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Abstract
This study conducts a thorough investigation into the behaviors of analysis ensemble spreads linked to stratospheric sudden warming (SSW) events. A stratosphere-resolving ensemble data assimilation system is used here to document the evolution of analysis spread leading up to a pair of warming events. Precursory signals of the increased ensemble spreads were found a few days prior to two SSW events that occurred during December 2018 and August–September 2019 in the Northern and Southern Hemispheres, respectively. The signals appeared in the upper and middle stratosphere and did not appear at lower heights. When the signals appeared, it was found that both tendency by forecast and analysis increment in a forecast-analysis (data assimilation) cycle simultaneously became large. An empirical orthogonal function analysis showed that the dominant structures of the precursory signals were equivalent barotropic and were 90° out-of-phase with the analysis ensemble-mean field. Over the same period, the upper and middle stratosphere became more susceptible to barotropic instability than in their previous states. We conclude that the differing growth of barotropically unstable modes across ensemble members can amplify spread during the lead-up to SSW events.
Significance Statement
Winds in the winter stratospheric polar vortex are typically westerly. Occasionally, however, warming over the pole leads to a reversal of the flow through a process known as stratospheric sudden warming. These events are difficult to predict even in state-of-the-art analysis and forecasting systems. In this study, we identify a precursor signal in the form of increased ensemble spread that appears to originate from differing realizations of growing barotropic modes across the ensemble. This signal could serve as a useful forecasting tool by enhancing situational awareness in the lead-up to potential stratospheric sudden warming events.
Abstract
This study conducts a thorough investigation into the behaviors of analysis ensemble spreads linked to stratospheric sudden warming (SSW) events. A stratosphere-resolving ensemble data assimilation system is used here to document the evolution of analysis spread leading up to a pair of warming events. Precursory signals of the increased ensemble spreads were found a few days prior to two SSW events that occurred during December 2018 and August–September 2019 in the Northern and Southern Hemispheres, respectively. The signals appeared in the upper and middle stratosphere and did not appear at lower heights. When the signals appeared, it was found that both tendency by forecast and analysis increment in a forecast-analysis (data assimilation) cycle simultaneously became large. An empirical orthogonal function analysis showed that the dominant structures of the precursory signals were equivalent barotropic and were 90° out-of-phase with the analysis ensemble-mean field. Over the same period, the upper and middle stratosphere became more susceptible to barotropic instability than in their previous states. We conclude that the differing growth of barotropically unstable modes across ensemble members can amplify spread during the lead-up to SSW events.
Significance Statement
Winds in the winter stratospheric polar vortex are typically westerly. Occasionally, however, warming over the pole leads to a reversal of the flow through a process known as stratospheric sudden warming. These events are difficult to predict even in state-of-the-art analysis and forecasting systems. In this study, we identify a precursor signal in the form of increased ensemble spread that appears to originate from differing realizations of growing barotropic modes across the ensemble. This signal could serve as a useful forecasting tool by enhancing situational awareness in the lead-up to potential stratospheric sudden warming events.
Abstract
A scheme for integration of atmospheric equations containing terms with differing time scales is developed. The method employs a filtered leapfrog scheme utilizing a fourth-order implicit time filter with one function evaluation per time step to compute slow-propagating phenomena such as advection and rotation. The terms involving fast-propagating modes are handled implicitly with an unconditionally stable method that permits application of larger time steps and faster computations compared to fully explicit treatment. Implementation using explicit and recurrent formulation is provided. Stability analysis demonstrates that the method is conditionally stable for any combination of frequencies involved in the slow and fast terms as they approach the origin. The implicit filter used in the method damps the computational modes without noticeably sacrificing the accuracy of the physical mode. The O[(Δt 4)] accuracy for amplitude errors achieved by the implicitly filtered leapfrog is preserved in applications where terms responsible for fast propagation are integrated with a semi-implicit method. Detailed formulation of the method for soundproof nonhydrostatic anelastic equations is provided. Procedures for implementation in global spectral shallow-water models are also given. Examples comparing numerical and analytical solutions for linear gravity waves demonstrate the accuracy of the scheme. The performance is also shown in more practical nonlinear applications, where numerical solutions accomplished by the method are evaluated against those computed from a scheme where the slow terms are handled by the third-order Runge–Kutta scheme. It demonstrates that the method is able to accurately resolve fine-scale dynamics of Kelvin–Helmholtz shear instabilities, the evolution of density current, and nonlinear drifts of twin tropical cyclones.
