Browse
Abstract
Based on the conditional nonlinear optimal perturbation (CNOP) approach, the impact of the optimally growing initial errors on the mesoscale predictability of typical mei-yu front heavy precipitation events over eastern China was explored. First, based on a nonlinear optimization system built using the high-resolution Weather Research and Forecasting Model and particle swarm optimization algorithm, the CNOPs for three heavy precipitation cases were obtained. The CNOPs as the optimally growing initial errors caused the largest forecast errors and made the 24-h accumulated precipitation stronger than any other kind of initial errors. Moreover, the CNOPs showed faster growth and saturation than the random errors in space, highlighting the importance of the initial error with specific spatial structure in the meso- and convective-scale processes. Despite different CNOPs having particular spatial structures, the large amplitudes of the CNOPs at lower levels were mainly located in the rainband along the mei-yu front. Although the spectral energies of the CNOPs increased with increasing scales, the forecast error growth for the CNOPs generally followed the well-known three-stage conceptual model. Moreover, the large-scale and large-amplitude initial errors in the CNOPs were the most influential in terms of the forecast quality. This suggests that reducing large-scale initial errors can potentially improve the forecast accuracy. However, the mesoscale predictability of the mei-yu front heavy precipitation events is inherently limited, for which the moist convection was found to be the main reason.
Abstract
Based on the conditional nonlinear optimal perturbation (CNOP) approach, the impact of the optimally growing initial errors on the mesoscale predictability of typical mei-yu front heavy precipitation events over eastern China was explored. First, based on a nonlinear optimization system built using the high-resolution Weather Research and Forecasting Model and particle swarm optimization algorithm, the CNOPs for three heavy precipitation cases were obtained. The CNOPs as the optimally growing initial errors caused the largest forecast errors and made the 24-h accumulated precipitation stronger than any other kind of initial errors. Moreover, the CNOPs showed faster growth and saturation than the random errors in space, highlighting the importance of the initial error with specific spatial structure in the meso- and convective-scale processes. Despite different CNOPs having particular spatial structures, the large amplitudes of the CNOPs at lower levels were mainly located in the rainband along the mei-yu front. Although the spectral energies of the CNOPs increased with increasing scales, the forecast error growth for the CNOPs generally followed the well-known three-stage conceptual model. Moreover, the large-scale and large-amplitude initial errors in the CNOPs were the most influential in terms of the forecast quality. This suggests that reducing large-scale initial errors can potentially improve the forecast accuracy. However, the mesoscale predictability of the mei-yu front heavy precipitation events is inherently limited, for which the moist convection was found to be the main reason.
Abstract
Intense deep convection and large mesoscale convective systems (MCSs) are known to occur downstream of the Andes in subtropical South America. Deep convection is often focused along the Sierras de Córdoba (SDC) in the afternoon and then rapidly grows upscale and moves to the east overnight. However, how the Andes and SDC impact the life cycle of MCSs under varying synoptic conditions is not well understood. Two sets of terrain-modification experiments using WRF are used to investigate the impact of topography in different synoptic regimes. The first set is run on the 13–14 December 2018 MCS case from RELAMPAGO, which featured a deep synoptic trough, strong lee cyclogenesis near the SDC, an enhanced low-level jet, and rapid upscale growth of an MCS. When the Andes are reduced by 50%, the lee cyclone and low-level jet that develop are weaker than with the full Andes, and the resulting MCS is weak and moves faster to the east. When the SDC are removed, few differences between the environment and resulting MCS relative to the control run are seen. A second set of experiments are run on the 25–26 January 2019 case in which a large MCS developed over the SDC and remained tied there for an extended period under weak synoptic forcing. The experiment that produces the most similar MCS to the control is when the Andes are reduced by 50% while maintaining the height of the SDC, suggesting the SDC may play a more important role in the MCS life cycle under quiescent synoptic conditions.
