Abstract The Warn-on-Forecast (WoF) program, driven by advanced data assimilation and ensemble design of numerical weather prediction (NWP) systems, seeks to advance 0–3-h NWP to aid National Weather Service warnings for thunderstorm-induced hazards. An early prototype of the WoF prediction system is the National Severe Storms Laboratory (NSSL) Experimental WoF System for ensembles (NEWSe), which comprises 36 ensemble members with varied initial conditions and parameterization suites. In the present study, real-time 3-h quantitative precipitation forecasts (QPFs) during spring 2016 from NEWSe members are compared against those from two real-time deterministic systems: the operational High Resolution Rapid Refresh (HRRR, version 1) and an upgraded, experimental configuration of the HRRR. All three model systems were run at 3-km horizontal grid spacing and differ in initialization, particularly in the radar data assimilation methods. It is the impact of this difference that is evaluated herein using both traditional and scale-aware verification schemes. NEWSe, evaluated deterministically for each member, shows marked improvement over the two HRRR versions for 0–3-h QPFs, especially at higher thresholds and smaller spatial scales. This improvement diminishes with forecast lead time. The experimental HRRR model, which became operational as HRRR version 2 in August 2016, also provides added skill over HRRR version 1.
Abstract The purpose of this study is to demonstrate the capability of an experimental, weather‐adaptive, high‐resolution, deterministic Warn‐on‐Forecast (WoF) analysis and forecast system (WoF3DVAR‐AFS) for predicting high‐impact severe weather events that occurred during the Hazardous Weather Testbed 2019 Spring Forecast Experiments. WoF3DVAR‐AFS uses a three‐dimensional variational (3DVAR) method as its core data assimilation system and the Advanced Research Version of the Weather Research and Forecasting (WRF‐ARW) model as its forward model. Surface measurements provided in meteorological aviation reports and the Oklahoma Mesonet, Doppler radar data, and spaceborne total lightning observations provided by the Geostationary Lightning Mapper are assimilated at 15‐min frequency over a target domain determined by the “Day 1” Convective Outlook product from the Storm Prediction Center. The chief goal of this system is to complement probabilistic forecasts generated by ensemble analysis and forecast systems, such as the experimental Warn‐on‐Forecast System (WoFS) with a higher‐resolution deterministic member to aid forecasters' decision‐making. We performed both qualitative and quantitative evaluations on 0–6 hr forecasts launched hourly from 1900 to 0300 UTC the next day for each of the 12 cases. Aggregated subjective forecast evaluation metrics from each individual case, as well as detailed comparison against available verification datasets, suggest that the forecasts are generally skillful in terms of composite reflectivity fields, quantitative precipitation forecasts, and the strength and location of rotation tracks and damaging winds. This study presents initial efforts to assess the performance of WoF3DVAR‐AFS and provides possible directions for further improvements, including the development of a weather‐adaptive, dual‐resolution analysis and forecast system hybrid with an ensemble system, such as the experimental Warn‐on‐Forecast system.
Abstract Landfalling tropical cyclones (TCs) are among the greatest natural threats to life and property in the United States, since they can produce multiple hazards associated with convective storms over a wide region. Of these hazards, tornadoes within TC rainbands pose a particularly difficult forecast problem owing to their rapid evolution and their frequent occurrence coincident with additional hazards, such as flash flooding and damaging winds. During the 2017 Atlantic hurricane season, Hurricanes Harvey and Irma impacted the continental United States, causing significant loss of life and billions of dollars in property damage. Application of the Warn-on-Forecast (WoF) concept of short-term, probabilistic guidance of convective hazards (Stensrud et al. 2009, 2013), including the potential for tornadoes within TCs, offers the ability to provide forecasters with valuable tools for prioritizing the relative risk from multiple convective threats and effectively communicating them to the public.