Abstract
A scheme for integration of atmospheric equations containing terms with differing time scales is developed. The method employs a filtered leapfrog scheme utilizing a fourth-order implicit time filter with one function evaluation per time step to compute slow-propagating phenomena such as advection and rotation. The terms involving fast-propagating modes are handled implicitly with an unconditionally stable method that permits application of larger time steps and faster computations compared to fully explicit treatment. Implementation using explicit and recurrent formulation is provided. Stability analysis demonstrates that the method is conditionally stable for any combination of frequencies involved in the slow and fast terms as they approach the origin. The implicit filter used in the method damps the computational modes without noticeably sacrificing the accuracy of the physical mode. The O[(Δt 4)] accuracy for amplitude errors achieved by the implicitly filtered leapfrog is preserved in applications where terms responsible for fast propagation are integrated with a semi-implicit method. Detailed formulation of the method for soundproof nonhydrostatic anelastic equations is provided. Procedures for implementation in global spectral shallow-water models are also given. Examples comparing numerical and analytical solutions for linear gravity waves demonstrate the accuracy of the scheme. The performance is also shown in more practical nonlinear applications, where numerical solutions accomplished by the method are evaluated against those computed from a scheme where the slow terms are handled by the third-order Runge–Kutta scheme. It demonstrates that the method is able to accurately resolve fine-scale dynamics of Kelvin–Helmholtz shear instabilities, the evolution of density current, and nonlinear drifts of twin tropical cyclones.
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.
Significance Statement
Streamwise vorticity currents (SVCs) are features within thunderstorms hypothesized to strengthen updraft rotation and increase the likelihood of tornado formation. SVCs in a suite of 19 thunderstorm simulations are analyzed to investigate how they develop, if their characteristics differ between storms, and how often they precede tornado production. The rotation in an SVC is amplified as air accelerates toward the updraft, which is the main process contributing to SVC formation. The likelihood of SVCs may vary with differences in the winds 3–6 km above the ground. These findings may aid in developing strategies for better observing SVCs.
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.
Significance Statement
Streamwise vorticity currents (SVCs) are features within thunderstorms hypothesized to strengthen updraft rotation and increase the likelihood of tornado formation. SVCs in a suite of 19 thunderstorm simulations are analyzed to investigate how they develop, if their characteristics differ between storms, and how often they precede tornado production. The rotation in an SVC is amplified as air accelerates toward the updraft, which is the main process contributing to SVC formation. The likelihood of SVCs may vary with differences in the winds 3–6 km above the ground. These findings may aid in developing strategies for better observing SVCs.
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, 600–800-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, with 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 mean 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.
Significance Statement
Dramatic increases in NWP model skill will be required over the coming decades. This paper describes the role of major deployments of accurate profiling sensors in achieving that goal and presents an example from the Second Wind Forecast Improvement Program (WFIP2). Wind-profile data from scanning Doppler lidars were used to evaluate two versions of HRRR, the original and an updated version, and nested versions of each. This study focuses on the ability of updated HRRR versions to improve upon predicting a regional sea-breeze flow, which was found to generate large errors by the original HRRR. Updates to the full-scale HRRR versions produced mixed results, but the finer-mesh versions consistently reduced model errors.
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, 600–800-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, with 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 mean 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.
Significance Statement
Dramatic increases in NWP model skill will be required over the coming decades. This paper describes the role of major deployments of accurate profiling sensors in achieving that goal and presents an example from the Second Wind Forecast Improvement Program (WFIP2). Wind-profile data from scanning Doppler lidars were used to evaluate two versions of HRRR, the original and an updated version, and nested versions of each. This study focuses on the ability of updated HRRR versions to improve upon predicting a regional sea-breeze flow, which was found to generate large errors by the original HRRR. Updates to the full-scale HRRR versions produced mixed results, but the finer-mesh versions consistently reduced model errors.
