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Abstract
Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations. However, only a few recent studies have focused on ensemble postprocessing of wind gust forecasts, despite its importance for severe weather warnings. Here, we provide a comprehensive review and systematic comparison of eight statistical and machine learning methods for probabilistic wind gust forecasting via ensemble postprocessing that can be divided in three groups: state-of-the-art postprocessing techniques from statistics [ensemble model output statistics (EMOS), member-by-member postprocessing, isotonic distributional regression], established machine learning methods (gradient-boosting extended EMOS, quantile regression forests), and neural network–based approaches (distributional regression network, Bernstein quantile network, histogram estimation network). The methods are systematically compared using 6 years of data from a high-resolution, convection-permitting ensemble prediction system that was run operationally at the German weather service, and hourly observations at 175 surface weather stations in Germany. While all postprocessing methods yield calibrated forecasts and are able to correct the systematic errors of the raw ensemble predictions, incorporating information from additional meteorological predictor variables beyond wind gusts leads to significant improvements in forecast skill. In particular, we propose a flexible framework of locally adaptive neural networks with different probabilistic forecast types as output, which not only significantly outperform all benchmark postprocessing methods but also learn physically consistent relations associated with the diurnal cycle, especially the evening transition of the planetary boundary layer.
Abstract
Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations. However, only a few recent studies have focused on ensemble postprocessing of wind gust forecasts, despite its importance for severe weather warnings. Here, we provide a comprehensive review and systematic comparison of eight statistical and machine learning methods for probabilistic wind gust forecasting via ensemble postprocessing that can be divided in three groups: state-of-the-art postprocessing techniques from statistics [ensemble model output statistics (EMOS), member-by-member postprocessing, isotonic distributional regression], established machine learning methods (gradient-boosting extended EMOS, quantile regression forests), and neural network–based approaches (distributional regression network, Bernstein quantile network, histogram estimation network). The methods are systematically compared using 6 years of data from a high-resolution, convection-permitting ensemble prediction system that was run operationally at the German weather service, and hourly observations at 175 surface weather stations in Germany. While all postprocessing methods yield calibrated forecasts and are able to correct the systematic errors of the raw ensemble predictions, incorporating information from additional meteorological predictor variables beyond wind gusts leads to significant improvements in forecast skill. In particular, we propose a flexible framework of locally adaptive neural networks with different probabilistic forecast types as output, which not only significantly outperform all benchmark postprocessing methods but also learn physically consistent relations associated with the diurnal cycle, especially the evening transition of the planetary boundary layer.
Abstract
Major airstreams in tropical cyclones (TCs) are rarely described from a Lagrangian perspective. Such a perspective, however, is required to account for asymmetries and time dependence of the TC circulation. We present a procedure that identifies main airstreams in TCs based on trajectory clustering. The procedure takes into account the TC’s large degree of inherent symmetry and is suitable for a very large number of trajectories
Abstract
Major airstreams in tropical cyclones (TCs) are rarely described from a Lagrangian perspective. Such a perspective, however, is required to account for asymmetries and time dependence of the TC circulation. We present a procedure that identifies main airstreams in TCs based on trajectory clustering. The procedure takes into account the TC’s large degree of inherent symmetry and is suitable for a very large number of trajectories
Abstract
We investigate the practical predictability limits of deep convection in a state-of-the-art, high-resolution, limited-area ensemble prediction system. A combination of sophisticated predictability measures, namely, believable and decorrelation scale, are applied to determine the predictable scales of short-term forecasts in a hierarchy of model configurations. First, we consider an idealized perfect model setup that includes both small-scale and synoptic-scale perturbations. We find increased predictability in the presence of orography and a strongly beneficial impact of radar data assimilation, which extends the forecast horizon by up to 6 h. Second, we examine realistic COSMO-KENDA simulations, including assimilation of radar and conventional data and a representation of model errors, for a convectively active two-week summer period over Germany. The results confirm increased predictability in orographic regions. We find that both latent heat nudging and ensemble Kalman filter assimilation of radar data lead to increased forecast skill, but the impact is smaller than in the idealized experiments. This highlights the need to assimilate spatially and temporally dense data, but also indicates room for further improvement. Finally, the examination of operational COSMO-DE-EPS ensemble forecasts for three summer periods confirms the beneficial impact of orography in a statistical sense and also reveals increased predictability in weather regimes controlled by synoptic forcing, as defined by the convective adjustment time scale.
