A 16-yr (2007-22) climatology of drylines is presented. Constructed using NOAA Weather Prediction Center (WPC) surface analyses, this climatology addresses the limitations of season, time of day, and region of previous dryline studies by using the full surface analysis archive to include drylines throughout the entire day at 3-h increments for the entire year over the contiguous United States. Severe storm reports and NWS-issued severe thunderstorms and tornado warnings are used to associate individual drylines with severe (or potentially severe) convection. March-June are the months with the greatest frequency of drylines and dryline-associated severe thunderstorms. Regardless of season, average dryline longitude over the course of a day mimics the conceptual model of daily dryline evolution with an eastward shift during the day and westward retreat overnight. The overwhelming majority of WPC-analyzed drylines are located in the southern Great Plains, particularly southwest Texas through the Texas Panhandle, and on average are furthest west in summer and furthest east in winter. A significant increase in the number of analyzed drylines and dryline days per year is identified in the WPC surface analysis archive. This increase is consistent across all analysis times and between drylines associated with and without severe convection. However, using a machine learning model to automate dryline detection, no statistically significant trend is found over this same analysis period. Thus, the increase in WPC-analyzed drylines during the 16-yr period is likely to be the result of nonmeteorological factors. SIGNIFICANCE STATEMENT: Past studies of the dryline, a boundary important to severe convective storms and the hydrology of the central United States, have only focused on the spring months. This study conducts the first year-round climatology of the dryline and their association with severe storms using 16 years of Weather Prediction Center (WPC) surface analyses. There is a significant increase in dryline frequency over the 16-yr analysis period, but this is attributed to nonmeteorological factors by comparison to an automated dryline identification model. Future work will expand this climatology back through the twentieth century and expand the analysis on the variability of drylines and their association with severe storms.

Hill et al. demonstrated promising results for 1-8-day severe weather predictions using a random forest (RF) trained with Global Ensemble Forecast System reforecasts (GEFS/R) and applied to operational GEFS forecasts. However, the skill of the reforecasts may be affected by using fewer members and coarser initial conditions relative to operational GEFS forecasts. Thus, this work builds on Hill et al. by formulating and testing a similar RF model using operational GEFS data for training and testing, instead of reforecasts, to produce 1-15-day severe weather predictions. Prior to training RFs from operational and reforecast GEFS data, feature engineering experiments were conducted for optimization and assessing performance sensitivities. These experiments found 1) there was only modest degradation in forecast skill with decreasing numbers of members; 2) RFs performed best using simple ensemble means as predictors rather than percentiles or individual members; 3) thermodynamic predictors were most impactful to RF performance; and 4) a multi-model RF combining predictors from GEFS and ECMWF's Integrated Forecasting System improved upon single-model RFs. Using the optimal RF configuration, detailed performance characteristics were presented and four case studies were analyzed. Finally, the optimal RF trained on operational GEFS forecasts significantly outperformed an identically config-ured RF trained on reforecasts. The superior performance in the RF trained from operational data could be related to operational GEFS forecasts themselves having better performance than the reforecasts and/or the storm report database better sampling severe weather in more recent years. SIGNIFICANCE STATEMENT: This study builds on previous work that developed automated 1-8-day severe weather forecasts using machine learning, which are widely used operationally. Using the same machine learning concepts, we have developed an improved algorithm and extended the lead times to 15 days. Thus, results from this study are expected to improve extended range severe weather forecasting capabilities.

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.

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.

Prediction of severe convective storms at time scales of 2-4 weeks is of interest to forecasters and stakeholders due to their impacts on life and property. Prediction of severe convective storms on this time scale is challenging, since the large-scale weather patterns that drive this activity begin to lose dynamic predictability beyond week 1. Previous work related to severe convective storms on the subseasonal time scale has mostly focused on observed relationships with teleconnections. The skill of numerical weather prediction forecasts of convective-related variables has been comparatively less explored. In this study over the United States, a forecast evaluation of variables relevant in the prediction of severe convective storms is conducted using Global Ensemble Forecast System, version 12, reforecasts at lead times of up to 4 weeks. We fi nd that kinematic and thermodynamic fi elds are predicted with skill out to week 3 in some cases, while composite parameters struggle to achieve meaningful skill into week 2. Additionally, using a novel method of weekly summations of daily maximum composite parameters, we suggest that the aggregation of certain variables may assist in providing additional predictability beyond week 1. These results should serve as a reference for forecast skill for the relevant fi elds and help inform the development of convective forecasting tools at time scales beyond current operational products.

