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Abstract
Tornadoes produced by right-moving supercells (RMs) and quasi-linear convective systems (QLCSs) are compared across the contiguous United States for the period 2003–21, based on the maximum F/EF-scale rating per hour on a 40-km horizontal grid. The frequency of QLCS tornadoes has increased dramatically since 2003, while the frequency of RM tornadoes has decreased during that same period. The finding of prior work that the most common damage rating for QLCS tornadoes at night is EF1 persists in this larger, independent sample. A comparison of WSR-88D radar attributes between RM and QLCS tornadoes shows no appreciable differences between EF0 tornadoes produced by either convective mode. Differences become apparent for EF1–2 tornadoes, where rotational velocity is larger and velocity couplet diameter is smaller for RM tornadoes compared to QLCS tornadoes. The frequency of tornadic debris signatures (TDSs) in dual-polarization data is also larger for EF1–2 RM tornadoes when controlling for tornadoes sampled relatively close to the radar sites and in those occurring during daylight versus overnight. The weaker rotational velocities, broader velocity couplet diameters, and lower frequencies of TDSs both close to the radar and at night for QLCS EF1 tornadoes suggest that a combination of inadequate radar sampling and occasional misclassification of wind damage may be responsible for the irregularities in the historical record of QLCS tornado reports.
Significance Statement
A comparison of radar attributes between tornadoes with right-moving supercells and squall-line mesovortices suggests some irregularities in squall-line tornado records in the contiguous United States. The irregularities appear to be the result of both inadequate radar sampling for the relatively shallow squall-line tornadoes and occasional misclassification of wind damage with the lack of other corroborating evidence, especially overnight.
Abstract
Tornadoes produced by right-moving supercells (RMs) and quasi-linear convective systems (QLCSs) are compared across the contiguous United States for the period 2003–21, based on the maximum F/EF-scale rating per hour on a 40-km horizontal grid. The frequency of QLCS tornadoes has increased dramatically since 2003, while the frequency of RM tornadoes has decreased during that same period. The finding of prior work that the most common damage rating for QLCS tornadoes at night is EF1 persists in this larger, independent sample. A comparison of WSR-88D radar attributes between RM and QLCS tornadoes shows no appreciable differences between EF0 tornadoes produced by either convective mode. Differences become apparent for EF1–2 tornadoes, where rotational velocity is larger and velocity couplet diameter is smaller for RM tornadoes compared to QLCS tornadoes. The frequency of tornadic debris signatures (TDSs) in dual-polarization data is also larger for EF1–2 RM tornadoes when controlling for tornadoes sampled relatively close to the radar sites and in those occurring during daylight versus overnight. The weaker rotational velocities, broader velocity couplet diameters, and lower frequencies of TDSs both close to the radar and at night for QLCS EF1 tornadoes suggest that a combination of inadequate radar sampling and occasional misclassification of wind damage may be responsible for the irregularities in the historical record of QLCS tornado reports.
Significance Statement
A comparison of radar attributes between tornadoes with right-moving supercells and squall-line mesovortices suggests some irregularities in squall-line tornado records in the contiguous United States. The irregularities appear to be the result of both inadequate radar sampling for the relatively shallow squall-line tornadoes and occasional misclassification of wind damage with the lack of other corroborating evidence, especially overnight.
Abstract
One of the most prominent mesoscale phenomena in the coastal zone is the sea-breeze/land-breeze circulation. The pattern and its implications for the weather in coastal areas are well described, and with mesoscale-resolving operational NWP models the circulation can be captured. In this study, a straightforward method to identify sea and land breezes based on the change in wind direction in the column above a grid point on the coastline is presented. The method was tested for southern Sweden using archived output from the HARMONIE-AROME model with promising results, describing both the seasonal and diurnal cycles well. In areas with a complex coastline, such as narrow straits, the concept of the land–sea breeze becomes less clear, and several ways to address this problem for the suggested method are discussed. With an operational index of the sea and land breezes, the forecaster can better understand and express the weather situation and add value for people in the coastal zone. Further, the indices can be used to study systematic biases in the model and to create climatologies of the sea and land breezes.
Significance Statement
A wind pattern that is frequently occurring in the coastal zone is the sea-breeze/land-breeze circulation, and the purpose of this study is to test a new method to automatically identify sea breezes and land breezes in weather forecasts. Knowing if a sea breeze or a land breeze is occurring is helpful for the operational weather forecaster in understanding the weather situation. It can also be used to study systematic model behavior, for example, errors in the forecast temperature during sea-breeze conditions. The method has been tested for seven coastal sites in Sweden and shows promising results both in case studies and multiyear statistics.
