Browse
Abstract
The realism of convective organization in operational convection-permitting model simulations is objectively assessed, with a particular focus on the mesoscale aspects, such as convective mode. A tracking and classification algorithm is applied to observed radar reflectivity and simulated radar reflectivity from the operational ACCESS-C convection-permitting forecast domain over northern Australia between October 2020 and May 2022, and the characteristics of real and simulated convective organization are compared. Mesoscale convective systems from the operational forecast model are approximately twice as likely to be oriented parallel to the ambient wind and ambient wind shear than those observed by radar, indicating a bias toward the “training line” systems typically associated with more extreme rainfall. During highly humid active monsoon conditions, simulated convective systems have larger ground-relative speeds than systems observed in radar. Although there is less than 5% difference between the ratios of simulated and observed trailing, leading and parallel stratiform system observations, significant differences exist in other wind shear–based classifications. For instance, in absolute terms, simulated systems are 10%–35% less likely to be upshear tilted, and 15%–30% less likely to be downshear propagating than observed systems, suggesting errors in simulated cold pool characteristics.
Significance Statement
Remarkable progress has been made simulating thunderstorms in operational weather forecasting computer models. While some details of individual storm clouds may be unrealistic, how these storm clouds self-organize, i.e., cluster and regenerate, can be explicitly simulated, with this organization often appearing realistic. However, assessing the realism of this organization in an objective, systematic way has proven challenging. Here we assess organized convection in Australia’s current high-resolution weather prediction model. In some respects, simulated storm clouds organize realistically. High-altitude icy cloud mostly trails behind groups of storm clouds in both simulations and reality. In other respects, organization is unrealistic. Simulated storm clouds are twice as likely to orient along the mean wind direction than in reality, likely contributing to extreme rainfall biases.
Abstract
The realism of convective organization in operational convection-permitting model simulations is objectively assessed, with a particular focus on the mesoscale aspects, such as convective mode. A tracking and classification algorithm is applied to observed radar reflectivity and simulated radar reflectivity from the operational ACCESS-C convection-permitting forecast domain over northern Australia between October 2020 and May 2022, and the characteristics of real and simulated convective organization are compared. Mesoscale convective systems from the operational forecast model are approximately twice as likely to be oriented parallel to the ambient wind and ambient wind shear than those observed by radar, indicating a bias toward the “training line” systems typically associated with more extreme rainfall. During highly humid active monsoon conditions, simulated convective systems have larger ground-relative speeds than systems observed in radar. Although there is less than 5% difference between the ratios of simulated and observed trailing, leading and parallel stratiform system observations, significant differences exist in other wind shear–based classifications. For instance, in absolute terms, simulated systems are 10%–35% less likely to be upshear tilted, and 15%–30% less likely to be downshear propagating than observed systems, suggesting errors in simulated cold pool characteristics.
Significance Statement
Remarkable progress has been made simulating thunderstorms in operational weather forecasting computer models. While some details of individual storm clouds may be unrealistic, how these storm clouds self-organize, i.e., cluster and regenerate, can be explicitly simulated, with this organization often appearing realistic. However, assessing the realism of this organization in an objective, systematic way has proven challenging. Here we assess organized convection in Australia’s current high-resolution weather prediction model. In some respects, simulated storm clouds organize realistically. High-altitude icy cloud mostly trails behind groups of storm clouds in both simulations and reality. In other respects, organization is unrealistic. Simulated storm clouds are twice as likely to orient along the mean wind direction than in reality, likely contributing to extreme rainfall biases.
