Browse
Abstract
This paper describes a novel machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple ML techniques and utilizing diverse data sources. Our multimodal framework, called Hurricast, efficiently combines spatial–temporal data with statistical data by extracting features with deep learning encoder–decoder architectures and predicting with gradient-boosted trees. We evaluate our models in the North Atlantic and eastern Pacific basins in 2016–19 for 24-h lead-time track and intensity forecasts and show they achieve comparable mean absolute error and skill to current operational forecast models while computing in seconds. Furthermore, the inclusion of Hurricast into an operational forecast consensus model could improve upon the National Hurricane Center’s official forecast, thus highlighting the complementary properties with existing approaches. In summary, our work demonstrates that utilizing machine learning techniques to combine different data sources can lead to new opportunities in tropical cyclone forecasting.
Significance Statement
Machine learning techniques have not been fully explored for improving tropical cyclone movement and intensity changes. This work shows how advanced machine learning techniques combined with routinely available information can be used to improve 24-h tropical cyclone forecasts efficiently. The successes demonstrated for 24-h forecasts provide a recipe for improving predictions for longer lead times, further reducing forecast uncertainties and benefiting society.
Abstract
This paper describes a novel machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple ML techniques and utilizing diverse data sources. Our multimodal framework, called Hurricast, efficiently combines spatial–temporal data with statistical data by extracting features with deep learning encoder–decoder architectures and predicting with gradient-boosted trees. We evaluate our models in the North Atlantic and eastern Pacific basins in 2016–19 for 24-h lead-time track and intensity forecasts and show they achieve comparable mean absolute error and skill to current operational forecast models while computing in seconds. Furthermore, the inclusion of Hurricast into an operational forecast consensus model could improve upon the National Hurricane Center’s official forecast, thus highlighting the complementary properties with existing approaches. In summary, our work demonstrates that utilizing machine learning techniques to combine different data sources can lead to new opportunities in tropical cyclone forecasting.
Significance Statement
Machine learning techniques have not been fully explored for improving tropical cyclone movement and intensity changes. This work shows how advanced machine learning techniques combined with routinely available information can be used to improve 24-h tropical cyclone forecasts efficiently. The successes demonstrated for 24-h forecasts provide a recipe for improving predictions for longer lead times, further reducing forecast uncertainties and benefiting society.
Abstract
The special observing periods (SOPs) of the Year of Polar Prediction present an opportunity to assess the skill of numerical weather prediction (NWP) models operating over the Antarctic, many of which assimilated additional observations during an SOP to produce some of the most observationally informed model output to date for the Antarctic region and permitting closer examination of model performance under various configurations and parameterizations. This intercomparison evaluates six NWP models spanning global and limited domains, coupled and uncoupled, operating in the Antarctic during the austral summer SOP between 16 November 2018 and 15 February 2019. Model performance varies regionally between each model and parameter; however, the majority of models were found to be warm biased over the continent with respect to ERA5 at analysis, some with biases growing to 3.5 K over land after 48 h. Temperature biases over sea ice were found to be strongly correlated between analysis and 48 h in uncoupled models, but that this correlation can be reduced through coupling to a sea ice model. Surface pressure and 500-hPa geopotential height forecasts and biases were found to be strongly correlated over open ocean in all models, and wind speed forecasts were found to be generally more skillful at higher resolutions with the exception of fast modeled winds over sloping terrain in PolarWRF. Surface sensible and latent heat flux forecasts and biases produced diverse correlations, varying by model, parameter, and gridcell classification. Of the models evaluated, those which couple atmosphere, sea ice, and ocean typically exhibited stronger skill.
