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Abstract
The Laseyer is a very local and uncommon wind storm in a narrow and steep valley in northeastern Switzerland. Whereas the ambient wind is from west to north-west, the strong surface wind in the valley is from the east, leading to gust speeds that become dangerous for the local train running into the valley to the Wasserauen station. To minimize the risk of derailment and to improve passenger comfort, the train service provider Appenzeller Bahnen (AB) has developed a new warning algorithm in close collaboration with academia (ETH Zurich) and the Swiss national weather service (Meteoswiss). The aim is to accurately predict the Laseyer wind storm several hours in advance, but also to reduce the number of false alarms. The new warning system is based on the Meteoswiss operational ensemble prediction system at 1.1 km horizontal mesh size, which is then used in combination with an observation-based machine learning approach to probabilistically forecast Laseyer events up to 30 hours in advance. A particular challenge in developing the new system was to introduce the customer, AB, to the modern concept of probabilistic numerical weather prediction, which requires a careful risk assessment by the customer. Hence, the development of the warning system is a process in which the customer and the warning provider closely collaborate and specify the final warning products to be delivered operationally. The operation of the new warning system during the 2021-22 Laseyer season shows that it is working successfully, and also indicates that the warning thresholds in the warning algorithm can be adjusted in the future to minimize false alarms without increasing the number of in missed events.
Abstract
The Laseyer is a very local and uncommon wind storm in a narrow and steep valley in northeastern Switzerland. Whereas the ambient wind is from west to north-west, the strong surface wind in the valley is from the east, leading to gust speeds that become dangerous for the local train running into the valley to the Wasserauen station. To minimize the risk of derailment and to improve passenger comfort, the train service provider Appenzeller Bahnen (AB) has developed a new warning algorithm in close collaboration with academia (ETH Zurich) and the Swiss national weather service (Meteoswiss). The aim is to accurately predict the Laseyer wind storm several hours in advance, but also to reduce the number of false alarms. The new warning system is based on the Meteoswiss operational ensemble prediction system at 1.1 km horizontal mesh size, which is then used in combination with an observation-based machine learning approach to probabilistically forecast Laseyer events up to 30 hours in advance. A particular challenge in developing the new system was to introduce the customer, AB, to the modern concept of probabilistic numerical weather prediction, which requires a careful risk assessment by the customer. Hence, the development of the warning system is a process in which the customer and the warning provider closely collaborate and specify the final warning products to be delivered operationally. The operation of the new warning system during the 2021-22 Laseyer season shows that it is working successfully, and also indicates that the warning thresholds in the warning algorithm can be adjusted in the future to minimize false alarms without increasing the number of in missed events.
Abstract
This study explores reasons for differences in discriminations of nontornadic and tornadic supercell environments between a recent study of field project (FP) radiosonde observations and RUC/RAP-based studies. Two differences are explored: 1) differences in relative skill between near-ground and deeper-layer storm-relative helicity (SRH) and 2) differences in skill for storm-relative winds (SRWs) seen in observed soundings that are not seen in RUC/RAP-based analyses. Results show that RUC/RAP-derived near-ground SRH continues to show larger skill than deeper-layer SRH for springtime, afternoon/evening cases over the plains (the “FP” domain), although 0-1-km SRH becomes more skillful than 0–500 m SRH. The skill of kinematic variables decreases over the FP domain, as the skill of mixed-layer CAPE (MLCAPE) and the percent of the low-level horizontal vorticity that is streamwise increases for significant tornadoes. Large skill is found for mean ground-relative winds (GRWs) over all layers tested, but the skill of SRWs using Bunkers motion is relatively small. The field project dataset is shown to be biased toward particularly high-end nontornadic supercells, with more tornado-favorable mixed-layer lifted condensation levels (MLLCLs), lapse rates, and low-level shear/SRH compared to the nontornadic cases in the RUC/RAP dataset over the FP domain. The skill of deeper-layer SRH, GRWs, SRWs, and MLCAPE are unusually large in the field project sample, which highlights variables that may increase the likelihood of tornadoes when other variables that relate to supercell tornado production (low-level shear/SRH and MLLCLs) are already in a tornado-favorable range. The skill of deeper-layer kinematic variables is particularly evident when observed storm motions are used instead of Bunkers motion.