Abstract
We investigate the practical predictability limits of deep convection in a state-of-the-art, high-resolution, limited-area ensemble prediction system. A combination of sophisticated predictability measures, namely, believable and decorrelation scale, are applied to determine the predictable scales of short-term forecasts in a hierarchy of model configurations. First, we consider an idealized perfect model setup that includes both small-scale and synoptic-scale perturbations. We find increased predictability in the presence of orography and a strongly beneficial impact of radar data assimilation, which extends the forecast horizon by up to 6 h. Second, we examine realistic COSMO-KENDA simulations, including assimilation of radar and conventional data and a representation of model errors, for a convectively active two-week summer period over Germany. The results confirm increased predictability in orographic regions. We find that both latent heat nudging and ensemble Kalman filter assimilation of radar data lead to increased forecast skill, but the impact is smaller than in the idealized experiments. This highlights the need to assimilate spatially and temporally dense data, but also indicates room for further improvement. Finally, the examination of operational COSMO-DE-EPS ensemble forecasts for three summer periods confirms the beneficial impact of orography in a statistical sense and also reveals increased predictability in weather regimes controlled by synoptic forcing, as defined by the convective adjustment time scale.
Abstract
Kilometer-scale models allow for an explicit simulation of deep convective overturning but many subgrid processes that are crucial for convective initiation are still poorly represented. This leads to biases such as insufficient convection triggering and late peak of summertime convection. A physically based stochastic perturbation scheme (PSP) for subgrid processes has been proposed (Kober and Craig) that targets the coupling between subgrid turbulence and resolved convection. The first part of this study presents four modifications to this PSP scheme for subgrid turbulence: an autoregressive, continuously evolving random field; a limitation of the perturbations to the boundary layer that removes artificial convection at night; a mask that turns off perturbations in precipitating columns to retain coherent structures; and nondivergent wind perturbations that drastically increase the effectiveness of the vertical velocity perturbations. In a revised version, PSP2, the combined modifications retain the physically based coupling to the boundary layer scheme of the original scheme while removing undesirable side effects. This has the potential to improve predictions of convective initiation in kilometer-scale models while minimizing other biases. The second part of the study focuses on perturbations to account for convective initiation by subgrid orography. Here the mechanical lifting effect is modeled by introducing vertical and horizontal wind perturbations of an orographically induced gravity wave. The resulting perturbations lead to enhanced convective initiation over mountainous terrain. However, the total benefit of this scheme is unclear and we do not adopt the scheme in our revised configuration.
Abstract
Kilometer-scale models allow for an explicit simulation of deep convective overturning but many subgrid processes that are crucial for convective initiation are still poorly represented. This leads to biases such as insufficient convection triggering and late peak of summertime convection. A physically based stochastic perturbation scheme (PSP) for subgrid processes has been proposed (Kober and Craig) that targets the coupling between subgrid turbulence and resolved convection. The first part of this study presents four modifications to this PSP scheme for subgrid turbulence: an autoregressive, continuously evolving random field; a limitation of the perturbations to the boundary layer that removes artificial convection at night; a mask that turns off perturbations in precipitating columns to retain coherent structures; and nondivergent wind perturbations that drastically increase the effectiveness of the vertical velocity perturbations. In a revised version, PSP2, the combined modifications retain the physically based coupling to the boundary layer scheme of the original scheme while removing undesirable side effects. This has the potential to improve predictions of convective initiation in kilometer-scale models while minimizing other biases. The second part of the study focuses on perturbations to account for convective initiation by subgrid orography. Here the mechanical lifting effect is modeled by introducing vertical and horizontal wind perturbations of an orographically induced gravity wave. The resulting perturbations lead to enhanced convective initiation over mountainous terrain. However, the total benefit of this scheme is unclear and we do not adopt the scheme in our revised configuration.