Significance Statement
We evaluated the performance of six numerical weather prediction models operating over the Antarctic during the Year of Polar Prediction austral summer special observing period (16 November 2018–15 February 2019). Our analysis found that several models were as much as 3.5 K warmer than the reference analysis (ERA5) at 48 h over land and were strongly correlated over sea ice in uncoupled models; however, this correlation is reduced through coupling to a sea ice model. Surface pressure biases are communicated to the midtroposphere over the ocean at larger spatial scales, while higher resolution showed an increase in positive wind biases at longer forecasts. Surface turbulent heat fluxes produced complex correlations with other forecast parameters, which should be quantified in future studies. Coupled models that included an ocean/sea ice component typically performed better; providing evidence that the inclusion of such components leads to improved model performance, even at short time scales such as these.
Abstract
The special observing periods (SOPs) of the Year of Polar Prediction present an opportunity to assess the skill of numerical weather prediction (NWP) models operating over the Antarctic, many of which assimilated additional observations during an SOP to produce some of the most observationally informed model output to date for the Antarctic region and permitting closer examination of model performance under various configurations and parameterizations. This intercomparison evaluates six NWP models spanning global and limited domains, coupled and uncoupled, operating in the Antarctic during the austral summer SOP between 16 November 2018 and 15 February 2019. Model performance varies regionally between each model and parameter; however, the majority of models were found to be warm biased over the continent with respect to ERA5 at analysis, some with biases growing to 3.5 K over land after 48 h. Temperature biases over sea ice were found to be strongly correlated between analysis and 48 h in uncoupled models, but that this correlation can be reduced through coupling to a sea ice model. Surface pressure and 500-hPa geopotential height forecasts and biases were found to be strongly correlated over open ocean in all models, and wind speed forecasts were found to be generally more skillful at higher resolutions with the exception of fast modeled winds over sloping terrain in PolarWRF. Surface sensible and latent heat flux forecasts and biases produced diverse correlations, varying by model, parameter, and gridcell classification. Of the models evaluated, those which couple atmosphere, sea ice, and ocean typically exhibited stronger skill.
Significance Statement
We evaluated the performance of six numerical weather prediction models operating over the Antarctic during the Year of Polar Prediction austral summer special observing period (16 November 2018–15 February 2019). Our analysis found that several models were as much as 3.5 K warmer than the reference analysis (ERA5) at 48 h over land and were strongly correlated over sea ice in uncoupled models; however, this correlation is reduced through coupling to a sea ice model. Surface pressure biases are communicated to the midtroposphere over the ocean at larger spatial scales, while higher resolution showed an increase in positive wind biases at longer forecasts. Surface turbulent heat fluxes produced complex correlations with other forecast parameters, which should be quantified in future studies. Coupled models that included an ocean/sea ice component typically performed better; providing evidence that the inclusion of such components leads to improved model performance, even at short time scales such as these.
Abstract
Deep learning models have been shown to perform well in terms of the radar-based precipitation nowcasting problem when compared with advection-based models. Yet, most of the existing literature has used locally trained models, and relatively little is known about how to construct deep learning–based nowcasting models applicable in wider domains. We conduct experiments for precipitation nowcasting using deep learning models spanning the region around Japan with the Japan Meteorological Agency radar precipitation dataset with 1-km spatial resolution and 5-min temporal resolution. We trained the model with radar data sampled from all over Japan, then applied transfer learning with regional data. Through this experiment, it is found that combining data from all over Japan is effective in improving the forecast performance of heavy precipitation with deep learning models.
Significance Statement
This paper proposes a methodological improvement for the problem of short-term rainfall forecasting (nowcasting) using deep learning. We trained deep learning models with data sampled from all over Japan, then applied transfer learning with regional data. We compared the performance metrics of several modeling approaches and demonstrated that learning from heavy precipitation examples collected from wider domains improves the performance significantly. This finding suggests that there are a lot of commonalities in the time evolution of rainfall patterns at different geographical locations, which could be exploited to improve the model’s performance. A future research topic could be to learn from a larger dataset, including precipitation datasets from other countries.