Abstract
This study explores reasons for differences in discriminations of nontornadic and tornadic supercell environments between a recent study of field project (FP) radiosonde observations and RUC/RAP-based studies. Two differences are explored: 1) differences in relative skill between near-ground and deeper-layer storm-relative helicity (SRH) and 2) differences in skill for storm-relative winds (SRWs) seen in observed soundings that are not seen in RUC/RAP-based analyses. Results show that RUC/RAP-derived near-ground SRH continues to show larger skill than deeper-layer SRH for springtime, afternoon/evening cases over the plains (the “FP” domain), although 0-1-km SRH becomes more skillful than 0–500 m SRH. The skill of kinematic variables decreases over the FP domain, as the skill of mixed-layer CAPE (MLCAPE) and the percent of the low-level horizontal vorticity that is streamwise increases for significant tornadoes. Large skill is found for mean ground-relative winds (GRWs) over all layers tested, but the skill of SRWs using Bunkers motion is relatively small. The field project dataset is shown to be biased toward particularly high-end nontornadic supercells, with more tornado-favorable mixed-layer lifted condensation levels (MLLCLs), lapse rates, and low-level shear/SRH compared to the nontornadic cases in the RUC/RAP dataset over the FP domain. The skill of deeper-layer SRH, GRWs, SRWs, and MLCAPE are unusually large in the field project sample, which highlights variables that may increase the likelihood of tornadoes when other variables that relate to supercell tornado production (low-level shear/SRH and MLLCLs) are already in a tornado-favorable range. The skill of deeper-layer kinematic variables is particularly evident when observed storm motions are used instead of Bunkers motion.
Abstract
The paper presents the development of a high-resolution mesoscale atmospheric numerical model Advanced Research WRF (ARW 3.7, henceforth ARW)-based operational forecast system, and evaluation of its surface wind forecasts using buoys and satellite data in the Indian Ocean. This was set up as part of the ocean state forecasting system to force the operational ocean models at the Indian National Centre for Ocean Information Services (INCOIS). Evaluation of winds is carried out by comparing the ARW forecasts with ocean buoys and Scatterometer observations during 2016. The analysis is conducted separately with coastal and open ocean buoys, revealing marginally better performance in the open ocean simulations. The comparison of ARW forecasted winds against offshore buoy winds show mean differences for wind speed and direction of −0.1 m/s and −3.3°, with RMSEs of 2.1m/s and 39.0° and correlation of 0.75 and 0.42, respectively. At coastal regimes, the mean differences for wind speed and direction are 0.6 m/s and 2.6°, with RMSEs of 2.2 m/s and 57° and correlations of 0.63 and 0.60, respectively. ARW model performs reasonably well compared to the NCMRWF model in capturing wind speed variability in the Arabian Sea compared to the Bay of Bengal. Particularly during the VSCS Vardah, the ARW model provided more accurate forecasts (high skill score 80-90%) compared to the NCMRWF model. These results indicate the efficacy of the WRF-based forecast system in predicting surface wind fields in the Indian Ocean region, particularly in the coastal areas, thus endorsing its use for an effective operational ocean forecasting at INCOIS.
