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Abstract
Intense deep convection and large mesoscale convective systems (MCSs) are known to occur downstream of the Andes in subtropical South America. Deep convection is often focused along the Sierras de Córdoba (SDC) in the afternoon and then rapidly grows upscale and moves to the east overnight. However, how the Andes and SDC impact the life cycle of MCSs under varying synoptic conditions is not well understood. Two sets of terrain-modification experiments using WRF are used to investigate the impact of topography in different synoptic regimes. The first set is run on the 13–14 December 2018 MCS case from RELAMPAGO, which featured a deep synoptic trough, strong lee cyclogenesis near the SDC, an enhanced low-level jet, and rapid upscale growth of an MCS. When the Andes are reduced by 50%, the lee cyclone and low-level jet that develop are weaker than with the full Andes, and the resulting MCS is weak and moves faster to the east. When the SDC are removed, few differences between the environment and resulting MCS relative to the control run are seen. A second set of experiments are run on the 25–26 January 2019 case in which a large MCS developed over the SDC and remained tied there for an extended period under weak synoptic forcing. The experiment that produces the most similar MCS to the control is when the Andes are reduced by 50% while maintaining the height of the SDC, suggesting the SDC may play a more important role in the MCS life cycle under quiescent synoptic conditions.
Abstract
Intense deep convection and large mesoscale convective systems (MCSs) are known to occur downstream of the Andes in subtropical South America. Deep convection is often focused along the Sierras de Córdoba (SDC) in the afternoon and then rapidly grows upscale and moves to the east overnight. However, how the Andes and SDC impact the life cycle of MCSs under varying synoptic conditions is not well understood. Two sets of terrain-modification experiments using WRF are used to investigate the impact of topography in different synoptic regimes. The first set is run on the 13–14 December 2018 MCS case from RELAMPAGO, which featured a deep synoptic trough, strong lee cyclogenesis near the SDC, an enhanced low-level jet, and rapid upscale growth of an MCS. When the Andes are reduced by 50%, the lee cyclone and low-level jet that develop are weaker than with the full Andes, and the resulting MCS is weak and moves faster to the east. When the SDC are removed, few differences between the environment and resulting MCS relative to the control run are seen. A second set of experiments are run on the 25–26 January 2019 case in which a large MCS developed over the SDC and remained tied there for an extended period under weak synoptic forcing. The experiment that produces the most similar MCS to the control is when the Andes are reduced by 50% while maintaining the height of the SDC, suggesting the SDC may play a more important role in the MCS life cycle under quiescent synoptic conditions.
Abstract
To better understand the conditions that favor tropical cyclone (TC) rapid intensification (RI), this study assesses environmental and storm-scale characteristics that differentiate TCs that undergo RI from TCs that undergo slow intensification (SI). This comparison is performed between analog TC pairs that have similar initial intensity, vertical wind shear, and maximum potential intensity. Differences in the characteristics of RI and SI TCs in the North Atlantic and western North Pacific basins are evaluated by compositing and comparing data from the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5) and the Gridded Satellite (GridSat) dataset. In the period leading up to the start of RI, RI TCs tend to have a stronger and deeper vortex that is more vertically aligned than SI TCs. Additionally, surface latent heat fluxes are significantly larger in RI TCs prior to the intensity change period, compared to SI TCs. The largest surface latent heat flux differences are initially located to the left of shear; subsequently, upshear and right-of-shear differences amplify, resulting in a more symmetric distribution of surface latent heat fluxes in RI TCs. Increasing azimuthal symmetry of surface latent heat fluxes in RI TCs, together with an increasing azimuthal symmetry of horizontal moisture flux convergence, promote the upshear migration of convection in RI TCs. These differences, and their evolution before and during the intensity change period, are hypothesized to support the persistence and invigoration of upshear convection and, thus, a more symmetric latent heating pattern that favors RI.
Abstract
To better understand the conditions that favor tropical cyclone (TC) rapid intensification (RI), this study assesses environmental and storm-scale characteristics that differentiate TCs that undergo RI from TCs that undergo slow intensification (SI). This comparison is performed between analog TC pairs that have similar initial intensity, vertical wind shear, and maximum potential intensity. Differences in the characteristics of RI and SI TCs in the North Atlantic and western North Pacific basins are evaluated by compositing and comparing data from the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5) and the Gridded Satellite (GridSat) dataset. In the period leading up to the start of RI, RI TCs tend to have a stronger and deeper vortex that is more vertically aligned than SI TCs. Additionally, surface latent heat fluxes are significantly larger in RI TCs prior to the intensity change period, compared to SI TCs. The largest surface latent heat flux differences are initially located to the left of shear; subsequently, upshear and right-of-shear differences amplify, resulting in a more symmetric distribution of surface latent heat fluxes in RI TCs. Increasing azimuthal symmetry of surface latent heat fluxes in RI TCs, together with an increasing azimuthal symmetry of horizontal moisture flux convergence, promote the upshear migration of convection in RI TCs. These differences, and their evolution before and during the intensity change period, are hypothesized to support the persistence and invigoration of upshear convection and, thus, a more symmetric latent heating pattern that favors RI.
