Browse
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.
Abstract
This study uses the convective adjustment time scale to identify the climatological frequency of equilibrium and non-equilibrium convection in different parts of the Contiguous United States (CONUS) as modeled by the operational convection-allowing High-Resolution Rapid Refresh (HRRR) forecast system. We find a qualitatively different climatology in the northern and southern domains separated by the 40°N parallel. The convective adjustment time scale picks up the fact that convection over the northern domains is governed by synoptic flow (leading to equilibrium) while locally forced, non-equilibrium convection dominates over the southern domains. Using a machine learning algorithm, we demonstrate that the convective adjustment timescale diagnostic provides a sensible classification that agrees with the underlying dynamics of equilibrium and non-equilibrium convection. Furthermore, the convective adjustment time scale can indicate the model quantitative precipitation forecast (QPF) quality, as it correctly reflects the higher QPF skill for precipitation under strong synoptic forcing. This diagnostic based on the strength of forcing for convection will be employed in future studies across different parts of CONUS to objectively distinguish different weather situations and explore the potential connection to warm-season precipitation predictability.
Abstract
This study uses the convective adjustment time scale to identify the climatological frequency of equilibrium and non-equilibrium convection in different parts of the Contiguous United States (CONUS) as modeled by the operational convection-allowing High-Resolution Rapid Refresh (HRRR) forecast system. We find a qualitatively different climatology in the northern and southern domains separated by the 40°N parallel. The convective adjustment time scale picks up the fact that convection over the northern domains is governed by synoptic flow (leading to equilibrium) while locally forced, non-equilibrium convection dominates over the southern domains. Using a machine learning algorithm, we demonstrate that the convective adjustment timescale diagnostic provides a sensible classification that agrees with the underlying dynamics of equilibrium and non-equilibrium convection. Furthermore, the convective adjustment time scale can indicate the model quantitative precipitation forecast (QPF) quality, as it correctly reflects the higher QPF skill for precipitation under strong synoptic forcing. This diagnostic based on the strength of forcing for convection will be employed in future studies across different parts of CONUS to objectively distinguish different weather situations and explore the potential connection to warm-season precipitation predictability.
Abstract
This study examined the dominant structure and characteristics of synoptic-scale (2–8-day periods) waves over northern Eurasia during 40 summer seasons (June–August, 1979–2018). The synoptic-scale wave patterns are isolated using an extended empirical orthogonal function (EEOF) analysis on the 300-hPa geopotential height anomalies, and a composite based on atmospheric circulation fields and gridded precipitation product. The wave patterns are classified into two types from two pairs of EEOF modes. These two different wave types are defined as the polar frontal (PF) mode and Arctic frontal (AF) mode, respectively. The PF-mode waves are initiated in the North Atlantic sector to the west of the British Isles. They propagate eastward across Siberia into the North Pacific, and produce precipitation mainly over the Eurasian polar frontal zone. The AF-mode wave train arcs along the climatological Arctic frontal zone (AFZ). The AF-mode waves originate near the Scandinavian Peninsula. Their eastward passage brings precipitation along the AFZ. The development of the synoptic-scale waves is reflected by unique background conditions over northern Eurasia. The lower-tropospheric baroclinicity in southern Siberia and central Asia favored the baroclinic growth of the PF-mode waves. The AF-mode waves are trapped in the well-organized baroclinic zone along the north coast of the Eurasian continent. The baroclinic zone is coupled with a band of large meridional gradient of potential vorticity in the upper-troposphere, suggesting that this band acts as a waveguide for the AF-mode waves.