Abstract
The paper presents the development of a high-resolution mesoscale atmospheric numerical model Advanced Research WRF (ARW 3.7, henceforth ARW)-based operational forecast system, and evaluation of its surface wind forecasts using buoys and satellite data in the Indian Ocean. This was set up as part of the ocean state forecasting system to force the operational ocean models at the Indian National Centre for Ocean Information Services (INCOIS). Evaluation of winds is carried out by comparing the ARW forecasts with ocean buoys and Scatterometer observations during 2016. The analysis is conducted separately with coastal and open ocean buoys, revealing marginally better performance in the open ocean simulations. The comparison of ARW forecasted winds against offshore buoy winds show mean differences for wind speed and direction of −0.1 m/s and −3.3°, with RMSEs of 2.1m/s and 39.0° and correlation of 0.75 and 0.42, respectively. At coastal regimes, the mean differences for wind speed and direction are 0.6 m/s and 2.6°, with RMSEs of 2.2 m/s and 57° and correlations of 0.63 and 0.60, respectively. ARW model performs reasonably well compared to the NCMRWF model in capturing wind speed variability in the Arabian Sea compared to the Bay of Bengal. Particularly during the VSCS Vardah, the ARW model provided more accurate forecasts (high skill score 80-90%) compared to the NCMRWF model. These results indicate the efficacy of the WRF-based forecast system in predicting surface wind fields in the Indian Ocean region, particularly in the coastal areas, thus endorsing its use for an effective operational ocean forecasting at INCOIS.
Abstract
This paper examines ice particle reorganization by three-dimensional horizontal kinematic flows within the comma head regions of two U.S. East Coast winter storms and the effect of reorganization on particle concentrations within snowbands in each storm. In these simplified experiments, the kinematic flows are from the initialization of the HRRR model. Ice particles falling through the comma head were started from either 9-, 8-, or 7-km altitude, spaced every 200 m, and were transported north or northwest, arriving within the north or northwest half of the primary snowband in each storm. The greatest particle concentration enhancement within each band was a factor of 2.32–3.84 for the 16–17 December 2020 storm and 1.76–2.32 for the 29–30 January 2022 storm. Trajectory analyses for particles originating at 4 km on the southeast side of the comma head beneath the dry slot showed that this region supplied particles to the south side of the band with particle enhancements of factor of 1.36–2.08 for the 16–17 December 2020 storm and 1.04–2.16 for the 29–30 January 2022 storm. Snowfall within the bands had two source regions: 1) on the north/northwestern side, from ice particles falling from the comma head, and 2) on the southeastern side, from particles forming at or below 4-km altitude and transported northwestward by low-level flow off the Atlantic. While the findings give information on the source of particles in the bands, they do not definitively determine the cause of precipitation banding since other factors, such as large-scale ascent and embedded convection, also contribute to snow growth.
Significance Statement
Wintertime storms along the east coast of North America can produce heavy snowfall, high winds, coastal flooding, and cold temperatures, resulting in major economic impacts within the northeast U.S. urban corridor. The heaviest snowfall typically occurs within snowbands, elongated narrow regions identifiable by high reflectivity on radar. This paper examines the potential sources of the ice particles contributing to the snowbands and how the flow fields throughout the storm can contribute to enhanced particle concentrations within the bands.
Abstract
This paper examines ice particle reorganization by three-dimensional horizontal kinematic flows within the comma head regions of two U.S. East Coast winter storms and the effect of reorganization on particle concentrations within snowbands in each storm. In these simplified experiments, the kinematic flows are from the initialization of the HRRR model. Ice particles falling through the comma head were started from either 9-, 8-, or 7-km altitude, spaced every 200 m, and were transported north or northwest, arriving within the north or northwest half of the primary snowband in each storm. The greatest particle concentration enhancement within each band was a factor of 2.32–3.84 for the 16–17 December 2020 storm and 1.76–2.32 for the 29–30 January 2022 storm. Trajectory analyses for particles originating at 4 km on the southeast side of the comma head beneath the dry slot showed that this region supplied particles to the south side of the band with particle enhancements of factor of 1.36–2.08 for the 16–17 December 2020 storm and 1.04–2.16 for the 29–30 January 2022 storm. Snowfall within the bands had two source regions: 1) on the north/northwestern side, from ice particles falling from the comma head, and 2) on the southeastern side, from particles forming at or below 4-km altitude and transported northwestward by low-level flow off the Atlantic. While the findings give information on the source of particles in the bands, they do not definitively determine the cause of precipitation banding since other factors, such as large-scale ascent and embedded convection, also contribute to snow growth.
