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Abstract
In this paper, a new nonlinear forcing singular vector (NFSV) approach is proposed to provide mutually independent optimally combined modes of initial perturbations and model perturbations (C-NFSVs) in ensemble forecasts. The C-NFSVs are a group of optimally growing structures that take into account the impact of the interaction between the initial errors and the model errors effectively, generalizing the original NFSV for simulations of the impact of the model errors. The C-NFSVs method is tested in the context of the Lorenz-96 model to demonstrate its potential to improve ensemble forecast skills. This method is compared with the orthogonal conditional nonlinear optimal perturbations (O-CNOPs) method for estimating only the initial uncertainties and the orthogonal NFSVs (O-NFSVs) for estimating only the model uncertainties. The results demonstrate that when both the initial perturbations and model perturbations are introduced in the forecasting system, the C-NFSVs are much more capable of achieving higher ensemble forecasting skills. The use of a deep learning approach as a remedy for the expensive computational costs of the C-NFSVs is evaluated. The results show that learning the impact of the C-NFSVs on the ensemble provides a useful and efficient alternative for the operational implementation of C-NFSVs in forecasting suites dealing with the combined effects of the initial errors and the model errors.
Significance Statement
A new ensemble forecasting method for dealing with combined effects of initial errors and model errors, i.e., the C-NFSVs, is proposed, which is an extension of the NFSV approach for simulating the model error effects in ensemble forecasts. The C-NFSVs provide mutually independent optimally combined modes of initial perturbations and model perturbations. This new method is tested for generating ensemble forecasts in the context of the Lorenz-96 model, and there are indications that the optimally growing structures may provide reliable ensemble forecasts. Furthermore, it is found that a hybrid dynamical–deep learning approach could be a potential avenue for real-time ensemble forecasting systems when perturbations combine the impact of the initial and the model errors.
Abstract
In this paper, a new nonlinear forcing singular vector (NFSV) approach is proposed to provide mutually independent optimally combined modes of initial perturbations and model perturbations (C-NFSVs) in ensemble forecasts. The C-NFSVs are a group of optimally growing structures that take into account the impact of the interaction between the initial errors and the model errors effectively, generalizing the original NFSV for simulations of the impact of the model errors. The C-NFSVs method is tested in the context of the Lorenz-96 model to demonstrate its potential to improve ensemble forecast skills. This method is compared with the orthogonal conditional nonlinear optimal perturbations (O-CNOPs) method for estimating only the initial uncertainties and the orthogonal NFSVs (O-NFSVs) for estimating only the model uncertainties. The results demonstrate that when both the initial perturbations and model perturbations are introduced in the forecasting system, the C-NFSVs are much more capable of achieving higher ensemble forecasting skills. The use of a deep learning approach as a remedy for the expensive computational costs of the C-NFSVs is evaluated. The results show that learning the impact of the C-NFSVs on the ensemble provides a useful and efficient alternative for the operational implementation of C-NFSVs in forecasting suites dealing with the combined effects of the initial errors and the model errors.
Significance Statement
A new ensemble forecasting method for dealing with combined effects of initial errors and model errors, i.e., the C-NFSVs, is proposed, which is an extension of the NFSV approach for simulating the model error effects in ensemble forecasts. The C-NFSVs provide mutually independent optimally combined modes of initial perturbations and model perturbations. This new method is tested for generating ensemble forecasts in the context of the Lorenz-96 model, and there are indications that the optimally growing structures may provide reliable ensemble forecasts. Furthermore, it is found that a hybrid dynamical–deep learning approach could be a potential avenue for real-time ensemble forecasting systems when perturbations combine the impact of the initial and the model errors.