Abstract
Deep learning models have been shown to perform well in terms of the radar-based precipitation nowcasting problem when compared with advection-based models. Yet, most of the existing literature has used locally trained models, and relatively little is known about how to construct deep learning–based nowcasting models applicable in wider domains. We conduct experiments for precipitation nowcasting using deep learning models spanning the region around Japan with the Japan Meteorological Agency radar precipitation dataset with 1-km spatial resolution and 5-min temporal resolution. We trained the model with radar data sampled from all over Japan, then applied transfer learning with regional data. Through this experiment, it is found that combining data from all over Japan is effective in improving the forecast performance of heavy precipitation with deep learning models.
Significance Statement
This paper proposes a methodological improvement for the problem of short-term rainfall forecasting (nowcasting) using deep learning. We trained deep learning models with data sampled from all over Japan, then applied transfer learning with regional data. We compared the performance metrics of several modeling approaches and demonstrated that learning from heavy precipitation examples collected from wider domains improves the performance significantly. This finding suggests that there are a lot of commonalities in the time evolution of rainfall patterns at different geographical locations, which could be exploited to improve the model’s performance. A future research topic could be to learn from a larger dataset, including precipitation datasets from other countries.
Abstract
When highly resolved precipitation forecasts are verified against observations, displacement errors tend to overshadow all other aspects of forecast quality. The appropriate treatment and explicit measurement of such errors remains a challenging task. This study explores a new verification technique that uses the phase of complex wavelet coefficients to quantify spatially varying displacements. Idealized and realistic test cases from the MesoVICT project demonstrate that our approach yields helpful results in a variety of situations where popular alternatives may struggle. Potential benefits of very high spatial resolutions can be identified even when the observational dataset is coarsely resolved itself. The new score can furthermore be applied not only to precipitation but also variables such as wind speed and potential temperature, thereby overcoming a limitation of many established location scores.
Significance Statement
One important requirement for a useful weather forecast is its ability to predict the placement of weather events such as cold fronts, low pressure systems, or groups of thunderstorms. Errors in the predicted location are not easy to quantify: some established quality measures combine location and other error sources in one score, others are only applicable if the data contain well-defined and easily identifiable objects. Here we introduce an alternative location score that avoids such assumptions and is thus widely applicable. As an additional benefit, we can separate displacement errors into different spatial scales and localize them on a weather map.
Abstract
When highly resolved precipitation forecasts are verified against observations, displacement errors tend to overshadow all other aspects of forecast quality. The appropriate treatment and explicit measurement of such errors remains a challenging task. This study explores a new verification technique that uses the phase of complex wavelet coefficients to quantify spatially varying displacements. Idealized and realistic test cases from the MesoVICT project demonstrate that our approach yields helpful results in a variety of situations where popular alternatives may struggle. Potential benefits of very high spatial resolutions can be identified even when the observational dataset is coarsely resolved itself. The new score can furthermore be applied not only to precipitation but also variables such as wind speed and potential temperature, thereby overcoming a limitation of many established location scores.
Significance Statement
One important requirement for a useful weather forecast is its ability to predict the placement of weather events such as cold fronts, low pressure systems, or groups of thunderstorms. Errors in the predicted location are not easy to quantify: some established quality measures combine location and other error sources in one score, others are only applicable if the data contain well-defined and easily identifiable objects. Here we introduce an alternative location score that avoids such assumptions and is thus widely applicable. As an additional benefit, we can separate displacement errors into different spatial scales and localize them on a weather map.