Abstract
This study examined the dominant structure and characteristics of synoptic-scale (2–8-day periods) waves over northern Eurasia during 40 summer seasons (June–August, 1979–2018). The synoptic-scale wave patterns are isolated using an extended empirical orthogonal function (EEOF) analysis on the 300-hPa geopotential height anomalies, and a composite based on atmospheric circulation fields and gridded precipitation product. The wave patterns are classified into two types from two pairs of EEOF modes. These two different wave types are defined as the polar frontal (PF) mode and Arctic frontal (AF) mode, respectively. The PF-mode waves are initiated in the North Atlantic sector to the west of the British Isles. They propagate eastward across Siberia into the North Pacific, and produce precipitation mainly over the Eurasian polar frontal zone. The AF-mode wave train arcs along the climatological Arctic frontal zone (AFZ). The AF-mode waves originate near the Scandinavian Peninsula. Their eastward passage brings precipitation along the AFZ. The development of the synoptic-scale waves is reflected by unique background conditions over northern Eurasia. The lower-tropospheric baroclinicity in southern Siberia and central Asia favored the baroclinic growth of the PF-mode waves. The AF-mode waves are trapped in the well-organized baroclinic zone along the north coast of the Eurasian continent. The baroclinic zone is coupled with a band of large meridional gradient of potential vorticity in the upper-troposphere, suggesting that this band acts as a waveguide for the AF-mode waves.
Abstract
Data from rawinsondes launched during intensive observation periods (IOPs) of the Ontario Winter Lake-effect Systems (OWLeS) field project reveal that elevated mixed layers (EMLs) in the lower troposphere were relatively common near Lake Ontario during OWLeS lake-effect events. Conservatively, EMLs exist in 193 of the 290 OWLeS IOP soundings. The distribution of EML base pressure derived from the OWLeS IOP soundings reveals two classes of EML, one that has a relatively low-elevation base (900 – 750 hPa) and one that has a relatively high-elevation base (750 – 500 hPa). It is hypothesized that the former class of EML, which is the focus of this research, is, at times, the result of mesoscale processes related to individual Great Lakes. WRF reanalysis fields from a case study during the OWLeS field project provide evidence of two means by which low-elevation base EMLs can originate from the lake-effect boundary layer convection and associated mesoscale circulations. First, such EMLs can form within the upper-level outflow branches of mesoscale solenoidal circulations. Evacuated Great Lake-modified convective boundary layer air aloft then lies above ambient air of a greater static stability, forming EMLs. Second, such EMLs can form in the absence of a mesoscale solenoidal circulation when Great Lake-modified convective boundary layers overrun ambient air of a greater density. The reanalysis fields show that EMLs and layers of reduced static stability tied to Great Lake-modified convective boundary layers can extend downwind for hundreds of kilometers from their areas of formation. Operational implications and avenues for future research are discussed.
Abstract
Data from rawinsondes launched during intensive observation periods (IOPs) of the Ontario Winter Lake-effect Systems (OWLeS) field project reveal that elevated mixed layers (EMLs) in the lower troposphere were relatively common near Lake Ontario during OWLeS lake-effect events. Conservatively, EMLs exist in 193 of the 290 OWLeS IOP soundings. The distribution of EML base pressure derived from the OWLeS IOP soundings reveals two classes of EML, one that has a relatively low-elevation base (900 – 750 hPa) and one that has a relatively high-elevation base (750 – 500 hPa). It is hypothesized that the former class of EML, which is the focus of this research, is, at times, the result of mesoscale processes related to individual Great Lakes. WRF reanalysis fields from a case study during the OWLeS field project provide evidence of two means by which low-elevation base EMLs can originate from the lake-effect boundary layer convection and associated mesoscale circulations. First, such EMLs can form within the upper-level outflow branches of mesoscale solenoidal circulations. Evacuated Great Lake-modified convective boundary layer air aloft then lies above ambient air of a greater static stability, forming EMLs. Second, such EMLs can form in the absence of a mesoscale solenoidal circulation when Great Lake-modified convective boundary layers overrun ambient air of a greater density. The reanalysis fields show that EMLs and layers of reduced static stability tied to Great Lake-modified convective boundary layers can extend downwind for hundreds of kilometers from their areas of formation. Operational implications and avenues for future research are discussed.
Abstract
While Tropical Cyclone (TC) risk is a global concern, high regional differences exist in the quality of available data. This paper introduces InCyc, a globally consistent database of high-resolution full-physics simulations of historical cyclones. InCyc is designed to facilitate analysis of TC wind risk across basins and is made available to research institutions. We illustrate the value of this database with a case study focused on key wind risk parameters, namely the location and intensity of peak winds for the North Atlantic and western North Pacific basins. A novel approach based on random forest algorithms is introduced to predict the full distribution of these TC wind risk parameters. Based on a leave-one-storm-out evaluation, the analysis of the predictions shows how this innovative approach compares to other parametric models commonly used for wind risk assessment. We finally discuss why capturing the full distribution of variability is crucial as well as the broader use in the context of TC risk assessment systems (i.e. “catastrophe models”).
