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Abstract
Recent research has demonstrated a relationship between convectively coupled Kelvin waves (CCKWs) and tropical cyclogenesis, likely due to the influence of CCKWs on the large-scale environment. However, it remains unclear which environmental factors are most important and how they connect to TC genesis processes. Using a 39-yr database of African easterly waves (AEWs) to create composites of reanalysis and satellite data, it is shown that genesis may be facilitated by CCKW-driven modifications to convection and moisture. First, stand-alone composites of genesis demonstrate the significant role of environmental preconditioning and convective aggregation. A moist static energy variance budget indicates that convective aggregation during genesis is dominated by feedbacks between convection and longwave radiation. These processes begin over two days prior to genesis, supporting previous observational work. Shifting attention to CCKWs, up to 76% of developing AEWs encounter at least one CCKW in their lifetime. An increase in genesis events following convectively active CCKW phases is found, corroborating earlier studies. A decrease in genesis events following convectively suppressed phases is also identified. Using CCKW-centered composites, we show that the convectively active CCKW phases enhance convection and moisture content in the vicinity of AEWs prior to genesis. Furthermore, enhanced convective activity is the main discriminator between AEW–CCKW interactions that result in genesis versus those that do not. This analysis suggests that CCKWs may influence genesis through environmental preconditioning and radiative–convective feedbacks, among other factors. A secondary finding is that AEW attributes as far east as central Africa may be predictive of downstream genesis.
Significance Statement
The purpose of this work is to investigate how one type of atmospheric wave, known as convectively coupled Kelvin waves (CCKWs), impacts the formation (“genesis”) of tropical cyclones. Forecasting of genesis remains a significant challenge, so identifying how CCKWs influence this process could help improve forecasts and give communities greater lead times. Our results show that CCKWs could temporarily make genesis more likely by increasing atmospheric moisture content and convective activity. While not all CCKWs lead to genesis, those that do are associated with a particularly strong increase in convection. This provides a potential tool for forecasters monitoring CCKWs and TC genesis in real time and motivates follow-up work on this topic in numerical models.
Abstract
Recent research has demonstrated a relationship between convectively coupled Kelvin waves (CCKWs) and tropical cyclogenesis, likely due to the influence of CCKWs on the large-scale environment. However, it remains unclear which environmental factors are most important and how they connect to TC genesis processes. Using a 39-yr database of African easterly waves (AEWs) to create composites of reanalysis and satellite data, it is shown that genesis may be facilitated by CCKW-driven modifications to convection and moisture. First, stand-alone composites of genesis demonstrate the significant role of environmental preconditioning and convective aggregation. A moist static energy variance budget indicates that convective aggregation during genesis is dominated by feedbacks between convection and longwave radiation. These processes begin over two days prior to genesis, supporting previous observational work. Shifting attention to CCKWs, up to 76% of developing AEWs encounter at least one CCKW in their lifetime. An increase in genesis events following convectively active CCKW phases is found, corroborating earlier studies. A decrease in genesis events following convectively suppressed phases is also identified. Using CCKW-centered composites, we show that the convectively active CCKW phases enhance convection and moisture content in the vicinity of AEWs prior to genesis. Furthermore, enhanced convective activity is the main discriminator between AEW–CCKW interactions that result in genesis versus those that do not. This analysis suggests that CCKWs may influence genesis through environmental preconditioning and radiative–convective feedbacks, among other factors. A secondary finding is that AEW attributes as far east as central Africa may be predictive of downstream genesis.
Significance Statement
The purpose of this work is to investigate how one type of atmospheric wave, known as convectively coupled Kelvin waves (CCKWs), impacts the formation (“genesis”) of tropical cyclones. Forecasting of genesis remains a significant challenge, so identifying how CCKWs influence this process could help improve forecasts and give communities greater lead times. Our results show that CCKWs could temporarily make genesis more likely by increasing atmospheric moisture content and convective activity. While not all CCKWs lead to genesis, those that do are associated with a particularly strong increase in convection. This provides a potential tool for forecasters monitoring CCKWs and TC genesis in real time and motivates follow-up work on this topic in numerical models.
