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Abstract
A potential replacement candidate for the aging operational WSR-88D infrastructure currently in place is the phased array radar (PAR) system. The current WSR-88Ds take ~5 min to produce a full volumetric scan of the atmosphere, whereas PAR technology allows for full volumetric scanning of the same atmosphere every ~1 min. How this increase in temporal frequency of radar observations might affect the National Severe Storms Laboratory’s (NSSL) Warn-on-Forecast system (WoFS), which is a storm-scale ensemble data assimilation and forecast system for severe convective weather, is unclear. Since radar data assimilation is critical for the WoFS, this study explores the optimal temporal frequency of PAR observations for storm-scale data assimilation using the 31 May 2013 El Reno, Oklahoma, tornadic supercell event. The National Severe Storms Laboratory’s National Weather Radar Testbed PAR in Norman, Oklahoma, began scanning this event more than an hour before the first (and strongest) tornado developed near El Reno, and scanned most of the tornadic supercell’s evolution. Several experiments using various cycling and data frequencies to synchronously and asynchronously assimilate these PAR observations are conducted to produce analyses and very short-term forecasts of the El Reno supercell. Forecasts of low-level reflectivity and midlevel updraft helicity are subjectively evaluated and objectively verified using spatial and object-based techniques. Results indicate that assimilating more frequent PAR observations can lead to more accurate analyses and probabilistic forecasts of the El Reno supercell at longer lead times. Hence, PAR is a promising radar platform for WoFS.
Abstract
A potential replacement candidate for the aging operational WSR-88D infrastructure currently in place is the phased array radar (PAR) system. The current WSR-88Ds take ~5 min to produce a full volumetric scan of the atmosphere, whereas PAR technology allows for full volumetric scanning of the same atmosphere every ~1 min. How this increase in temporal frequency of radar observations might affect the National Severe Storms Laboratory’s (NSSL) Warn-on-Forecast system (WoFS), which is a storm-scale ensemble data assimilation and forecast system for severe convective weather, is unclear. Since radar data assimilation is critical for the WoFS, this study explores the optimal temporal frequency of PAR observations for storm-scale data assimilation using the 31 May 2013 El Reno, Oklahoma, tornadic supercell event. The National Severe Storms Laboratory’s National Weather Radar Testbed PAR in Norman, Oklahoma, began scanning this event more than an hour before the first (and strongest) tornado developed near El Reno, and scanned most of the tornadic supercell’s evolution. Several experiments using various cycling and data frequencies to synchronously and asynchronously assimilate these PAR observations are conducted to produce analyses and very short-term forecasts of the El Reno supercell. Forecasts of low-level reflectivity and midlevel updraft helicity are subjectively evaluated and objectively verified using spatial and object-based techniques. Results indicate that assimilating more frequent PAR observations can lead to more accurate analyses and probabilistic forecasts of the El Reno supercell at longer lead times. Hence, PAR is a promising radar platform for WoFS.
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.
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.
Abstract
The Geostationary Operational Environmental Satellite-R (GOES-R) Geostationary Lightning Mapper (GLM) instrument detects total lightning rate at high temporal and spatial resolution over the Americas and adjacent oceanic regions. The GLM observations provide detection and monitoring of deep electrified convection. This study explores the impact of assimilating the GLM-derived flash extent density (FED) on the analyses and short-term forecasts of two severe weather events into an experimental Warn-on-Forecast system (WoFS) using the ensemble Kalman filter data assimilation technique. Sensitivity experiments are conducted using two tornadic severe storm events: one with a line of individual supercells and the other one with both isolated cells and a severe convective line. The control experiment (CTRL) assimilates conventional surface observations and geostationary satellite cloud water path into WoFS. Additional experiments also assimilate either GLM FED or radar data (RAD), or a combination of both (RAD+GLM). It is found that assimilating GLM data in the absence of radar data into the WoFS improves the short-term forecast skill over CTRL in one case, while in the other case it degrades the forecast skill by generating weaker cold pools and overly suppressing convection, mainly owing to assimilating zero FED values in the trailing stratiform regions. Assimilating unexpectedly low FED values in some regions due to low GLM detection efficiency also accounts for the poorer forecasts. Although RAD provides superior forecasts over GLM, the combination RAD+GLM shows further gains in both cases. Additional observation operators should consider different storm types and GLM detection efficiency.