Abstract
One of the most prominent mesoscale phenomena in the coastal zone is the sea-breeze/land-breeze circulation. The pattern and its implications for the weather in coastal areas are well described, and with mesoscale-resolving operational NWP models the circulation can be captured. In this study, a straightforward method to identify sea and land breezes based on the change in wind direction in the column above a grid point on the coastline is presented. The method was tested for southern Sweden using archived output from the HARMONIE-AROME model with promising results, describing both the seasonal and diurnal cycles well. In areas with a complex coastline, such as narrow straits, the concept of the land–sea breeze becomes less clear, and several ways to address this problem for the suggested method are discussed. With an operational index of the sea and land breezes, the forecaster can better understand and express the weather situation and add value for people in the coastal zone. Further, the indices can be used to study systematic biases in the model and to create climatologies of the sea and land breezes.
Significance Statement
A wind pattern that is frequently occurring in the coastal zone is the sea-breeze/land-breeze circulation, and the purpose of this study is to test a new method to automatically identify sea breezes and land breezes in weather forecasts. Knowing if a sea breeze or a land breeze is occurring is helpful for the operational weather forecaster in understanding the weather situation. It can also be used to study systematic model behavior, for example, errors in the forecast temperature during sea-breeze conditions. The method has been tested for seven coastal sites in Sweden and shows promising results both in case studies and multiyear statistics.
Abstract
Infrequent lightning flashes occurring outside of surface precipitation pose challenges to Impact-Based Decision Support Services (IDSS) for outdoor activities. This paper examines the remote sensing observations from an event on 20 August 2019 where multiple cloud-to-ground flashes occurred over 10 km outside surface precipitation (lowest radar tilt reflectivity < 10 dBZ and no evidence of surface precipitation) in a trailing stratiform region of a mesoscale convective system. The goal is to demonstrate the fusion of radar with multiple lightning observations and a lightning risk model to demonstrate how reflectivity and differential reflectivity combined provided the best indicator for the potential of lightning where all of the other lightning safety methods failed. A total of 13 lightning flashes were observed by the Geostationary Lightning Mapper (GLM) within the trailing stratiform region between 2100 and 2300 UTC. The average size of the 13 lightning flashes was 3184 km2, with an average total optical energy of 7734 fJ. A total of 75 NLDN flash locations were coincident with the 13 GLM flashes, resulting in an average of 5.8 NLDN flashes [in-cloud (IC) and cloud-to-ground (CG)] per GLM flash. In total, five of the GLM flashes contained at least one positive cloud-to-ground flash (+CG) flash identified by the NLDN, with peak amplitudes ranging between 66 and 136 kA. All eight CG flashes identified by the NLDN were located more than 10 km outside surface precipitation. The only indication of the potential of these infrequently large flashes was the presence of depolarization streaks in differential reflectivity (Z DR) and enhanced reflectivity near the melting layer.
Abstract
Infrequent lightning flashes occurring outside of surface precipitation pose challenges to Impact-Based Decision Support Services (IDSS) for outdoor activities. This paper examines the remote sensing observations from an event on 20 August 2019 where multiple cloud-to-ground flashes occurred over 10 km outside surface precipitation (lowest radar tilt reflectivity < 10 dBZ and no evidence of surface precipitation) in a trailing stratiform region of a mesoscale convective system. The goal is to demonstrate the fusion of radar with multiple lightning observations and a lightning risk model to demonstrate how reflectivity and differential reflectivity combined provided the best indicator for the potential of lightning where all of the other lightning safety methods failed. A total of 13 lightning flashes were observed by the Geostationary Lightning Mapper (GLM) within the trailing stratiform region between 2100 and 2300 UTC. The average size of the 13 lightning flashes was 3184 km2, with an average total optical energy of 7734 fJ. A total of 75 NLDN flash locations were coincident with the 13 GLM flashes, resulting in an average of 5.8 NLDN flashes [in-cloud (IC) and cloud-to-ground (CG)] per GLM flash. In total, five of the GLM flashes contained at least one positive cloud-to-ground flash (+CG) flash identified by the NLDN, with peak amplitudes ranging between 66 and 136 kA. All eight CG flashes identified by the NLDN were located more than 10 km outside surface precipitation. The only indication of the potential of these infrequently large flashes was the presence of depolarization streaks in differential reflectivity (Z DR) and enhanced reflectivity near the melting layer.