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.

During the 2021 Spring Forecasting Experiment (SFE), the usefulness of the experimental Warn-onForecast 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 s 5 70 km is recommended to create hourly practically perfect fields in future experiments.

This article provides a brief, technical narrative of the WoFS journey to a cloud-based high-performance computing (HPC) system, including some of the technological challenges encountered and solutions found. Also, discussed are a few new components that are in development for cloud-based Warn-on-Forecast System (cb-WoFS), such as the cloud infrastructure project for managing resources, as well as a new web application that manages cb-WoFS runs. An important initial step in our cloud journey is containerizing all of the compiled applications, such as WRF, GSI, and EnKF and their dependencies like netCDF and MPI. With these applications compiled within an Apptainer container, WoFS can run on any local or cloud-based HPC cluster that supports MPI. Furthermore, an additional software layer was developed that creates and manages cloud vendor resources. This layer, which is referred to as the WoFS framework, contains the workflow required to run cb-WoFS, as well as management for other aspects of cb-WoFS (including but not limited to creation of HPC pools in the end-to-end workflow, runtime notifications, and database management). This additional layer was developed to separate the WoFS business logic from vendor-specific API calls. The WoFS framework exposes features through its service library, which is then referenced by the newly developed cb-WoFS web application and other cloud applications. This makes WoFS a complete end-to-end cloud-based application, where an administrator can launch a model run, manage resources, and view output all within a single web app.

Hail forecasts produced by the CAM-HAILCAST pseudo-Lagrangian hail size forecasting model were evaluated during the 2019, 2020, and 2021 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiments (SFEs). As part of this evaluation, HWT SFE participants were polled about their definition of a “good” hail forecast. Participants were presented with two different verification methods conducted over three different spatiotemporal scales, and were then asked to subjectively evaluate the hail forecast as well as the different verification methods themselves. Results recommended use of multiple verification methods tailored to the type of forecast expected by the end-user interpreting and applying the forecast. The hail forecasts evaluated during this period included an implementation of CAM-HAILCAST in the Limited Area Model of the Unified Forecast System with the Finite Volume 3 (FV3) dynamical core. Evaluation of FV3-HAILCAST over both 1- and 24-h periods found continued improvement from 2019 to 2021. The improvement was largely a result of wide intervariability among FV3 ensemble members with different microphysics parameterizations in 2019 lessening significantly during 2020 and 2021. Overprediction throughout the diurnal cycle also lessened by 2021. A combination of both upscaling neighborhood verification and an object-based technique that only retained matched convective objects was necessary to understand the improvement, agreeing with the HWT SFE participants’ recommendations for multiple verification methods. Significance Statement “Good” forecasts of hail can be determined in multiple ways and must depend on both the performance of the guidance and the perspective of the end-user. This work looks at different verification strategies to capture the performance of the CAM-HAILCAST hail forecasting model across three years of the Spring Forecasting Experiment (SFE) in different parent models. Verification strategies were informed by SFE participant input via a survey. Skill variability among models decreased in SFE 2021 relative to prior SFEs. The FV3 model in 2021, compared to 2019, provided improved forecasts of both convective distribution and 38-mm (1.5 in.) hail size, as well as less overforecasting of convection from 1900 to 2300 UTC.