Abstract
Extratropical transition (ET) of tropical cyclones involves distinct changes of the cyclone’s structure that are not yet well understood. This study presents for the first time a comprehensive Lagrangian description of structure change near the inner core. A large sample of trajectories is computed from a convection-permitting numerical simulation of the ET of Tropical Storm Karl (2016). Three main airstreams are considered: those associated with the inner-core convection, inner-core descent, and the developing warm conveyor belt. Analysis of these airstreams is performed both in thermodynamic and physical space. Prior to ET, Karl is embedded in weak vertical wind shear and its intensity is impeded by excessive detrainment from the inner-core convection. At the start of ET, vertical shear increases and Karl intensifies, which is attributable to reduced detrainment and thus to the formation of a well-defined outflow layer. During ET, the thermodynamic changes of the environment impact Karl’s inner-core convection predominantly by a decrease of θe values in the inflow layer. Notably, notwithstanding Karl’s weak intensity, its inner core acts as a “containment vessel” that transports high-θe air into the increasingly hostile environment. Inner-core descent has two origins: (i) mostly from upshear-left above 4-km height in the environment and (ii) boundary layer air that ascends in the inner core first and then descends, performing rollercoaster-like trajectories. At the end of the tropical phase of ET, the developing warm conveyor belt comprises air masses from several different source regions, and only partly from the cyclone’s developing warm sector, as expected for extratropical cyclones.
Abstract
Extratropical transition (ET) of tropical cyclones involves distinct changes of the cyclone’s structure that are not yet well understood. This study presents for the first time a comprehensive Lagrangian description of structure change near the inner core. A large sample of trajectories is computed from a convection-permitting numerical simulation of the ET of Tropical Storm Karl (2016). Three main airstreams are considered: those associated with the inner-core convection, inner-core descent, and the developing warm conveyor belt. Analysis of these airstreams is performed both in thermodynamic and physical space. Prior to ET, Karl is embedded in weak vertical wind shear and its intensity is impeded by excessive detrainment from the inner-core convection. At the start of ET, vertical shear increases and Karl intensifies, which is attributable to reduced detrainment and thus to the formation of a well-defined outflow layer. During ET, the thermodynamic changes of the environment impact Karl’s inner-core convection predominantly by a decrease of θe values in the inflow layer. Notably, notwithstanding Karl’s weak intensity, its inner core acts as a “containment vessel” that transports high-θe air into the increasingly hostile environment. Inner-core descent has two origins: (i) mostly from upshear-left above 4-km height in the environment and (ii) boundary layer air that ascends in the inner core first and then descends, performing rollercoaster-like trajectories. At the end of the tropical phase of ET, the developing warm conveyor belt comprises air masses from several different source regions, and only partly from the cyclone’s developing warm sector, as expected for extratropical cyclones.
Abstract
Two diagnostics based on potential vorticity and the envelope of Rossby waves are used to investigate upscale error growth from a dynamical perspective. The diagnostics are applied to several cases of global, real-case ensemble simulations, in which the only difference between the ensemble members lies in the random seed of the stochastic convection scheme. Based on a tendency equation for the enstrophy error, the relative importance of individual processes to enstrophy-error growth near the tropopause is quantified. After the enstrophy error is saturated on the synoptic scale, the envelope diagnostic is used to investigate error growth up to the planetary scale. The diagnostics reveal distinct stages of the error growth: in the first 12 h, error growth is dominated by differences in the convection scheme. Differences in the upper-tropospheric divergent wind then project these diabatic errors into the tropopause region (day 0.5–2). The subsequent error growth (day 2–14.5) is governed by differences in the nonlinear near-tropopause dynamics. A fourth stage of the error growth is found up to 18 days when the envelope diagnostic indicates error growth from the synoptic up to the planetary scale. Previous ideas of the multiscale nature of upscale error growth are confirmed in general. However, a novel interpretation of the governing processes is provided. The insight obtained into the dynamics of upscale error growth may help to design representations of uncertainty in operational forecast models and to identify atmospheric conditions that are intrinsically prone to large error amplification.
Abstract
Two diagnostics based on potential vorticity and the envelope of Rossby waves are used to investigate upscale error growth from a dynamical perspective. The diagnostics are applied to several cases of global, real-case ensemble simulations, in which the only difference between the ensemble members lies in the random seed of the stochastic convection scheme. Based on a tendency equation for the enstrophy error, the relative importance of individual processes to enstrophy-error growth near the tropopause is quantified. After the enstrophy error is saturated on the synoptic scale, the envelope diagnostic is used to investigate error growth up to the planetary scale. The diagnostics reveal distinct stages of the error growth: in the first 12 h, error growth is dominated by differences in the convection scheme. Differences in the upper-tropospheric divergent wind then project these diabatic errors into the tropopause region (day 0.5–2). The subsequent error growth (day 2–14.5) is governed by differences in the nonlinear near-tropopause dynamics. A fourth stage of the error growth is found up to 18 days when the envelope diagnostic indicates error growth from the synoptic up to the planetary scale. Previous ideas of the multiscale nature of upscale error growth are confirmed in general. However, a novel interpretation of the governing processes is provided. The insight obtained into the dynamics of upscale error growth may help to design representations of uncertainty in operational forecast models and to identify atmospheric conditions that are intrinsically prone to large error amplification.