Abstract
Obtaining a faithful probabilistic depiction of moist convection is complicated by unknown errors in subgrid-scale physical parameterization schemes, invalid assumptions made by data assimilation (DA) techniques, and high system dimensionality. As an initial step toward untangling sources of uncertainty in convective weather regimes, we evaluate a novel Bayesian data assimilation methodology based on particle filtering within a WRF ensemble analysis and forecasting system. Unlike most geophysical DA methods, the particle filter (PF) represents prior and posterior error distributions nonparametrically rather than assuming a Gaussian distribution and can accept any type of likelihood function. This approach is known to reduce bias introduced by Gaussian approximations in low-dimensional and idealized contexts. The form of PF used in this research adopts a dimension-reduction strategy, making it affordable for typical weather applications. The present study examines posterior ensemble members and forecasts for select severe weather events between 2019 and 2020, comparing results from the PF with those from an ensemble Kalman filter (EnKF). We find that assimilating with a PF produces posterior quantities for microphysical variables that are more consistent with model climatology than comparable quantities from an EnKF, which we attribute to a reduction in DA bias. These differences are significant enough to impact the dynamic evolution of convective systems via cold pool strength and propagation, with impacts to forecast verification scores depending on the particular microphysics scheme. Our findings have broad implications for future approaches to the selection of physical parameterization schemes and parameter estimation within preexisting data assimilation frameworks.
Significance Statement
The accurate prediction of severe storms using numerical weather models depends on effective parameterization schemes for small-scale processes and the assimilation of incomplete observational data in a manner that faithfully represents the probabilistic state of the atmosphere. Current generation methods for data assimilation typically assume a standard form for the error distributions of relevant quantities, which can introduce bias that not only hinders numerical prediction, but that can also confound the characterization of errors from the model itself. The current study performs data assimilation using a novel method that does not make such assumptions and explores characteristics of resulting model fields and forecasts that might make such a method useful for improving model parameterization schemes.
Abstract
Obtaining a faithful probabilistic depiction of moist convection is complicated by unknown errors in subgrid-scale physical parameterization schemes, invalid assumptions made by data assimilation (DA) techniques, and high system dimensionality. As an initial step toward untangling sources of uncertainty in convective weather regimes, we evaluate a novel Bayesian data assimilation methodology based on particle filtering within a WRF ensemble analysis and forecasting system. Unlike most geophysical DA methods, the particle filter (PF) represents prior and posterior error distributions nonparametrically rather than assuming a Gaussian distribution and can accept any type of likelihood function. This approach is known to reduce bias introduced by Gaussian approximations in low-dimensional and idealized contexts. The form of PF used in this research adopts a dimension-reduction strategy, making it affordable for typical weather applications. The present study examines posterior ensemble members and forecasts for select severe weather events between 2019 and 2020, comparing results from the PF with those from an ensemble Kalman filter (EnKF). We find that assimilating with a PF produces posterior quantities for microphysical variables that are more consistent with model climatology than comparable quantities from an EnKF, which we attribute to a reduction in DA bias. These differences are significant enough to impact the dynamic evolution of convective systems via cold pool strength and propagation, with impacts to forecast verification scores depending on the particular microphysics scheme. Our findings have broad implications for future approaches to the selection of physical parameterization schemes and parameter estimation within preexisting data assimilation frameworks.
Significance Statement
The accurate prediction of severe storms using numerical weather models depends on effective parameterization schemes for small-scale processes and the assimilation of incomplete observational data in a manner that faithfully represents the probabilistic state of the atmosphere. Current generation methods for data assimilation typically assume a standard form for the error distributions of relevant quantities, which can introduce bias that not only hinders numerical prediction, but that can also confound the characterization of errors from the model itself. The current study performs data assimilation using a novel method that does not make such assumptions and explores characteristics of resulting model fields and forecasts that might make such a method useful for improving model parameterization schemes.
Abstract
Numerous low-level vortices are initiated downwind of the Hoggar Mountains and progress toward the Atlantic coast on the northern path of African easterly waves (AEWs). These vortices occur mostly in July and August and more specifically when the northern position of the Saharan heat low (SHL) generates stronger and vertically expanded easterly winds over the Hoggar Mountains. At synoptic time scales, a composite analysis reveals that vortex initiation and westward motion are also statistically triggered by a reinforcement of these easterly winds by a wide and persistent high pressure anomaly developing around the Strait of Gibraltar and by a weak wave trough approaching from the east. The vortices are generated in the lee of the Hoggar, about 1000 km west of this approaching trough, and intensify rapidly. The evolution of the vortex perturbation is afterward comparable with the known evolution of the AEWs of the northern path and suggest a growth due to dry barotropic and baroclinic processes induced in particular by the strong cyclonic shear between the reinforced easterly winds and the monsoon flow. These results show that vortex genesis promoted by changes in orographic forcing due to the strengthening of easterly winds over the Hoggar Mountains is a source of intensification of the northern path of AEWs in July and August. These results also provide a possible mechanism to explain the role of the SHL and of particular midlatitude intraseasonal disturbances on the intensity of these waves.