Abstract
The devastating winds in extratropical cyclones can be assigned to different mesoscale flows. How these strong winds are transported to the surface is discussed for the Mediterranean windstorm Adrian (Vaia), which caused extensive damage in Corsica in October 2018. A mesoscale analysis based on a kilometer-scale simulation with the Meso-NH model shows that the strongest winds come from a cold conveyor belt (CCB). The focus then shifts to a large-eddy simulation (LES) for which the strongest winds over the sea are located in a convective boundary layer. Convection is organized into coherent turbulent structures in the form of convective rolls. It is their downward branches that contribute most to the nonlocal transport of strong winds from the CCB to the surface layer. On landing, the convective rolls break up because of the complex topography of Corsica. Sensitivity experiments to horizontal grid spacing show similar organization of boundary layer rolls across the resolution. A comparative analysis of the kinetic energy spectra suggests that a grid spacing of 200 m is sufficient to represent the vertical transport of strong winds through convective rolls. Contrary to LES, convective rolls are not resolved in the kilometer-scale simulation and surface winds are overestimated due to excessive momentum transport. These results highlight the importance of convective rolls for the generation of surface wind gusts and the need to better represent them in boundary layer parameterizations.
Abstract
The devastating winds in extratropical cyclones can be assigned to different mesoscale flows. How these strong winds are transported to the surface is discussed for the Mediterranean windstorm Adrian (Vaia), which caused extensive damage in Corsica in October 2018. A mesoscale analysis based on a kilometer-scale simulation with the Meso-NH model shows that the strongest winds come from a cold conveyor belt (CCB). The focus then shifts to a large-eddy simulation (LES) for which the strongest winds over the sea are located in a convective boundary layer. Convection is organized into coherent turbulent structures in the form of convective rolls. It is their downward branches that contribute most to the nonlocal transport of strong winds from the CCB to the surface layer. On landing, the convective rolls break up because of the complex topography of Corsica. Sensitivity experiments to horizontal grid spacing show similar organization of boundary layer rolls across the resolution. A comparative analysis of the kinetic energy spectra suggests that a grid spacing of 200 m is sufficient to represent the vertical transport of strong winds through convective rolls. Contrary to LES, convective rolls are not resolved in the kilometer-scale simulation and surface winds are overestimated due to excessive momentum transport. These results highlight the importance of convective rolls for the generation of surface wind gusts and the need to better represent them in boundary layer parameterizations.
Abstract
This study combines operational reforecasts (2001–21) with results from a lower-resolution 41-yr reforecast (1980–2020) to provide a robust assessment of wintertime Euro-Atlantic regimes and their modulation by tropospheric and stratospheric teleconnection pathways in the European Centre for Medium-Range Weather Forecasts (ECMWF) Subseasonal to Seasonal Prediction project (S2S). In both operational and lower-resolution reforecasts, the climatological properties of wintertime Euro-Atlantic regimes, including regime structures, frequencies, and transition probabilities, are accurately simulated at S2S lead times. However, the 41-yr reforecasts allow us to diagnose substantial errors in regime statistics when conditioned on modes of intraseasonal-to-interannual variability. In particular, ECMWF reforecasts underestimate the response of the North Atlantic Oscillation (NAO) to the Madden–Julian oscillation (MJO) and fail to reproduce the modulation of MJO–NAO teleconnections by El Niño–Southern Oscillation (ENSO). Teleconnection and atmospheric wave diagnostics highlight two specific issues that are likely to contribute to these conditional errors in ECMWF reforecasts: (i) insufficient propagation of Rossby wave activity from the Pacific to the Atlantic following MJO phase 3 during El Niño conditions, when the direct tropospheric teleconnection pathway is most active; and (ii) an underestimated response of the stratospheric polar vortex following MJO phase 8 during La Niña conditions, when the indirect stratospheric teleconnection pathway is most active. Improving the representation of tropospheric and stratospheric teleconnection pathways is thus a priority for improving ECMWF forecasts of extratropical weather regimes and their associated surface impacts.
Significance Statement
Subseasonal to Seasonal Prediction project (S2S) forecasts are used operationally at ECMWF to provide early warning of cold conditions in Europe associated with persistent large-scale circulation patterns known as weather regimes. On average, ECMWF reforecasts accurately simulate wintertime Euro-Atlantic regime structures and frequencies at S2S lead times. However, regime forecasts show substantial errors when we restrict our analysis to certain phases of intraseasonal and interannual variability, such as El Niño–Southern Oscillation (ENSO). These errors are related to deficiencies in the simulated response of weather regimes to well-predicted variability in the tropics. Improving the representation of such tropical–extratropical teleconnections will improve predictions of extratropical weather regimes and their associated surface impacts.