Abstract
Intense deep convection and large mesoscale convective systems (MCSs) are known to occur downstream of the Andes in subtropical South America. Deep convection is often focused along the Sierras de Córdoba (SDC) in the afternoon and then rapidly grows upscale and moves to the east overnight. However, how the Andes and SDC impact the life cycle of MCSs under varying synoptic conditions is not well understood. Two sets of terrain-modification experiments using WRF are used to investigate the impact of topography in different synoptic regimes. The first set is run on the 13–14 December 2018 MCS case from RELAMPAGO, which featured a deep synoptic trough, strong lee cyclogenesis near the SDC, an enhanced low-level jet, and rapid upscale growth of an MCS. When the Andes are reduced by 50%, the lee cyclone and low-level jet that develop are weaker than with the full Andes, and the resulting MCS is weak and moves faster to the east. When the SDC are removed, few differences between the environment and resulting MCS relative to the control run are seen. A second set of experiments are run on the 25–26 January 2019 case in which a large MCS developed over the SDC and remained tied there for an extended period under weak synoptic forcing. The experiment that produces the most similar MCS to the control is when the Andes are reduced by 50% while maintaining the height of the SDC, suggesting the SDC may play a more important role in the MCS life cycle under quiescent synoptic conditions.
Abstract
This paper presents a multiscale hybrid ensemble–variational (EnVar) data assimilation strategy with an hourly rapid update aiming to improve analysis of convection via radar observations and of convective environment via conventional observations. In this multiscale hybrid EnVar strategy, the ensemble members are updated by assimilating conventional data using an EnKF to provide the hybrid EnVar with flow-dependent background error covariance (BEC). A two-step approach is employed in the hybrid EnVar to achieve improved multiscale analysis by assimilating radar data and conventional data, respectively, in two successive steps. This two-step procedure enables the applications of different BEC tuning factors and different hybrid weights for radar and conventional observations. In addition, this study also examines the impacts of the flow-dependent BEC generated with and without radar data assimilation in EnKF on the performance of hybrid EnVar analysis and ensuing convective forecasting. The multiscale hybrid EnVar strategy was first evaluated through a comparison with 3DVar and EnKF using a convective rainfall case. Quantitative verifications for both precipitation and environmental variables demonstrated that the hybrid EnVar system with an optimal multiscale configuration outperformed both the 3DVar and EnKF. The multiscale hybrid EnVar strategy was then evaluated through a series of sensitivity experiments. It was shown that the two-step assimilation strategy outperformed the one-step for both the precipitation and environmental variables, and the ensemble BEC generated without radar data assimilation led to improved hybrid EnVar analysis over that with radar data assimilation by better representing uncertainties in convective environment and reducing spurious spatial and multivariate correlations.
Abstract
This paper presents a multiscale hybrid ensemble–variational (EnVar) data assimilation strategy with an hourly rapid update aiming to improve analysis of convection via radar observations and of convective environment via conventional observations. In this multiscale hybrid EnVar strategy, the ensemble members are updated by assimilating conventional data using an EnKF to provide the hybrid EnVar with flow-dependent background error covariance (BEC). A two-step approach is employed in the hybrid EnVar to achieve improved multiscale analysis by assimilating radar data and conventional data, respectively, in two successive steps. This two-step procedure enables the applications of different BEC tuning factors and different hybrid weights for radar and conventional observations. In addition, this study also examines the impacts of the flow-dependent BEC generated with and without radar data assimilation in EnKF on the performance of hybrid EnVar analysis and ensuing convective forecasting. The multiscale hybrid EnVar strategy was first evaluated through a comparison with 3DVar and EnKF using a convective rainfall case. Quantitative verifications for both precipitation and environmental variables demonstrated that the hybrid EnVar system with an optimal multiscale configuration outperformed both the 3DVar and EnKF. The multiscale hybrid EnVar strategy was then evaluated through a series of sensitivity experiments. It was shown that the two-step assimilation strategy outperformed the one-step for both the precipitation and environmental variables, and the ensemble BEC generated without radar data assimilation led to improved hybrid EnVar analysis over that with radar data assimilation by better representing uncertainties in convective environment and reducing spurious spatial and multivariate correlations.
Abstract
This study examines the application of latent heat nudging (LHN) for the assimilation of radar-derived precipitation rates in the 2.5-km High-Resolution Deterministic Prediction System (HRDPS) operated by Environment and Climate Change Canada. One goal of this study is to document the overall impact of applying LHN in the most recent operational implementation of the HRDPS. On average, for 110 forecasts conducted over a 2-month period in 2016, LHN is shown to improve a composite NWP index (measuring the average change in RMSE over many variables and observation types) by ∼1.5% compared to a control experiment with no LHN. For winds between 400 and 100 hPa, reductions in RMSE between 1% and 4% are found for lead times up to 48 h. For precipitation, improvements for lead times up to 6 h are shown. Another goal of this study is to quantify the relative contributions for certain components of the LHN implementation. The features being investigated are the inclusion of LHN within the assimilation cycle, the use of idealized heating profiles and the conservation of relative humidity. The 2-month forecasting experiments are conducted with these components deactivated in turn. By comparing these experiments, it is found that the inclusion of LHN in the assimilation cycle has the largest impact. It is also found that the use of idealized profiles in locations where precipitation is observed but not simulated is a critical aspect of this implementation of LHN. A case study is also provided to illustrate the impact of LHN on altitude winds.