Abstract
Numerous low-level vortices are initiated downwind of the Hoggar Mountains and progress toward the Atlantic coast on the northern path of African easterly waves (AEWs). These vortices occur mostly in July and August and more specifically when the northern position of the Saharan heat low (SHL) generates stronger and vertically expanded easterly winds over the Hoggar Mountains. At synoptic time scales, a composite analysis reveals that vortex initiation and westward motion are also statistically triggered by a reinforcement of these easterly winds by a wide and persistent high pressure anomaly developing around the Strait of Gibraltar and by a weak wave trough approaching from the east. The vortices are generated in the lee of the Hoggar, about 1000 km west of this approaching trough, and intensify rapidly. The evolution of the vortex perturbation is afterward comparable with the known evolution of the AEWs of the northern path and suggest a growth due to dry barotropic and baroclinic processes induced in particular by the strong cyclonic shear between the reinforced easterly winds and the monsoon flow. These results show that vortex genesis promoted by changes in orographic forcing due to the strengthening of easterly winds over the Hoggar Mountains is a source of intensification of the northern path of AEWs in July and August. These results also provide a possible mechanism to explain the role of the SHL and of particular midlatitude intraseasonal disturbances on the intensity of these waves.
Abstract
Numerous questions remain regarding the influence of environmental inhomogeneities on supercell evolution. Motivated by this topic, this study associates cell-merger occurrence with supercell evolution and tornado production during the prolific 27–28 April 2011 outbreak in the U.S. Southeast. This event included 29 discrete supercells that produced 102 tornadoes and featured 300 cell mergers. Cell-merger frequency increased for supercells that initiated farthest east, possibly owing to changes in overall convective coverage over time. There is some signal for stronger mesocyclones to be associated with more mergers in the primary supercell’s forward flank. There is also a slight tendency for supercells that encounter more cell mergers to produce tornadoes more quickly, especially for those that formed away from a significant zonal boundary. However, there is a slight tendency for supercells spawning the longest-lived tornadoes (especially those with durations over 60 min) to be associated with fewer cell mergers during the 15-min window preceding tornadogenesis. Of particular importance, a significant inverse relationship exists between premerger mesocyclone strength and the subsequent change in mesocyclone strength during the merger (i.e., weaker mesocyclones tended to strengthen as a result of the merger, and vice versa). These findings highlight the influence that cell mergers can have on supercell evolution and tornado production—even within an incredibly volatile environment—and motivate future work exploring the physical processes involved and ways to translate these findings into experimental techniques or guidance for operational forecasters.
Significance Statement
The prolific 27–28 April 2011 supercell tornado outbreak in the U.S. Southeast featured 29 supercells that produced 102 tornadoes. This study analyzes mergers between these tornadic supercells and 300 weaker cells to determine if the mergers corresponded with important supercell characteristics. This appeared to be the case during this event; cell-merger events tended to be associated with tornadic periods of the supercells’ life cycles and influenced low- and midlevel mesocyclone strength, supercell evolutionary time scales, and subsequent tornado duration. These results are important for (i) better understanding of different supercell evolutionary paths in similar background environments and (ii) motivating future work in investigating experimental products related to these findings.
Abstract
Numerous questions remain regarding the influence of environmental inhomogeneities on supercell evolution. Motivated by this topic, this study associates cell-merger occurrence with supercell evolution and tornado production during the prolific 27–28 April 2011 outbreak in the U.S. Southeast. This event included 29 discrete supercells that produced 102 tornadoes and featured 300 cell mergers. Cell-merger frequency increased for supercells that initiated farthest east, possibly owing to changes in overall convective coverage over time. There is some signal for stronger mesocyclones to be associated with more mergers in the primary supercell’s forward flank. There is also a slight tendency for supercells that encounter more cell mergers to produce tornadoes more quickly, especially for those that formed away from a significant zonal boundary. However, there is a slight tendency for supercells spawning the longest-lived tornadoes (especially those with durations over 60 min) to be associated with fewer cell mergers during the 15-min window preceding tornadogenesis. Of particular importance, a significant inverse relationship exists between premerger mesocyclone strength and the subsequent change in mesocyclone strength during the merger (i.e., weaker mesocyclones tended to strengthen as a result of the merger, and vice versa). These findings highlight the influence that cell mergers can have on supercell evolution and tornado production—even within an incredibly volatile environment—and motivate future work exploring the physical processes involved and ways to translate these findings into experimental techniques or guidance for operational forecasters.
