐on‐Forecast System (WoFS). The new LDA scheme first assigns a pseudo relative humidity between the cloud base and a specific layer based on the FED value. Then at each model layer, the pseudo relative humidity is converted to pseudo dewpoint temperature according to the corresponding air temperature. Some sensitivity experiments are performed to investigate how to assign and use GLM/FED in an optimum way. The impact of assimilating this pseudo dewpoint temperature on a short‐term severe weather forecast is preliminarily assessed in this proof‐of‐concept study. A high‐impact weather event in Kansas on 24 May 2021 is used to evaluate the performance of the new scheme on analyses and subsequent short‐term forecasts. The results show that the assimilation of additional FED‐based dewpoint temperature observations along with radar, satellite radiance, and cloud water can improve short‐term (3‐hr) forecast skill in terms of quantitative and qualitative verifications against the observations. The improvement is primarily due to the direct and indirect adjustment of dynamic and thermodynamic conditions through the LDA process. More specifically, the assimilation of FED‐based dewpoint temperature, in addition to the other observations currently used in WoFS, tends to enhance the ingredients required for thunderstorm formation, namely moisture, instability, and lifting mechanism.

Quantification of NSSL Warn-on-Forecast System Accuracy by Storm Age Using Object-Based Verification

Abstract The National Severe Storm Laboratory’s Warn-on-Forecast System (WoFS) is a convection-allowing ensemble with rapidly cycled data assimilation (DA) of various satellite and radar datasets designed for prediction at 0–6-h lead time of hazardous weather. With the focus on short lead times, WoFS predictive accuracy is strongly dependent on its ability to accurately initialize and depict the evolution of ongoing storms. Since it takes multiple DA cycles to fully “spin up” ongoing storms, predictive skill is likely a function of storm age at the time of model initialization, meaning that older storms that have been through several DA cycles will be forecast with greater accuracy than newer storms that initiate just before model initialization or at any point after. To quantify this relationship, we apply an object-based spatial tracking and verification approach to map differences in the probability of detection (POD), in space–time, of predicted storm objects from WoFS with respect to Multi-Radar Multi-Sensor (MRMS) reflectivity objects. Object-tracking/matching statistics are computed for all suitable and available WoFS cases from 2017 to 2021. Our results indicate sharply increasing POD with increasing storm age for lead times within 3 h. PODs were about 0.3 for storm objects that emerge 2–3 h after model initialization, while for storm objects that were at least an hour old at the time of model initialization by DA, PODs ranged from around 0.7 to 0.9 depending on the lead time. These results should aid in forecaster interpretation of WoFS, as well as guide WoFS developers on improving the model and DA system. Significance Statement The Warn-on-Forecast System (WoFS) is a collection of weather models designed to predict individual thunderstorms. Before the models can predict storms, they must ingest radar and satellite observations to put existing storms into the models. Because storms develop at different times, more observations will exist for some storms in the model domain than others, which results in WoFS forecasts with different accuracy for different storms. This paper estimates the differences in accuracy for storms that have existed for a long time and those that have not by tracking observed and predicted storms. We find that the likelihood of WoFS accurately predicting a thunderstorm nearly doubles if the storm has existed for over an hour prior to the forecast. Understanding this relationship between storm age and forecast accuracy will help forecasters better use WoFS predictions and guide future research to improve WoFS forecasts.

An Experimental 1-km Warn-on-Forecast System for Hazardous Weather Events

Abstract An experimental Warn-on-Forecast System (WoFS) ensemble data assimilation (DA) and prediction system at 1-km grid spacing is developed and tested using two landfalling tropical cyclone (TC) events, one springtime severe thunderstorm event, and one summertime flash flood event. To evaluate the impact of DA at 1-km grid spacing, two experiments are conducted. One experiment, namely, the WoFS-1km, generates 3-h ensemble forecasts from the 1-km WoFS analyses while another experiment, namely, the Downscaled-1km, generates 3-h ensemble forecasts from downscaled 3-km analyses. With 1-km DA, the two landfalling TC events and the summertime event show some improvement in predicting high reflectivity, while the springtime event performs worse. Meanwhile, WoFS-1km is slightly better at predicting heavier precipitation (>20 mm h−1) with lower bias. However, heavy precipitation spatial placement error is only mitigated in one TC event and the summertime event with 1-km DA but is neutral or worse in the other two events. Object-based verification for rotation objects indicates that WoFS-1km performs better in one of the TC events, but worse in the springtime event with lower probability of detection and higher false alarm ratio due to fewer strong rotation objects being generated. The forecast skill of WoFS-1km for the springtime event is degraded mainly because the convective cores do not sufficiently develop as the forecast advances. The conditional benefits from 1-km DA in this study highlights the need for evaluation of a larger sample of convective storm cases and further development of the system.