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Abstract
We present the formulation and optimization of a Runge–Kutta-type time-stepping scheme for solving the shallow-water equations, aimed at substantially increasing the effective allowable time step over that of comparable methods. This scheme, called FB-RK(3,2), uses weighted forward–backward averaging of thickness data to advance the momentum equation. The weights for this averaging are chosen with an optimization process that employs a von Neumann–type analysis, ensuring that the weights maximize the admittable Courant number. Through a simplified local truncation error analysis and numerical experiments, we show that the method is at least second-order in time for any choice of weights and exhibits low dispersion and dissipation errors for well-resolved waves. Further, we show that an optimized FB-RK(3,2) can take time steps up to 2.8 times as large as a popular three-stage, third-order strong stability-preserving Runge–Kutta method in a quasi-linear test case. In fully nonlinear shallow-water test cases relevant to oceanic and atmospheric flows, FB-RK(3,2) outperforms SSPRK3 in admittable time step by factors roughly between 1.6 and 2.2, making the scheme approximately twice as computationally efficient with little to no effect on solution quality.
Significance Statement
The purpose of this work is to develop and optimize time-stepping schemes for models relevant to oceanic and atmospheric flows. Specifically, for the shallow-water equations we optimize for schemes that can take time steps as large as possible while retaining solution quality. We find that our optimized schemes can take time steps between 1.6 and 2.2 times larger than schemes that cost the same number of floating point operations, translating directly to a corresponding speedup. Our ultimate goal is to use these schemes in climate-scale simulations.
Abstract
We present the formulation and optimization of a Runge–Kutta-type time-stepping scheme for solving the shallow-water equations, aimed at substantially increasing the effective allowable time step over that of comparable methods. This scheme, called FB-RK(3,2), uses weighted forward–backward averaging of thickness data to advance the momentum equation. The weights for this averaging are chosen with an optimization process that employs a von Neumann–type analysis, ensuring that the weights maximize the admittable Courant number. Through a simplified local truncation error analysis and numerical experiments, we show that the method is at least second-order in time for any choice of weights and exhibits low dispersion and dissipation errors for well-resolved waves. Further, we show that an optimized FB-RK(3,2) can take time steps up to 2.8 times as large as a popular three-stage, third-order strong stability-preserving Runge–Kutta method in a quasi-linear test case. In fully nonlinear shallow-water test cases relevant to oceanic and atmospheric flows, FB-RK(3,2) outperforms SSPRK3 in admittable time step by factors roughly between 1.6 and 2.2, making the scheme approximately twice as computationally efficient with little to no effect on solution quality.
Significance Statement
The purpose of this work is to develop and optimize time-stepping schemes for models relevant to oceanic and atmospheric flows. Specifically, for the shallow-water equations we optimize for schemes that can take time steps as large as possible while retaining solution quality. We find that our optimized schemes can take time steps between 1.6 and 2.2 times larger than schemes that cost the same number of floating point operations, translating directly to a corresponding speedup. Our ultimate goal is to use these schemes in climate-scale simulations.
Abstract
The radius of maximum wind R max, an important parameter in tropical cyclone (TC) ocean surface wind structure, is currently resolved by only a few sensors so that, in most cases, it is estimated subjectively or via crude statistical models. Recently, a semiempirical model relying on an outer wind radius, intensity, and latitude was fit to best-track data. In this study we revise this semiempirical model and discuss its physical basis. While intensity and latitude are taken from best-track data, R max observations from high-resolution (3 km) spaceborne synthetic aperture radar (SAR) and wind radii from an intercalibrated dataset of medium-resolution radiometers and scatterometers are considered to revise the model coefficients. The new version of the model is then applied to the period 2010–20 and yields R max reanalyses and trends that are more accurate than best-track data. SAR measurements corroborate that fundamental conservation principles constrain the radial wind structure on average, endorsing the physical basis of the model. Observations highlight that departures from the average conservation situation are mainly explained by wind profile shape variations, confirming the model’s physical basis, which further shows that radial inflow, boundary layer depth, and drag coefficient also play roles. Physical understanding will benefit from improved observations of the near-core region from accumulated SAR observations and future missions. In the meantime, the revised model offers an efficient tool to provide guidance on R max when a radiometer or scatterometer observation is available, for either operations or reanalysis purposes.