Significance Statement
Wintertime storms along the east coast of North America can produce heavy snowfall, high winds, coastal flooding, and cold temperatures, resulting in major economic impacts within the northeast U.S. urban corridor. The heaviest snowfall typically occurs within snowbands, elongated narrow regions identifiable by high reflectivity on radar. This paper examines the potential sources of the ice particles contributing to the snowbands and how the flow fields throughout the storm can contribute to enhanced particle concentrations within the bands.
Abstract
A user-focused verification approach for evaluating probability forecasts of binary outcomes (also known as probabilistic classifiers) is demonstrated that (i) is based on proper scoring rules, (ii) focuses on user decision thresholds, and (iii) provides actionable insights. It is argued that when categorical performance diagrams and the critical success index are used to evaluate overall predictive performance, rather than the discrimination ability of probabilistic forecasts, they may produce misleading results. Instead, Murphy diagrams are shown to provide a better understanding of the overall predictive performance as a function of user probabilistic decision threshold. We illustrate how to select a proper scoring rule, based on the relative importance of different user decision thresholds, and how this choice impacts scores of overall predictive performance and supporting measures of discrimination and calibration. These approaches and ideas are demonstrated using several probabilistic thunderstorm forecast systems as well as synthetic forecast data. Furthermore, a fair method for comparing the performance of probabilistic and categorical forecasts is illustrated using the fixed risk multicategorical (FIRM) score, which is a proper scoring rule directly connected to values on the Murphy diagram. While the methods are illustrated using thunderstorm forecasts, they are applicable for evaluating probabilistic forecasts for any situation with binary outcomes.
Significance Statement
Recently, several papers have presented verification results for probabilistic forecasts using so-called categorical performance diagrams, which summarize multiple verification metrics. While categorical performance diagrams measure discrimination ability, we demonstrate how they can potentially lead to incorrect conclusions when evaluating overall predictive performance of probabilistic forecasts. By reviewing recent advances in the statistical literature, we show a comprehensive approach for the meteorological community that (i) does not reward a forecaster who “hedges” their forecast, (ii) focuses on the importance of the forecast user’s decision threshold(s), and (iii) provides actionable insights. Additionally, we present an approach for fairly comparing the skill of categorical forecasts to probabilistic forecasts.
Abstract
A user-focused verification approach for evaluating probability forecasts of binary outcomes (also known as probabilistic classifiers) is demonstrated that (i) is based on proper scoring rules, (ii) focuses on user decision thresholds, and (iii) provides actionable insights. It is argued that when categorical performance diagrams and the critical success index are used to evaluate overall predictive performance, rather than the discrimination ability of probabilistic forecasts, they may produce misleading results. Instead, Murphy diagrams are shown to provide a better understanding of the overall predictive performance as a function of user probabilistic decision threshold. We illustrate how to select a proper scoring rule, based on the relative importance of different user decision thresholds, and how this choice impacts scores of overall predictive performance and supporting measures of discrimination and calibration. These approaches and ideas are demonstrated using several probabilistic thunderstorm forecast systems as well as synthetic forecast data. Furthermore, a fair method for comparing the performance of probabilistic and categorical forecasts is illustrated using the fixed risk multicategorical (FIRM) score, which is a proper scoring rule directly connected to values on the Murphy diagram. While the methods are illustrated using thunderstorm forecasts, they are applicable for evaluating probabilistic forecasts for any situation with binary outcomes.