Abstract
Convection-permitting resolutions, which refer to kilometer-scale horizontal grid spacings, have become increasingly popular in regional numerical weather prediction and climate studies. However, this resolution range is in the gray zone for the simulation of convection, where conventional cumulus convection and subgrid-scale (SGS) turbulence parameterizations are inadequate for such grid spacings due to invalid assumptions and simplifications. Recent studies demonstrated that the magnitudes of SGS fluxes of momentum and scalars are comparable to those of resolved fluxes at convection-permitting resolutions and that horizontal SGS components are as important as the vertical SGS component. Thus, it appears necessary to adapt available schemes to model the SGS effects of convective motions for the gray zone. Here, we investigated the efficacy of separately parameterizing the vertical and horizontal SGS effects in improving the convection-permitting simulation of Typhoon Vicente (2012). To represent the vertical SGS turbulence effect, we evaluated the Grell-3, Tiedtke, and multiscale Kain–Fritsch (MSKF) schemes in the Weather Research and Forecasting (WRF) Model; the MSKF scheme is scale adaptive, whereas the other two are conventional cumulus schemes. For horizontal SGS turbulence, we evaluated the effects of the traditional Smagorinsky scheme and our newly developed reconstruction and nonlinear anisotropy (RNA) model, which models not only downgradient diffusion but also backscatter. We found that the simulation combining the MSKF and RNA schemes exhibits the best skill in predicting precipitation, especially rainfall extremes. The advantages are rooted in the MSKF scheme’s scale-awareness and parameterized cloud–radiation feedback and in the backscatter-enabling capability of the RNA model.
Significance Statement
Operational numerical weather prediction and some climate simulations have approached kilometer-scale horizontal resolutions, called convection-permitting resolutions. However, details of convective storms are not well represented at these resolutions, and small-scale fluid motions can potentially impact the overall simulation performance. In practice, the effects of such unresolved turbulent eddies were once neglected. We suggest representing these effects in the vertical and horizontal directions with an adaptive cumulus convection parameterization and an advanced turbulence model, respectively, which significantly improve the simulation of tropical cyclones. This framework allows us to adapt convection schemes developed by the mesoscale modeling community and turbulence schemes studied by large-eddy simulation groups for representing three-dimensional turbulence in the convection-permitting regime.
Abstract
Convection-permitting resolutions, which refer to kilometer-scale horizontal grid spacings, have become increasingly popular in regional numerical weather prediction and climate studies. However, this resolution range is in the gray zone for the simulation of convection, where conventional cumulus convection and subgrid-scale (SGS) turbulence parameterizations are inadequate for such grid spacings due to invalid assumptions and simplifications. Recent studies demonstrated that the magnitudes of SGS fluxes of momentum and scalars are comparable to those of resolved fluxes at convection-permitting resolutions and that horizontal SGS components are as important as the vertical SGS component. Thus, it appears necessary to adapt available schemes to model the SGS effects of convective motions for the gray zone. Here, we investigated the efficacy of separately parameterizing the vertical and horizontal SGS effects in improving the convection-permitting simulation of Typhoon Vicente (2012). To represent the vertical SGS turbulence effect, we evaluated the Grell-3, Tiedtke, and multiscale Kain–Fritsch (MSKF) schemes in the Weather Research and Forecasting (WRF) Model; the MSKF scheme is scale adaptive, whereas the other two are conventional cumulus schemes. For horizontal SGS turbulence, we evaluated the effects of the traditional Smagorinsky scheme and our newly developed reconstruction and nonlinear anisotropy (RNA) model, which models not only downgradient diffusion but also backscatter. We found that the simulation combining the MSKF and RNA schemes exhibits the best skill in predicting precipitation, especially rainfall extremes. The advantages are rooted in the MSKF scheme’s scale-awareness and parameterized cloud–radiation feedback and in the backscatter-enabling capability of the RNA model.
Significance Statement
Operational numerical weather prediction and some climate simulations have approached kilometer-scale horizontal resolutions, called convection-permitting resolutions. However, details of convective storms are not well represented at these resolutions, and small-scale fluid motions can potentially impact the overall simulation performance. In practice, the effects of such unresolved turbulent eddies were once neglected. We suggest representing these effects in the vertical and horizontal directions with an adaptive cumulus convection parameterization and an advanced turbulence model, respectively, which significantly improve the simulation of tropical cyclones. This framework allows us to adapt convection schemes developed by the mesoscale modeling community and turbulence schemes studied by large-eddy simulation groups for representing three-dimensional turbulence in the convection-permitting regime.