Abstract
Recurving tropical cyclones (TCs) in the western North Pacific often cause heavy rainfall events (HREs) in East Asia. However, how their interactions with midlatitude flows alter the characteristics of HREs remains unclear. The present study examines the synoptic–dynamic characteristics of HREs directly resulting from TCs in South Korea with a focus on the role of midlatitude baroclinic condition. The HREs are categorized into two clusters based on midlatitude tropopause patterns: strongly (C1) and weakly (C2) baroclinic conditions. C1, which is common in late summer, is characterized by a well-defined trough–ridge couplet and jet streak at the tropopause. As TCs approach, the trough–ridge couplet amplifies, but is anchored by divergent TC outflow. This leads to phase locking of the upstream trough with TCs and thereby prompts substantial structural changes of TCs reminiscent of extratropical transition. The synergistic TC–midlatitude flow interactions allow for widely enhanced quasigeostrophic forcing for ascent to the north of the TC center. This allows HREs to occur even before TC landfall with more inland rainfall than C2 HREs. In contrast, C2, which is mainly observed in midsummer, does not accompany the undulating tropopause. In the absence of strong interactions with midlatitude flows, TCs rapidly dissipate after HREs while maintaining their tropical features. The upward motion is confined to the inherent TC convection, and thus HREs occur only when TCs are located in the vicinity of the country. These findings suggest that midlatitude baroclinic condition determines the spatial extent of TC rainfall and the timing of TC-induced HREs in South Korea.
Significance Statement
This study suggests that the midlatitude flows can substantially modulate heavy rainfall events directly caused by tropical cyclones. By analyzing the 42-yr tropical cyclone–induced heavy rainfall events in South Korea, it is found that tropical cyclones and midlatitude flows strongly interact with each other, especially when the midlatitude flows meander in conjunction with a strong jet stream. Their synergistic interactions result in a poleward expansion of the tropical cyclones’ precipitation shields, leading to heavy rainfall events even before they make landfall in the country. Consequently, it is advisable to carefully monitor the midlatitude conditions as well as tropical cyclones themselves as earlier heavy rainfall warnings may be necessary depending on the former.
Abstract
Recurving tropical cyclones (TCs) in the western North Pacific often cause heavy rainfall events (HREs) in East Asia. However, how their interactions with midlatitude flows alter the characteristics of HREs remains unclear. The present study examines the synoptic–dynamic characteristics of HREs directly resulting from TCs in South Korea with a focus on the role of midlatitude baroclinic condition. The HREs are categorized into two clusters based on midlatitude tropopause patterns: strongly (C1) and weakly (C2) baroclinic conditions. C1, which is common in late summer, is characterized by a well-defined trough–ridge couplet and jet streak at the tropopause. As TCs approach, the trough–ridge couplet amplifies, but is anchored by divergent TC outflow. This leads to phase locking of the upstream trough with TCs and thereby prompts substantial structural changes of TCs reminiscent of extratropical transition. The synergistic TC–midlatitude flow interactions allow for widely enhanced quasigeostrophic forcing for ascent to the north of the TC center. This allows HREs to occur even before TC landfall with more inland rainfall than C2 HREs. In contrast, C2, which is mainly observed in midsummer, does not accompany the undulating tropopause. In the absence of strong interactions with midlatitude flows, TCs rapidly dissipate after HREs while maintaining their tropical features. The upward motion is confined to the inherent TC convection, and thus HREs occur only when TCs are located in the vicinity of the country. These findings suggest that midlatitude baroclinic condition determines the spatial extent of TC rainfall and the timing of TC-induced HREs in South Korea.
Significance Statement
This study suggests that the midlatitude flows can substantially modulate heavy rainfall events directly caused by tropical cyclones. By analyzing the 42-yr tropical cyclone–induced heavy rainfall events in South Korea, it is found that tropical cyclones and midlatitude flows strongly interact with each other, especially when the midlatitude flows meander in conjunction with a strong jet stream. Their synergistic interactions result in a poleward expansion of the tropical cyclones’ precipitation shields, leading to heavy rainfall events even before they make landfall in the country. Consequently, it is advisable to carefully monitor the midlatitude conditions as well as tropical cyclones themselves as earlier heavy rainfall warnings may be necessary depending on the former.