Abstract
This study combines operational reforecasts (2001–21) with results from a lower-resolution 41-yr reforecast (1980–2020) to provide a robust assessment of wintertime Euro-Atlantic regimes and their modulation by tropospheric and stratospheric teleconnection pathways in the European Centre for Medium-Range Weather Forecasts (ECMWF) Subseasonal to Seasonal Prediction project (S2S). In both operational and lower-resolution reforecasts, the climatological properties of wintertime Euro-Atlantic regimes, including regime structures, frequencies, and transition probabilities, are accurately simulated at S2S lead times. However, the 41-yr reforecasts allow us to diagnose substantial errors in regime statistics when conditioned on modes of intraseasonal-to-interannual variability. In particular, ECMWF reforecasts underestimate the response of the North Atlantic Oscillation (NAO) to the Madden–Julian oscillation (MJO) and fail to reproduce the modulation of MJO–NAO teleconnections by El Niño–Southern Oscillation (ENSO). Teleconnection and atmospheric wave diagnostics highlight two specific issues that are likely to contribute to these conditional errors in ECMWF reforecasts: (i) insufficient propagation of Rossby wave activity from the Pacific to the Atlantic following MJO phase 3 during El Niño conditions, when the direct tropospheric teleconnection pathway is most active; and (ii) an underestimated response of the stratospheric polar vortex following MJO phase 8 during La Niña conditions, when the indirect stratospheric teleconnection pathway is most active. Improving the representation of tropospheric and stratospheric teleconnection pathways is thus a priority for improving ECMWF forecasts of extratropical weather regimes and their associated surface impacts.
Significance Statement
Subseasonal to Seasonal Prediction project (S2S) forecasts are used operationally at ECMWF to provide early warning of cold conditions in Europe associated with persistent large-scale circulation patterns known as weather regimes. On average, ECMWF reforecasts accurately simulate wintertime Euro-Atlantic regime structures and frequencies at S2S lead times. However, regime forecasts show substantial errors when we restrict our analysis to certain phases of intraseasonal and interannual variability, such as El Niño–Southern Oscillation (ENSO). These errors are related to deficiencies in the simulated response of weather regimes to well-predicted variability in the tropics. Improving the representation of such tropical–extratropical teleconnections will improve predictions of extratropical weather regimes and their associated surface impacts.
Abstract
Traditional ensemble Kalman filter data assimilation methods make implicit assumptions of Gaussianity and linearity that are strongly violated by many important Earth system applications. For instance, bounded quantities like the amount of a tracer and sea ice fractional coverage cannot be accurately represented by a Gaussian that is unbounded by definition. Nonlinear relations between observations and model state variables abound. Examples include the relation between a remotely sensed radiance and the column of atmospheric temperatures, or the relation between cloud amount and water vapor quantity. Part I of this paper described a very general data assimilation framework for computing observation increments for non-Gaussian prior distributions and likelihoods. These methods can respect bounds and other non-Gaussian aspects of observed variables. However, these benefits can be lost when observation increments are used to update state variables using the linear regression that is part of standard ensemble Kalman filter algorithms. Here, regression of observation increments is performed in a space where variables are transformed by the probit and probability integral transforms, a specific type of Gaussian anamorphosis. This method can enforce appropriate bounds for all quantities and deal much more effectively with nonlinear relations between observations and state variables. Important enhancements like localization and inflation can be performed in the transformed space. Results are provided for idealized bivariate distributions and for cycling assimilation in a low-order dynamical system. Implications for improved data assimilation across Earth system applications are discussed.