Abstract
A local long-lived convective system developed at midnight over inland South China, producing record-breaking rainfall in Guangzhou on 7 May 2017. This study examines the physical processes responsible for nocturnal convection initiation (CI) and growth. Observational analyses show that the CI occurs in the warm sector under weakly forced synoptic conditions at 500 hPa, while moderate but nocturnally enhanced low-level southeasterlies with a mesoscale moist tongue at 925 hPa intrude inland from the northern South China Sea. Convection-permitting model results show that mesoscale low-level convergence and increased moisture at the leading edge of the southeasterlies are favorable for CI dynamically and thermodynamically. Local ascent and potential instability are further enhanced by orographic lifting and warm moist air from the urban surface, respectively, which trigger convection in northern Guangzhou. The mesoscale moist tongue of southeasterly flows then meets convectively generated outflows, thereby maintaining strong updrafts and continuously triggering back-building convective cells in eastern Guangzhou. Sensitivity tests are conducted to estimate the relative roles of ambient southeasterly moist tongue and urban thermal effects. The southeasterly moist tongue provides moisture that is crucial for CI, while warm moist air from the urban surface is lifted at the leading edge of the southeasterlies and locally facilitates convection. Therefore, the mesoscale processes of lifting and moistening due to nocturnal southeasterlies and their strong interaction with the local factors (orographic lifting, urban heating, and cold-pool-related ascent) provide the sustained lifting and instability crucial for triggering the local long-lived convective systems. The multiscale processes shed light on the understanding of the nocturnal warm-sector heavy rainfall inland.
Abstract
A local long-lived convective system developed at midnight over inland South China, producing record-breaking rainfall in Guangzhou on 7 May 2017. This study examines the physical processes responsible for nocturnal convection initiation (CI) and growth. Observational analyses show that the CI occurs in the warm sector under weakly forced synoptic conditions at 500 hPa, while moderate but nocturnally enhanced low-level southeasterlies with a mesoscale moist tongue at 925 hPa intrude inland from the northern South China Sea. Convection-permitting model results show that mesoscale low-level convergence and increased moisture at the leading edge of the southeasterlies are favorable for CI dynamically and thermodynamically. Local ascent and potential instability are further enhanced by orographic lifting and warm moist air from the urban surface, respectively, which trigger convection in northern Guangzhou. The mesoscale moist tongue of southeasterly flows then meets convectively generated outflows, thereby maintaining strong updrafts and continuously triggering back-building convective cells in eastern Guangzhou. Sensitivity tests are conducted to estimate the relative roles of ambient southeasterly moist tongue and urban thermal effects. The southeasterly moist tongue provides moisture that is crucial for CI, while warm moist air from the urban surface is lifted at the leading edge of the southeasterlies and locally facilitates convection. Therefore, the mesoscale processes of lifting and moistening due to nocturnal southeasterlies and their strong interaction with the local factors (orographic lifting, urban heating, and cold-pool-related ascent) provide the sustained lifting and instability crucial for triggering the local long-lived convective systems. The multiscale processes shed light on the understanding of the nocturnal warm-sector heavy rainfall inland.
Abstract
This study investigates the organizational modes of convective storms and associated severe weather in spring and summer (March–August) of 2015–19 over southern China. These storms are classified into three major organizational structures (cellular, linear, and nonlinear), including 10 dominant morphologies. In general, cellular systems are most frequent, followed by linear systems. Convective storms are common in spring, increasing markedly from April to June, and peak in June. Convective storm cases are usually longer lived in spring, while shorter lived in summer. They also present pronounced diurnal variations, with a primary peak in the afternoon and several secondary peaks during the night to the morning. Approximately 79.7% of initial convection clearly exhibits a dominant eastward movement, with a faster moving speed in spring. Convective storms frequently evolve among organizational modes during their life spans. Linear systems produce the most severe weather observations, in which convective lines with trailing stratiform rain are most prolific. Bow echoes are most efficient in producing severe weather events among all systems, despite their rare occurrences. In spring, lines with parallel stratiform rain are abundant producers of severe wind events, ranking the second highest probability. In summer, embedded lines produce the second largest proportion of intense rainfall events, whereas lines with leading stratiform rain are most efficient in generating extremely intense rainfall and thus pose a distinct flooding threat. Broken lines produce the largest proportion of severe weather events among cellular storms. In contrast, nonlinear systems possess the weakest capability to produce severe weather events.