Abstract
Three-dimensional hurricane boundary layer (BL) structure is investigated during secondary eyewall formation, as portrayed in a high-resolution, full-physics simulation of Hurricane Earl (2010). This is the second part of a study on the evolution of BL structure during vortex decay. As in part 1 of this work, the BL’s azimuthal structure was linked to vertical wind shear and storm motion. Measures of shear magnitude and translational speed in Earl were comparable to Hurricane Irma (2017) in part 1, but the orientation of one vector relative to the other differed, which contributed to different structural evolutions between the two cases. Shear and storm motion influence the shape of low-level radial flow, which in turn influences patterns of spinup and spindown associated with the advection of absolute angular momentum M. Positive agradient forcing associated with the import of M in the inner core elicits dynamically restorative outflow near the BL top, which in this case was asymmetric and intense at times prior to eyewall replacement. These asymmetries associated with shear and storm motion provide an explanation for BL convergence and spinup at the BL top outside the radius of maximum wind (RMW), which affects inertial stability and agradient forcing outside the RMW in a feedback loop. The feedback process may have facilitated the development of a secondary wind maximum over approximately two days, which culminated in eyewall replacement.
Significance Statement
In this second part of a two-part study, a simulation of Hurricane Earl in 2010 is used to analyze the cylindrical structure of the lowest 2.5 km of the atmosphere, which include the boundary layer. Structure at times when Earl weakened prior to and during a secondary eyewall formation is of primary concern. During the secondary eyewall formation, wind and thermal fields had substantial azimuthal structure, which was linked to the state of the environment. It is found that the azimuthal structure could be important to how the secondary eyewall formed in this simulation. A discussion and motivation for further investigating the lower-atmospheric azimuthal structure of hurricanes in the context of storm intensity is provided.
Abstract
Three-dimensional hurricane boundary layer (BL) structure is investigated during secondary eyewall formation, as portrayed in a high-resolution, full-physics simulation of Hurricane Earl (2010). This is the second part of a study on the evolution of BL structure during vortex decay. As in part 1 of this work, the BL’s azimuthal structure was linked to vertical wind shear and storm motion. Measures of shear magnitude and translational speed in Earl were comparable to Hurricane Irma (2017) in part 1, but the orientation of one vector relative to the other differed, which contributed to different structural evolutions between the two cases. Shear and storm motion influence the shape of low-level radial flow, which in turn influences patterns of spinup and spindown associated with the advection of absolute angular momentum M. Positive agradient forcing associated with the import of M in the inner core elicits dynamically restorative outflow near the BL top, which in this case was asymmetric and intense at times prior to eyewall replacement. These asymmetries associated with shear and storm motion provide an explanation for BL convergence and spinup at the BL top outside the radius of maximum wind (RMW), which affects inertial stability and agradient forcing outside the RMW in a feedback loop. The feedback process may have facilitated the development of a secondary wind maximum over approximately two days, which culminated in eyewall replacement.
Significance Statement
In this second part of a two-part study, a simulation of Hurricane Earl in 2010 is used to analyze the cylindrical structure of the lowest 2.5 km of the atmosphere, which include the boundary layer. Structure at times when Earl weakened prior to and during a secondary eyewall formation is of primary concern. During the secondary eyewall formation, wind and thermal fields had substantial azimuthal structure, which was linked to the state of the environment. It is found that the azimuthal structure could be important to how the secondary eyewall formed in this simulation. A discussion and motivation for further investigating the lower-atmospheric azimuthal structure of hurricanes in the context of storm intensity is provided.