Abstract
This study examines the application of latent heat nudging (LHN) for the assimilation of radar-derived precipitation rates in the 2.5-km High-Resolution Deterministic Prediction System (HRDPS) operated by Environment and Climate Change Canada. One goal of this study is to document the overall impact of applying LHN in the most recent operational implementation of the HRDPS. On average, for 110 forecasts conducted over a 2-month period in 2016, LHN is shown to improve a composite NWP index (measuring the average change in RMSE over many variables and observation types) by ∼1.5% compared to a control experiment with no LHN. For winds between 400 and 100 hPa, reductions in RMSE between 1% and 4% are found for lead times up to 48 h. For precipitation, improvements for lead times up to 6 h are shown. Another goal of this study is to quantify the relative contributions for certain components of the LHN implementation. The features being investigated are the inclusion of LHN within the assimilation cycle, the use of idealized heating profiles and the conservation of relative humidity. The 2-month forecasting experiments are conducted with these components deactivated in turn. By comparing these experiments, it is found that the inclusion of LHN in the assimilation cycle has the largest impact. It is also found that the use of idealized profiles in locations where precipitation is observed but not simulated is a critical aspect of this implementation of LHN. A case study is also provided to illustrate the impact of LHN on altitude winds.
Abstract
A challenge arising from the local Bayesian assimilation of data in an atmospheric flow simulation is the imbalances it may introduce. Acoustic fast-mode imbalances of the order of the slower dynamics can be negated by employing a blended numerical model with seamless access to the compressible and the soundproof pseudo-incompressible dynamics. Here, the blended modeling strategy by Benacchio et al. is upgraded in an advanced numerical framework and extended with a Bayesian local ensemble data assimilation method. Upon assimilation of data, the model configuration is switched to the pseudo-incompressible regime for one time step. After that, the model configuration is switched back to the compressible model for the duration of the assimilation window. The switching between model regimes is repeated for each subsequent assimilation window. An improved blending strategy for the numerical model ensures that a single time step in the pseudo-incompressible regime is sufficient to suppress imbalances coming from the initialization and data assimilation. This improvement is based on three innovations: (i) the association of pressure fields computed at different stages of the numerical integration with actual time levels, (ii) a conversion of pressure-related variables between the model regimes derived from low Mach number asymptotics, and (iii) a judicious selection of the pressure variables used in converting numerical model states when a switch of models occurs. Idealized two-dimensional traveling vortex and buoyancy-driven bubble convection experiments show that acoustic imbalances arising from data assimilation can be eliminated by using this blended model, thereby achieving balanced analysis fields.
Significance Statement
Weather forecasting models use a combination of physics-based algorithms and meteorological measurements. A problem with combining outputs from the model with measurements of the atmosphere is that insignificant signals may generate noise and compromise the physical soundness of weather-relevant processes. By selecting atmospheric processes through the toggling of parameters in a mixed model, we propose to suppress the undesirable signals in an efficient way and retain the physical features of solutions produced by the model. The approach is validated here for acoustic imbalances using a compressible/pseudo-incompressible model pair. This development has the potential to improve the techniques used to bring observations into models and with them the quality of atmospheric model output.