Abstract One primary goal of annual Spring Forecasting Experiments (SFEs), which are coorganized by NOAA’s National Severe Storms Laboratory and Storm Prediction Center and conducted in the National Oceanic and Atmospheric Administration’s (NOAA) Hazardous Weather Testbed, is documenting performance characteristics of experimental, convection-allowing modeling systems (CAMs). Since 2007, the number of CAMs (including CAM ensembles) examined in the SFEs has increased dramatically, peaking at six different CAM ensembles in 2015. Meanwhile, major advances have been made in creating, importing, processing, verifying, and developing tools for analyzing and visualizing these large and complex datasets. However, progress toward identifying optimal CAM ensemble configurations has been inhibited because the different CAM systems have been independently designed, making it difficult to attribute differences in performance characteristics. Thus, for the 2016 SFE, a much more coordinated effort among many collaborators was made by agreeing on a set of model specifications (e.g., model version, grid spacing, domain size, and physics) so that the simulations contributed by each collaborator could be combined to form one large, carefully designed ensemble known as the Community Leveraged Unified Ensemble (CLUE). The 2016 CLUE was composed of 65 members contributed by five research institutions and represents an unprecedented effort to enable an evidence-driven decision process to help guide NOAA’s operational modeling efforts. Eight unique experiments were designed within the CLUE framework to examine issues directly relevant to the design of NOAA’s future operational CAM-based ensembles. This article will highlight the CLUE design and present results from one of the experiments examining the impact of single versus multicore CAM ensemble configurations.
Abstract A multiscale analysis of the significant nocturnal tornado outbreak in Tennessee on 2–3 March 2020 is presented. This outbreak included several significant tornadoes and resulted in the second most fatalities (25) and most injuries (309) of all nocturnal tornado events in Tennessee in 1950–2020. The two deadliest tornadoes struck Nashville (EF3 intensity) and Cookeville (EF4) resulting in 5 and 19 fatalities, respectively. The supercell responsible for the tornado outbreak initiated at 0330 UTC 3 March within a region of warm frontogenesis in western Tennessee. Throughout its life cycle, the supercell was located in a region of convective available potential energy near 1000 J kg −1 and 0–1-km storm-relative helicity over 350 m 2 s −2 . Retrospective 3-h forecasts from the experimental Warn-on-Forecast System (WoFS) convection-allowing ensemble initialized after the parent supercell initiated indicated a high probability, high severity scenario for tornadoes across Tennessee and into Nashville through 0700 UTC. Earlier WoFS forecasts indicated a low probability, high severity scenario owing to uncertainty in the initiation of supercells. The presence of these supercells was sensitive to the upstream thermodynamic conditions and warm frontogenesis regions that were inherited from the lateral boundary conditions. In all, this study highlights the potential of the WoFS ensemble to contribute useful probabilistic severe weather information to the short-term forecast process during a nocturnal significant tornado outbreak.
Abstract This first part of a two-part study on storm-scale radar and satellite data assimilation provides an overview of a multicase study conducted as part of the NOAA Warn-on-Forecast (WoF) project. The NSSL Experimental WoF System for ensembles (NEWS-e) is used to produce storm-scale analyses and forecasts of six diverse severe weather events from spring 2013 and 2014. In this study, only Doppler reflectivity and radial velocity observations (and, when available, surface mesonet data) are assimilated into a 36-member, storm-scale ensemble using an ensemble Kalman filter (EnKF) approach. A series of 1-h ensemble forecasts are then initialized from storm-scale analyses during the 1-h period preceding the onset of storm reports. Of particular interest is the ability of these 0–1-h ensemble forecasts to reproduce the low-level rotational characteristics of supercell thunderstorms, as well as other convective hazards. For the tornado-producing thunderstorms considered in this study, ensemble probabilistic forecasts of low-level rotation generally indicated a rotating thunderstorm approximately 30 min before the time of first observed tornado. Displacement errors (often to the north of tornado-affected areas) associated with vorticity swaths were greatest in those forecasts launched 30–60 min before the time of first tornado. Similar forecasts were produced for a tornadic mesovortex along the leading edge of a bow echo and, again, highlighted a well-defined vorticity swath as much as 30 min prior to the first tornado.
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.
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