Abstract
In addition to initial conditions, uncertainty in model physics can also influence the practical predictability of tropical cyclones. In this study, the influence that various magnitudes of uncertainty in the surface exchange coefficients of momentum (Cd ) and enthalpy (Ck ) can have on an otherwise highly predictable major hurricane (Hurricane Patricia) is compared with that resulting from climatological environmental initial condition uncertainty and the intrinsic limit for this case. As the systematic uncertainty in Cd and Ck is reduced from 40% to 1%, the simulated uncertainty in the intensity and structure is substantially reduced and approaches the intrinsic limit when uncertainty is reduced to 1%. In addition, the forecasted intensity and structure uncertainty only becomes less than that resulting from climatological environmental initial condition uncertainty once the systematic uncertainty in Cd and Ck is reduced to ∼10%, highlighting the strong influence of model error in limiting TC predictability. If Cd and Ck are perturbed stochastically, instead of systematically, it is shown that the influence on the simulated intensity and structure is negligible and nearly identical to the intrinsic limit, regardless of the magnitude of the stochastic Cd and Ck perturbations. While the magnitude of the stochastic Cd and Ck perturbations are comparable to the systematic perturbations, the stochastic perturbations are shown to not substantially perturb the time-integrated inner-core fluxes of momentum or enthalpy that predominantly determine simulated tropical cyclone intensity. Last, it is shown that the kinetic energy error growth behavior varies with the radius, azimuthal wavenumber, and ensemble design.
Significance Statement
The air–sea energy exchange beneath hurricanes is highly uncertain but strongly influences intensity. In this study, the influences of different magnitudes of surface-exchange coefficient uncertainty on the simulated intensity of an intense hurricane is compared with that resulting from environmental initial condition uncertainty and the intrinsic predictability limit. The main takeaway is that current surface-exchange coefficient uncertainties result in larger intensity uncertainty than environmental initial condition uncertainty, and substantial improvements in predictions are possible if current surface-exchange coefficient uncertainties are reduced. Furthermore, it is shown that randomly perturbing the surface-exchange coefficients at each point in space and time is not the best approach to account for the influences of this uncertain physical process on hurricane prediction because it has minimal influence on the simulated intensity.
Abstract
In addition to initial conditions, uncertainty in model physics can also influence the practical predictability of tropical cyclones. In this study, the influence that various magnitudes of uncertainty in the surface exchange coefficients of momentum (Cd ) and enthalpy (Ck ) can have on an otherwise highly predictable major hurricane (Hurricane Patricia) is compared with that resulting from climatological environmental initial condition uncertainty and the intrinsic limit for this case. As the systematic uncertainty in Cd and Ck is reduced from 40% to 1%, the simulated uncertainty in the intensity and structure is substantially reduced and approaches the intrinsic limit when uncertainty is reduced to 1%. In addition, the forecasted intensity and structure uncertainty only becomes less than that resulting from climatological environmental initial condition uncertainty once the systematic uncertainty in Cd and Ck is reduced to ∼10%, highlighting the strong influence of model error in limiting TC predictability. If Cd and Ck are perturbed stochastically, instead of systematically, it is shown that the influence on the simulated intensity and structure is negligible and nearly identical to the intrinsic limit, regardless of the magnitude of the stochastic Cd and Ck perturbations. While the magnitude of the stochastic Cd and Ck perturbations are comparable to the systematic perturbations, the stochastic perturbations are shown to not substantially perturb the time-integrated inner-core fluxes of momentum or enthalpy that predominantly determine simulated tropical cyclone intensity. Last, it is shown that the kinetic energy error growth behavior varies with the radius, azimuthal wavenumber, and ensemble design.
Significance Statement
The air–sea energy exchange beneath hurricanes is highly uncertain but strongly influences intensity. In this study, the influences of different magnitudes of surface-exchange coefficient uncertainty on the simulated intensity of an intense hurricane is compared with that resulting from environmental initial condition uncertainty and the intrinsic predictability limit. The main takeaway is that current surface-exchange coefficient uncertainties result in larger intensity uncertainty than environmental initial condition uncertainty, and substantial improvements in predictions are possible if current surface-exchange coefficient uncertainties are reduced. Furthermore, it is shown that randomly perturbing the surface-exchange coefficients at each point in space and time is not the best approach to account for the influences of this uncertain physical process on hurricane prediction because it has minimal influence on the simulated intensity.