Abstract
The Geostationary Operational Environmental Satellite-R (GOES-R) Geostationary Lightning Mapper (GLM) instrument detects total lightning rate at high temporal and spatial resolution over the Americas and adjacent oceanic regions. The GLM observations provide detection and monitoring of deep electrified convection. This study explores the impact of assimilating the GLM-derived flash extent density (FED) on the analyses and short-term forecasts of two severe weather events into an experimental Warn-on-Forecast system (WoFS) using the ensemble Kalman filter data assimilation technique. Sensitivity experiments are conducted using two tornadic severe storm events: one with a line of individual supercells and the other one with both isolated cells and a severe convective line. The control experiment (CTRL) assimilates conventional surface observations and geostationary satellite cloud water path into WoFS. Additional experiments also assimilate either GLM FED or radar data (RAD), or a combination of both (RAD+GLM). It is found that assimilating GLM data in the absence of radar data into the WoFS improves the short-term forecast skill over CTRL in one case, while in the other case it degrades the forecast skill by generating weaker cold pools and overly suppressing convection, mainly owing to assimilating zero FED values in the trailing stratiform regions. Assimilating unexpectedly low FED values in some regions due to low GLM detection efficiency also accounts for the poorer forecasts. Although RAD provides superior forecasts over GLM, the combination RAD+GLM shows further gains in both cases. Additional observation operators should consider different storm types and GLM detection efficiency.
Abstract
The goal of the National Oceanic and Atmospheric Administration’s (NOAA) Warn-on-Forecast (WoF) program is to provide frequently updating, probabilistic model guidance that will enable National Weather Service (NWS) forecasters to produce more continuous communication of hazardous weather threats (e.g., heavy rainfall, flash floods, damaging wind, large hail, and tornadoes) between the watch and warning temporal and spatial scales. To evaluate the application of this WoF concept for probabilistic short-term flash flood prediction, the 0–3-h rainfall forecasts from NOAA National Severe Storms Laboratory’s (NSSL) experimental WoF System (WoFS) were integrated as the forcing to the NWS operational hydrologic modeling core within the Flooded Locations and Simulated Hydrographs (FLASH) system. Initial assessment of the potential impacts of probabilistic short-term flash flood forecasts from this coupled atmosphere–hydrology (WoFS-FLASH) modeling system were evaluated in the 2018 Hydrometeorology Testbed Multi-Radar Multi-Sensor Hydrology experiment held in Norman, Oklahoma. During the 3-week experiment period, a total of nine NWS forecasters analyzed three retrospective flash flood events in archive mode. This study will describe specifically what information participants extracted from the WoFS-FLASH products during these three archived events, and how this type of information is expected to impact operational decision-making processes. Overall feedback from the testbed participants’ evaluations show promise for the coupled NSSL WoFS-FLASH system probabilistic flash flood model guidance to enable earlier assessment and detection of flash flood threats and to advance the current warning lead time for these events.
Abstract
The goal of the National Oceanic and Atmospheric Administration’s (NOAA) Warn-on-Forecast (WoF) program is to provide frequently updating, probabilistic model guidance that will enable National Weather Service (NWS) forecasters to produce more continuous communication of hazardous weather threats (e.g., heavy rainfall, flash floods, damaging wind, large hail, and tornadoes) between the watch and warning temporal and spatial scales. To evaluate the application of this WoF concept for probabilistic short-term flash flood prediction, the 0–3-h rainfall forecasts from NOAA National Severe Storms Laboratory’s (NSSL) experimental WoF System (WoFS) were integrated as the forcing to the NWS operational hydrologic modeling core within the Flooded Locations and Simulated Hydrographs (FLASH) system. Initial assessment of the potential impacts of probabilistic short-term flash flood forecasts from this coupled atmosphere–hydrology (WoFS-FLASH) modeling system were evaluated in the 2018 Hydrometeorology Testbed Multi-Radar Multi-Sensor Hydrology experiment held in Norman, Oklahoma. During the 3-week experiment period, a total of nine NWS forecasters analyzed three retrospective flash flood events in archive mode. This study will describe specifically what information participants extracted from the WoFS-FLASH products during these three archived events, and how this type of information is expected to impact operational decision-making processes. Overall feedback from the testbed participants’ evaluations show promise for the coupled NSSL WoFS-FLASH system probabilistic flash flood model guidance to enable earlier assessment and detection of flash flood threats and to advance the current warning lead time for these events.
Abstract
Improving the forecasting and communication of weather hazards such as urban floods and extreme winds has been recognized by the World Meteorological Organization (WMO) as a priority for international weather research. The WMO has established a 10-yr High-Impact Weather Project (HIWeather) to address global challenges and accelerate progress on scientific and social solutions. In this review, key challenges in hazard forecasting are first illustrated and summarized via four examples of high-impact weather events. Following this, a synthesis of the requirements, current status, and future research in observations, multiscale data assimilation, multiscale ensemble forecasting, and multiscale coupled hazard modeling is provided.