Abstract
The extratropical transition (ET) of tropical cyclones often has an important impact on the nature and predictability of the midlatitude flow. This review synthesizes the current understanding of the dynamical and physical processes that govern this impact and highlights the relationship of downstream development during ET to high-impact weather, with a focus on downstream regions. It updates a previous review from 2003 and identifies new and emerging challenges and future research needs. First, the mechanisms through which the transitioning cyclone impacts the midlatitude flow in its immediate vicinity are discussed. This “direct impact” manifests in the formation of a jet streak and the amplification of a ridge directly downstream of the cyclone. This initial flow modification triggers or amplifies a midlatitude Rossby wave packet, which disperses the impact of ET into downstream regions (downstream impact) and may contribute to the formation of high-impact weather. Details are provided concerning the impact of ET on forecast uncertainty in downstream regions and on the impact of observations on forecast skill. The sources and characteristics of the following key features and processes that may determine the manifestation of the impact of ET on the midlatitude flow are discussed: the upper-tropospheric divergent outflow, mainly associated with latent heat release in the troposphere below, and the phasing between the transitioning cyclone and the midlatitude wave pattern. Improving the representation of diabatic processes during ET in models and a climatological assessment of the ET’s impact on downstream high-impact weather are examples for future research directions.
Abstract
The extratropical transition (ET) of tropical cyclones often has an important impact on the nature and predictability of the midlatitude flow. This review synthesizes the current understanding of the dynamical and physical processes that govern this impact and highlights the relationship of downstream development during ET to high-impact weather, with a focus on downstream regions. It updates a previous review from 2003 and identifies new and emerging challenges and future research needs. First, the mechanisms through which the transitioning cyclone impacts the midlatitude flow in its immediate vicinity are discussed. This “direct impact” manifests in the formation of a jet streak and the amplification of a ridge directly downstream of the cyclone. This initial flow modification triggers or amplifies a midlatitude Rossby wave packet, which disperses the impact of ET into downstream regions (downstream impact) and may contribute to the formation of high-impact weather. Details are provided concerning the impact of ET on forecast uncertainty in downstream regions and on the impact of observations on forecast skill. The sources and characteristics of the following key features and processes that may determine the manifestation of the impact of ET on the midlatitude flow are discussed: the upper-tropospheric divergent outflow, mainly associated with latent heat release in the troposphere below, and the phasing between the transitioning cyclone and the midlatitude wave pattern. Improving the representation of diabatic processes during ET in models and a climatological assessment of the ET’s impact on downstream high-impact weather are examples for future research directions.
Abstract
Tropical cyclones that evolve from a nontropical origin and undergo tropical transition (TT) play a prominent role in cyclogenesis in the North Atlantic Ocean. They pose a special challenge for predictions, as they often emerge at the end of a multiscale cascade of atmospheric processes. Here we use operational European Centre for Medium-Range Weather Forecasts ensemble predictions to investigate the TT of North Atlantic Hurricane Chris (2012), whose formation was preceded by the merger of two potential vorticity (PV) maxima, eventually resulting in the cyclone-inducing PV streamer. The principal goal is to elucidate the dynamic and thermodynamic processes governing the predictability of Chris’s cyclogenesis and subsequent TT. Dynamic time warping is applied to identify ensemble tracks that are similar to the analysis track. This technique permits small temporal and spatial shifts in the development. The formation of the pre-Chris cyclone is predicted by those members that also predict the merging of the two PV maxima. The PV streamer’s shape and its position relative to the pre-Chris cyclone determine whether the cyclone follows the TT pathway. The transitioning cyclones are located inside a favorable region of high equivalent potential temperatures that result from a warm seclusion underneath the cyclonic roll-up of the PV streamer. A systematic investigation of consecutive ensemble forecasts indicates that sudden changes in ensemble statistics of cyclone metrics are linked to specific events. The present case exemplifies how a novel combination of Eulerian and cyclone-relative ensemble forecast analysis tools allow inference of physical causes of abrupt changes in predictability.