Abstract
An approach to reduce forecast data to coastal waveguide coordinates is described and demonstrated, informed by the literature on coastally trapped waves (CTWs). All discussion is limited to the Australian mainland but the approach is generally relevant to regions where CTWs influence sea level, including the Americas and Africa. The approach does not produce new forecasts, but aims to focus forecaster attention on aspects of sea level forecasts prominent on the long Australian coast. The approach also explicitly addresses spatial issues associated with measuring coastal paths. Coastal paths are scale dependent and forecast models discretize the coastal boundary differently. A well-defined coastal path is required for the quantitative application of CTW concepts such as propagation distance and offshore direction. The relevance of coastally trapped signals and remote forcing is documented in the oceanographic literature, but is effectively unknown to the general public and rarely mentioned in press reports of sea level events such as nuisance flooding. Routine presentation of forecast guidance in waveguide coordinates could contribute to the transfer of oceanographic research understanding into forecast narratives. In addition, the approach can facilitate quantitative forecast evaluations that target CTW properties. Two ocean forecast systems are contrasted in this framework for the Australian mainland. One year of daily forecasts are compared, with indications that model baroclinicity is of practical relevance.
Abstract
An approach to reduce forecast data to coastal waveguide coordinates is described and demonstrated, informed by the literature on coastally trapped waves (CTWs). All discussion is limited to the Australian mainland but the approach is generally relevant to regions where CTWs influence sea level, including the Americas and Africa. The approach does not produce new forecasts, but aims to focus forecaster attention on aspects of sea level forecasts prominent on the long Australian coast. The approach also explicitly addresses spatial issues associated with measuring coastal paths. Coastal paths are scale dependent and forecast models discretize the coastal boundary differently. A well-defined coastal path is required for the quantitative application of CTW concepts such as propagation distance and offshore direction. The relevance of coastally trapped signals and remote forcing is documented in the oceanographic literature, but is effectively unknown to the general public and rarely mentioned in press reports of sea level events such as nuisance flooding. Routine presentation of forecast guidance in waveguide coordinates could contribute to the transfer of oceanographic research understanding into forecast narratives. In addition, the approach can facilitate quantitative forecast evaluations that target CTW properties. Two ocean forecast systems are contrasted in this framework for the Australian mainland. One year of daily forecasts are compared, with indications that model baroclinicity is of practical relevance.
Abstract
It is argued here that even with the development of objective algorithms, convection-allowing numerical models, and artificial intelligence/machine learning, conceptual models will still be useful for forecasters until all these methods can fully satisfy the forecast requirements in the future. Conceptual models can help forecasters form forecast ideas quickly. They also can make up for the deficiencies of the numerical model and other objective methods. Furthermore, they can help forecasters understand the weather, and then help the forecasters lock in on the key features affecting the forecast as soon as possible. Ultimately, conceptual models can help the forecaster serve the end users faster, and better understand the forecast results during the service process. Based on the above considerations, construction of new conceptual models should have the following characteristics: 1) be guided by purpose, 2) focus on improving the ability of forecasters, 3) have multiangle consideration, 4) have multiscale fusion, and 5) need to be tested and corrected continuously. The traditional conceptual models used for forecasts of severe convective weather should be replaced gradually by new models that incorporate these principles.
Abstract
It is argued here that even with the development of objective algorithms, convection-allowing numerical models, and artificial intelligence/machine learning, conceptual models will still be useful for forecasters until all these methods can fully satisfy the forecast requirements in the future. Conceptual models can help forecasters form forecast ideas quickly. They also can make up for the deficiencies of the numerical model and other objective methods. Furthermore, they can help forecasters understand the weather, and then help the forecasters lock in on the key features affecting the forecast as soon as possible. Ultimately, conceptual models can help the forecaster serve the end users faster, and better understand the forecast results during the service process. Based on the above considerations, construction of new conceptual models should have the following characteristics: 1) be guided by purpose, 2) focus on improving the ability of forecasters, 3) have multiangle consideration, 4) have multiscale fusion, and 5) need to be tested and corrected continuously. The traditional conceptual models used for forecasts of severe convective weather should be replaced gradually by new models that incorporate these principles.