Abstract
Traditional ensemble Kalman filter data assimilation methods make implicit assumptions of Gaussianity and linearity that are strongly violated by many important Earth system applications. For instance, bounded quantities like the amount of a tracer and sea ice fractional coverage cannot be accurately represented by a Gaussian that is unbounded by definition. Nonlinear relations between observations and model state variables abound. Examples include the relation between a remotely sensed radiance and the column of atmospheric temperatures, or the relation between cloud amount and water vapor quantity. Part I of this paper described a very general data assimilation framework for computing observation increments for non-Gaussian prior distributions and likelihoods. These methods can respect bounds and other non-Gaussian aspects of observed variables. However, these benefits can be lost when observation increments are used to update state variables using the linear regression that is part of standard ensemble Kalman filter algorithms. Here, regression of observation increments is performed in a space where variables are transformed by the probit and probability integral transforms, a specific type of Gaussian anamorphosis. This method can enforce appropriate bounds for all quantities and deal much more effectively with nonlinear relations between observations and state variables. Important enhancements like localization and inflation can be performed in the transformed space. Results are provided for idealized bivariate distributions and for cycling assimilation in a low-order dynamical system. Implications for improved data assimilation across Earth system applications are discussed.
Abstract
The Advanced Baseline Imager (ABI) aboard the GOES-16 and GOES-17 satellites provides high-resolution observations of cloud structures that could be highly beneficial for convective-scale DA. However, only clear-air radiance observations are typically assimilated at operational centers due to a variety of problems associated with cloudy radiance data. As such, many questions remain about how to best assimilate all-sky radiance data, especially when using hybrid DA systems such as EnVar wherein a nonlinear observation operator can lead to cost function gradient imbalance and slow minimization. Here, we develop new methods for assimilating all-sky radiance observations in EnVar using the novel Rapid Refresh Forecasting System (RRFS) that utilizes the Finite-Volume Cubed-Sphere (FV3) model. We first modify the EnVar solver by directly including brightness temperature (Tb ) as a state variable. This modification improves the balance of the cost function gradient and speeds up minimization. Including Tb as a state variable also improves the model fit to observations and increases forecast skill compared to utilizing a standard state vector configuration. We also evaluate the impact of assimilating ABI all-sky radiances in RRFS for a severe convective event in the central Great Plains. Assimilating the radiance observations results in better spinup of a tornadic supercell. These data also aid in suppressing spurious convection by reducing the snow hydrometeor content near the tropopause and weakening spurious anvil clouds. The all-sky radiance observations pair well with reflectivity observations that remove primarily liquid hydrometeors (i.e., rain) closer to the surface. Additionally, the benefits of assimilating the ABI observations continue into the forecast period, especially for localized convective events.
Abstract
The Advanced Baseline Imager (ABI) aboard the GOES-16 and GOES-17 satellites provides high-resolution observations of cloud structures that could be highly beneficial for convective-scale DA. However, only clear-air radiance observations are typically assimilated at operational centers due to a variety of problems associated with cloudy radiance data. As such, many questions remain about how to best assimilate all-sky radiance data, especially when using hybrid DA systems such as EnVar wherein a nonlinear observation operator can lead to cost function gradient imbalance and slow minimization. Here, we develop new methods for assimilating all-sky radiance observations in EnVar using the novel Rapid Refresh Forecasting System (RRFS) that utilizes the Finite-Volume Cubed-Sphere (FV3) model. We first modify the EnVar solver by directly including brightness temperature (Tb ) as a state variable. This modification improves the balance of the cost function gradient and speeds up minimization. Including Tb as a state variable also improves the model fit to observations and increases forecast skill compared to utilizing a standard state vector configuration. We also evaluate the impact of assimilating ABI all-sky radiances in RRFS for a severe convective event in the central Great Plains. Assimilating the radiance observations results in better spinup of a tornadic supercell. These data also aid in suppressing spurious convection by reducing the snow hydrometeor content near the tropopause and weakening spurious anvil clouds. The all-sky radiance observations pair well with reflectivity observations that remove primarily liquid hydrometeors (i.e., rain) closer to the surface. Additionally, the benefits of assimilating the ABI observations continue into the forecast period, especially for localized convective events.