Significance Statement
Under the influence of the East Asian summer monsoon, severe weather events produced by convective storms occur frequently in China, leading to serious natural disasters. Numerous studies have demonstrated that the morphologies of convective storms are helpful to improve our understanding and prediction of convective storms. However, fewer attempts have been made to examine the convective morphologies over southern China. We aim to reveal the general features of convective organizational modes (e.g., frequencies, durations, variations, etc.) and determine which particular types of severe weather are more or less likely to be associated with particular convective morphologies. These results are of benefit to local forecasters for better anticipating the storm types and issuing warnings for related hazardous weather.
Abstract
This study investigates the organizational modes of convective storms and associated severe weather in spring and summer (March–August) of 2015–19 over southern China. These storms are classified into three major organizational structures (cellular, linear, and nonlinear), including 10 dominant morphologies. In general, cellular systems are most frequent, followed by linear systems. Convective storms are common in spring, increasing markedly from April to June, and peak in June. Convective storm cases are usually longer lived in spring, while shorter lived in summer. They also present pronounced diurnal variations, with a primary peak in the afternoon and several secondary peaks during the night to the morning. Approximately 79.7% of initial convection clearly exhibits a dominant eastward movement, with a faster moving speed in spring. Convective storms frequently evolve among organizational modes during their life spans. Linear systems produce the most severe weather observations, in which convective lines with trailing stratiform rain are most prolific. Bow echoes are most efficient in producing severe weather events among all systems, despite their rare occurrences. In spring, lines with parallel stratiform rain are abundant producers of severe wind events, ranking the second highest probability. In summer, embedded lines produce the second largest proportion of intense rainfall events, whereas lines with leading stratiform rain are most efficient in generating extremely intense rainfall and thus pose a distinct flooding threat. Broken lines produce the largest proportion of severe weather events among cellular storms. In contrast, nonlinear systems possess the weakest capability to produce severe weather events.
Significance Statement
Under the influence of the East Asian summer monsoon, severe weather events produced by convective storms occur frequently in China, leading to serious natural disasters. Numerous studies have demonstrated that the morphologies of convective storms are helpful to improve our understanding and prediction of convective storms. However, fewer attempts have been made to examine the convective morphologies over southern China. We aim to reveal the general features of convective organizational modes (e.g., frequencies, durations, variations, etc.) and determine which particular types of severe weather are more or less likely to be associated with particular convective morphologies. These results are of benefit to local forecasters for better anticipating the storm types and issuing warnings for related hazardous weather.
Abstract
A dynamical vortex initialization (DVI) scheme is implemented on unstructured meshes for the global model MPAS for typhoon forecasts. The DVI extracts the departure vortex within a specified radius of the vortex center and implants this vortex at the observed vortex location in continuously cycled 1-h integrations of the model. The cycling integration is stopped when either the simulated central sea level pressure or maximum wind speed of the typhoon has reached the value in the best track data, denoted as P-match or V-match, respectively. The DVI may spin up the initial vortex with a more contracting eyewall, but still keeping the same size of the outer vortex. Forecasts for 16 typhoons over the western North Pacific in 2015–20 are investigated. Predictions from the experiments with the 60–15-km variable-resolution MPAS mesh show that both P-match and V-match significantly improve the track forecasts, where V-match mostly requires less cycle runs than P-match. Cycling results with P-match or V-match are also dependent on the choice of physics suites within MPAS. Positive impacts are larger for V-match than P-match using the mesoscale reference physics suite, with significantly improved track forecasts and earlier intensity forecasts. Intensity differences resulting from the DVI have gradually decreased with forecast time, which are closely correlated to the differences in the averaged tropospheric potential vorticity of the inner vortex. The DVI with the 60–15–3-km variable-resolution mesh also works well and improves intensity forecasts. The DVI can also help produce asymmetric structures and spin up inner vortex cores for typhoons near high topography, which leads to improved intensity forecasts.