Significance Statement
Recently, several papers have presented verification results for probabilistic forecasts using so-called categorical performance diagrams, which summarize multiple verification metrics. While categorical performance diagrams measure discrimination ability, we demonstrate how they can potentially lead to incorrect conclusions when evaluating overall predictive performance of probabilistic forecasts. By reviewing recent advances in the statistical literature, we show a comprehensive approach for the meteorological community that (i) does not reward a forecaster who “hedges” their forecast, (ii) focuses on the importance of the forecast user’s decision threshold(s), and (iii) provides actionable insights. Additionally, we present an approach for fairly comparing the skill of categorical forecasts to probabilistic forecasts.
Abstract
Though discrete supercells are usually emphasized in severe weather forecasting, hazard production is often preceded by their interaction with external features. Past studies have examined the impacts of cell mergers, boundaries, other supercells, convective systems, etc., but usually in isolation. Here, we investigate 230 significant tornadoes, 246 significant hail events, and 191 null cases across the United States using WSR-88D data. We find that in over 90% of cases, supercells that produced significant hazards were accompanied by external features. These features varied between hazards; for example, hailstorms were more frequently near boundaries than tornadic storms. That said, the positions of these features with respect to the storm (and storm-relative inflow) distinguished between hazard potential and type. For example, tornadic storms were predominantly on the more-unstable side of a boundary, while non-tornadic storms and hailstorms were on the less-unstable side. Similarly, tornadic storms had more cells in their rear flanks than forward flanks, while hailstorms had more cells in their forward flanks than rear flanks. Although these conditions were observed regardless of the background environment, they were affected by certain variables in the vertical profile, especially in tornadic cases. Namely, when storm-relative inflow was stronger and lifting condensation level (LCL) was lower, tornadic storms were accompanied by more rear-flank cells that were closer to the storm, more directly opposite the storm-relative inflow, for a longer period of time. We propose that these interactions likely modulate hazard potential, in ways that are not accounted for in traditional environmental parameter-based forecasting.
Abstract
Though discrete supercells are usually emphasized in severe weather forecasting, hazard production is often preceded by their interaction with external features. Past studies have examined the impacts of cell mergers, boundaries, other supercells, convective systems, etc., but usually in isolation. Here, we investigate 230 significant tornadoes, 246 significant hail events, and 191 null cases across the United States using WSR-88D data. We find that in over 90% of cases, supercells that produced significant hazards were accompanied by external features. These features varied between hazards; for example, hailstorms were more frequently near boundaries than tornadic storms. That said, the positions of these features with respect to the storm (and storm-relative inflow) distinguished between hazard potential and type. For example, tornadic storms were predominantly on the more-unstable side of a boundary, while non-tornadic storms and hailstorms were on the less-unstable side. Similarly, tornadic storms had more cells in their rear flanks than forward flanks, while hailstorms had more cells in their forward flanks than rear flanks. Although these conditions were observed regardless of the background environment, they were affected by certain variables in the vertical profile, especially in tornadic cases. Namely, when storm-relative inflow was stronger and lifting condensation level (LCL) was lower, tornadic storms were accompanied by more rear-flank cells that were closer to the storm, more directly opposite the storm-relative inflow, for a longer period of time. We propose that these interactions likely modulate hazard potential, in ways that are not accounted for in traditional environmental parameter-based forecasting.
Abstract
Recent operationally driven research has generated a framework, known as the three ingredients method and mesovortex warning system, that can help forecasters anticipate mesovortex development and issue warnings within quasi-linear convective systems (QLCSs). However, dual-polarization radar data has not yet been incorporated into this framework. Therefore, several dual- and single-polarization radar signatures associated with QLCS mesovortices were analyzed to determine if they could provide additional information about mesovortex development and intensity. An analysis of 167 mesovortices showed that 1) K DP drops precede ∼95% of mesovortices and provide an initial indication of where a mesovortex may develop; 2) midlevel K DP cores are a potentially useful precursor signature because they precede a majority of mesovortices and have higher magnitudes for mesovortices that produce wind damage or tornadoes; 3) low-level K DP cores and areas of enhanced spectrum width have higher magnitudes for mesovortices that produce wind damage or tornadoes but tend to develop at about the same time as the mesovortex, which makes them more useful as diagnostic than as predictive signatures; and 4) as range from the radar increases, the radar signatures become less useful in anticipating mesovortex intensity but can still be used to anticipate mesovortex development or build confidence in mesovortex existence.