Abstract
Improving the forecasting and communication of weather hazards such as urban floods and extreme winds has been recognized by the World Meteorological Organization (WMO) as a priority for international weather research. The WMO has established a 10-yr High-Impact Weather Project (HIWeather) to address global challenges and accelerate progress on scientific and social solutions. In this review, key challenges in hazard forecasting are first illustrated and summarized via four examples of high-impact weather events. Following this, a synthesis of the requirements, current status, and future research in observations, multiscale data assimilation, multiscale ensemble forecasting, and multiscale coupled hazard modeling is provided.
Abstract
Assimilation of dual-polarization (dual-pol) observations provides more accurate storm-scale analyses to initialize forecasts of severe convective thunderstorms. This study investigates the impact assimilating experimental sector-scan dual-pol observations has on storm-scale ensemble forecasts and how this impact changes over different data assimilation (DA) windows using the ensemble Kalman filter (EnKF). Ensemble forecasts are initialized after 30, 45, and 60 min of DA for two sets of experiments that assimilate either reflectivity and radial velocity only (EXPZ) or reflectivity and radial velocity plus differential reflectivity (EXPZZDR). This study uses the 31 May 2013 Oklahoma event, which included multiple storms that produced tornadoes and severe hail, with a focus on two storms that impacted El Reno and Stillwater during the event. The earliest initialized forecast of EXPZZDR better predicts the evolution of the El Reno storm than EXPZ, but the two sets of experiments become similar at subsequent forecast times. However, the later EXPZZDR forecasts of the Stillwater storm, which organized toward the end of the DA window, produce improved results compared to EXPZ, in which the storm is less intense and weakens. Evaluation of forecast products for supercell mesocyclones [updraft helicity (UH)] and hail show similar results, with earlier EXPZZDR forecasts better predicting the UH swaths of the El Reno storm and later forecasts producing improved UH and hail swaths for the Stillwater storm. The results indicate that the assimilation of Z DR over fewer DA cycles can produce improved forecasts when DA windows sufficiently cover storms during their initial development and organization.
Abstract
Assimilation of dual-polarization (dual-pol) observations provides more accurate storm-scale analyses to initialize forecasts of severe convective thunderstorms. This study investigates the impact assimilating experimental sector-scan dual-pol observations has on storm-scale ensemble forecasts and how this impact changes over different data assimilation (DA) windows using the ensemble Kalman filter (EnKF). Ensemble forecasts are initialized after 30, 45, and 60 min of DA for two sets of experiments that assimilate either reflectivity and radial velocity only (EXPZ) or reflectivity and radial velocity plus differential reflectivity (EXPZZDR). This study uses the 31 May 2013 Oklahoma event, which included multiple storms that produced tornadoes and severe hail, with a focus on two storms that impacted El Reno and Stillwater during the event. The earliest initialized forecast of EXPZZDR better predicts the evolution of the El Reno storm than EXPZ, but the two sets of experiments become similar at subsequent forecast times. However, the later EXPZZDR forecasts of the Stillwater storm, which organized toward the end of the DA window, produce improved results compared to EXPZ, in which the storm is less intense and weakens. Evaluation of forecast products for supercell mesocyclones [updraft helicity (UH)] and hail show similar results, with earlier EXPZZDR forecasts better predicting the UH swaths of the El Reno storm and later forecasts producing improved UH and hail swaths for the Stillwater storm. The results indicate that the assimilation of Z DR over fewer DA cycles can produce improved forecasts when DA windows sufficiently cover storms during their initial development and organization.
Abstract
The increasing maturity of the Warn-on-Forecast System (WoFS) coupled with the now operational GOES-16 satellite allows for the first time a comprehensive analysis of the relative impacts of assimilating GOES-16 all-sky 6.2-, 6.9-, and 7.3-μm channel radiances compared to other radar and satellite observations. The WoFS relies on cloud property retrievals such as cloud water path, which have been proven to increase forecast skill compared to only assimilating radar data and other conventional observations. The impacts of assimilating clear-sky radiances have also been explored and shown to provide useful information on midtropospheric moisture content in the near-storm environment. Assimilation of all-sky radiances adds a layer of complexity and is tested to determine its effectiveness across four events occurring in the spring and summer of 2019. Qualitative and object-based verification of severe weather and the near-storm environment are used to assess the impact of assimilating all-sky radiances compared to the current model configuration. We focus our study through the entire WoFS analysis and forecasting cycle (1900–0600 UTC, daily) so that the impacts throughout the evolution of convection from initiation to large upscale growth can be assessed. Overall, assimilating satellite data improves forecasts relative to radar-only assimilation experiments. The retrieval method with clear-sky radiances performs best overall, but assimilating all-sky radiances does have very positive impacts in certain conditions. In particular, all-sky radiance assimilation improved convective initiation forecast of severe storms in several instances. This work represents an initial attempt at assimilating all-sky radiances into the WoFS and additional research is ongoing to further improve forecast skill.