Abstract
A multiscale analysis of the significant nocturnal tornado outbreak in Tennessee on 2–3 March 2020 is presented. This outbreak included several significant tornadoes and resulted in the second most fatalities (25) and most injuries (309) of all nocturnal tornado events in Tennessee in 1950–2020. The two deadliest tornadoes struck Nashville (EF3 intensity) and Cookeville (EF4) resulting in 5 and 19 fatalities, respectively. The supercell responsible for the tornado outbreak initiated at 0330 UTC 3 March within a region of warm frontogenesis in western Tennessee. Throughout its life cycle, the supercell was located in a region of convective available potential energy near 1000 J kg−1 and 0–1-km storm-relative helicity over 350 m2 s−2. Retrospective 3-h forecasts from the experimental Warn-on-Forecast System (WoFS) convection-allowing ensemble initialized after the parent supercell initiated indicated a high probability, high severity scenario for tornadoes across Tennessee and into Nashville through 0700 UTC. Earlier WoFS forecasts indicated a low probability, high severity scenario owing to uncertainty in the initiation of supercells. The presence of these supercells was sensitive to the upstream thermodynamic conditions and warm frontogenesis regions that were inherited from the lateral boundary conditions. In all, this study highlights the potential of the WoFS ensemble to contribute useful probabilistic severe weather information to the short-term forecast process during a nocturnal significant tornado outbreak.
Abstract
A multiscale analysis of the significant nocturnal tornado outbreak in Tennessee on 2–3 March 2020 is presented. This outbreak included several significant tornadoes and resulted in the second most fatalities (25) and most injuries (309) of all nocturnal tornado events in Tennessee in 1950–2020. The two deadliest tornadoes struck Nashville (EF3 intensity) and Cookeville (EF4) resulting in 5 and 19 fatalities, respectively. The supercell responsible for the tornado outbreak initiated at 0330 UTC 3 March within a region of warm frontogenesis in western Tennessee. Throughout its life cycle, the supercell was located in a region of convective available potential energy near 1000 J kg−1 and 0–1-km storm-relative helicity over 350 m2 s−2. Retrospective 3-h forecasts from the experimental Warn-on-Forecast System (WoFS) convection-allowing ensemble initialized after the parent supercell initiated indicated a high probability, high severity scenario for tornadoes across Tennessee and into Nashville through 0700 UTC. Earlier WoFS forecasts indicated a low probability, high severity scenario owing to uncertainty in the initiation of supercells. The presence of these supercells was sensitive to the upstream thermodynamic conditions and warm frontogenesis regions that were inherited from the lateral boundary conditions. In all, this study highlights the potential of the WoFS ensemble to contribute useful probabilistic severe weather information to the short-term forecast process during a nocturnal significant tornado outbreak.
Abstract
Tornadoes produced by quasi-linear convective systems (QLCSs) in low instability environments present distinctive challenges for forecasters. This study analyzes a population of 56 vortices (all cyclonic) in a full-physics, case study simulation to examine vortex characteristics and their relationships to the pre-line environment. Peak surface vortex intensity correlates with peak vortex depth, peak surface wind speed, and vortex pathlength. The strongest vortices are the deepest and longest lived, implying that they would be most detectable. The modeled surface vortices are primarily associated with gust front cusps and bow echoes, line breaks, and supercell-like features. Strong vortices frequently have sustained, superposed surface vorticity and near-ground updrafts for several minutes. Although weak vortices lack this superposition, they often exhibit impressive midlevel vorticity and midlevel updrafts. The environments of the weak and strong vortices are similar with small, yet statistically significant, differences in several thermodynamic and kinematic fields. The profiles near strong vortices have more low-level CAPE, steeper lapse rates, and stronger deep-layer vertical wind shear. However, the small magnitudes of the differences imply that forecasters might struggle to discriminate well between nontornadic and tornadic environments in high-shear, low-CAPE events. Despite the similarities, the profiles produce distinct reflectivity, updraft, and vertical vorticity distributions in idealized cloud model simulations. The most intense updrafts and vortices in the idealized runs occur when the environmental profiles from the strong vortex cases are combined with a QLCS orientation more normal to the lower-tropospheric vertical wind shear.