Abstract
The conditions associated with tropical cyclones undergoing downshear reformation are explored for the North Atlantic basin from 1998 to 2020. These storms were compared to analog tropical cyclones with similar intensity, vertical wind shear, and maximum potential intensity, but did not undergo downshear reformation. Storm-centered, shear-relative composites were generated using ERA5 and GridSat-B1 data. Downshear reformation predominately occurs for tropical cyclones of tropical storm intensity embedded in moderate vertical wind shear. A comparison between composites suggests that reformed storms are characterized by greater low-level and midtropospheric relative humidity downshear, larger surface latent heat fluxes downshear and left of shear, and larger low-level equivalent potential temperatures and CAPE right of shear. These factors increase thermodynamic favorability, building a reservoir of potential energy and decreasing dry air entrainment, promoting sustained convection downshear, and favoring the development of a new center.
Significance Statement
The development of a new low-level circulation center in tropical cyclones that replaces the original center, called downshear reformation, can affect the structure and intensity of storms, representing a challenge in forecasting tropical cyclones. While there have been a handful of case studies on downshear reformation, this study aims to more comprehensively understand the conditions that favor downshear reformation by comparing a large set of North Atlantic tropical cyclones that underwent reformation with a similar set of tropical cyclones that did not undergo reformation. Tropical cyclones that undergo reformation have a moister environment, larger surface evaporation, and higher low-level instability in specific regions that help sustain deep, downshear convection that favors the development of a new center.
Abstract
The conditions associated with tropical cyclones undergoing downshear reformation are explored for the North Atlantic basin from 1998 to 2020. These storms were compared to analog tropical cyclones with similar intensity, vertical wind shear, and maximum potential intensity, but did not undergo downshear reformation. Storm-centered, shear-relative composites were generated using ERA5 and GridSat-B1 data. Downshear reformation predominately occurs for tropical cyclones of tropical storm intensity embedded in moderate vertical wind shear. A comparison between composites suggests that reformed storms are characterized by greater low-level and midtropospheric relative humidity downshear, larger surface latent heat fluxes downshear and left of shear, and larger low-level equivalent potential temperatures and CAPE right of shear. These factors increase thermodynamic favorability, building a reservoir of potential energy and decreasing dry air entrainment, promoting sustained convection downshear, and favoring the development of a new center.
Significance Statement
The development of a new low-level circulation center in tropical cyclones that replaces the original center, called downshear reformation, can affect the structure and intensity of storms, representing a challenge in forecasting tropical cyclones. While there have been a handful of case studies on downshear reformation, this study aims to more comprehensively understand the conditions that favor downshear reformation by comparing a large set of North Atlantic tropical cyclones that underwent reformation with a similar set of tropical cyclones that did not undergo reformation. Tropical cyclones that undergo reformation have a moister environment, larger surface evaporation, and higher low-level instability in specific regions that help sustain deep, downshear convection that favors the development of a new center.
Abstract
Old descriptive diaries are important sources of daily weather conditions before modern instrumental measurements were available. A previous study demonstrated the potential of reconstructing historical weather at a high temporal resolution by assimilating cloud cover converted from descriptive diaries. However, cloud cover often exhibits a non-Gaussian distribution, which violates the basic assumptions of most data assimilation schemes. In this study, we applied a Gaussian transformation (GT) approach to cloud cover data assimilation and conducted observing system simulation experiments (OSSEs) using 20 observation points over Japan. We performed experiments to assimilate cloud cover with large observational errors using the Global Spectral Model (GSM) and a local ensemble transform Kalman filter (LETKF). Without GT, meridional wind and temperature exhibited deteriorations in the lower troposphere compared with the experiment with no observations. In contrast, GT reduced the 2-month root-mean-square errors (RMSEs) by 5%–15% throughout the troposphere for wind, temperature, and specific humidity fields. Significant improvements include zonal wind at 500 hPa and temperature at 850 hPa with 6.4% and 7.3% improvements by GT, respectively, compared with the experiment without GT. We further demonstrate that the additional GT application to the precipitation background field improves precipitation estimation by 12.2%, with pronounced improvements over regions with monthly precipitation of less than 150 mm. We also explored the impact of cloud cover GT on a global scale and confirmed improvements extending from around the observation sites. Our results demonstrate the potential of GT in high-resolution historical weather reconstruction using old descriptive diaries.