Abstract
A dynamical vortex initialization (DVI) scheme is implemented on unstructured meshes for the global model MPAS for typhoon forecasts. The DVI extracts the departure vortex within a specified radius of the vortex center and implants this vortex at the observed vortex location in continuously cycled 1-h integrations of the model. The cycling integration is stopped when either the simulated central sea level pressure or maximum wind speed of the typhoon has reached the value in the best track data, denoted as P-match or V-match, respectively. The DVI may spin up the initial vortex with a more contracting eyewall, but still keeping the same size of the outer vortex. Forecasts for 16 typhoons over the western North Pacific in 2015–20 are investigated. Predictions from the experiments with the 60–15-km variable-resolution MPAS mesh show that both P-match and V-match significantly improve the track forecasts, where V-match mostly requires less cycle runs than P-match. Cycling results with P-match or V-match are also dependent on the choice of physics suites within MPAS. Positive impacts are larger for V-match than P-match using the mesoscale reference physics suite, with significantly improved track forecasts and earlier intensity forecasts. Intensity differences resulting from the DVI have gradually decreased with forecast time, which are closely correlated to the differences in the averaged tropospheric potential vorticity of the inner vortex. The DVI with the 60–15–3-km variable-resolution mesh also works well and improves intensity forecasts. The DVI can also help produce asymmetric structures and spin up inner vortex cores for typhoons near high topography, which leads to improved intensity forecasts.
Abstract
Horizontal convective rolls (HCRs) with aspect ratios ≥ 5, called wide HCRs, are observed over land from WSR-88D radar reflectivity observations in clear air over central Oklahoma. Results indicate that wide HCRs are a natural part of the daily HCR life cycle, occurring most frequently from 1500 to 1700 UTC and from 2300 to 2400 UTC, with the HCRs having aspect ratios ∼ 3 during the rest of their lifetime. Wide HCRs are most likely to be observed from HCRs with lifetimes longer than 5 h. Results show that for HCRs lasting for more than 5 h, 12% have aspect ratios ≥ 5 during HCR formation, whereas 50% of have aspect ratios ≥ 5 at dissipation. An evaluation of radar observations from 50 cases of long-lived HCRs suggests the wide HCRs that occur in tandem with HCR formation early in the day develop in situ with a large aspect ratio. In contrast, the cases of wide HCRs that form late in the day most often appear to develop as specific HCR wavelengths are maintained while roll circulations with smaller wavelengths dissipate. These ephemeral wide HCRs over land deserve attention as the mechanisms leading to their formation are unclear.
Significance Statement
The atmospheric boundary layer extends from the ground up to a typical daytime height between 500 m and 3 km. Within this layer, the flow is often turbulent during the daytime, although there are common structures that help to organize the flow patterns. One of these structures is a field of horizontal counterrotating helical circulations, with parallel upwelling and downwelling zones. This study shows that the separation distance between these long parallel lines of upward and downward motion changes during the day and can be quite large when compared to the depth of the boundary layer, both early in the day and late in the day. Reasons for this behavior are unclear and deserve attention, as the boundary layer is where we spend our lives and has a large influence on our daily activities.
Abstract
Horizontal convective rolls (HCRs) with aspect ratios ≥ 5, called wide HCRs, are observed over land from WSR-88D radar reflectivity observations in clear air over central Oklahoma. Results indicate that wide HCRs are a natural part of the daily HCR life cycle, occurring most frequently from 1500 to 1700 UTC and from 2300 to 2400 UTC, with the HCRs having aspect ratios ∼ 3 during the rest of their lifetime. Wide HCRs are most likely to be observed from HCRs with lifetimes longer than 5 h. Results show that for HCRs lasting for more than 5 h, 12% have aspect ratios ≥ 5 during HCR formation, whereas 50% of have aspect ratios ≥ 5 at dissipation. An evaluation of radar observations from 50 cases of long-lived HCRs suggests the wide HCRs that occur in tandem with HCR formation early in the day develop in situ with a large aspect ratio. In contrast, the cases of wide HCRs that form late in the day most often appear to develop as specific HCR wavelengths are maintained while roll circulations with smaller wavelengths dissipate. These ephemeral wide HCRs over land deserve attention as the mechanisms leading to their formation are unclear.