Abstract
A challenge arising from the local Bayesian assimilation of data in an atmospheric flow simulation is the imbalances it may introduce. Acoustic fast-mode imbalances of the order of the slower dynamics can be negated by employing a blended numerical model with seamless access to the compressible and the soundproof pseudo-incompressible dynamics. Here, the blended modeling strategy by Benacchio et al. is upgraded in an advanced numerical framework and extended with a Bayesian local ensemble data assimilation method. Upon assimilation of data, the model configuration is switched to the pseudo-incompressible regime for one time step. After that, the model configuration is switched back to the compressible model for the duration of the assimilation window. The switching between model regimes is repeated for each subsequent assimilation window. An improved blending strategy for the numerical model ensures that a single time step in the pseudo-incompressible regime is sufficient to suppress imbalances coming from the initialization and data assimilation. This improvement is based on three innovations: (i) the association of pressure fields computed at different stages of the numerical integration with actual time levels, (ii) a conversion of pressure-related variables between the model regimes derived from low Mach number asymptotics, and (iii) a judicious selection of the pressure variables used in converting numerical model states when a switch of models occurs. Idealized two-dimensional traveling vortex and buoyancy-driven bubble convection experiments show that acoustic imbalances arising from data assimilation can be eliminated by using this blended model, thereby achieving balanced analysis fields.
Significance Statement
Weather forecasting models use a combination of physics-based algorithms and meteorological measurements. A problem with combining outputs from the model with measurements of the atmosphere is that insignificant signals may generate noise and compromise the physical soundness of weather-relevant processes. By selecting atmospheric processes through the toggling of parameters in a mixed model, we propose to suppress the undesirable signals in an efficient way and retain the physical features of solutions produced by the model. The approach is validated here for acoustic imbalances using a compressible/pseudo-incompressible model pair. This development has the potential to improve the techniques used to bring observations into models and with them the quality of atmospheric model output.
Abstract
A new lightning-flash and convective initiation climatology is developed over the Alpine area, one of the hotspots for lightning activity in Europe. The climatology uses cloud-to-ground (CG) data from the European Cooperation for Lightning Detection (EUCLID) network, occurring from 2005 to 2019. The CG lightning data are gridded at a resolution of approximately 2 km and 10 min. A new and simple method of identifying convective initiation (CI) events applies a spatiotemporal mask to the CG data to determine CI timing and location. Although the method depends on a few empirical thresholds, sensitivity tests show the results to be robust. The maximum activity for both CG flashes and CI events is observed from mid-May to mid-September, with a peak at the end of July; the peak in the diurnal cycle occurs in the afternoon. CI is mainly concentrated over and around the Alps, particularly in northern and northeastern Italy. Since many thunderstorms follow the prevailing midlatitude westerly flow, a peak of CG flashes extends from the mountains into the plains and coastal areas of northeastern Italy and Slovenia. CG flashes and CI events over the sea/coast occur less frequently than in plains and mountains, have a weaker diurnal cycle, and have a seasonal maximum in autumn instead of summer.
Abstract
A new lightning-flash and convective initiation climatology is developed over the Alpine area, one of the hotspots for lightning activity in Europe. The climatology uses cloud-to-ground (CG) data from the European Cooperation for Lightning Detection (EUCLID) network, occurring from 2005 to 2019. The CG lightning data are gridded at a resolution of approximately 2 km and 10 min. A new and simple method of identifying convective initiation (CI) events applies a spatiotemporal mask to the CG data to determine CI timing and location. Although the method depends on a few empirical thresholds, sensitivity tests show the results to be robust. The maximum activity for both CG flashes and CI events is observed from mid-May to mid-September, with a peak at the end of July; the peak in the diurnal cycle occurs in the afternoon. CI is mainly concentrated over and around the Alps, particularly in northern and northeastern Italy. Since many thunderstorms follow the prevailing midlatitude westerly flow, a peak of CG flashes extends from the mountains into the plains and coastal areas of northeastern Italy and Slovenia. CG flashes and CI events over the sea/coast occur less frequently than in plains and mountains, have a weaker diurnal cycle, and have a seasonal maximum in autumn instead of summer.
Abstract
A theoretical, numerical-modeling-based examination of the sensitivity of vortex sheets along airmass boundaries to the following three characteristics is presented: 1) boundary-normal component of the vertical wind shear, 2) boundary-parallel component of the vertical wind shear, and 3) temperature perturbation within the parent air mass of the boundary. The overall aim of this work is to advance understanding of the sensitivity of micro-α- to meso-γ-scale vortex generation along airmass boundaries to the ambient environment. Density currents are simulated in a 2D domain that does not allow baroclinic generation of near-surface vertical vorticity (ζ ns) with parameterized latent heating for convection initiated at the associated airmass boundary and Coriolis turned on. Despite the absence of baroclinically generated ζ ns, with Coriolis turned on and without any boundary-parallel shear, ζ ns more than two orders of magnitude larger than planetary vorticity is generated along the boundary and located within the cold air. The magnitude of ζ ns is found to increase with increasing boundary-normal shear with statistically significant intra-experiment separations. Near-surface vertical vorticity ζ ns is found to scale inversely with boundary-parallel shear with a transition to negative leading-edge ζ ns in several of the larger boundary-normal shear simulations. An inverse and statistically significant relationship is found between ζ ns and the temperature perturbation within the parent air mass of the boundary (Δθ), and is a direct consequence of the dependence of boundary propagation speed on Δθ.