Abstract
The increasing maturity of the Warn-on-Forecast System (WoFS) coupled with the now operational GOES-16 satellite allows for the first time a comprehensive analysis of the relative impacts of assimilating GOES-16 all-sky 6.2-, 6.9-, and 7.3-μm channel radiances compared to other radar and satellite observations. The WoFS relies on cloud property retrievals such as cloud water path, which have been proven to increase forecast skill compared to only assimilating radar data and other conventional observations. The impacts of assimilating clear-sky radiances have also been explored and shown to provide useful information on midtropospheric moisture content in the near-storm environment. Assimilation of all-sky radiances adds a layer of complexity and is tested to determine its effectiveness across four events occurring in the spring and summer of 2019. Qualitative and object-based verification of severe weather and the near-storm environment are used to assess the impact of assimilating all-sky radiances compared to the current model configuration. We focus our study through the entire WoFS analysis and forecasting cycle (1900–0600 UTC, daily) so that the impacts throughout the evolution of convection from initiation to large upscale growth can be assessed. Overall, assimilating satellite data improves forecasts relative to radar-only assimilation experiments. The retrieval method with clear-sky radiances performs best overall, but assimilating all-sky radiances does have very positive impacts in certain conditions. In particular, all-sky radiance assimilation improved convective initiation forecast of severe storms in several instances. This work represents an initial attempt at assimilating all-sky radiances into the WoFS and additional research is ongoing to further improve forecast skill.
Abstract
An object-based verification method for short-term, storm-scale probabilistic forecasts was developed and applied to mesocyclone guidance produced by the experimental Warn-on-Forecast System (WoFS) in 63 cases from 2017 to 2018. The probabilistic mesocyclone guidance was generated by calculating gridscale ensemble probabilities from WoFS forecasts of updraft helicity (UH) in layers 2–5 km (midlevel) and 0–2 km (low-level) above ground level (AGL) aggregated over 60-min periods. The resulting ensemble probability swaths are associated with individual thunderstorms and treated as objects with a single, representative probability value prescribed. A mesocyclone probability object, conceptually, is a region bounded by the ensemble forecast envelope of a mesocyclone track for a given thunderstorm over 1 h. The mesocyclone probability objects were matched against rotation track objects in Multi-Radar Multi-Sensor data using the total interest score, but with the maximum displacement varied between 0, 9, 15, and 30 km. Forecast accuracy and reliability were assessed at four different forecast lead time periods: 0–60, 30–90, 60–120, and 90–150 min. In the 0–60-min forecast period, the low-level UH probabilistic forecasts had a POD, FAR, and CSI of 0.46, 0.45, and 0.31, respectively, with a probability threshold of 22.2% (the threshold of maximum CSI). In the 90–150-min forecast period, the POD and CSI dropped to 0.39 and 0.27 while FAR remained relatively unchanged. Forecast probabilities > 60% overpredicted the likelihood of observed mesocyclones in the 0–60-min period; however, reliability improved when allowing larger maximum displacements for object matching and at longer lead times.
Abstract
An object-based verification method for short-term, storm-scale probabilistic forecasts was developed and applied to mesocyclone guidance produced by the experimental Warn-on-Forecast System (WoFS) in 63 cases from 2017 to 2018. The probabilistic mesocyclone guidance was generated by calculating gridscale ensemble probabilities from WoFS forecasts of updraft helicity (UH) in layers 2–5 km (midlevel) and 0–2 km (low-level) above ground level (AGL) aggregated over 60-min periods. The resulting ensemble probability swaths are associated with individual thunderstorms and treated as objects with a single, representative probability value prescribed. A mesocyclone probability object, conceptually, is a region bounded by the ensemble forecast envelope of a mesocyclone track for a given thunderstorm over 1 h. The mesocyclone probability objects were matched against rotation track objects in Multi-Radar Multi-Sensor data using the total interest score, but with the maximum displacement varied between 0, 9, 15, and 30 km. Forecast accuracy and reliability were assessed at four different forecast lead time periods: 0–60, 30–90, 60–120, and 90–150 min. In the 0–60-min forecast period, the low-level UH probabilistic forecasts had a POD, FAR, and CSI of 0.46, 0.45, and 0.31, respectively, with a probability threshold of 22.2% (the threshold of maximum CSI). In the 90–150-min forecast period, the POD and CSI dropped to 0.39 and 0.27 while FAR remained relatively unchanged. Forecast probabilities > 60% overpredicted the likelihood of observed mesocyclones in the 0–60-min period; however, reliability improved when allowing larger maximum displacements for object matching and at longer lead times.
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 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
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.
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.
