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Abstract
Streamwise vorticity currents (SVCs) have been hypothesized to enhance low-level mesocyclones within supercell thunderstorms and perhaps increase the likelihood of tornadogenesis. Recent observational studies have confirmed the existence of SVCs in supercells and numerical simulations have allowed for further investigation of SVCs. A suite of 19 idealized supercell simulations with varying midlevel shear orientations is analyzed to determine how SVC formation and characteristics may differ between storms. In our simulations, SVCs develop on the cold side of left-flank convergence boundaries and their updraft-relative positions are partially dependent on downdraft location. The magnitude, duration, and mean depth of SVCs do not differ significantly between simulations or between SVCs that precede tornado-like vortices (TLVs) and those that do not. Trajectories initialized within SVCs reveal two primary airstreams, one that flows through an SVC for the majority of its length, and another that originates in the modified inflow in the forward flank and then merges with the SVC. Vorticity budgets calculated along trajectories reveal that the first airstream exhibits significantly greater maximum streamwise vorticity magnitudes than the second airstream. The vorticity budgets also indicate that stretching of horizontal streamwise vorticity is the dominant contributor to the large values of streamwise vorticity within the SVCs. TLV formation does not require the development of an SVC beforehand; 44% of TLVs in the simulations are preceded by SVCs. When an SVC occurs, it is followed by a TLV 53% of the time, indicating not all SVCs lead to TLV formation.
Abstract
Streamwise vorticity currents (SVCs) have been hypothesized to enhance low-level mesocyclones within supercell thunderstorms and perhaps increase the likelihood of tornadogenesis. Recent observational studies have confirmed the existence of SVCs in supercells and numerical simulations have allowed for further investigation of SVCs. A suite of 19 idealized supercell simulations with varying midlevel shear orientations is analyzed to determine how SVC formation and characteristics may differ between storms. In our simulations, SVCs develop on the cold side of left-flank convergence boundaries and their updraft-relative positions are partially dependent on downdraft location. The magnitude, duration, and mean depth of SVCs do not differ significantly between simulations or between SVCs that precede tornado-like vortices (TLVs) and those that do not. Trajectories initialized within SVCs reveal two primary airstreams, one that flows through an SVC for the majority of its length, and another that originates in the modified inflow in the forward flank and then merges with the SVC. Vorticity budgets calculated along trajectories reveal that the first airstream exhibits significantly greater maximum streamwise vorticity magnitudes than the second airstream. The vorticity budgets also indicate that stretching of horizontal streamwise vorticity is the dominant contributor to the large values of streamwise vorticity within the SVCs. TLV formation does not require the development of an SVC beforehand; 44% of TLVs in the simulations are preceded by SVCs. When an SVC occurs, it is followed by a TLV 53% of the time, indicating not all SVCs lead to TLV formation.
Abstract
Airborne Doppler radar reflectivity data collected in hurricanes on the NOAA P-3 aircraft between 1997 and 2021 were parsed into different modes of precipitation: stratiform precipitation, shallow convection, moderate convection, and deep convection. Stratiform precipitation was the most frequent precipitation mode with 82.6% of all observed precipitation while deep convection was the most infrequent at 1.3%. When stratified by 12-hr intensity change, intensifying TCs had a greater areal coverage of total convection in the eyewall compared to weakening and steady-state TCs. The largest difference in the azimuthal distributions in the precipitation modes was in deep convection, which was mostly confined to the downshear-left quadrant in weakening and steady-state hurricanes and more symmetrically distributed in intensifying hurricanes. For all intensity change categories, the most symmetrically distributed precipitation mode was stratiform rain.
To build upon the results of a recent thermodynamic study, the precipitation data were recategorized for hurricanes experiencing deep-layer wind shear with either a northerly-component or southerly-component. Like intensifying storms, hurricanes that experienced northerly-component shear had a more symmetric distribution of deep convection than southerly-component shear storms, which had a distribution of deep convection that resembled weakening storms. The greatest difference in the precipitation distributions between the shear direction groups were in major hurricanes experiencing moderate (4.5–11 m s−1) wind shear values. Consistent with previous airborne radar studies, the results suggest that considering the distribution of deep convection and the thermodynamic distributions associated with differing environmental wind shear direction could aid TC intensity forecasts.