Abstract
Tornadoes produced by quasi-linear convective systems (QLCSs) in low instability environments present distinctive challenges for forecasters. This study analyzes a population of 56 vortices (all cyclonic) in a full-physics, case study simulation to examine vortex characteristics and their relationships to the pre-line environment. Peak surface vortex intensity correlates with peak vortex depth, peak surface wind speed, and vortex pathlength. The strongest vortices are the deepest and longest lived, implying that they would be most detectable. The modeled surface vortices are primarily associated with gust front cusps and bow echoes, line breaks, and supercell-like features. Strong vortices frequently have sustained, superposed surface vorticity and near-ground updrafts for several minutes. Although weak vortices lack this superposition, they often exhibit impressive midlevel vorticity and midlevel updrafts. The environments of the weak and strong vortices are similar with small, yet statistically significant, differences in several thermodynamic and kinematic fields. The profiles near strong vortices have more low-level CAPE, steeper lapse rates, and stronger deep-layer vertical wind shear. However, the small magnitudes of the differences imply that forecasters might struggle to discriminate well between nontornadic and tornadic environments in high-shear, low-CAPE events. Despite the similarities, the profiles produce distinct reflectivity, updraft, and vertical vorticity distributions in idealized cloud model simulations. The most intense updrafts and vortices in the idealized runs occur when the environmental profiles from the strong vortex cases are combined with a QLCS orientation more normal to the lower-tropospheric vertical wind shear.
Abstract
An early morning tornado outbreak occurred on 13 April 2020 in the Central Savannah River Area. Multiple significant tornadoes were reported, resulting in fatalities and injuries. While the operational tornado warnings had positive lead times, the convective mode (quasi-linear convective system) increased the warning decision complexity. The timing of the event [0500–0600 local time (LT)] also made NWS-to-public communication difficult. The experimental NSSL Warn-on-Forecast System (WoFS) was run retrospectively for this case. The WoFS consists of 3–6-h ensemble forecasts initialized every 30 min, and the goals of the system are to bridge the gap between severe weather watches and warnings and to increase warning lead times. Multiple WoFS forecasts were initialized leading up to the first tornado report; those initialized prior to tornado warning issuance have high ensemble probabilities of low-level rotation in the appropriate areas based on subsequent tornado reports. This case highlights another example of the usefulness of WoFS before its eventual transition to operations. Using the WoFS forecasts, kinematic and thermodynamic storm–environment relationships are analyzed using ensemble sensitivity analysis (ESA). The analyses suggest variations in the mesoscale environmental vertical wind profile are not as influential on mesovortex intensity as variations in the thermodynamic environment. Surface observations recorded prior to the tornado outbreak reveal subtle temperature and moisture gradients that may be the impetus for mesovortex intensification and tornadogenesis.
Abstract
An early morning tornado outbreak occurred on 13 April 2020 in the Central Savannah River Area. Multiple significant tornadoes were reported, resulting in fatalities and injuries. While the operational tornado warnings had positive lead times, the convective mode (quasi-linear convective system) increased the warning decision complexity. The timing of the event [0500–0600 local time (LT)] also made NWS-to-public communication difficult. The experimental NSSL Warn-on-Forecast System (WoFS) was run retrospectively for this case. The WoFS consists of 3–6-h ensemble forecasts initialized every 30 min, and the goals of the system are to bridge the gap between severe weather watches and warnings and to increase warning lead times. Multiple WoFS forecasts were initialized leading up to the first tornado report; those initialized prior to tornado warning issuance have high ensemble probabilities of low-level rotation in the appropriate areas based on subsequent tornado reports. This case highlights another example of the usefulness of WoFS before its eventual transition to operations. Using the WoFS forecasts, kinematic and thermodynamic storm–environment relationships are analyzed using ensemble sensitivity analysis (ESA). The analyses suggest variations in the mesoscale environmental vertical wind profile are not as influential on mesovortex intensity as variations in the thermodynamic environment. Surface observations recorded prior to the tornado outbreak reveal subtle temperature and moisture gradients that may be the impetus for mesovortex intensification and tornadogenesis.