Significance Statement
The atmospheric boundary layer extends from the ground up to a typical daytime height between 500 m and 3 km. Within this layer, the flow is often turbulent during the daytime, although there are common structures that help to organize the flow patterns. One of these structures is a field of horizontal counterrotating helical circulations, with parallel upwelling and downwelling zones. This study shows that the separation distance between these long parallel lines of upward and downward motion changes during the day and can be quite large when compared to the depth of the boundary layer, both early in the day and late in the day. Reasons for this behavior are unclear and deserve attention, as the boundary layer is where we spend our lives and has a large influence on our daily activities.
Abstract
An historic outbreak of tornadoes impacted a large swath of the eastern United States on 26–28 April 2011. The most severe series of tornadoes was associated with numerous classic supercell thunderstorms that developed across the Southeast during the afternoon and evening of 27 April and continued into the predawn of 28 April. This study documents characteristics of these storms with respect to tornado production and mesocyclone strength during different periods of each storm’s life cycle. The supercells initiated in four quasi-distinct spatiotemporal regions, with each cluster exhibiting slightly different evolutionary traits and tornado production. These included differences in the mean times between convection initiation and the time of first tornadogenesis for each supercell, as well as variations in overall and significant tornado production. This suggests that mesoscale environmental differences, such as proximity to a mesoscale boundary, and/or storm-scale events strongly influenced the variety of supercell evolutionary paths that were observed during this event, even in the presence of a synoptic-scale background environment extremely favorable for supercell and tornado production. The azimuthal shear products from the Multi-Year Reanalysis of Remotely Sensed Storms database perform well in discriminating between mesocyclones associated with ongoing weak, strong, and violent tornadoes during the event. Furthermore, mean azimuthal shear values during pre-tornadic (e.g., within 30 min of tornadogenesis) and tornadic phases are significantly larger than those during nontornadic phases. This warrants further study of azimuthal shear characteristics in different environments and its potential usefulness in aiding real-time forecasting efforts.
Significance Statement
This study documents the prolific supercell tornado outbreak that occurred in the southeastern United States on 27–28 April 2011. We associate tornado families with their parent supercells and use a radar-derived database to quantify changes in mesocyclone strength. We show that a variety of supercell evolutionary paths occurred during the event that were somewhat distinct based on where and when each supercell initiated. We also find significant differences between supercell intensity, characterized using azimuthal shear as a measure of mesocyclone strength, during nontornadic periods as opposed to the 30-min window prior to tornadogenesis. These findings are relevant for both researchers and operational forecasters and motivate future work to better understand relationships and processes influencing supercells and their background environments.
Abstract
An historic outbreak of tornadoes impacted a large swath of the eastern United States on 26–28 April 2011. The most severe series of tornadoes was associated with numerous classic supercell thunderstorms that developed across the Southeast during the afternoon and evening of 27 April and continued into the predawn of 28 April. This study documents characteristics of these storms with respect to tornado production and mesocyclone strength during different periods of each storm’s life cycle. The supercells initiated in four quasi-distinct spatiotemporal regions, with each cluster exhibiting slightly different evolutionary traits and tornado production. These included differences in the mean times between convection initiation and the time of first tornadogenesis for each supercell, as well as variations in overall and significant tornado production. This suggests that mesoscale environmental differences, such as proximity to a mesoscale boundary, and/or storm-scale events strongly influenced the variety of supercell evolutionary paths that were observed during this event, even in the presence of a synoptic-scale background environment extremely favorable for supercell and tornado production. The azimuthal shear products from the Multi-Year Reanalysis of Remotely Sensed Storms database perform well in discriminating between mesocyclones associated with ongoing weak, strong, and violent tornadoes during the event. Furthermore, mean azimuthal shear values during pre-tornadic (e.g., within 30 min of tornadogenesis) and tornadic phases are significantly larger than those during nontornadic phases. This warrants further study of azimuthal shear characteristics in different environments and its potential usefulness in aiding real-time forecasting efforts.