Abstract
Airborne Doppler radar reflectivity data collected in hurricanes on the NOAA P-3 aircraft between 1997 and 2021 were parsed into different modes of precipitation: stratiform precipitation, shallow convection, moderate convection, and deep convection. Stratiform precipitation was the most frequent precipitation mode with 82.6% of all observed precipitation while deep convection was the most infrequent at 1.3%. When stratified by 12-hr intensity change, intensifying TCs had a greater areal coverage of total convection in the eyewall compared to weakening and steady-state TCs. The largest difference in the azimuthal distributions in the precipitation modes was in deep convection, which was mostly confined to the downshear-left quadrant in weakening and steady-state hurricanes and more symmetrically distributed in intensifying hurricanes. For all intensity change categories, the most symmetrically distributed precipitation mode was stratiform rain.
To build upon the results of a recent thermodynamic study, the precipitation data were recategorized for hurricanes experiencing deep-layer wind shear with either a northerly-component or southerly-component. Like intensifying storms, hurricanes that experienced northerly-component shear had a more symmetric distribution of deep convection than southerly-component shear storms, which had a distribution of deep convection that resembled weakening storms. The greatest difference in the precipitation distributions between the shear direction groups were in major hurricanes experiencing moderate (4.5–11 m s−1) wind shear values. Consistent with previous airborne radar studies, the results suggest that considering the distribution of deep convection and the thermodynamic distributions associated with differing environmental wind shear direction could aid TC intensity forecasts.
Abstract
The Targeted Observation by Radars and UAS of Supercells (TORUS) field project observed two supercells on 8 June 2019 in northwestern Kansas and far eastern Colorado. Although these storms occurred in close spatial and temporal proximity, their evolutions were markedly different. The first storm struggled to maintain itself and eventually dissipated. Meanwhile, the second supercell developed just after and slightly to the south of where the first storm dissipated, and then tracked over almost the same location before rapidly intensifying and going on to produce several tornadoes. The objective of this study is to determine why the first storm struggled to survive and failed to produce mesocyclonic tornadoes while the second storm thrived and was cyclically tornadic. Analysis relies on observations collected by the TORUS project–including UAS transects and profiles, mobile soundings, surface mobile mesonet transects, and dual-Doppler wind syntheses from the NOAA P-3 tail Doppler radars. Our results indicate that rapid changes in the low-level wind profile, the second supercell’s interaction with two mesoscale boundaries, an interaction with a rapidly-intensifying new updraft just to its west, and the influence of a strong outflow surge likely account for much of the second supercell’s increased strength and tornado production. The rapid evolution of the low-level wind profile may have been most important in raising the probability of the second supercell becoming tornadic, with the new updraft and the outflow surge leading to a favorable storm-scale evolution that increased this probability further.
Abstract
The Targeted Observation by Radars and UAS of Supercells (TORUS) field project observed two supercells on 8 June 2019 in northwestern Kansas and far eastern Colorado. Although these storms occurred in close spatial and temporal proximity, their evolutions were markedly different. The first storm struggled to maintain itself and eventually dissipated. Meanwhile, the second supercell developed just after and slightly to the south of where the first storm dissipated, and then tracked over almost the same location before rapidly intensifying and going on to produce several tornadoes. The objective of this study is to determine why the first storm struggled to survive and failed to produce mesocyclonic tornadoes while the second storm thrived and was cyclically tornadic. Analysis relies on observations collected by the TORUS project–including UAS transects and profiles, mobile soundings, surface mobile mesonet transects, and dual-Doppler wind syntheses from the NOAA P-3 tail Doppler radars. Our results indicate that rapid changes in the low-level wind profile, the second supercell’s interaction with two mesoscale boundaries, an interaction with a rapidly-intensifying new updraft just to its west, and the influence of a strong outflow surge likely account for much of the second supercell’s increased strength and tornado production. The rapid evolution of the low-level wind profile may have been most important in raising the probability of the second supercell becoming tornadic, with the new updraft and the outflow surge leading to a favorable storm-scale evolution that increased this probability further.