Abstract
While Tropical Cyclone (TC) risk is a global concern, high regional differences exist in the quality of available data. This paper introduces InCyc, a globally consistent database of high-resolution full-physics simulations of historical cyclones. InCyc is designed to facilitate analysis of TC wind risk across basins and is made available to research institutions. We illustrate the value of this database with a case study focused on key wind risk parameters, namely the location and intensity of peak winds for the North Atlantic and western North Pacific basins. A novel approach based on random forest algorithms is introduced to predict the full distribution of these TC wind risk parameters. Based on a leave-one-storm-out evaluation, the analysis of the predictions shows how this innovative approach compares to other parametric models commonly used for wind risk assessment. We finally discuss why capturing the full distribution of variability is crucial as well as the broader use in the context of TC risk assessment systems (i.e. “catastrophe models”).
Abstract
The success of the National Severe Storms Laboratory’s (NSSL) experimental Warn-on-Forecast System (WoFS) to provide useful probabilistic guidance of severe and hazardous weather is mostly due to the frequent assimilation of observations, especially radar observations. Phased-array radar (PAR) technology, which is a potential candidate to replace the current U.S. operational radar network, would allow for even more rapid assimilation of radar observations by providing full-volumetric scans of the atmosphere every ~1 min. Based on previous studies, more frequent PAR data assimilation can lead to improved forecasts, but it can also lead to ensemble underdispersion and suboptimal observation assimilation. The use of stochastic and perturbed parameter methods to increase ensemble spread is a potential solution to this problem. In this study, four stochastic and perturbed parameter methods are assessed using a 1-km-scale version of the WoFS and include the stochastic kinetic energy backscatter (SKEB) scheme, the physically-based stochastic perturbation (PSP) scheme, a fixed perturbed parameters (FPP) method, and a novel surface-model scheme blending (SMSB) method. Using NSSL PAR observations from the 9 May 2016 tornado outbreak, experiments are conducted to assess the impact of the methods individually, in different combinations, and with different cycling intervals. The results from these experiments reveal the potential benefits of stochastic and perturbed parameter methods for future versions of the WoFS. Stochastic and perturbed parameter methods can lead to more skillful forecasts during periods of storm development. Moreover, a combination of multiple methods can result in more skillful forecasts than using a single method.
Abstract
The success of the National Severe Storms Laboratory’s (NSSL) experimental Warn-on-Forecast System (WoFS) to provide useful probabilistic guidance of severe and hazardous weather is mostly due to the frequent assimilation of observations, especially radar observations. Phased-array radar (PAR) technology, which is a potential candidate to replace the current U.S. operational radar network, would allow for even more rapid assimilation of radar observations by providing full-volumetric scans of the atmosphere every ~1 min. Based on previous studies, more frequent PAR data assimilation can lead to improved forecasts, but it can also lead to ensemble underdispersion and suboptimal observation assimilation. The use of stochastic and perturbed parameter methods to increase ensemble spread is a potential solution to this problem. In this study, four stochastic and perturbed parameter methods are assessed using a 1-km-scale version of the WoFS and include the stochastic kinetic energy backscatter (SKEB) scheme, the physically-based stochastic perturbation (PSP) scheme, a fixed perturbed parameters (FPP) method, and a novel surface-model scheme blending (SMSB) method. Using NSSL PAR observations from the 9 May 2016 tornado outbreak, experiments are conducted to assess the impact of the methods individually, in different combinations, and with different cycling intervals. The results from these experiments reveal the potential benefits of stochastic and perturbed parameter methods for future versions of the WoFS. Stochastic and perturbed parameter methods can lead to more skillful forecasts during periods of storm development. Moreover, a combination of multiple methods can result in more skillful forecasts than using a single method.