Significance Statement
The prolific 27–28 April 2011 supercell tornado outbreak in the U.S. Southeast featured 29 supercells that produced 102 tornadoes. This study analyzes mergers between these tornadic supercells and 300 weaker cells to determine if the mergers corresponded with important supercell characteristics. This appeared to be the case during this event; cell-merger events tended to be associated with tornadic periods of the supercells’ life cycles and influenced low- and midlevel mesocyclone strength, supercell evolutionary time scales, and subsequent tornado duration. These results are important for (i) better understanding of different supercell evolutionary paths in similar background environments and (ii) motivating future work in investigating experimental products related to these findings.
Abstract
Unsteadiness and horizontal heterogeneities frequently characterize atmospheric motions, especially within convective storms, which are frequently studied using large-eddy simulations (LES). The models of near-surface turbulence employed by atmospheric LES, however, predominantly assume statistically steady and horizontally homogeneous conditions (known as the equilibrium approach). The primary objective of this work is to investigate the potential consequences of such unrealistic assumptions in simulations of tornadoes. Cloud Model 1 (CM1) LES runs are performed using three approaches to model near-surface turbulence: the “semi-slip” boundary condition (which is the most commonly used equilibrium approach), a recently proposed nonequilibrium approach that accounts for some of the effects of turbulence memory, and a nonequilibrium approach based on thin boundary layer equations (TBLE) originally proposed by the engineering community for smooth-wall boundary layer applications. To be adopted for atmospheric applications, the TBLE approach is modified to account for the surface roughness. The implementation of TBLE into CM1 is evaluated using LES results of an idealized, neutral atmospheric boundary layer. LES runs are then performed for an idealized tornado characterized by rapid evolution, strongly curved air parcel trajectories, and substantial horizontal heterogeneities. The semi-slip boundary condition, by design, always yields a surface shear stress opposite the horizontal wind at the lowest LES grid level. The nonequilibrium approaches of modeling near-surface turbulence allow for a range of surface-shear-stress directions and enhance the resolved turbulence and wind gusts. The TBLE approach even occasionally permits kinetic energy backscatter from unresolved to resolved scales.
Significance Statement
The traditional approach of modeling the near-surface turbulence is not suitable for a tornado characterized by rapid evolution, strongly curved air parcel trajectories, and substantial horizontal heterogeneities. To understand the influence of statistically unsteady and horizontally heterogeneous near-surface conditions on tornadoes, this work adopts a fairly sophisticated approach from the engineering community and implements it into a widely used atmospheric model with necessary modifications. Compared to the traditional approach, the newly implemented approach produces more turbulent near-surface winds, more flexible surface-drag directions, and stronger wind gusts. These findings suggest a simulated tornado is very sensitive to the modeling approach of near-surface turbulence.
Abstract
Unsteadiness and horizontal heterogeneities frequently characterize atmospheric motions, especially within convective storms, which are frequently studied using large-eddy simulations (LES). The models of near-surface turbulence employed by atmospheric LES, however, predominantly assume statistically steady and horizontally homogeneous conditions (known as the equilibrium approach). The primary objective of this work is to investigate the potential consequences of such unrealistic assumptions in simulations of tornadoes. Cloud Model 1 (CM1) LES runs are performed using three approaches to model near-surface turbulence: the “semi-slip” boundary condition (which is the most commonly used equilibrium approach), a recently proposed nonequilibrium approach that accounts for some of the effects of turbulence memory, and a nonequilibrium approach based on thin boundary layer equations (TBLE) originally proposed by the engineering community for smooth-wall boundary layer applications. To be adopted for atmospheric applications, the TBLE approach is modified to account for the surface roughness. The implementation of TBLE into CM1 is evaluated using LES results of an idealized, neutral atmospheric boundary layer. LES runs are then performed for an idealized tornado characterized by rapid evolution, strongly curved air parcel trajectories, and substantial horizontal heterogeneities. The semi-slip boundary condition, by design, always yields a surface shear stress opposite the horizontal wind at the lowest LES grid level. The nonequilibrium approaches of modeling near-surface turbulence allow for a range of surface-shear-stress directions and enhance the resolved turbulence and wind gusts. The TBLE approach even occasionally permits kinetic energy backscatter from unresolved to resolved scales.