Significance Statement
Research presented in this article aims to contribute to an improved understanding of the environmental controls on the generation of small-scale vortices along airmass boundaries. Vertical shear, both along and across an airmass boundary, as well as temperature of the air mass on the cool side of an airmass boundary are found to regulate the magnitude of near-surface vertical vorticity available to small-scale vortices.
Abstract
A theoretical, numerical-modeling-based examination of the sensitivity of vortex sheets along airmass boundaries to the following three characteristics is presented: 1) boundary-normal component of the vertical wind shear, 2) boundary-parallel component of the vertical wind shear, and 3) temperature perturbation within the parent air mass of the boundary. The overall aim of this work is to advance understanding of the sensitivity of micro-α- to meso-γ-scale vortex generation along airmass boundaries to the ambient environment. Density currents are simulated in a 2D domain that does not allow baroclinic generation of near-surface vertical vorticity (ζ ns) with parameterized latent heating for convection initiated at the associated airmass boundary and Coriolis turned on. Despite the absence of baroclinically generated ζ ns, with Coriolis turned on and without any boundary-parallel shear, ζ ns more than two orders of magnitude larger than planetary vorticity is generated along the boundary and located within the cold air. The magnitude of ζ ns is found to increase with increasing boundary-normal shear with statistically significant intra-experiment separations. Near-surface vertical vorticity ζ ns is found to scale inversely with boundary-parallel shear with a transition to negative leading-edge ζ ns in several of the larger boundary-normal shear simulations. An inverse and statistically significant relationship is found between ζ ns and the temperature perturbation within the parent air mass of the boundary (Δθ), and is a direct consequence of the dependence of boundary propagation speed on Δθ.
Significance Statement
Research presented in this article aims to contribute to an improved understanding of the environmental controls on the generation of small-scale vortices along airmass boundaries. Vertical shear, both along and across an airmass boundary, as well as temperature of the air mass on the cool side of an airmass boundary are found to regulate the magnitude of near-surface vertical vorticity available to small-scale vortices.
Abstract
To better understand the conditions that favor tropical cyclone (TC) rapid intensification (RI), this study assesses environmental and storm-scale characteristics that differentiate TCs that undergo RI from TCs that undergo slow intensification (SI). This comparison is performed between analog TC pairs that have similar initial intensity, vertical wind shear, and maximum potential intensity. Differences in the characteristics of RI and SI TCs in the North Atlantic and western North Pacific basins are evaluated by compositing and comparing data from the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5) and the Gridded Satellite (GridSat) dataset. In the period leading up to the start of RI, RI TCs tend to have a stronger and deeper vortex that is more vertically aligned than SI TCs. Additionally, surface latent heat fluxes are significantly larger in RI TCs prior to the intensity change period, compared to SI TCs. The largest surface latent heat flux differences are initially located to the left of shear; subsequently, upshear and right-of-shear differences amplify, resulting in a more symmetric distribution of surface latent heat fluxes in RI TCs. Increasing azimuthal symmetry of surface latent heat fluxes in RI TCs, together with an increasing azimuthal symmetry of horizontal moisture flux convergence, promote the upshear migration of convection in RI TCs. These differences, and their evolution before and during the intensity change period, are hypothesized to support the persistence and invigoration of upshear convection and, thus, a more symmetric latent heating pattern that favors RI.