Significance Statement
The purpose of this study is to look at weather radar features that might help forecasters predict the development and intensity of tornadoes and strong winds within linear thunderstorm systems. Our results show that the intensity and trends of some radar features are helpful in showing when these hazards might develop and how strong they might be, while other radar features are less helpful. This information can help forecasters focus on the most useful radar features and ultimately provide the best possible warnings.
Abstract
Recent operationally driven research has generated a framework, known as the three ingredients method and mesovortex warning system, that can help forecasters anticipate mesovortex development and issue warnings within quasi-linear convective systems (QLCSs). However, dual-polarization radar data has not yet been incorporated into this framework. Therefore, several dual- and single-polarization radar signatures associated with QLCS mesovortices were analyzed to determine if they could provide additional information about mesovortex development and intensity. An analysis of 167 mesovortices showed that 1) K DP drops precede ∼95% of mesovortices and provide an initial indication of where a mesovortex may develop; 2) midlevel K DP cores are a potentially useful precursor signature because they precede a majority of mesovortices and have higher magnitudes for mesovortices that produce wind damage or tornadoes; 3) low-level K DP cores and areas of enhanced spectrum width have higher magnitudes for mesovortices that produce wind damage or tornadoes but tend to develop at about the same time as the mesovortex, which makes them more useful as diagnostic than as predictive signatures; and 4) as range from the radar increases, the radar signatures become less useful in anticipating mesovortex intensity but can still be used to anticipate mesovortex development or build confidence in mesovortex existence.
Significance Statement
The purpose of this study is to look at weather radar features that might help forecasters predict the development and intensity of tornadoes and strong winds within linear thunderstorm systems. Our results show that the intensity and trends of some radar features are helpful in showing when these hazards might develop and how strong they might be, while other radar features are less helpful. This information can help forecasters focus on the most useful radar features and ultimately provide the best possible warnings.
Abstract
On 8 August 2023, a wind-driven wildfire pushed across the city of Lahaina, located in West Maui, Hawaii, resulting in at least 100 deaths and an estimated economic loss of 4–6 billion dollars. The Lahaina wildfire was associated with strong, dry downslope winds gusting to 31–41 m s−1 (60–80 kt; 1 kt ≈ 0.51 m s−1) that initiated the fire by damaging power infrastructure. The fire spread rapidly in invasive grasses growing in abandoned agricultural land upslope from Lahaina. This paper describes the synoptic and mesoscale meteorology associated with this event, as well as its predictability. Stronger-than-normal northeast trade winds, accompanied by a stable layer near the crest level of the West Maui Mountains, resulted in a high-amplitude mountain-wave response and a strong downslope windstorm. Mesoscale model predictions were highly accurate regarding the location, strength, and timing of the strong winds. Hurricane Dora, which passed approximately 1300 km to the south of Maui, does not appear to have had a significant impact on the occurrence and intensity of the winds associated with the wildfire event. The Maui wildfire was preceded by a wetter-than-normal winter and near-normal summer conditions.
Significance Statement
The 2023 Maui wildfire was one of the most damaging of the past century, with at least 100 fatalities. This paper describes the meteorological conditions associated with the event and demonstrates that excellent model forecasts made the threat foreseeable.