Abstract
Surface boundaries in supercells have been suspected of being important in the arrangement and concentration of vorticity for the development and intensification of tornadoes, but there has been little attention given to the effects of the underlying surface roughness on their behavior. This study investigates the impact of surface drag on the structure and evolution of these boundaries, their associated distribution of near-surface vorticity, and tornadogenesis and maintenance. Comparisons between idealized simulations without and with drag introduced in the mature stage of the storm prior to tornadogenesis reveal that the inclusion of surface drag substantially alters the low-level structure, particularly with respect to the number, location, and intensity of surface convergence boundaries. Substantial drag-generated horizontal vorticity induces rotor structures near the surface associated with the convergence boundaries in both the forward and rear flanks of the storm. Stretching of horizontal vorticity and subsequent tilting into the vertical along the convergence boundaries lead to elongated positive vertical vorticity sheets on the ascending branch of the rotors and the opposite on the descending branch. The larger near-surface pressure deficit associated with the faster development of the near-surface cyclone when drag is active creates a downward dynamic vertical pressure gradient force that suppresses vertical growth, leading to a weaker and wider tornado detached from the surrounding convergence boundaries. A conceptual model of the low-level structure of the tornadic supercell is presented that focuses on the contribution of surface drag, with the aim of adding more insight and complexity to previous conceptual models.
Abstract
Surface boundaries in supercells have been suspected of being important in the arrangement and concentration of vorticity for the development and intensification of tornadoes, but there has been little attention given to the effects of the underlying surface roughness on their behavior. This study investigates the impact of surface drag on the structure and evolution of these boundaries, their associated distribution of near-surface vorticity, and tornadogenesis and maintenance. Comparisons between idealized simulations without and with drag introduced in the mature stage of the storm prior to tornadogenesis reveal that the inclusion of surface drag substantially alters the low-level structure, particularly with respect to the number, location, and intensity of surface convergence boundaries. Substantial drag-generated horizontal vorticity induces rotor structures near the surface associated with the convergence boundaries in both the forward and rear flanks of the storm. Stretching of horizontal vorticity and subsequent tilting into the vertical along the convergence boundaries lead to elongated positive vertical vorticity sheets on the ascending branch of the rotors and the opposite on the descending branch. The larger near-surface pressure deficit associated with the faster development of the near-surface cyclone when drag is active creates a downward dynamic vertical pressure gradient force that suppresses vertical growth, leading to a weaker and wider tornado detached from the surrounding convergence boundaries. A conceptual model of the low-level structure of the tornadic supercell is presented that focuses on the contribution of surface drag, with the aim of adding more insight and complexity to previous conceptual models.
Abstract
Supercell storms are commonly responsible for severe hail, which is the costliest severe storm hazard in the United States and elsewhere. Radar observations of such storms are common and have been leveraged to estimate hail size and severe hail occurrence. However, many established relationships between radar-observed storm characteristics and severe hail occurrence have been found using data from few storms and in isolation from other radar metrics. This study leverages a 10-yr record of polarimetric Doppler radar observations in the United States to evaluate and compare radar observations of thousands of severe hail–producing supercells based on their maximum hail size. In agreement with prior studies, it is found that increasing hail size relates to increasing volume of high (≥50 dBZ) radar reflectivity, increasing midaltitude mesocyclone rotation (azimuthal shear), increasing storm-top divergence, and decreased differential reflectivity and copolar correlation coefficient at low levels (mostly below the environmental 0°C level). New insights include increasing vertical alignment of the storm mesocyclone with increasing hail size and a Doppler velocity spectrum width minimum aloft near storm center that increases in area with increasing hail size and is argued to indicate increasing updraft width. To complement the extensive radar analysis, near-storm environments from reanalyses are compared and indicate that the greatest environmental differences exist in the middle troposphere (within the hail growth region), especially the wind speed perpendicular to storm motion. Recommendations are given for future improvements to radar-based hail-size estimation.