Significance Statement
The traditional approach of modeling the near-surface turbulence is not suitable for a tornado characterized by rapid evolution, strongly curved air parcel trajectories, and substantial horizontal heterogeneities. To understand the influence of statistically unsteady and horizontally heterogeneous near-surface conditions on tornadoes, this work adopts a fairly sophisticated approach from the engineering community and implements it into a widely used atmospheric model with necessary modifications. Compared to the traditional approach, the newly implemented approach produces more turbulent near-surface winds, more flexible surface-drag directions, and stronger wind gusts. These findings suggest a simulated tornado is very sensitive to the modeling approach of near-surface turbulence.
Abstract
Convectively generated cold pools play a crucial role in the convection initiation and development, but observations of their vertical structure are insufficient. In this study, quantitative vertical evolutions of cold pools during a high-impact heavy rainfall event near the south coast of China were examined using observations from a 356-m-high Shenzhen Met-Tower, and their potential impacts on heavy rainfall were further discussed through high-resolution surface station network and radar observations. On 11 April 2019, heavy precipitation occurred near metropolitan Shenzhen, lasting for 50 min at its southern downtown and resulting in 11 deaths. During this event, a shallow cold pool was first observed by the tower and yielded a long-lasting cooling of 2.6 K. Approximately 1 h later, another deeper cold pool accompanied by a squall line was added from the west. This addition resulted in a more abrupt and intense surface temperature deficit (5.1 K) and stronger gusty winds (23 m s−1). When the two cold pools collided near Shenzhen, the low-level winds converged at their intersection, dynamically enhancing the heavy-rain-producing squall line. Moreover, the collision of the two cold pools reduced the temperature gradient at the northern edge of the merged cold pool, which could inhibit development of the squall line. The area south of the squall line became a relatively favorable environment for convection initiation, given the warm and moist oceanic environment. Consequently, the squall line turned northeast–southwest, forming a training line mode that was nearly parallel to the eastward movement. This training line mode prolonged the precipitation duration in the southern downtown area.
Abstract
Convectively generated cold pools play a crucial role in the convection initiation and development, but observations of their vertical structure are insufficient. In this study, quantitative vertical evolutions of cold pools during a high-impact heavy rainfall event near the south coast of China were examined using observations from a 356-m-high Shenzhen Met-Tower, and their potential impacts on heavy rainfall were further discussed through high-resolution surface station network and radar observations. On 11 April 2019, heavy precipitation occurred near metropolitan Shenzhen, lasting for 50 min at its southern downtown and resulting in 11 deaths. During this event, a shallow cold pool was first observed by the tower and yielded a long-lasting cooling of 2.6 K. Approximately 1 h later, another deeper cold pool accompanied by a squall line was added from the west. This addition resulted in a more abrupt and intense surface temperature deficit (5.1 K) and stronger gusty winds (23 m s−1). When the two cold pools collided near Shenzhen, the low-level winds converged at their intersection, dynamically enhancing the heavy-rain-producing squall line. Moreover, the collision of the two cold pools reduced the temperature gradient at the northern edge of the merged cold pool, which could inhibit development of the squall line. The area south of the squall line became a relatively favorable environment for convection initiation, given the warm and moist oceanic environment. Consequently, the squall line turned northeast–southwest, forming a training line mode that was nearly parallel to the eastward movement. This training line mode prolonged the precipitation duration in the southern downtown area.
Abstract
This article describes proposed revised methods for the statistical postprocessing of precipitation amount intended for the NOAA’s National Blend of Models using the Global Ensemble Forecast System version 12 data (GEFSv12). The procedure updates the previously established procedure of quantile mapping, weighting of sorted members, and dressing of the ensemble. The revised method leverages the long reforecast training dataset that has become available to improve quantile mapping of GEFSv12 data by eliminating the use of supplemental locations, that is, training data from other grid points. It establishes improved definitions of cumulative distributions through a spline-fitting approach. It provides updated algorithms for the weighting of sorted members based on closest-member histogram statistics, and it establishes an objective method for the dressing of the quantile-mapped, weighted ensemble. Verification statistics and case studies are provided in the accompanying article (Part II).