Abstract
On 8 August 2023, a wind-driven wildfire pushed across the city of Lahaina, located in West Maui, Hawaii, resulting in at least 100 deaths and an estimated economic loss of 4–6 billion dollars. The Lahaina wildfire was associated with strong, dry downslope winds gusting to 31–41 m s−1 (60–80 kt; 1 kt ≈ 0.51 m s−1) that initiated the fire by damaging power infrastructure. The fire spread rapidly in invasive grasses growing in abandoned agricultural land upslope from Lahaina. This paper describes the synoptic and mesoscale meteorology associated with this event, as well as its predictability. Stronger-than-normal northeast trade winds, accompanied by a stable layer near the crest level of the West Maui Mountains, resulted in a high-amplitude mountain-wave response and a strong downslope windstorm. Mesoscale model predictions were highly accurate regarding the location, strength, and timing of the strong winds. Hurricane Dora, which passed approximately 1300 km to the south of Maui, does not appear to have had a significant impact on the occurrence and intensity of the winds associated with the wildfire event. The Maui wildfire was preceded by a wetter-than-normal winter and near-normal summer conditions.
Significance Statement
The 2023 Maui wildfire was one of the most damaging of the past century, with at least 100 fatalities. This paper describes the meteorological conditions associated with the event and demonstrates that excellent model forecasts made the threat foreseeable.
Abstract
Development of an impact-based decision support forecasting tool for surface-transportation hazards requires consideration for what impacts the product is intended to capture and how to scale forecast information to impacts to then categorize impact severity. In this first part of the series, we discuss the motivation and intent of such a product, in addition to outlining the approach we take to leverage existing and new research to develop the product. Traffic disruptions (e.g., crashes, increased travel times, roadway restrictions, or closures) are the intended impacts, where impact severity levels are intended to scale to reflect the increasing severity of adverse driving conditions that can correlate with a need for enhanced mitigation efforts by motorists and/or transportation agencies (e.g., slowing down, avoiding travel, and imposing roadway restrictions or closures). Previous research on how weather and road conditions impact transportation and novel research herein to create a metric for crash impact based on precipitation type and local hour of the day are both intended to help scale weather forecasts to impacts. Impact severity classifications can ultimately be determined through consideration of any thresholds used by transportation agencies, in conjunction with the scaling metrics.
Significance Statement
Weather can profoundly impact surface transportation and motorist safety. Because of this and because there are no explicit tools available to forecasters to identify and communicate potential impacts to surface transportation, there is a desire for the development of such a forecast product. However, doing so requires careful consideration for what impacts are intended to be included, how weather corresponds to impacts, and how thresholds for impact severity should be defined. In this first part of the paper series, we outline each of these aspects and present novel research and approaches for the development of an impact-based forecast product specifically tailored to surface-transportation hazards. The product is ultimately intended to improve motorist safety and mobility on roads.
Abstract
Development of an impact-based decision support forecasting tool for surface-transportation hazards requires consideration for what impacts the product is intended to capture and how to scale forecast information to impacts to then categorize impact severity. In this first part of the series, we discuss the motivation and intent of such a product, in addition to outlining the approach we take to leverage existing and new research to develop the product. Traffic disruptions (e.g., crashes, increased travel times, roadway restrictions, or closures) are the intended impacts, where impact severity levels are intended to scale to reflect the increasing severity of adverse driving conditions that can correlate with a need for enhanced mitigation efforts by motorists and/or transportation agencies (e.g., slowing down, avoiding travel, and imposing roadway restrictions or closures). Previous research on how weather and road conditions impact transportation and novel research herein to create a metric for crash impact based on precipitation type and local hour of the day are both intended to help scale weather forecasts to impacts. Impact severity classifications can ultimately be determined through consideration of any thresholds used by transportation agencies, in conjunction with the scaling metrics.
Significance Statement
Weather can profoundly impact surface transportation and motorist safety. Because of this and because there are no explicit tools available to forecasters to identify and communicate potential impacts to surface transportation, there is a desire for the development of such a forecast product. However, doing so requires careful consideration for what impacts are intended to be included, how weather corresponds to impacts, and how thresholds for impact severity should be defined. In this first part of the paper series, we outline each of these aspects and present novel research and approaches for the development of an impact-based forecast product specifically tailored to surface-transportation hazards. The product is ultimately intended to improve motorist safety and mobility on roads.