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.
Significance Statement
Tornado development is sensitive to near-surface processes, including those associated with front-like boundaries between regions of airflow within the parent storm. However, observations and theory are insufficient to understand these phenomena, and numerical simulation remains vital. In our simulations, we find that a change in a parameter that controls how much the near-surface winds are reduced by friction (or drag) can substantially alter the storm behavior and tornado potential. We investigate how surface drag affects the low-level storm structure, the distribution of regions of near-surface rotation, and the development of tornadoes within the simulation. Our results provide insight into the role of surface drag and lead to an improved conceptual model of the near-surface structure of a tornadic storm.
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.
Significance Statement
Tornado development is sensitive to near-surface processes, including those associated with front-like boundaries between regions of airflow within the parent storm. However, observations and theory are insufficient to understand these phenomena, and numerical simulation remains vital. In our simulations, we find that a change in a parameter that controls how much the near-surface winds are reduced by friction (or drag) can substantially alter the storm behavior and tornado potential. We investigate how surface drag affects the low-level storm structure, the distribution of regions of near-surface rotation, and the development of tornadoes within the simulation. Our results provide insight into the role of surface drag and lead to an improved conceptual model of the near-surface structure of a tornadic storm.
Abstract
An accurate wind resource dataset is required for assessing the potential energy yield of floating offshore wind farms that are expected along the California outer continental shelf. The National Renewable Energy Laboratory has developed and disseminated an updated wind resource dataset offshore California, using the Weather Research and Forecasting model, referred to as the CA20 dataset. As compared to buoy lidar measurements that have become available recently, the CA20 dataset showed significant positive biases for 100-m wind speeds along northern California wind energy lease areas. To investigate the meteorological drivers for the model errors, we first consider two 1-year simulations run with two different planetary boundary layer (PBL) parameterizations: the Mellor-Yamada-Nakanishi-Niino (MYNN) PBL scheme (chosen configuration in the CA20 dataset) and the Yonsei University PBL scheme (which significantly reduces the bias in modeled winds). By comparing the 1-year simulations to the concurrent lidar buoy observations, we find that errors are larger with the MYNN PBL scheme in warm seasons. We then dive deeper into the analysis by running simulations for short-term (3-day) case studies to evaluate the sensitivity of initial/boundary condition forcings on model results. By analyzing the short-term simulations, we find that during synoptic scale northerly flows driven by the North Pacific High and inland thermal low, a coastal warm bias in the MYNN simulation is mainly responsible for the modeled wind speed bias by altering the boundary layer thermodynamics. The results of our analysis will help guide the creation of an updated version of the CA20 dataset.
Abstract
An accurate wind resource dataset is required for assessing the potential energy yield of floating offshore wind farms that are expected along the California outer continental shelf. The National Renewable Energy Laboratory has developed and disseminated an updated wind resource dataset offshore California, using the Weather Research and Forecasting model, referred to as the CA20 dataset. As compared to buoy lidar measurements that have become available recently, the CA20 dataset showed significant positive biases for 100-m wind speeds along northern California wind energy lease areas. To investigate the meteorological drivers for the model errors, we first consider two 1-year simulations run with two different planetary boundary layer (PBL) parameterizations: the Mellor-Yamada-Nakanishi-Niino (MYNN) PBL scheme (chosen configuration in the CA20 dataset) and the Yonsei University PBL scheme (which significantly reduces the bias in modeled winds). By comparing the 1-year simulations to the concurrent lidar buoy observations, we find that errors are larger with the MYNN PBL scheme in warm seasons. We then dive deeper into the analysis by running simulations for short-term (3-day) case studies to evaluate the sensitivity of initial/boundary condition forcings on model results. By analyzing the short-term simulations, we find that during synoptic scale northerly flows driven by the North Pacific High and inland thermal low, a coastal warm bias in the MYNN simulation is mainly responsible for the modeled wind speed bias by altering the boundary layer thermodynamics. The results of our analysis will help guide the creation of an updated version of the CA20 dataset.