Abstract
It is argued here that even with the development of objective algorithms, convection-allowing numerical models, and artificial intelligence/machine learning, conceptual models will still be useful for forecasters until all these methods can fully satisfy the forecast requirements in the future. Conceptual models can help forecasters form forecast ideas quickly. They also can make up for the deficiencies of the numerical model and other objective methods. Furthermore, they can help forecasters understand the weather, and then help the forecasters lock in on the key features affecting the forecast as soon as possible. Ultimately, conceptual models can help the forecaster serve the end users faster, and better understand the forecast results during the service process. Based on the above considerations, construction of new conceptual models should have the following characteristics: 1) be guided by purpose, 2) focus on improving the ability of forecasters, 3) have multiangle consideration, 4) have multiscale fusion, and 5) need to be tested and corrected continuously. The traditional conceptual models used for forecasts of severe convective weather should be replaced gradually by new models that incorporate these principles.
Abstract
It is argued here that even with the development of objective algorithms, convection-allowing numerical models, and artificial intelligence/machine learning, conceptual models will still be useful for forecasters until all these methods can fully satisfy the forecast requirements in the future. Conceptual models can help forecasters form forecast ideas quickly. They also can make up for the deficiencies of the numerical model and other objective methods. Furthermore, they can help forecasters understand the weather, and then help the forecasters lock in on the key features affecting the forecast as soon as possible. Ultimately, conceptual models can help the forecaster serve the end users faster, and better understand the forecast results during the service process. Based on the above considerations, construction of new conceptual models should have the following characteristics: 1) be guided by purpose, 2) focus on improving the ability of forecasters, 3) have multiangle consideration, 4) have multiscale fusion, and 5) need to be tested and corrected continuously. The traditional conceptual models used for forecasts of severe convective weather should be replaced gradually by new models that incorporate these principles.
Abstract
In 2001, the National Weather Service (NWS) began a Lightning Safety Awareness Campaign to reduce lightning-related fatalities in the United States. Although fatalities have decreased 41% since the campaign began, lightning still poses a significant threat to public safety as the majority of victims have little or no warning of cloud-to-ground lightning. This suggests it would be valuable to message the threat of lightning before it occurs, especially to NWS core partners that have the responsibility to protect large numbers of people. During the summer of 2018, a subset of forecasters from the Jacksonville, Florida, NWS Weather Forecast Office investigated if messaging the threat of cloud-to-ground (CG) lightning in developing convection was possible. Based on previous CG lightning forecasting research, forecasters incorporated new high-resolution Geostationary Operational Environmental Satellite (GOES)-16 Day Cloud Phase Distinction red–green–blue (RGB) composite imagery with Multi-Radar Multi-Sensor isothermal reflectivity and total lightning data to determine if there was enough confidence to message the threat of CG lightning before it occurred. This paper will introduce the Day Cloud Phase Distinction RGB composite, show how it can add value for short-term lightning forecasting, and provide an operational example illustrating how fusing these datasets together may be able to provide confidence and extend the lead time when messaging the threat of cloud-to-ground lightning before it occurs.
Abstract
In 2001, the National Weather Service (NWS) began a Lightning Safety Awareness Campaign to reduce lightning-related fatalities in the United States. Although fatalities have decreased 41% since the campaign began, lightning still poses a significant threat to public safety as the majority of victims have little or no warning of cloud-to-ground lightning. This suggests it would be valuable to message the threat of lightning before it occurs, especially to NWS core partners that have the responsibility to protect large numbers of people. During the summer of 2018, a subset of forecasters from the Jacksonville, Florida, NWS Weather Forecast Office investigated if messaging the threat of cloud-to-ground (CG) lightning in developing convection was possible. Based on previous CG lightning forecasting research, forecasters incorporated new high-resolution Geostationary Operational Environmental Satellite (GOES)-16 Day Cloud Phase Distinction red–green–blue (RGB) composite imagery with Multi-Radar Multi-Sensor isothermal reflectivity and total lightning data to determine if there was enough confidence to message the threat of CG lightning before it occurred. This paper will introduce the Day Cloud Phase Distinction RGB composite, show how it can add value for short-term lightning forecasting, and provide an operational example illustrating how fusing these datasets together may be able to provide confidence and extend the lead time when messaging the threat of cloud-to-ground lightning before it occurs.