Significance Statement
To reconstruct the historical weather, cloud cover information from old diaries can be used by incorporating high-resolution model simulations. However, cloud cover is not normally distributed and violates an important assumption when combining cloud cover observations with model simulations. Our results demonstrate that transforming the cloud cover distribution into a normal distribution could improve wind speed, temperature, and humidity fields in the model. We demonstrate the critical role of the transformation in a nonnormally distributed variable when combined with models and show the potential of diary-based weather information to reconstruct historical weather.
Abstract
Old descriptive diaries are important sources of daily weather conditions before modern instrumental measurements were available. A previous study demonstrated the potential of reconstructing historical weather at a high temporal resolution by assimilating cloud cover converted from descriptive diaries. However, cloud cover often exhibits a non-Gaussian distribution, which violates the basic assumptions of most data assimilation schemes. In this study, we applied a Gaussian transformation (GT) approach to cloud cover data assimilation and conducted observing system simulation experiments (OSSEs) using 20 observation points over Japan. We performed experiments to assimilate cloud cover with large observational errors using the Global Spectral Model (GSM) and a local ensemble transform Kalman filter (LETKF). Without GT, meridional wind and temperature exhibited deteriorations in the lower troposphere compared with the experiment with no observations. In contrast, GT reduced the 2-month root-mean-square errors (RMSEs) by 5%–15% throughout the troposphere for wind, temperature, and specific humidity fields. Significant improvements include zonal wind at 500 hPa and temperature at 850 hPa with 6.4% and 7.3% improvements by GT, respectively, compared with the experiment without GT. We further demonstrate that the additional GT application to the precipitation background field improves precipitation estimation by 12.2%, with pronounced improvements over regions with monthly precipitation of less than 150 mm. We also explored the impact of cloud cover GT on a global scale and confirmed improvements extending from around the observation sites. Our results demonstrate the potential of GT in high-resolution historical weather reconstruction using old descriptive diaries.
Significance Statement
To reconstruct the historical weather, cloud cover information from old diaries can be used by incorporating high-resolution model simulations. However, cloud cover is not normally distributed and violates an important assumption when combining cloud cover observations with model simulations. Our results demonstrate that transforming the cloud cover distribution into a normal distribution could improve wind speed, temperature, and humidity fields in the model. We demonstrate the critical role of the transformation in a nonnormally distributed variable when combined with models and show the potential of diary-based weather information to reconstruct historical weather.
Abstract
Severe storms produce hazardous weather phenomena, such as large hail, damaging winds, and tornadoes. However, relationships between convective parameters and confirmed severe weather occurrences are poorly quantified in south-central Brazil. This study explores severe weather reports and measurements from newly available datasets. Hail, damaging wind, and tornado reports are sourced from the PREVOTS project from June 2018 to December 2021, while measurements of convectively induced wind gusts from 1996 to 2019 are obtained from METAR reports and from Brazil’s operational network of automated weather stations. Proximal convective parameters were computed from ERA5 reanalysis for these reports and used to perform a discriminant analysis using mixed-layer CAPE and deep-layer shear (DLS). Compared to other regions, thermodynamic parameters associated with severe weather episodes exhibit lower magnitudes in south-central Brazil. DLS displays better performance in distinguishing different types of hazardous weather, but does not discriminate well between distinct severity levels. To address the sensitivity of the discriminant analysis to distinct environmental regimes and hazard types, five different discriminants are assessed. These include discriminants for any severe storm, severe hail only, severe wind gust only, and all environments but broken into “high” and “low” CAPE regimes. The best performance of the discriminant analysis is found for the “high” CAPE regime, followed by the severe wind regime. All discriminants demonstrate that DLS plays a more important role in conditioning Brazilian severe storm environments than other regions, confirming the need to ensure that parameters and discriminants are tuned to local severe weather conditions.