Abstract
The radius of maximum wind R max, an important parameter in tropical cyclone (TC) ocean surface wind structure, is currently resolved by only a few sensors so that, in most cases, it is estimated subjectively or via crude statistical models. Recently, a semiempirical model relying on an outer wind radius, intensity, and latitude was fit to best-track data. In this study we revise this semiempirical model and discuss its physical basis. While intensity and latitude are taken from best-track data, R max observations from high-resolution (3 km) spaceborne synthetic aperture radar (SAR) and wind radii from an intercalibrated dataset of medium-resolution radiometers and scatterometers are considered to revise the model coefficients. The new version of the model is then applied to the period 2010–20 and yields R max reanalyses and trends that are more accurate than best-track data. SAR measurements corroborate that fundamental conservation principles constrain the radial wind structure on average, endorsing the physical basis of the model. Observations highlight that departures from the average conservation situation are mainly explained by wind profile shape variations, confirming the model’s physical basis, which further shows that radial inflow, boundary layer depth, and drag coefficient also play roles. Physical understanding will benefit from improved observations of the near-core region from accumulated SAR observations and future missions. In the meantime, the revised model offers an efficient tool to provide guidance on R max when a radiometer or scatterometer observation is available, for either operations or reanalysis purposes.
Abstract
This study investigates the effects of lakes in mountainous terrain on the evolution of supercell thunderstorms. With a newly developed radar-based, mesocyclone-detection algorithm, a recent study has characterized the occurrence and evolution of supercell thunderstorms in the Swiss Alpine region. That study highlights the influence of orography on both storm intensity and occurrence frequency. To disentangle the different influential factors, an idealized modeling framework is established here using the mesoscale model CM1. The modeling scenarios are based on a high-CAPE environment with unidirectional shear, where a warm bubble serves to initiate the convection. Mimicking the environment of the southern Prealps in central Europe, scenarios with a high mountain ridge, valleys and lakes are explored. The effect on the supercells of the slopes, high-altitude terrain and moisture sources emphasizes the highly localized nature of terrain effects, leading to a heterogeneous intensity lifecycle with transitory enhancement and weakening of the supercell. The dynamic and thermodynamic impact of mountain valleys with lakes increases the range of atmospheric conditions that supports supercellular development through horizontal vorticity production, increased storm relative helicity and higher moisture content. This influence results in a systematic location dependence of the frequency, intensity and lifetime of supercells, as also found in observations.
Abstract
This study investigates the effects of lakes in mountainous terrain on the evolution of supercell thunderstorms. With a newly developed radar-based, mesocyclone-detection algorithm, a recent study has characterized the occurrence and evolution of supercell thunderstorms in the Swiss Alpine region. That study highlights the influence of orography on both storm intensity and occurrence frequency. To disentangle the different influential factors, an idealized modeling framework is established here using the mesoscale model CM1. The modeling scenarios are based on a high-CAPE environment with unidirectional shear, where a warm bubble serves to initiate the convection. Mimicking the environment of the southern Prealps in central Europe, scenarios with a high mountain ridge, valleys and lakes are explored. The effect on the supercells of the slopes, high-altitude terrain and moisture sources emphasizes the highly localized nature of terrain effects, leading to a heterogeneous intensity lifecycle with transitory enhancement and weakening of the supercell. The dynamic and thermodynamic impact of mountain valleys with lakes increases the range of atmospheric conditions that supports supercellular development through horizontal vorticity production, increased storm relative helicity and higher moisture content. This influence results in a systematic location dependence of the frequency, intensity and lifetime of supercells, as also found in observations.
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.