Abstract
Tropical cyclones that evolve from a nontropical origin and undergo tropical transition (TT) play a prominent role in cyclogenesis in the North Atlantic Ocean. They pose a special challenge for predictions, as they often emerge at the end of a multiscale cascade of atmospheric processes. Here we use operational European Centre for Medium-Range Weather Forecasts ensemble predictions to investigate the TT of North Atlantic Hurricane Chris (2012), whose formation was preceded by the merger of two potential vorticity (PV) maxima, eventually resulting in the cyclone-inducing PV streamer. The principal goal is to elucidate the dynamic and thermodynamic processes governing the predictability of Chris’s cyclogenesis and subsequent TT. Dynamic time warping is applied to identify ensemble tracks that are similar to the analysis track. This technique permits small temporal and spatial shifts in the development. The formation of the pre-Chris cyclone is predicted by those members that also predict the merging of the two PV maxima. The PV streamer’s shape and its position relative to the pre-Chris cyclone determine whether the cyclone follows the TT pathway. The transitioning cyclones are located inside a favorable region of high equivalent potential temperatures that result from a warm seclusion underneath the cyclonic roll-up of the PV streamer. A systematic investigation of consecutive ensemble forecasts indicates that sudden changes in ensemble statistics of cyclone metrics are linked to specific events. The present case exemplifies how a novel combination of Eulerian and cyclone-relative ensemble forecast analysis tools allow inference of physical causes of abrupt changes in predictability.
Abstract
The extratropical transition (ET) of tropical cyclones (TCs) can significantly influence the evolution of the midlatitude flow. However, the interaction between recurving TCs and upstream upper-level troughs features a large and partly unexplained case-to-case variability. In this study, a synoptic, feature-based climatology of TC–trough interactions is constructed to discriminate recurving TCs that interact with decelerating and accelerating troughs. Upper-level troughs reducing their eastward propagation speed during the interaction with recurving TCs exhibit phase locking with lower-level temperature anomalies and are linked to pronounced downstream Rossby wave amplification. Conversely, accelerating troughs do not exhibit phase locking and are associated with a nonsignificant downstream impact. Irrotational outflow near the tropopause associated with latent heat release in regions of heavy precipitation near the transitioning storm can promote phase locking (via enhancement of trough deceleration) and further enhance the downstream impact (via advection of air with low potential vorticity in the direction of the waveguide). These different impacts affect the probability of atmospheric blocking at the end of the Pacific storm track, which is generally higher if a TC–trough interaction occurs in the western North Pacific. Blocking in the eastern North Pacific is up to 3 times more likely than climatology if an interaction between a TC and a decelerating trough occurs upstream, whereas no statistical deviation with respect to climatology is observed for accelerating troughs. The outlined results support the hypothesis that differences in phase locking can explain the observed variability in the downstream impact of ET.
Abstract
The extratropical transition (ET) of tropical cyclones (TCs) can significantly influence the evolution of the midlatitude flow. However, the interaction between recurving TCs and upstream upper-level troughs features a large and partly unexplained case-to-case variability. In this study, a synoptic, feature-based climatology of TC–trough interactions is constructed to discriminate recurving TCs that interact with decelerating and accelerating troughs. Upper-level troughs reducing their eastward propagation speed during the interaction with recurving TCs exhibit phase locking with lower-level temperature anomalies and are linked to pronounced downstream Rossby wave amplification. Conversely, accelerating troughs do not exhibit phase locking and are associated with a nonsignificant downstream impact. Irrotational outflow near the tropopause associated with latent heat release in regions of heavy precipitation near the transitioning storm can promote phase locking (via enhancement of trough deceleration) and further enhance the downstream impact (via advection of air with low potential vorticity in the direction of the waveguide). These different impacts affect the probability of atmospheric blocking at the end of the Pacific storm track, which is generally higher if a TC–trough interaction occurs in the western North Pacific. Blocking in the eastern North Pacific is up to 3 times more likely than climatology if an interaction between a TC and a decelerating trough occurs upstream, whereas no statistical deviation with respect to climatology is observed for accelerating troughs. The outlined results support the hypothesis that differences in phase locking can explain the observed variability in the downstream impact of ET.