Abstract
Severe storms produce hazardous weather phenomena, such as large hail, damaging winds, and tornadoes. However, relationships between convective parameters and confirmed severe weather occurrences are poorly quantified in south-central Brazil. This study explores severe weather reports and measurements from newly available datasets. Hail, damaging wind, and tornado reports are sourced from the PREVOTS project from June 2018 to December 2021, while measurements of convectively induced wind gusts from 1996 to 2019 are obtained from METAR reports and from Brazil’s operational network of automated weather stations. Proximal convective parameters were computed from ERA5 reanalysis for these reports and used to perform a discriminant analysis using mixed-layer CAPE and deep-layer shear (DLS). Compared to other regions, thermodynamic parameters associated with severe weather episodes exhibit lower magnitudes in south-central Brazil. DLS displays better performance in distinguishing different types of hazardous weather, but does not discriminate well between distinct severity levels. To address the sensitivity of the discriminant analysis to distinct environmental regimes and hazard types, five different discriminants are assessed. These include discriminants for any severe storm, severe hail only, severe wind gust only, and all environments but broken into “high” and “low” CAPE regimes. The best performance of the discriminant analysis is found for the “high” CAPE regime, followed by the severe wind regime. All discriminants demonstrate that DLS plays a more important role in conditioning Brazilian severe storm environments than other regions, confirming the need to ensure that parameters and discriminants are tuned to local severe weather conditions.
Abstract
Tropical cyclones that intensify abruptly experience “rapid intensification.” Rapid intensification remains a formidable forecast challenge, in part because the underlying science has not been settled. One way to reconcile the debates and inconsistencies in the literature is to presume that different forms (or modes) of rapid intensification exist. The present study provides evidence in support of this hypothesis by documenting two modes of rapid intensification in a global convection-permitting simulation and the HURDAT2 database. The “marathon mode” is characterized by a moderately paced and long-lived intensification period, whereas the “sprint mode” is characterized by explosive and short-lived intensification bursts. Differences between the modes were also found in initial vortex structure (well defined versus poorly defined), nature of intensification (symmetric versus asymmetric), and environmental conditions (weak shear versus strong shear). Collectively, these differences indicate that the two modes involve distinct intensification mechanisms. Recognizing the existence of multiple intensification modes may help to better understand and predict rapid intensification by, for example, explaining the lack of consensus in the literature, or by raising awareness that rapid intensification in strongly sheared cyclones is not just an exception to a rule, but a typical process.
Significance Statement
Hurricanes are serious threats to society—in particular those that suddenly and quickly intensify before striking land. Forecasting these “rapid intensification” events is a challenge, in part because we do not fully understand the science behind rapid intensification. This study furthers our understanding of hurricane rapid intensification by documenting that rapid intensification comes in different types. Specifically, we show that one type of rapid intensification happens under conditions that meteorologists have thought would lessen the chances of intensification. Awareness of such a type of rapid intensification could lead to better predictions of hurricane intensity because forecasters are more cognizant of this type of event and the conditions in which they occur.
Abstract
Tropical cyclones that intensify abruptly experience “rapid intensification.” Rapid intensification remains a formidable forecast challenge, in part because the underlying science has not been settled. One way to reconcile the debates and inconsistencies in the literature is to presume that different forms (or modes) of rapid intensification exist. The present study provides evidence in support of this hypothesis by documenting two modes of rapid intensification in a global convection-permitting simulation and the HURDAT2 database. The “marathon mode” is characterized by a moderately paced and long-lived intensification period, whereas the “sprint mode” is characterized by explosive and short-lived intensification bursts. Differences between the modes were also found in initial vortex structure (well defined versus poorly defined), nature of intensification (symmetric versus asymmetric), and environmental conditions (weak shear versus strong shear). Collectively, these differences indicate that the two modes involve distinct intensification mechanisms. Recognizing the existence of multiple intensification modes may help to better understand and predict rapid intensification by, for example, explaining the lack of consensus in the literature, or by raising awareness that rapid intensification in strongly sheared cyclones is not just an exception to a rule, but a typical process.
