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- Author or Editor: Curtis L. Walker x
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Abstract
Forecast systems provide decision support for end users ranging from the solar energy industry to municipalities concerned with road safety. Pavement temperature is an important variable when considering vehicle response to various weather conditions. A complex relationship exists between tire and pavement temperatures that affects vehicle performance. Many forecast systems suffer from inaccurate radiation forecasts resulting in part from the inability to model different types of clouds and their influence on radiation. This research focuses on forecast improvement by determining how cloud type impacts pavement temperature and the amount of shortwave radiation reaching the surface. The study region is the Great Plains where surface radiation data were obtained from the High Plains Regional Climate Center’s Automated Weather Data Network stations. Pavement temperature data were obtained from the Meteorological Assimilation Data Ingest System. Cloud-type identification was possible via the Naval Research Laboratory Cloud Classification algorithm, and clouds were subsequently sorted into five distinct groups: clear conditions, low clouds, middle clouds, high clouds, and cumuliform clouds. Statistical analyses during the daytime in June 2011 revealed that cloud cover lowered pavement temperatures by up to approximately 10°C and dampened downwelling shortwave radiation by up to 400 W m−2. These pavement temperatures and surface radiation observations were strongly correlated, with a maximum correlation coefficient of 0.83. A comparison between cloud-type group identified and cloud cover observed from satellite images provided a measure of confidence in the results and identified cautions with using satellite-based cloud detection.
Abstract
Forecast systems provide decision support for end users ranging from the solar energy industry to municipalities concerned with road safety. Pavement temperature is an important variable when considering vehicle response to various weather conditions. A complex relationship exists between tire and pavement temperatures that affects vehicle performance. Many forecast systems suffer from inaccurate radiation forecasts resulting in part from the inability to model different types of clouds and their influence on radiation. This research focuses on forecast improvement by determining how cloud type impacts pavement temperature and the amount of shortwave radiation reaching the surface. The study region is the Great Plains where surface radiation data were obtained from the High Plains Regional Climate Center’s Automated Weather Data Network stations. Pavement temperature data were obtained from the Meteorological Assimilation Data Ingest System. Cloud-type identification was possible via the Naval Research Laboratory Cloud Classification algorithm, and clouds were subsequently sorted into five distinct groups: clear conditions, low clouds, middle clouds, high clouds, and cumuliform clouds. Statistical analyses during the daytime in June 2011 revealed that cloud cover lowered pavement temperatures by up to approximately 10°C and dampened downwelling shortwave radiation by up to 400 W m−2. These pavement temperatures and surface radiation observations were strongly correlated, with a maximum correlation coefficient of 0.83. A comparison between cloud-type group identified and cloud cover observed from satellite images provided a measure of confidence in the results and identified cautions with using satellite-based cloud detection.
Abstract
This study utilizes a winter severity index (WSI) to characterize the impacts of High (Great) Plains winter storms during the 2006/07–2018/19 winter seasons across Nebraska and the Colorado Front Range. Winter storms are specifically defined based on the severity of their meteorological impacts and are required to influence a majority of Department of Transportation (DOT) districts within both states. Following their identification, winter storms are examined using a jet-centered framework based on the two leading modes of North Pacific jet (NPJ) and North Atlantic jet (NAJ) variability. The analysis reveals that a retracted or equatorward-shifted NPJ establishes a highly amplified flow pattern conducive to cyclogenesis over the central United States, while a poleward- or equatorward-shifted NAJ favors the development of a strongly baroclinic environment across the study region that serves as a focal region for cyclogenesis and precipitation. Composite analyses of winter storms that rank in the top 25% and bottom 25% in terms of their aggregate WSI are also performed to identify characteristics of the synoptic-scale evolution that discriminate between “high impact” and “low impact” events, respectively. High-impact events are found to feature a more amplified upper-tropospheric flow pattern over the eastern North Pacific and western United States relative to low-impact events, which subsequently favors stronger cyclogenesis over the southern plains. The integration of jet regimes with winter storm severity metrics as part of this study offers the potential to enhance impact-based decision support services and provide the weather enterprise, and its stakeholders, with critical life-saving information.
Abstract
This study utilizes a winter severity index (WSI) to characterize the impacts of High (Great) Plains winter storms during the 2006/07–2018/19 winter seasons across Nebraska and the Colorado Front Range. Winter storms are specifically defined based on the severity of their meteorological impacts and are required to influence a majority of Department of Transportation (DOT) districts within both states. Following their identification, winter storms are examined using a jet-centered framework based on the two leading modes of North Pacific jet (NPJ) and North Atlantic jet (NAJ) variability. The analysis reveals that a retracted or equatorward-shifted NPJ establishes a highly amplified flow pattern conducive to cyclogenesis over the central United States, while a poleward- or equatorward-shifted NAJ favors the development of a strongly baroclinic environment across the study region that serves as a focal region for cyclogenesis and precipitation. Composite analyses of winter storms that rank in the top 25% and bottom 25% in terms of their aggregate WSI are also performed to identify characteristics of the synoptic-scale evolution that discriminate between “high impact” and “low impact” events, respectively. High-impact events are found to feature a more amplified upper-tropospheric flow pattern over the eastern North Pacific and western United States relative to low-impact events, which subsequently favors stronger cyclogenesis over the southern plains. The integration of jet regimes with winter storm severity metrics as part of this study offers the potential to enhance impact-based decision support services and provide the weather enterprise, and its stakeholders, with critical life-saving information.