Abstract
Supercell storms are commonly responsible for severe hail, which is the costliest severe storm hazard in the United States and elsewhere. Radar observations of such storms are common and have been leveraged to estimate hail size and severe hail occurrence. However, many established relationships between radar-observed storm characteristics and severe hail occurrence have been found using data from few storms and in isolation from other radar metrics. This study leverages a 10-yr record of polarimetric Doppler radar observations in the United States to evaluate and compare radar observations of thousands of severe hail–producing supercells based on their maximum hail size. In agreement with prior studies, it is found that increasing hail size relates to increasing volume of high (≥50 dBZ) radar reflectivity, increasing midaltitude mesocyclone rotation (azimuthal shear), increasing storm-top divergence, and decreased differential reflectivity and copolar correlation coefficient at low levels (mostly below the environmental 0°C level). New insights include increasing vertical alignment of the storm mesocyclone with increasing hail size and a Doppler velocity spectrum width minimum aloft near storm center that increases in area with increasing hail size and is argued to indicate increasing updraft width. To complement the extensive radar analysis, near-storm environments from reanalyses are compared and indicate that the greatest environmental differences exist in the middle troposphere (within the hail growth region), especially the wind speed perpendicular to storm motion. Recommendations are given for future improvements to radar-based hail-size estimation.
Abstract
Lightning flash observations are closely associated with the development of convective clouds and have a potential for convective-scale data assimilation with high-resolution numerical weather prediction models. A main challenge with the ensemble Kalman filter (EnKF) is that no ensemble members have nonzero lightning flashes in the places where a lightning flash is observed. In this situation, different model states provide all zero lightning, and the EnKF cannot assimilate the nonzero lightning data effectively. This problem is known as the zero-gradient issue. This study addresses the zero-gradient issue by adding regression-based ensemble perturbations derived from a statistical relationship between simulated lightning and atmospheric variables in the whole computational domain. Regression-based ensemble perturbations are applied if the number of ensemble members with nonzero lightning flashes is smaller than a prescribed threshold (Nmin). Observing system simulation experiments for a heavy precipitation event in Japan show that regression-based ensemble perturbations increase the ensemble spread and successfully induce the analysis increments associated with convection even if only a few members have nonzero lightning flashes. Furthermore, applying regression-based ensemble perturbations improves the forecast accuracy of precipitation although the improvement is sensitive to the choice of Nmin.
Significance Statement
This study develops an effective method to use lightning flash observations for weather prediction. Lightning flash observations include precious information of the inner structure of clouds, but their effective use for weather prediction is not straightforward since a weather prediction model often misses observed lightning flashes. Our new method uses ensemble-generated statistical relationships to compensate for the misses and successfully improves the forecast accuracy of heavy rains in a simulated case. Our future work will test the method with real observation data.
Abstract
Lightning flash observations are closely associated with the development of convective clouds and have a potential for convective-scale data assimilation with high-resolution numerical weather prediction models. A main challenge with the ensemble Kalman filter (EnKF) is that no ensemble members have nonzero lightning flashes in the places where a lightning flash is observed. In this situation, different model states provide all zero lightning, and the EnKF cannot assimilate the nonzero lightning data effectively. This problem is known as the zero-gradient issue. This study addresses the zero-gradient issue by adding regression-based ensemble perturbations derived from a statistical relationship between simulated lightning and atmospheric variables in the whole computational domain. Regression-based ensemble perturbations are applied if the number of ensemble members with nonzero lightning flashes is smaller than a prescribed threshold (Nmin). Observing system simulation experiments for a heavy precipitation event in Japan show that regression-based ensemble perturbations increase the ensemble spread and successfully induce the analysis increments associated with convection even if only a few members have nonzero lightning flashes. Furthermore, applying regression-based ensemble perturbations improves the forecast accuracy of precipitation although the improvement is sensitive to the choice of Nmin.
Significance Statement
This study develops an effective method to use lightning flash observations for weather prediction. Lightning flash observations include precious information of the inner structure of clouds, but their effective use for weather prediction is not straightforward since a weather prediction model often misses observed lightning flashes. Our new method uses ensemble-generated statistical relationships to compensate for the misses and successfully improves the forecast accuracy of heavy rains in a simulated case. Our future work will test the method with real observation data.
Abstract
High-resolution numerical models have been used to develop statistical models of the enhancement of sea surface fluxes resulting from spatial variability of sea surface wind. In particular, studies have shown that flux enhancement is not a deterministic function of the resolved state. Previous studies focused on single geographical areas or used a single high-resolution numerical model. This study extends the development of such statistical models by considering six different high-resolution models, four different geographical regions, and three different 10-day periods, allowing for a systematic investigation of the robustness of both the deterministic and stochastic parts of the data-driven parameterization. Results indicate that the deterministic part, based on regressing the unresolved normalized flux onto resolved-scale normalized flux and precipitation, is broadly robust across different models, regions, and time periods. The statistical features of the stochastic part of the model (spatial and temporal autocorrelation and parameters of a Gaussian process fit to the regression residual) are also found to be robust and not strongly sensitive to the underlying model, modeled geographical region, or time period studied. Best-fit Gaussian process parameters display robust spatial heterogeneity across models, indicating potential for improvements to the statistical model. These results illustrate the potential for the development of a generic, explicitly stochastic parameterization of sea surface flux enhancements dependent on wind variability.