This study evaluates simulated radiance forecasts from a series of controlled experiments consisting of FV3-LAM forecasts with different configurations of model physics and vertical resolution. The forecasts were produced during the 2020 Hazardous Weather Testbed Spring Forecasting Experiments on the same forecast cases. The evaluation includes grid-point, neighborhood-based and object-based verification. The experiments include forecasts that were identical except for the physics (EMC-LAM vs. EMC-LAMx), vertical resolution (EMC-LAMx vs. NSSL-LAM), or combined initial conditions, physics and vertical resolution (GSL-LAM). It is found that the EMC-LAM generally provided better simulated radiance forecasts than the other three configurations at most forecast lead times, due to its unique physics configuration. All configurations generally over-forecasted high level clouds. EMC-LAM reduced the over-forecasting of high clouds, but also under-forecasted the coverage of mid-level clouds. In contrast, at early lead times the EMC-LAM had relatively poor performance relative to the other forecasts. Furthermore, EMC-LAM was an outlier in terms of the vertical structure of clouds. It is also found that the NSSL-LAM consistently improved upon the EMC-LAMx, which had fewer vertical levels than NSSL-LAM. Compared to EMC-LAMx, NSSL-LAM had less cloud over-forecasting bias, especially with small cloud objects, and less overall error. The differences between EMC-LAMx and GSL-LAM were generally much smaller than the differences between EMC-LAMx and EMC-LAM/NSSL-LAM. Finally, it is found that a non-linear bias correction conditioned on symmetric brightness temperature reduced the overall root-mean-square error by about a factor of 2 while improving the unrealistic vertical structure of clouds in the EMC-LAM.

© 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

As part of NOAA’s Hazardous Weather Testbed Spring Forecasting Experiment (SFE) in 2020, an international collaboration yielded a set of real-time convection-allowing model (CAM) forecasts over the contiguous United States in which the model configurations and initial/boundary conditions were varied in a controlled manner. Three model configurations were employed, among which the Finite Volume Cubed-Sphere (FV3), Unified Model (UM), and Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model dynamical cores were represented. Two runs were produced for each configuration: one driven by NOAA’s Global Forecast System for initial and boundary conditions, and the other driven by the Met Office’s operational global UM. For 32 cases during SFE2020, these runs were initialized at 0000 UTC and integrated for 36 h. Objective verification of model fields relevant to convective forecasting illuminates differences in the influence of configuration versus driving model pertinent to the ongoing problem of optimizing spread and skill in CAM ensembles. The UM and WRF configurations tend to outperform FV3 for forecasts of precipitation, thermodynamics, and simulated radar reflectivity; using a driving model with the native CAM core also tends to produce better skill in aggregate. Reflectivity and thermodynamic forecasts were found to cluster more by configuration than by driving model at lead times greater than 18 h. The two UM configuration experiments had notably similar solutions that, despite competitive aggregate skill, had large errors in the diurnal convective cycle.

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

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 StatementThe 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.

Recent research has shown that random forests (RFs) can create skillful probabilistic severe weather hazard forecasts from numerical weather prediction (NWP) ensemble data. However, it remains unclear how RFs use NWP data and how predictors should be generated from NWP ensembles. This paper compares two methods for creating RFs for next-day severe weather prediction using simulated forecast data from the convection-allowing High-Resolution Ensemble Forecast System, version 2.1 (HREFv2.1). The first method uses predictors from individual ensemble members (IM) at the point of prediction, while the second uses ensemble mean (EM) predictors at multiple spatial points. IM and EM RFs are trained with all predictors as well as predictor subsets, and the Python module tree interpreter (TI) is used to assess RF variable importance and the relationships learned by the RFs. Results show that EM RFs have better objective skill compared to similarly configured IM RFs for all hazards, presumably because EM predictors contain less noise. In both IM and EM RFs, storm variables are found to be most important, followed by index and environment variables. Interestingly, RFs created from storm and index variables tend to produce forecasts with greater or equal skill than those from the all-predictor RFs. TI analysis shows that the RFs emphasize different predictors for different hazards in a way that makes physical sense. Further, TI shows that RFs create calibrated hazard probabilities based on complex, multivariate relationships that go well beyond thresholding 2-5-km updraft helicity.