Abstract
To better understand the conditions that favor tropical cyclone (TC) rapid intensification (RI), this study assesses environmental and storm-scale characteristics that differentiate TCs that undergo RI from TCs that undergo slow intensification (SI). This comparison is performed between analog TC pairs that have similar initial intensity, vertical wind shear, and maximum potential intensity. Differences in the characteristics of RI and SI TCs in the North Atlantic and western North Pacific basins are evaluated by compositing and comparing data from the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5) and the Gridded Satellite (GridSat) dataset. In the period leading up to the start of RI, RI TCs tend to have a stronger and deeper vortex that is more vertically aligned than SI TCs. Additionally, surface latent heat fluxes are significantly larger in RI TCs prior to the intensity change period, compared to SI TCs. The largest surface latent heat flux differences are initially located to the left of shear; subsequently, upshear and right-of-shear differences amplify, resulting in a more symmetric distribution of surface latent heat fluxes in RI TCs. Increasing azimuthal symmetry of surface latent heat fluxes in RI TCs, together with an increasing azimuthal symmetry of horizontal moisture flux convergence, promote the upshear migration of convection in RI TCs. These differences, and their evolution before and during the intensity change period, are hypothesized to support the persistence and invigoration of upshear convection and, thus, a more symmetric latent heating pattern that favors RI.
Abstract
Three-dimensional hurricane boundary layer (BL) structure is investigated during secondary eyewall formation, as portrayed in a high-resolution, full-physics simulation of Hurricane Earl (2010). This is the second part of a study on the evolution of BL structure during vortex decay. As in part 1 of this work, the BL’s azimuthal structure was linked to vertical wind shear and storm motion. Measures of shear magnitude and translational speed in Earl were comparable to Hurricane Irma (2017) in part 1, but the orientation of one vector relative to the other differed, which contributed to different structural evolutions between the two cases. Shear and storm motion influence the shape of low-level radial flow, which in turn influences patterns of spinup and spindown associated with the advection of absolute angular momentum M. Positive agradient forcing associated with the import of M in the inner core elicits dynamically restorative outflow near the BL top, which in this case was asymmetric and intense at times prior to eyewall replacement. These asymmetries associated with shear and storm motion provide an explanation for BL convergence and spinup at the BL top outside the radius of maximum wind (RMW), which affects inertial stability and agradient forcing outside the RMW in a feedback loop. The feedback process may have facilitated the development of a secondary wind maximum over approximately two days, which culminated in eyewall replacement.
Significance Statement
In this second part of a two-part study, a simulation of Hurricane Earl in 2010 is used to analyze the cylindrical structure of the lowest 2.5 km of the atmosphere, which include the boundary layer. Structure at times when Earl weakened prior to and during a secondary eyewall formation is of primary concern. During the secondary eyewall formation, wind and thermal fields had substantial azimuthal structure, which was linked to the state of the environment. It is found that the azimuthal structure could be important to how the secondary eyewall formed in this simulation. A discussion and motivation for further investigating the lower-atmospheric azimuthal structure of hurricanes in the context of storm intensity is provided.
Abstract
Three-dimensional hurricane boundary layer (BL) structure is investigated during secondary eyewall formation, as portrayed in a high-resolution, full-physics simulation of Hurricane Earl (2010). This is the second part of a study on the evolution of BL structure during vortex decay. As in part 1 of this work, the BL’s azimuthal structure was linked to vertical wind shear and storm motion. Measures of shear magnitude and translational speed in Earl were comparable to Hurricane Irma (2017) in part 1, but the orientation of one vector relative to the other differed, which contributed to different structural evolutions between the two cases. Shear and storm motion influence the shape of low-level radial flow, which in turn influences patterns of spinup and spindown associated with the advection of absolute angular momentum M. Positive agradient forcing associated with the import of M in the inner core elicits dynamically restorative outflow near the BL top, which in this case was asymmetric and intense at times prior to eyewall replacement. These asymmetries associated with shear and storm motion provide an explanation for BL convergence and spinup at the BL top outside the radius of maximum wind (RMW), which affects inertial stability and agradient forcing outside the RMW in a feedback loop. The feedback process may have facilitated the development of a secondary wind maximum over approximately two days, which culminated in eyewall replacement.
Significance Statement
In this second part of a two-part study, a simulation of Hurricane Earl in 2010 is used to analyze the cylindrical structure of the lowest 2.5 km of the atmosphere, which include the boundary layer. Structure at times when Earl weakened prior to and during a secondary eyewall formation is of primary concern. During the secondary eyewall formation, wind and thermal fields had substantial azimuthal structure, which was linked to the state of the environment. It is found that the azimuthal structure could be important to how the secondary eyewall formed in this simulation. A discussion and motivation for further investigating the lower-atmospheric azimuthal structure of hurricanes in the context of storm intensity is provided.