Significance Statement
This study documents the prolific supercell tornado outbreak that occurred in the southeastern United States on 27–28 April 2011. We associate tornado families with their parent supercells and use a radar-derived database to quantify changes in mesocyclone strength. We show that a variety of supercell evolutionary paths occurred during the event that were somewhat distinct based on where and when each supercell initiated. We also find significant differences between supercell intensity, characterized using azimuthal shear as a measure of mesocyclone strength, during nontornadic periods as opposed to the 30-min window prior to tornadogenesis. These findings are relevant for both researchers and operational forecasters and motivate future work to better understand relationships and processes influencing supercells and their background environments.
Abstract
The transformation stage of extratropical transition characterizes the process by which a tropical cyclone transforms into an extratropical cyclone at higher latitudes in a cooler, more baroclinic environment. A 2006 study connects extremes in transformation-stage duration, post-transformation intensity change, and post-transformation thermal structure for North Atlantic basin tropical cyclones to synoptic-scale environmental variability. However, the 2006 study’s findings are derived from coarse atmospheric analyses that include fictitious tropical cyclone vortices applied to small samples with substantial variability between cases. This study updates the 2006 study’s findings using larger sample sizes, improvements in atmospheric reanalysis resolution and fidelity, and advances in scientific understanding over the last two decades. Transformation-stage duration is primarily a function of the duration that a transforming cyclone remains in an environment supportive of tropical development after entering a region supportive of baroclinic development. Post-transformation intensity-change composites are distinguished primarily by whether proper phasing is achieved between the transforming cyclone and upstream trough following the transformation stage. Finally, post-transformation thermal structure is distinguished primarily by whether the transforming cyclone moves into a strongly confluent synoptic-scale environment following the transformation stage. This study also presents the first composite analyses of North Atlantic tropical cyclones that maintain a lower-tropospheric warm-core structure post-transformation, termed instant warm-seclusion cyclones, which have previously only been diagnosed in case studies of individual North Atlantic tropical cyclones and for a limited climatology of western North Pacific tropical cyclones. These cyclones, comprising approximately one-third of all cases, are characterized by the transforming TC becoming negatively tilted with respect to the upstream trough and undergoing cyclonic Rossby wave breaking.
Abstract
The transformation stage of extratropical transition characterizes the process by which a tropical cyclone transforms into an extratropical cyclone at higher latitudes in a cooler, more baroclinic environment. A 2006 study connects extremes in transformation-stage duration, post-transformation intensity change, and post-transformation thermal structure for North Atlantic basin tropical cyclones to synoptic-scale environmental variability. However, the 2006 study’s findings are derived from coarse atmospheric analyses that include fictitious tropical cyclone vortices applied to small samples with substantial variability between cases. This study updates the 2006 study’s findings using larger sample sizes, improvements in atmospheric reanalysis resolution and fidelity, and advances in scientific understanding over the last two decades. Transformation-stage duration is primarily a function of the duration that a transforming cyclone remains in an environment supportive of tropical development after entering a region supportive of baroclinic development. Post-transformation intensity-change composites are distinguished primarily by whether proper phasing is achieved between the transforming cyclone and upstream trough following the transformation stage. Finally, post-transformation thermal structure is distinguished primarily by whether the transforming cyclone moves into a strongly confluent synoptic-scale environment following the transformation stage. This study also presents the first composite analyses of North Atlantic tropical cyclones that maintain a lower-tropospheric warm-core structure post-transformation, termed instant warm-seclusion cyclones, which have previously only been diagnosed in case studies of individual North Atlantic tropical cyclones and for a limited climatology of western North Pacific tropical cyclones. These cyclones, comprising approximately one-third of all cases, are characterized by the transforming TC becoming negatively tilted with respect to the upstream trough and undergoing cyclonic Rossby wave breaking.