Abstract
High-resolution numerical models have been used to develop statistical models of the enhancement of sea surface fluxes resulting from spatial variability of sea surface wind. In particular, studies have shown that flux enhancement is not a deterministic function of the resolved state. Previous studies focused on single geographical areas or used a single high-resolution numerical model. This study extends the development of such statistical models by considering six different high-resolution models, four different geographical regions, and three different 10-day periods, allowing for a systematic investigation of the robustness of both the deterministic and stochastic parts of the data-driven parameterization. Results indicate that the deterministic part, based on regressing the unresolved normalized flux onto resolved-scale normalized flux and precipitation, is broadly robust across different models, regions, and time periods. The statistical features of the stochastic part of the model (spatial and temporal autocorrelation and parameters of a Gaussian process fit to the regression residual) are also found to be robust and not strongly sensitive to the underlying model, modeled geographical region, or time period studied. Best-fit Gaussian process parameters display robust spatial heterogeneity across models, indicating potential for improvements to the statistical model. These results illustrate the potential for the development of a generic, explicitly stochastic parameterization of sea surface flux enhancements dependent on wind variability.
Abstract
Tropical transition (TT) is a cyclogenesis process in which a baroclinic disturbance is transformed into a tropical cyclone. Many studies have analyzed TT events over the North Atlantic. This study assesses TT processes from a possible subtropical cyclone to Tropical Storm Kirogi at a relatively high latitude over the western North Pacific in an environment of enhanced baroclinicity in August 2012. Analyses based on satellite observations, the JRA-55 reanalysis, and a simulation with 2.5-km horizontal grid spacing demonstrate three stages during the TT: the baroclinic, intermediate, and convective stages. Over the baroclinic stage, Kirogi had an asymmetric comma-shaped cloud pattern with convection in the northern and eastern parts of the cyclone. This convection is attributed to quasigeostrophic forcing and frontogenesis associated with advection of warm and moist air. Vorticity locally generated by this convection was advected to the cyclone center by cyclone-relative northerly flow. Kirogi also had a shallow warm-core structure due to the interaction with an upper-level cold trough extending from the midlatitudes. In the intermediate stage, the warm and moist air in the lower troposphere and the cold trough in the upper troposphere wrapped around Kirogi. In the convective stage, Kirogi attained characteristics of a typical tropical cyclone with convection concentrated near the cyclone center and a deep warm-core structure. These results demonstrate that baroclinic processes can directly trigger formation of a tropical storm at relatively high latitudes over the western North Pacific in a similar manner to that over the North Atlantic.
Significance Statement
Tropical cyclogenesis is an important process for early identification of tropical cyclone hazards. Tropical transition is a tropical cyclogenesis process that is triggered by a subtropical or extratropical disturbance. It is unique to relatively high latitudes and has social importance particularly for midlatitude countries. There have been fewer studies on tropical transition over the western North Pacific than over the North Atlantic. This study demonstrates the dynamics of a distinct tropical transition event that led to the formation of Tropical Storm Kirogi (2012) at a relatively high latitude over the western North Pacific.