Abstract
In addition to initial conditions, uncertainty in model physics can also influence the practical predictability of tropical cyclones. In this study, the influence that various magnitudes of uncertainty in the surface exchange coefficients of momentum (Cd ) and enthalpy (Ck ) can have on an otherwise highly predictable major hurricane (Hurricane Patricia) is compared with that resulting from climatological environmental initial condition uncertainty and the intrinsic limit for this case. As the systematic uncertainty in Cd and Ck is reduced from 40% to 1%, the simulated uncertainty in the intensity and structure is substantially reduced and approaches the intrinsic limit when uncertainty is reduced to 1%. In addition, the forecasted intensity and structure uncertainty only becomes less than that resulting from climatological environmental initial condition uncertainty once the systematic uncertainty in Cd and Ck is reduced to ∼10%, highlighting the strong influence of model error in limiting TC predictability. If Cd and Ck are perturbed stochastically, instead of systematically, it is shown that the influence on the simulated intensity and structure is negligible and nearly identical to the intrinsic limit, regardless of the magnitude of the stochastic Cd and Ck perturbations. While the magnitude of the stochastic Cd and Ck perturbations are comparable to the systematic perturbations, the stochastic perturbations are shown to not substantially perturb the time-integrated inner-core fluxes of momentum or enthalpy that predominantly determine simulated tropical cyclone intensity. Last, it is shown that the kinetic energy error growth behavior varies with the radius, azimuthal wavenumber, and ensemble design.
Significance Statement
The air–sea energy exchange beneath hurricanes is highly uncertain but strongly influences intensity. In this study, the influences of different magnitudes of surface-exchange coefficient uncertainty on the simulated intensity of an intense hurricane is compared with that resulting from environmental initial condition uncertainty and the intrinsic predictability limit. The main takeaway is that current surface-exchange coefficient uncertainties result in larger intensity uncertainty than environmental initial condition uncertainty, and substantial improvements in predictions are possible if current surface-exchange coefficient uncertainties are reduced. Furthermore, it is shown that randomly perturbing the surface-exchange coefficients at each point in space and time is not the best approach to account for the influences of this uncertain physical process on hurricane prediction because it has minimal influence on the simulated intensity.
Abstract
In addition to initial conditions, uncertainty in model physics can also influence the practical predictability of tropical cyclones. In this study, the influence that various magnitudes of uncertainty in the surface exchange coefficients of momentum (Cd ) and enthalpy (Ck ) can have on an otherwise highly predictable major hurricane (Hurricane Patricia) is compared with that resulting from climatological environmental initial condition uncertainty and the intrinsic limit for this case. As the systematic uncertainty in Cd and Ck is reduced from 40% to 1%, the simulated uncertainty in the intensity and structure is substantially reduced and approaches the intrinsic limit when uncertainty is reduced to 1%. In addition, the forecasted intensity and structure uncertainty only becomes less than that resulting from climatological environmental initial condition uncertainty once the systematic uncertainty in Cd and Ck is reduced to ∼10%, highlighting the strong influence of model error in limiting TC predictability. If Cd and Ck are perturbed stochastically, instead of systematically, it is shown that the influence on the simulated intensity and structure is negligible and nearly identical to the intrinsic limit, regardless of the magnitude of the stochastic Cd and Ck perturbations. While the magnitude of the stochastic Cd and Ck perturbations are comparable to the systematic perturbations, the stochastic perturbations are shown to not substantially perturb the time-integrated inner-core fluxes of momentum or enthalpy that predominantly determine simulated tropical cyclone intensity. Last, it is shown that the kinetic energy error growth behavior varies with the radius, azimuthal wavenumber, and ensemble design.
Significance Statement
The air–sea energy exchange beneath hurricanes is highly uncertain but strongly influences intensity. In this study, the influences of different magnitudes of surface-exchange coefficient uncertainty on the simulated intensity of an intense hurricane is compared with that resulting from environmental initial condition uncertainty and the intrinsic predictability limit. The main takeaway is that current surface-exchange coefficient uncertainties result in larger intensity uncertainty than environmental initial condition uncertainty, and substantial improvements in predictions are possible if current surface-exchange coefficient uncertainties are reduced. Furthermore, it is shown that randomly perturbing the surface-exchange coefficients at each point in space and time is not the best approach to account for the influences of this uncertain physical process on hurricane prediction because it has minimal influence on the simulated intensity.