Abstract
Accurately representing model-based sources of uncertainty is essential for the development of reliable ensemble prediction systems for NWP applications. Uncertainties in discretizations, algorithmic approximations, and diabatic and unresolved processes combine to influence forecast skill in a flow-dependent way. An emerging approach designed to provide a process-level representation of these potential error sources, stochastically perturbed parameterizations (SPP), is introduced into the Canadian operational Global Ensemble Prediction System. This implementation extends the SPP technique beyond its typical application to free parameters in the physics suite by sampling uncertainty both within the dynamical core and at the formulation level using “error models” when multiple physical closures are available. Because SPP perturbs components within the model, internal consistency is ensured and conservation properties are not affected. The full SPP scheme is shown to increase ensemble spread to keep pace with error growth on a global scale. The sensitivity of the ensemble to each independently perturbed “element” is then assessed, with those responsible for the bulk of the response analyzed in more detail. Perturbations to surface exchange coefficients and the turbulent mixing length have a leading impact on near-surface statistics. Aloft, a tropically focused error model representing uncertainty in the advection scheme is found to initiate growing perturbations on the subtropical jet that lead to forecast improvements at higher latitudes. The results of Part I suggest that SPP has the potential to serve as a reliable representation of model uncertainty for ensemble NWP applications.
Significance Statement
Ensemble systems account for the negative impact that uncertainties in prediction models have on forecasts. Here, uncertain model parameters and algorithms are subjected to perturbations representing impact on forecast errors. By initiating error growth within the model calculations, the equally skillful members of the ensemble remain physically realistic and self-consistent, which is not guaranteed by other depictions of model error. This “stochastically perturbed parameterization” technique (SPP) comprises many small error sources, each analyzed in isolation. Each source is related to a limited set of processes, making it possible to determine how the individual perturbations affect the forecast. We conclude that SPP in the Canadian Global Ensemble Forecasting System produces realistic estimates of the impact of model uncertainties on forecast skill.
Abstract
Accurately representing model-based sources of uncertainty is essential for the development of reliable ensemble prediction systems for NWP applications. Uncertainties in discretizations, algorithmic approximations, and diabatic and unresolved processes combine to influence forecast skill in a flow-dependent way. An emerging approach designed to provide a process-level representation of these potential error sources, stochastically perturbed parameterizations (SPP), is introduced into the Canadian operational Global Ensemble Prediction System. This implementation extends the SPP technique beyond its typical application to free parameters in the physics suite by sampling uncertainty both within the dynamical core and at the formulation level using “error models” when multiple physical closures are available. Because SPP perturbs components within the model, internal consistency is ensured and conservation properties are not affected. The full SPP scheme is shown to increase ensemble spread to keep pace with error growth on a global scale. The sensitivity of the ensemble to each independently perturbed “element” is then assessed, with those responsible for the bulk of the response analyzed in more detail. Perturbations to surface exchange coefficients and the turbulent mixing length have a leading impact on near-surface statistics. Aloft, a tropically focused error model representing uncertainty in the advection scheme is found to initiate growing perturbations on the subtropical jet that lead to forecast improvements at higher latitudes. The results of Part I suggest that SPP has the potential to serve as a reliable representation of model uncertainty for ensemble NWP applications.
Significance Statement
Ensemble systems account for the negative impact that uncertainties in prediction models have on forecasts. Here, uncertain model parameters and algorithms are subjected to perturbations representing impact on forecast errors. By initiating error growth within the model calculations, the equally skillful members of the ensemble remain physically realistic and self-consistent, which is not guaranteed by other depictions of model error. This “stochastically perturbed parameterization” technique (SPP) comprises many small error sources, each analyzed in isolation. Each source is related to a limited set of processes, making it possible to determine how the individual perturbations affect the forecast. We conclude that SPP in the Canadian Global Ensemble Forecasting System produces realistic estimates of the impact of model uncertainties on forecast skill.