Significance Statement
Hurricanes are serious threats to society—in particular those that suddenly and quickly intensify before striking land. Forecasting these “rapid intensification” events is a challenge, in part because we do not fully understand the science behind rapid intensification. This study furthers our understanding of hurricane rapid intensification by documenting that rapid intensification comes in different types. Specifically, we show that one type of rapid intensification happens under conditions that meteorologists have thought would lessen the chances of intensification. Awareness of such a type of rapid intensification could lead to better predictions of hurricane intensity because forecasters are more cognizant of this type of event and the conditions in which they occur.
Abstract
This study aims to objectively identify storm-scale characteristics associated with tornado-like vortex (TLV) formation in an ensemble of high-resolution supercell simulations. An ensemble of 51 supercells is created using Cloud Model version 1 (CM1). The first member is initialized using a base state populated by the Rapid Update Cycle (RUC) proximity sounding near El Reno, Oklahoma, on 24 May 2011. The other 50 ensemble members are created by randomly perturbing the base state after a supercell has formed. There is considerable spread between ensemble members, with some supercells producing strong, long-lived TLVs, while others do not produce a TLV at all. The ensemble is analyzed using the ensemble sensitivity analysis (ESA) technique, uncovering storm-scale characteristics that are dynamically relevant to TLV formation. In the rear flank, divergence at the surface southeast of the TLV helps converge and contract existing vertical vorticity, but there is no meaningful sensitivity to rear-flank outflow temperature. In the forward flank, warm temperatures within the cold pool are important to TLV production and magnitude. The longitudinal positioning of strong streamwise vorticity is also a clear indicator of TLV formation and strength, especially within 5 min of when the TLV is measured.
Significance Statement
Tornadoes that form in supercell thunderstorms (long-lived storms with a rotating updraft) are heavily influenced by the features created by the storm itself, such as the temperature of a downdraft. In this study, many different iterations of a strong supercell thunderstorm are simulated, in which tornado-like features are formed at different times with widely different strengths. A statistical method is used to identify what the storms had in common when they produced a tornado-like feature, and what they had in common when one failed to form. This study is important because it highlights which storm features are most influential to tornado formation using an objective method, with results that can be used when observing supercells in the field.
Abstract
This study aims to objectively identify storm-scale characteristics associated with tornado-like vortex (TLV) formation in an ensemble of high-resolution supercell simulations. An ensemble of 51 supercells is created using Cloud Model version 1 (CM1). The first member is initialized using a base state populated by the Rapid Update Cycle (RUC) proximity sounding near El Reno, Oklahoma, on 24 May 2011. The other 50 ensemble members are created by randomly perturbing the base state after a supercell has formed. There is considerable spread between ensemble members, with some supercells producing strong, long-lived TLVs, while others do not produce a TLV at all. The ensemble is analyzed using the ensemble sensitivity analysis (ESA) technique, uncovering storm-scale characteristics that are dynamically relevant to TLV formation. In the rear flank, divergence at the surface southeast of the TLV helps converge and contract existing vertical vorticity, but there is no meaningful sensitivity to rear-flank outflow temperature. In the forward flank, warm temperatures within the cold pool are important to TLV production and magnitude. The longitudinal positioning of strong streamwise vorticity is also a clear indicator of TLV formation and strength, especially within 5 min of when the TLV is measured.
Significance Statement
Tornadoes that form in supercell thunderstorms (long-lived storms with a rotating updraft) are heavily influenced by the features created by the storm itself, such as the temperature of a downdraft. In this study, many different iterations of a strong supercell thunderstorm are simulated, in which tornado-like features are formed at different times with widely different strengths. A statistical method is used to identify what the storms had in common when they produced a tornado-like feature, and what they had in common when one failed to form. This study is important because it highlights which storm features are most influential to tornado formation using an objective method, with results that can be used when observing supercells in the field.