Severe weather probabilities are derived from the Warn-on-Forecast System (WoFS) run by NOAA's National Severe Storms Laboratory (NSSL) during spring 2018 using the random forest (RF) machine learning algorithm. Recent work has shown this method generates skillful and reliable forecasts when applied to convection-allowing model ensembles for the "Day 1" time range (i.e., 12-36-h lead times), but it has been tested in only one other study for lead times relevant to WoFS (e.g., 0-6 h). Thus, in this paper, various sets of WoFS predictors, which include both environment and storm-based fields, are input into a RF algorithm and trained using the occurrence of severe weather reports within 39 km of a point to produce severe weather probabilities at 0-3-h lead times. We analyze the skill and reliability of these forecasts, sensitivity to different sets of predictors, and avenues for further improvements. The RF algorithm produced very skillful and reliable severe weather probabilities and significantly outperformed baseline probabilities calculated by finding the best performing updraft helicity (UH) threshold and smoothing parameter. Experiments where different sets of predictors were used to derive RF probabilities revealed 1) storm attribute fields contributed significantly more skill than environmental fields, 2) 2-5 km AGL UH and maximum updraft speed were the best performing storm attribute fields, 3) the most skillful ensemble summary metric was a smoothed mean, and 4) the most skillful forecasts were obtained when smoothed UH from individual ensemble members were used as predictors.

During the 2019 Spring Forecasting Experiment in NOAA’s Hazardous Weather Testbed, two NWS forecasters issued experimental probabilistic forecasts of hail, tornadoes, and severe convective wind using NSSL’s Warn-on-Forecast System (WoFS). The aim was to explore forecast skill in the timeframe between severe convective watches and severe convective warnings during the peak of the spring convective season. Hourly forecasts issued during 2100 UTC–0000 UTC, valid from 0100–0200 UTC demonstrate how forecasts change with decreasing lead time. Across all 13 cases in this study, the descriptive outlook statistics (e.g., mean outlook area, number of contours) change slightly and the measures of outlook skill (e.g., Fractions Skill Score, reliability) improve incrementally with decreasing lead time. WoFS updraft helicity (UH) probabilities also improve slightly and less consistently with decreasing lead time, though both the WoFS and the forecasters generated skillful forecasts throughout. Larger skill differences with lead time emerge on a case-by-case basis, illustrating cases where forecasters consistently improved upon WoFS guidance, cases where the guidance and the forecasters recognized small-scale features as lead time decreased, and cases where the forecasters issued small areas of high probabilities using guidance and observations. While forecasts generally “honed in” on the reports with slightly smaller contours and higher probabilities, increased confidence could include higher certainty that severe weather would not occur (e.g., lower probabilities). Long-range (1–5 h) WoFS UH probabilities were skillful, and where the guidance erred, forecasters could adjust for those errors and increase their forecasts’ skill as lead time decreased.

This study assesses the impact of assimilating pseudo‐observations for water vapor mass derived from the Geostationary Operational Environmental Satellites GOES‐16 lightning data on short‐term quantitative precipitation forecasts (QPFs) over the contiguous United States (CONUS), with an emphasis given to regions characterized by an overall poor radar coverage. The GOES‐16/17 provide nearly uniform and high temporal resolution total lightning observations over most of the Americas. To leverage this information for convective scale weather forecasts, a three‐dimensional variational data assimilation (DA) package developed by the National Severe Storm Laboratory was employed to assimilate these data. To mimic operational settings, the Weather Research and Forecasting Model configuration used in NOAA Global Systems Laboratory's High‐Resolution Rapid Refresh Model version 4 was utilized. During the 2020 NOAA Hazardous Weather Testbed Spring Forecasting Experiment, four experiments were run in real time during a 29‐day period over CONUS and surrounding territories to assess the added value of GOES‐16 lightning over conventional radar data. Overall, the lightning DA (LDA) showed benefit in improving QPFs up to 6 hr, with the best improvements seen during the first 3 hr. Owing to notably larger lightning activity over the eastern CONUS, the most noticeable impacts from the LDA were seen there. Case‐by‐case analysis revealed that positive impacts from the LDA were seen over areas characterized by both good (eastern two thirds of CONUS) and poor radar coverage (western one third of CONUS). Highlighting the inherent difficulties in developing an optimal observation operator based on moisture for LDA applications, the GLM‐based DA experiments systematically overestimated rainfall over the eastern two thirds of CONUS while underestimating it over the western one third of CONUS.