Abstract
Prediction systems to enable Earth system predictability research on the subseasonal time scale have been developed with the Community Earth System Model, version 2 (CESM2) using two configurations that differ in their atmospheric components. One system uses the Community Atmosphere Model, version 6 (CAM6) with its top near 40 km, referred to as CESM2(CAM6). The other employs the Whole Atmosphere Community Climate Model, version 6 (WACCM6) whose top extends to ∼140 km, and it includes fully interactive tropospheric and stratospheric chemistry [CESM2(WACCM6)]. Both systems are utilized to carry out subseasonal reforecasts for the 1999–2020 period following the Subseasonal Experiment’s (SubX) protocol. Subseasonal prediction skill from both systems is compared to those of the National Oceanic and Atmospheric Administration CFSv2 and European Centre for Medium-Range Weather Forecasts (ECMWF) operational models. CESM2(CAM6) and CESM2(WACCM6) show very similar subseasonal prediction skill of 2-m temperature, precipitation, the Madden–Julian oscillation, and North Atlantic Oscillation to its previous version and to the NOAA CFSv2 model. Overall, skill of CESM2(CAM6) and CESM2(WACCM6) is a little lower than that of the ECMWF system. In addition to typical output provided by subseasonal prediction systems, CESM2 reforecasts provide comprehensive datasets for predictability research of multiple Earth system components, including three-dimensional output for many variables, and output specific to the mesosphere and lower-thermosphere (MLT) region from CESM2(WACCM6). It is shown that sudden stratosphere warming events, and the associated variability in the MLT, can be predicted ∼10 days in advance. Weekly real-time forecasts and reforecasts with CESM2(CAM6) and CESM2(WACCM6) are freely available.
Significance Statement
We describe here the design and prediction skill of two subseasonal prediction systems based on two configurations of the Community Earth System Model, version 2 (CESM2): CESM2 with the Community Atmosphere Model, version 6 [CESM2(CAM6)] and CESM 2 with Whole Atmosphere Community Climate Model, version 6 [CESM2(WACCM6)] as its atmospheric component. These two systems provide a foundation for community-model based subseasonal prediction research. The CESM2(WACCM6) system provides a novel capability to explore the predictability of the stratosphere, mesosphere, and lower thermosphere. Both CESM2(CAM6) and CESM2(WACCM6) demonstrate subseasonal surface prediction skill comparable to that of the NOAA CFSv2 model, and a little lower than that of the ECMWF forecasting system. CESM2 reforecasts provide a comprehensive dataset for predictability research of multiple aspects of the Earth system, including the whole atmosphere up to 140 km, land, and sea ice. Weekly real-time forecasts, reforecasts, and models are publicly available.
Abstract
Prediction systems to enable Earth system predictability research on the subseasonal time scale have been developed with the Community Earth System Model, version 2 (CESM2) using two configurations that differ in their atmospheric components. One system uses the Community Atmosphere Model, version 6 (CAM6) with its top near 40 km, referred to as CESM2(CAM6). The other employs the Whole Atmosphere Community Climate Model, version 6 (WACCM6) whose top extends to ∼140 km, and it includes fully interactive tropospheric and stratospheric chemistry [CESM2(WACCM6)]. Both systems are utilized to carry out subseasonal reforecasts for the 1999–2020 period following the Subseasonal Experiment’s (SubX) protocol. Subseasonal prediction skill from both systems is compared to those of the National Oceanic and Atmospheric Administration CFSv2 and European Centre for Medium-Range Weather Forecasts (ECMWF) operational models. CESM2(CAM6) and CESM2(WACCM6) show very similar subseasonal prediction skill of 2-m temperature, precipitation, the Madden–Julian oscillation, and North Atlantic Oscillation to its previous version and to the NOAA CFSv2 model. Overall, skill of CESM2(CAM6) and CESM2(WACCM6) is a little lower than that of the ECMWF system. In addition to typical output provided by subseasonal prediction systems, CESM2 reforecasts provide comprehensive datasets for predictability research of multiple Earth system components, including three-dimensional output for many variables, and output specific to the mesosphere and lower-thermosphere (MLT) region from CESM2(WACCM6). It is shown that sudden stratosphere warming events, and the associated variability in the MLT, can be predicted ∼10 days in advance. Weekly real-time forecasts and reforecasts with CESM2(CAM6) and CESM2(WACCM6) are freely available.