Abstract
Tornadoes that occur at night pose particularly dangerous societal risks, and these risks are amplified across the southeastern United States. The purpose of this study is to highlight some of the characteristics distinguishing the convective environment accompanying these events. This is accomplished by building upon previous research that assesses the predictive power of meteorological parameters. In particular, this study uses the Statistical Severe Convective Risk Assessment Model (SSCRAM) to determine how well convective parameters explain tornado potential across the Southeast during the months of November–May and during the 0300–1200 UTC (nocturnal) time frame. This study compares conditional tornado probabilities across the Southeast during November–May nocturnal hours to those probabilities for all other November–May environments across the contiguous United States. This study shows that effective bulk shear, effective storm-relative helicity, and effective-layer significant tornado parameter yield the strongest predictability for the November–May nocturnal Southeast regime among investigated parameters. This study demonstrates that November–May southeastern U.S. nocturnal predictability is generally similar to that within other regimes across the contiguous United States. However, selected ranges of multiple parameters are associated with slightly better predictability for the nocturnal Southeast regime. Additionally, this study assesses conditional November–May nocturnal tornado probabilities across a coastal domain embedded within the Southeast. Nocturnal coastal tornado predictability is shown to generally be lower than the other regimes. All of the differences highlight several forecast challenges, which this study analyzes in detail.
Abstract
Tornadoes that occur at night pose particularly dangerous societal risks, and these risks are amplified across the southeastern United States. The purpose of this study is to highlight some of the characteristics distinguishing the convective environment accompanying these events. This is accomplished by building upon previous research that assesses the predictive power of meteorological parameters. In particular, this study uses the Statistical Severe Convective Risk Assessment Model (SSCRAM) to determine how well convective parameters explain tornado potential across the Southeast during the months of November–May and during the 0300–1200 UTC (nocturnal) time frame. This study compares conditional tornado probabilities across the Southeast during November–May nocturnal hours to those probabilities for all other November–May environments across the contiguous United States. This study shows that effective bulk shear, effective storm-relative helicity, and effective-layer significant tornado parameter yield the strongest predictability for the November–May nocturnal Southeast regime among investigated parameters. This study demonstrates that November–May southeastern U.S. nocturnal predictability is generally similar to that within other regimes across the contiguous United States. However, selected ranges of multiple parameters are associated with slightly better predictability for the nocturnal Southeast regime. Additionally, this study assesses conditional November–May nocturnal tornado probabilities across a coastal domain embedded within the Southeast. Nocturnal coastal tornado predictability is shown to generally be lower than the other regimes. All of the differences highlight several forecast challenges, which this study analyzes in detail.
Abstract
This study presents the development and testing of two statistical models that simulate tornado potential and wind speed. This study reports on the first-ever development of two multiple regression–based models to assist warning forecasters in statistically simulating tornado probability and tornado wind speed in a diagnostic manner based on radar-observed tornado signature attributes and one environmental parameter. Based on a robust database, the radar-based storm-scale circulation attributes (strength, height above ground, clarity) combine with the effective-layer significant tornado parameter to establish a tornado probability. The second model adds the categorical presence (absence) of a tornadic debris signature to derive the tornado wind speed. While the fits of these models are considered somewhat modest, their regression coefficients generally offer physical consistency, based on findings from previous research. Furthermore, simulating these models on an independent dataset and other past cases featured in previous research reveals encouraging signals for accurately identifying higher potential for tornadoes. This statistical application using large-sample-size datasets can serve as a first step to streamlining the process of reproducibly quantifying tornado threats by service-providing organizations in a diagnostic manner, encouraging consistency in messaging scientifically sound information for the protection of life and property.
Abstract
This study presents the development and testing of two statistical models that simulate tornado potential and wind speed. This study reports on the first-ever development of two multiple regression–based models to assist warning forecasters in statistically simulating tornado probability and tornado wind speed in a diagnostic manner based on radar-observed tornado signature attributes and one environmental parameter. Based on a robust database, the radar-based storm-scale circulation attributes (strength, height above ground, clarity) combine with the effective-layer significant tornado parameter to establish a tornado probability. The second model adds the categorical presence (absence) of a tornadic debris signature to derive the tornado wind speed. While the fits of these models are considered somewhat modest, their regression coefficients generally offer physical consistency, based on findings from previous research. Furthermore, simulating these models on an independent dataset and other past cases featured in previous research reveals encouraging signals for accurately identifying higher potential for tornadoes. This statistical application using large-sample-size datasets can serve as a first step to streamlining the process of reproducibly quantifying tornado threats by service-providing organizations in a diagnostic manner, encouraging consistency in messaging scientifically sound information for the protection of life and property.