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 unoccupied aircraft system (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 unoccupied aircraft system (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
Large-scale meteorological pattern (LSMP)–based analysis is used in a novel way to understand meteorological conditions before and during short-duration dry spells over the northeastern United States. These LSMPs are useful to assess models and select better-performing models for future projections. Dry-spell events are identified from histograms of consecutive dry days below a daily precipitation threshold. Events lasting 12 days or longer, which correspond to ∼10% of dry-spell events, are examined. The 500-hPa streamfunction anomaly fields for the first 12 days of each event are time averaged, and k-means clustering is applied to isolate the dry-spell-related LSMPs. The first cluster has a strong low pressure anomaly over the Atlantic Ocean, southeast of the region, and is more common in winter and spring. The second cluster has strong high pressure over east-central North America and is most common during autumn. Over the region, both clusters have negative specific humidity anomalies, negative integrated vapor transport from the north, and subsidence associated with a midlatitude jet stream dipole structure that reinforces upper-level convergence. Subsidence is supported by cold-air advection in the first cluster and the location on the east side of the lower-level high pressure in the second cluster. Extratropical cyclone storm tracks are generally shifted southward of the region during the dry spells. Individual events lie on a continuum between two distinct clusters. These clusters have similar local, but different remote, properties. Although dry spells occur with greater frequency during drought months, most dry spells occur during nondrought months.
Significance Statement
This study examines the large-scale weather patterns and meteorological conditions associated with dry-spell events lasting at least 2 weeks while affecting the northeastern United States. A statistical approach groups events together on the basis of similar atmospheric features. We find two distinct sets of patterns that we call large-scale meteorological patterns. These patterns reduce moisture, foster localized sinking, and shift the storm track southward along the Atlantic seaboard, all of which reduce precipitation. Besides greater understanding, knowing the meteorological patterns during short-term dryness in the region provides an important tool to assess how well atmospheric models reproduce these specific patterns. More dry spells occur in nondrought months than in drought months, which means that dry spells can occur without preexisting drought conditions.
Abstract
Large-scale meteorological pattern (LSMP)–based analysis is used in a novel way to understand meteorological conditions before and during short-duration dry spells over the northeastern United States. These LSMPs are useful to assess models and select better-performing models for future projections. Dry-spell events are identified from histograms of consecutive dry days below a daily precipitation threshold. Events lasting 12 days or longer, which correspond to ∼10% of dry-spell events, are examined. The 500-hPa streamfunction anomaly fields for the first 12 days of each event are time averaged, and k-means clustering is applied to isolate the dry-spell-related LSMPs. The first cluster has a strong low pressure anomaly over the Atlantic Ocean, southeast of the region, and is more common in winter and spring. The second cluster has strong high pressure over east-central North America and is most common during autumn. Over the region, both clusters have negative specific humidity anomalies, negative integrated vapor transport from the north, and subsidence associated with a midlatitude jet stream dipole structure that reinforces upper-level convergence. Subsidence is supported by cold-air advection in the first cluster and the location on the east side of the lower-level high pressure in the second cluster. Extratropical cyclone storm tracks are generally shifted southward of the region during the dry spells. Individual events lie on a continuum between two distinct clusters. These clusters have similar local, but different remote, properties. Although dry spells occur with greater frequency during drought months, most dry spells occur during nondrought months.
Significance Statement
This study examines the large-scale weather patterns and meteorological conditions associated with dry-spell events lasting at least 2 weeks while affecting the northeastern United States. A statistical approach groups events together on the basis of similar atmospheric features. We find two distinct sets of patterns that we call large-scale meteorological patterns. These patterns reduce moisture, foster localized sinking, and shift the storm track southward along the Atlantic seaboard, all of which reduce precipitation. Besides greater understanding, knowing the meteorological patterns during short-term dryness in the region provides an important tool to assess how well atmospheric models reproduce these specific patterns. More dry spells occur in nondrought months than in drought months, which means that dry spells can occur without preexisting drought conditions.