Significance Statement
We describe here the design and prediction skill of two subseasonal prediction systems based on two configurations of the Community Earth System Model, version 2 (CESM2): CESM2 with the Community Atmosphere Model, version 6 [CESM2(CAM6)] and CESM 2 with Whole Atmosphere Community Climate Model, version 6 [CESM2(WACCM6)] as its atmospheric component. These two systems provide a foundation for community-model based subseasonal prediction research. The CESM2(WACCM6) system provides a novel capability to explore the predictability of the stratosphere, mesosphere, and lower thermosphere. Both CESM2(CAM6) and CESM2(WACCM6) demonstrate subseasonal surface prediction skill comparable to that of the NOAA CFSv2 model, and a little lower than that of the ECMWF forecasting system. CESM2 reforecasts provide a comprehensive dataset for predictability research of multiple aspects of the Earth system, including the whole atmosphere up to 140 km, land, and sea ice. Weekly real-time forecasts, reforecasts, and models are publicly available.
Abstract
Tornado motion changes occurring with major internal rear-flank momentum surges are examined in three significant tornado-producing supercells. The analysis primarily uses fixed-site Doppler radar data, but also utilizes in situ and videographic observations when available. In the cases examined, the peak lowest-level remotely sensed or in situ rear-flank surge wind speeds ranged from 48 to at least 63 m s−1. Contemporaneous with major surges impacting the tornadoes and their parent low-level mesocyclones, longer-duration tornado heading changes were leftward and ranged from 30° to 55°. In all cases, the tornado speed increased substantially upon surge impact, with tornado speeds approximately doubling in two of the events. A storm-relative change in the hook echo orientation accompanied the major surges and provided a signal that a marked leftward heading change for an ongoing tornado was under way. Concurrent with the surge interaction, the hook echo tip and associated low-level mesocyclone turned leftward while also moving in a storm-relative downshear direction. The major rear-flank internal surges influenced tornado motion such that a generally favorable storm updraft-relative position was maintained. In all cases, the tornado lasted well beyond (≥21 min) the time of the surge-associated left turn with no evident marked loss of intensity until well down-track of the turn. The local momentum balance between outflow and inflow that bounds the tornado or its parent circulation, especially the directionality evolution of the bounding momentum, is the most apparent explanation for tornado down-track or off-track accelerations in the featured events.
Abstract
Tornado motion changes occurring with major internal rear-flank momentum surges are examined in three significant tornado-producing supercells. The analysis primarily uses fixed-site Doppler radar data, but also utilizes in situ and videographic observations when available. In the cases examined, the peak lowest-level remotely sensed or in situ rear-flank surge wind speeds ranged from 48 to at least 63 m s−1. Contemporaneous with major surges impacting the tornadoes and their parent low-level mesocyclones, longer-duration tornado heading changes were leftward and ranged from 30° to 55°. In all cases, the tornado speed increased substantially upon surge impact, with tornado speeds approximately doubling in two of the events. A storm-relative change in the hook echo orientation accompanied the major surges and provided a signal that a marked leftward heading change for an ongoing tornado was under way. Concurrent with the surge interaction, the hook echo tip and associated low-level mesocyclone turned leftward while also moving in a storm-relative downshear direction. The major rear-flank internal surges influenced tornado motion such that a generally favorable storm updraft-relative position was maintained. In all cases, the tornado lasted well beyond (≥21 min) the time of the surge-associated left turn with no evident marked loss of intensity until well down-track of the turn. The local momentum balance between outflow and inflow that bounds the tornado or its parent circulation, especially the directionality evolution of the bounding momentum, is the most apparent explanation for tornado down-track or off-track accelerations in the featured events.