Abstract
This article describes proposed revised methods for the statistical postprocessing of precipitation amount intended for the NOAA’s National Blend of Models using the Global Ensemble Forecast System version 12 data (GEFSv12). The procedure updates the previously established procedure of quantile mapping, weighting of sorted members, and dressing of the ensemble. The revised method leverages the long reforecast training dataset that has become available to improve quantile mapping of GEFSv12 data by eliminating the use of supplemental locations, that is, training data from other grid points. It establishes improved definitions of cumulative distributions through a spline-fitting approach. It provides updated algorithms for the weighting of sorted members based on closest-member histogram statistics, and it establishes an objective method for the dressing of the quantile-mapped, weighted ensemble. Verification statistics and case studies are provided in the accompanying article (Part II).
Abstract
This second part of the series presents results from verifying a precipitation forecast calibration method discussed in the first part, based on quantile mapping (QM), weighting of sorted members, and dressing of the ensemble. NOAA’s Global Ensemble Forecast System, version 12 (GEFSv12), reforecasts were used in this study. The method was validated with preoperational GEFSv12 forecasts from December 2017 to November 2019. The method is proposed as an enhancement for GEFSv12 precipitation postprocessing in NOAA’s National Blend of Models. The first part described adaptations to the methodology to leverage the ∼20-yr GEFSv12 reforecast data. As shown here in this part, when compared with probabilistic quantitative precipitation forecasts from the raw ensemble, the adapted method produced downscaled, high-resolution forecasts that were significantly more reliable and skillful than raw ensemble-derived probabilities, especially at shorter lead times (i.e., <5 days) and for forecasts of events from light precipitation to >10 mm (6 h)−1. Cool-season events in the western United States were especially improved when the QM algorithm was applied, providing a statistical downscaling with realistic smaller-scale detail related to terrain features. The method provided less value added for forecasts of longer lead times and for the heaviest precipitation.
Abstract
This second part of the series presents results from verifying a precipitation forecast calibration method discussed in the first part, based on quantile mapping (QM), weighting of sorted members, and dressing of the ensemble. NOAA’s Global Ensemble Forecast System, version 12 (GEFSv12), reforecasts were used in this study. The method was validated with preoperational GEFSv12 forecasts from December 2017 to November 2019. The method is proposed as an enhancement for GEFSv12 precipitation postprocessing in NOAA’s National Blend of Models. The first part described adaptations to the methodology to leverage the ∼20-yr GEFSv12 reforecast data. As shown here in this part, when compared with probabilistic quantitative precipitation forecasts from the raw ensemble, the adapted method produced downscaled, high-resolution forecasts that were significantly more reliable and skillful than raw ensemble-derived probabilities, especially at shorter lead times (i.e., <5 days) and for forecasts of events from light precipitation to >10 mm (6 h)−1. Cool-season events in the western United States were especially improved when the QM algorithm was applied, providing a statistical downscaling with realistic smaller-scale detail related to terrain features. The method provided less value added for forecasts of longer lead times and for the heaviest precipitation.
Abstract
In large-eddy simulations (LES), it is crucial to ensure that discretization errors do not contaminate the subgrid effect of the turbulence model in a wavelength range larger than the effective resolution. Recently, we showed that a seventh- or eighth-order accuracy is required for advection terms in planetary boundary layer simulations when using conventional gridpoint methods. However, a significant amount of communication between parallel computers is necessary to achieve high-order accuracy in gridpoint methods, and this can degrade computational efficiency. The discontinuous Galerkin method (DGM) is a promising approach for overcoming these limitations. Therefore, this study focuses on the numerical criteria of the DGM at LES from the viewpoint of numerical diffusion and dispersion. We extend our earlier study to the DGM framework and clarify the necessary order of the polynomial (p). We find that p = 4 is required based on the numerical criteria at the grid spacing of O(10) m with sufficiently scale-selective modal filters. The examination of temporal accuracy suggests that the fourth-order is sufficient when a fully explicit temporal scheme is used. In addition, we investigate the effect of hyperupwinding that is usually met when the Rusanov flux is employed in the low Mach number flows. It suggests that the choice of numerical flux has little effect on simulation results when the high-order DGM is used. Furthermore, we perform a series of LES in the planetary boundary layer and confirm that the indication obtained from the criteria holds for an actual LES.