Abstract
Tropical transition (TT) is a cyclogenesis process in which a baroclinic disturbance is transformed into a tropical cyclone. Many studies have analyzed TT events over the North Atlantic. This study assesses TT processes from a possible subtropical cyclone to Tropical Storm Kirogi at a relatively high latitude over the western North Pacific in an environment of enhanced baroclinicity in August 2012. Analyses based on satellite observations, the JRA-55 reanalysis, and a simulation with 2.5-km horizontal grid spacing demonstrate three stages during the TT: the baroclinic, intermediate, and convective stages. Over the baroclinic stage, Kirogi had an asymmetric comma-shaped cloud pattern with convection in the northern and eastern parts of the cyclone. This convection is attributed to quasigeostrophic forcing and frontogenesis associated with advection of warm and moist air. Vorticity locally generated by this convection was advected to the cyclone center by cyclone-relative northerly flow. Kirogi also had a shallow warm-core structure due to the interaction with an upper-level cold trough extending from the midlatitudes. In the intermediate stage, the warm and moist air in the lower troposphere and the cold trough in the upper troposphere wrapped around Kirogi. In the convective stage, Kirogi attained characteristics of a typical tropical cyclone with convection concentrated near the cyclone center and a deep warm-core structure. These results demonstrate that baroclinic processes can directly trigger formation of a tropical storm at relatively high latitudes over the western North Pacific in a similar manner to that over the North Atlantic.
Significance Statement
Tropical cyclogenesis is an important process for early identification of tropical cyclone hazards. Tropical transition is a tropical cyclogenesis process that is triggered by a subtropical or extratropical disturbance. It is unique to relatively high latitudes and has social importance particularly for midlatitude countries. There have been fewer studies on tropical transition over the western North Pacific than over the North Atlantic. This study demonstrates the dynamics of a distinct tropical transition event that led to the formation of Tropical Storm Kirogi (2012) at a relatively high latitude over the western North Pacific.
Abstract
Doppler-lidar wind-profile measurements at three sites were used to evaluate NWP model errors from two versions of NOAA’s 3-km-grid HRRR model, to see whether updates in the latest version-4 reduced errors when compared against the original version-1. Nested (750-m-grid) versions of each were also tested to see how grid spacing affected forecast skill. The measurements were part of the field phase of the Second Wind Forecasting Improvement Project (WFIP2), an 18-month deployment into central Oregon/Washington, a major wind-energy producing region. This study focuses on errors in simulating marine intrusions, a summertime, 600-to-800-m deep, regional sea-breeze flow found to generate large errors. HRRR errors proved to be complex and site dependent. The most prominent error resulted from a premature drop in modeled marine-intrusion wind speeds after local midnight, when lidar-measured winds of greater than 8 m s−1 persisted through the next morning. These large negative errors were offset at low levels by positive errors due to excessive mixing, complicating the interpretation of model ‘improvement,’ such that the updates to the full-scale versions produced mixed results, sometimes enhancing but sometimes degrading model skill. Nesting consistently improved model performance, version-1’s nest producing the smallest errors overall. HRRR’s ability to represent the stages of sea-breeze forcing was evaluated using radiation-budget, surface-energy balance, and near-surface temperature measurements available during WFIP2. The significant site-to-site differences in model error and the complex nature of these errors means that field-measurement campaigns having dense arrays of profiling sensors are necessary to properly diagnose and characterize model errors, as part of a systematic approach to NWP model improvement.
Abstract
Doppler-lidar wind-profile measurements at three sites were used to evaluate NWP model errors from two versions of NOAA’s 3-km-grid HRRR model, to see whether updates in the latest version-4 reduced errors when compared against the original version-1. Nested (750-m-grid) versions of each were also tested to see how grid spacing affected forecast skill. The measurements were part of the field phase of the Second Wind Forecasting Improvement Project (WFIP2), an 18-month deployment into central Oregon/Washington, a major wind-energy producing region. This study focuses on errors in simulating marine intrusions, a summertime, 600-to-800-m deep, regional sea-breeze flow found to generate large errors. HRRR errors proved to be complex and site dependent. The most prominent error resulted from a premature drop in modeled marine-intrusion wind speeds after local midnight, when lidar-measured winds of greater than 8 m s−1 persisted through the next morning. These large negative errors were offset at low levels by positive errors due to excessive mixing, complicating the interpretation of model ‘improvement,’ such that the updates to the full-scale versions produced mixed results, sometimes enhancing but sometimes degrading model skill. Nesting consistently improved model performance, version-1’s nest producing the smallest errors overall. HRRR’s ability to represent the stages of sea-breeze forcing was evaluated using radiation-budget, surface-energy balance, and near-surface temperature measurements available during WFIP2. The significant site-to-site differences in model error and the complex nature of these errors means that field-measurement campaigns having dense arrays of profiling sensors are necessary to properly diagnose and characterize model errors, as part of a systematic approach to NWP model improvement.