Abstract
In line with the continued focus of the National Weather Service (NWS) to provide impact-based decision support services (IDSS) and effectively communicate potential impacts, a new IDSS forecasting tool for surface-transportation hazards is in development at the Weather Prediction Center: the hourly winter storm severity index (WSSI-H). This second part of the series outlines the current algorithms and thresholds for the components of the WSSI-H, which has been developed in line with the approach and considerations discussed in Part I of this series. These components—snow amount, ice accumulation, snow rate, liquid rate, and blowing and drifting snow—each address a specific hazard for motorists. The inclusion of metrics related to driving conditions for untreated road surfaces and time-of-day factoring for active precipitation types helps directly tie forecasted weather conditions to transportation impacts. Impact severity level thresholds are approximately in line with thresholds used by transportation agencies when considering various mitigation strategies (e.g., imposing speed restrictions or closing roadways). Whereas the product is not meant to forecast specific impacts (e.g., road closure or pileup), impact severity levels are designed to scale with increasingly poor travel conditions, which can prompt various mitigation efforts from motorists or transportation agencies to maintain safety. WSSI-H outputs for three winter events are discussed in depth to highlight the potential utility of the product. Overall, the WSSI-H is intended to provide high-resolution situational awareness of potential surface-transportation-related impacts and aid in enhanced collaborations between NWS forecasters and stakeholders like transportation agencies to improve motorist safety.
Significance Statement
A new impact-based forecast product designed to aid in situational awareness of potential impacts from surface-transportation-related hazards is in development. In this second part of the series, we outline the algorithms and thresholds for the various components of the product, where each component addresses a unique hazard. Product outputs for three winter events are presented to highlight the potential utility of the product in an operational forecast setting. Ultimately, enhanced collaboration between forecasters and transportation agencies alongside guidance from this product will bolster consistent messaging to motorists and improve safety and mobility on roads.
Abstract
In line with the continued focus of the National Weather Service (NWS) to provide impact-based decision support services (IDSS) and effectively communicate potential impacts, a new IDSS forecasting tool for surface-transportation hazards is in development at the Weather Prediction Center: the hourly winter storm severity index (WSSI-H). This second part of the series outlines the current algorithms and thresholds for the components of the WSSI-H, which has been developed in line with the approach and considerations discussed in Part I of this series. These components—snow amount, ice accumulation, snow rate, liquid rate, and blowing and drifting snow—each address a specific hazard for motorists. The inclusion of metrics related to driving conditions for untreated road surfaces and time-of-day factoring for active precipitation types helps directly tie forecasted weather conditions to transportation impacts. Impact severity level thresholds are approximately in line with thresholds used by transportation agencies when considering various mitigation strategies (e.g., imposing speed restrictions or closing roadways). Whereas the product is not meant to forecast specific impacts (e.g., road closure or pileup), impact severity levels are designed to scale with increasingly poor travel conditions, which can prompt various mitigation efforts from motorists or transportation agencies to maintain safety. WSSI-H outputs for three winter events are discussed in depth to highlight the potential utility of the product. Overall, the WSSI-H is intended to provide high-resolution situational awareness of potential surface-transportation-related impacts and aid in enhanced collaborations between NWS forecasters and stakeholders like transportation agencies to improve motorist safety.
Significance Statement
A new impact-based forecast product designed to aid in situational awareness of potential impacts from surface-transportation-related hazards is in development. In this second part of the series, we outline the algorithms and thresholds for the various components of the product, where each component addresses a unique hazard. Product outputs for three winter events are presented to highlight the potential utility of the product in an operational forecast setting. Ultimately, enhanced collaboration between forecasters and transportation agencies alongside guidance from this product will bolster consistent messaging to motorists and improve safety and mobility on roads.