Abstract
In large-eddy simulations (LES), it is crucial to ensure that discretization errors do not contaminate the subgrid effect of the turbulence model in a wavelength range larger than the effective resolution. Recently, we showed that a seventh- or eighth-order accuracy is required for advection terms in planetary boundary layer simulations when using conventional gridpoint methods. However, a significant amount of communication between parallel computers is necessary to achieve high-order accuracy in gridpoint methods, and this can degrade computational efficiency. The discontinuous Galerkin method (DGM) is a promising approach for overcoming these limitations. Therefore, this study focuses on the numerical criteria of the DGM at LES from the viewpoint of numerical diffusion and dispersion. We extend our earlier study to the DGM framework and clarify the necessary order of the polynomial (p). We find that p = 4 is required based on the numerical criteria at the grid spacing of O(10) m with sufficiently scale-selective modal filters. The examination of temporal accuracy suggests that the fourth-order is sufficient when a fully explicit temporal scheme is used. In addition, we investigate the effect of hyperupwinding that is usually met when the Rusanov flux is employed in the low Mach number flows. It suggests that the choice of numerical flux has little effect on simulation results when the high-order DGM is used. Furthermore, we perform a series of LES in the planetary boundary layer and confirm that the indication obtained from the criteria holds for an actual LES.
Abstract
Upper-ocean temperatures from 72 airborne expendable bathythermographs (AXBTs) collected during U.S. Air Force Hurricane Hunter flights into Hurricane Dorian (2019) over a 72-h period are examined. Three transects collected behind the storm reveal increased cross-track sea surface temperature gradient magnitudes as Dorian intensified to a category-5 hurricane and slowed while approaching the Bahamas. The cold wake, evident in vertical and horizontal cross sections from in situ and satellite sensors, appears as an expected response to tropical cyclone passage. Atypical, however, is the 2°C surface cooling observed over 36 h in a pair of transects ahead of hurricane force winds in Dorian, likely due to changes in the tropical cyclone’s translation speed and direction and/or proximity to the Gulf Stream and continental shelf. Collocated AXBT pairs document a dynamical regime shift from mixing to upwelling as Dorian slows and turns. Relationships between time-integrated wind stress and sea surface temperature indicate track-relative differences varying with storm translation speed and heading changes, paralleling the shift in cooling dynamics.
Significance Statement
We studied in situ and satellite ocean temperature observations beneath Hurricane Dorian (2019) as the storm moved slowly, turned north, and weakened near Grand Bahama Island. We found a distinct change in the spatial distribution of cool upper-ocean temperatures beneath the storm, which indicated a shift in the primary cooling mechanism from ocean mixing to upwelling. This mechanism shift is important because hurricanes depend on warm ocean temperatures for energy, and upwelling roughly doubles the area of cooling beneath the storm. Our results highlight the effects of large heading changes on the upper-ocean response beneath tropical cyclones, especially in tandem with slow translation speeds.
Abstract
Upper-ocean temperatures from 72 airborne expendable bathythermographs (AXBTs) collected during U.S. Air Force Hurricane Hunter flights into Hurricane Dorian (2019) over a 72-h period are examined. Three transects collected behind the storm reveal increased cross-track sea surface temperature gradient magnitudes as Dorian intensified to a category-5 hurricane and slowed while approaching the Bahamas. The cold wake, evident in vertical and horizontal cross sections from in situ and satellite sensors, appears as an expected response to tropical cyclone passage. Atypical, however, is the 2°C surface cooling observed over 36 h in a pair of transects ahead of hurricane force winds in Dorian, likely due to changes in the tropical cyclone’s translation speed and direction and/or proximity to the Gulf Stream and continental shelf. Collocated AXBT pairs document a dynamical regime shift from mixing to upwelling as Dorian slows and turns. Relationships between time-integrated wind stress and sea surface temperature indicate track-relative differences varying with storm translation speed and heading changes, paralleling the shift in cooling dynamics.
Significance Statement
We studied in situ and satellite ocean temperature observations beneath Hurricane Dorian (2019) as the storm moved slowly, turned north, and weakened near Grand Bahama Island. We found a distinct change in the spatial distribution of cool upper-ocean temperatures beneath the storm, which indicated a shift in the primary cooling mechanism from ocean mixing to upwelling. This mechanism shift is important because hurricanes depend on warm ocean temperatures for energy, and upwelling roughly doubles the area of cooling beneath the storm. Our results highlight the effects of large heading changes on the upper-ocean response beneath tropical cyclones, especially in tandem with slow translation speeds.