Abstract
Innovative technologies that support implementation of automated vehicles continue to develop at a rapid pace. These advances strive to increase efficiency and safety throughout the global transportation network. One important challenge to these emergent technologies that remains underappreciated is how the vehicles will perform in adverse weather. Each year, weather-related vehicular crashes account for approximately 21% of all highway crashes in the United States. These crashes result in over 5,300 fatalities, injure over 418,000 people, and cost billions of dollars in insurance claims, liability, emergency services, congestion delays, rehabilitation, and environmental damage annually. Automated vehicles have the potential to significantly mitigate these statistics; however, public, private, and academic partnerships between the meteorological and transportation communities must be established to develop solutions to weather impacts now. To date, such interactions have been sparse and largely contribute to a lack of awareness in how these two communities may collaborate together. The purpose of this manuscript is to call the meteorological community to action and proactive engagement with the transportation community. A secondary goal is to make the transportation community aware of the advantages of teaming with the weather enterprise. Automated vehicles will not only increase travel safety, but also have benefits to the meteorological community through increasing availability of high-resolution surface data observations. The future challenges of these emergent technologies in the context of road weather implications focus on vehicle situational awareness and technological sensing capability in all weather conditions, and transforming how drivers and vehicles are informed of weather threats beyond sensing capabilities.
Abstract
Innovative technologies that support implementation of automated vehicles continue to develop at a rapid pace. These advances strive to increase efficiency and safety throughout the global transportation network. One important challenge to these emergent technologies that remains underappreciated is how the vehicles will perform in adverse weather. Each year, weather-related vehicular crashes account for approximately 21% of all highway crashes in the United States. These crashes result in over 5,300 fatalities, injure over 418,000 people, and cost billions of dollars in insurance claims, liability, emergency services, congestion delays, rehabilitation, and environmental damage annually. Automated vehicles have the potential to significantly mitigate these statistics; however, public, private, and academic partnerships between the meteorological and transportation communities must be established to develop solutions to weather impacts now. To date, such interactions have been sparse and largely contribute to a lack of awareness in how these two communities may collaborate together. The purpose of this manuscript is to call the meteorological community to action and proactive engagement with the transportation community. A secondary goal is to make the transportation community aware of the advantages of teaming with the weather enterprise. Automated vehicles will not only increase travel safety, but also have benefits to the meteorological community through increasing availability of high-resolution surface data observations. The future challenges of these emergent technologies in the context of road weather implications focus on vehicle situational awareness and technological sensing capability in all weather conditions, and transforming how drivers and vehicles are informed of weather threats beyond sensing capabilities.
Abstract
Adverse weather conditions are responsible for millions of vehicular crashes, thousands of deaths, and billions of dollars per year in economic and congestion costs. Many transportation agencies utilize a performance or mobility metric to assess how well they maintain road access; however, there is only limited consideration of meteorological impacts to the success of their operations. This research develops the Nebraska winter severity index (NEWINS), which is a daily event-driven index derived for the Nebraska Department of Transportation (NDOT). The NEWINS includes a categorical storm classification framework to capture atmospheric conditions and possible road impacts across diverse spatial regions of Nebraska. A 10-yr (2006–16) winter season database of meteorological variables for Nebraska was obtained from the National Centers for Environmental Information. The NEWINS is based on a weighted linear combination applied to the collected storm classification database to measure severity. The NEWINS results were compared to other meteorological variables, many used in other agencies’ winter severity indices. This comparison verified the NEWINS robustness for the observed events for the 10-yr period. An assessment of the difference between days with observed snow versus days with accumulated snow revealed 39% fewer snow-accumulated days than snow-observed days. Furthermore, the NEWINS results highlighted the greater number of events during the 2009/10 winter season and the lack of events during the 2011/12 winter season. It is expected that the NEWINS could help transportation personnel allocate efficiently resources during adverse weather events. Moreover, the NEWINS framework can be used by other agencies to assess their weather sensitivity.
Abstract
Adverse weather conditions are responsible for millions of vehicular crashes, thousands of deaths, and billions of dollars per year in economic and congestion costs. Many transportation agencies utilize a performance or mobility metric to assess how well they maintain road access; however, there is only limited consideration of meteorological impacts to the success of their operations. This research develops the Nebraska winter severity index (NEWINS), which is a daily event-driven index derived for the Nebraska Department of Transportation (NDOT). The NEWINS includes a categorical storm classification framework to capture atmospheric conditions and possible road impacts across diverse spatial regions of Nebraska. A 10-yr (2006–16) winter season database of meteorological variables for Nebraska was obtained from the National Centers for Environmental Information. The NEWINS is based on a weighted linear combination applied to the collected storm classification database to measure severity. The NEWINS results were compared to other meteorological variables, many used in other agencies’ winter severity indices. This comparison verified the NEWINS robustness for the observed events for the 10-yr period. An assessment of the difference between days with observed snow versus days with accumulated snow revealed 39% fewer snow-accumulated days than snow-observed days. Furthermore, the NEWINS results highlighted the greater number of events during the 2009/10 winter season and the lack of events during the 2011/12 winter season. It is expected that the NEWINS could help transportation personnel allocate efficiently resources during adverse weather events. Moreover, the NEWINS framework can be used by other agencies to assess their weather sensitivity.