Abstract
For the numerical simulation of atmospheric flows that extend as high as the thermosphere, it is more appropriate to represent the upper boundary of the model domain as a material surface at constant pressure rather than one characterized by a rigid lid. Consequently, in adapting the Model for Prediction Across Scales (MPAS) for geospace applications, a modification of the height-based vertical coordinate is presented that permits the coordinate surfaces at upper levels to transition toward a constant pressure surface at the model’s upper boundary. This modification is conceptually similar to a terrain-following coordinate at low levels, but now modifies the coordinate surfaces at upper levels to conform to a constant pressure surface at the model top. Since this surface is evolving in time, the height of the upper boundary is adaptively adjusted to follow a designated constant pressure upper surface. This is accomplished by applying the hydrostatic equation to estimate the change in height along the boundary that is consistent with the vertical pressure gradient at the model top. This alteration in the vertical coordinate requires only minor modifications and little additional computational expense to the original height-based time-invariant terrain-following vertical coordinate employed in MPAS. The viability of this modified vertical coordinate formulation has been verified in a 2D prototype of MPAS for an idealized case of upper-level diurnal heating.
Significance Statement
Most atmospheric numerical models that use a height-based vertical coordinate employ a rigid lid at the top of the model domain. While a rigid lid works well for applications in the troposphere and stratosphere, it is not well suited for applications extending into the thermosphere where significant vertical expansion/contraction occurs due to deep heating/cooling of the atmosphere. This paper develops and tests a simple modification to the height-based coordinate formulation that allows the height of the upper boundary to adaptively follow a constant pressure surface. This added flexibility in the treatment of the upper domain boundary for height-based models may be particularly beneficial in facilitating their transition to a deep atmosphere configuration without significant retooling of the model numerics.
Abstract
For the numerical simulation of atmospheric flows that extend as high as the thermosphere, it is more appropriate to represent the upper boundary of the model domain as a material surface at constant pressure rather than one characterized by a rigid lid. Consequently, in adapting the Model for Prediction Across Scales (MPAS) for geospace applications, a modification of the height-based vertical coordinate is presented that permits the coordinate surfaces at upper levels to transition toward a constant pressure surface at the model’s upper boundary. This modification is conceptually similar to a terrain-following coordinate at low levels, but now modifies the coordinate surfaces at upper levels to conform to a constant pressure surface at the model top. Since this surface is evolving in time, the height of the upper boundary is adaptively adjusted to follow a designated constant pressure upper surface. This is accomplished by applying the hydrostatic equation to estimate the change in height along the boundary that is consistent with the vertical pressure gradient at the model top. This alteration in the vertical coordinate requires only minor modifications and little additional computational expense to the original height-based time-invariant terrain-following vertical coordinate employed in MPAS. The viability of this modified vertical coordinate formulation has been verified in a 2D prototype of MPAS for an idealized case of upper-level diurnal heating.
Significance Statement
Most atmospheric numerical models that use a height-based vertical coordinate employ a rigid lid at the top of the model domain. While a rigid lid works well for applications in the troposphere and stratosphere, it is not well suited for applications extending into the thermosphere where significant vertical expansion/contraction occurs due to deep heating/cooling of the atmosphere. This paper develops and tests a simple modification to the height-based coordinate formulation that allows the height of the upper boundary to adaptively follow a constant pressure surface. This added flexibility in the treatment of the upper domain boundary for height-based models may be particularly beneficial in facilitating their transition to a deep atmosphere configuration without significant retooling of the model numerics.
Abstract
Remote sensing data play a critical role in improving numerical weather prediction (NWP). However, the physical principles of radiation dictate that data voids frequently exist in physical space (e.g., subcloud area for satellite infrared radiance or no-precipitation region for radar reflectivity). Such data gaps impair the accuracy of initial conditions derived from data assimilation (DA), which has a negative impact on NWP. We use the barotropic vorticity equation to demonstrate the potential of deep learning augmented data assimilation (DDA), which involves reconstructing spatially complete pseudo-observation fields from incomplete observations and using them for DA. By training a convolutional autoencoder (CAE) with a long simulation at a coarse “forecast” resolution (T63), we obtained a deep learning approximation of the “reconstruction operator,” which maps spatially incomplete observations to a model state with full spatial coverage and resolution. The CAE was applied to an incomplete streamfunction observation (∼30% missing) from a high-resolution benchmark simulation and demonstrated satisfactory reconstruction performance, even when only very sparse (1/16 of T63 grid density) observations were used as input. When only spatially incomplete observations are used, the analysis fields obtained from ensemble square root filter (EnSRF) assimilation exhibit significant error. However, in DDA, when EnSRF takes in the combined data from the incomplete observations and CAE reconstruction, analysis error reduces significantly. Such gains are more pronounced with sparse observation and small ensemble size because the DDA analysis is much less sensitive to observation density and ensemble size than the conventional DA analysis, which is based solely on incomplete observations.
Significance Statement
Data assimilation plays a critical role in improving the skills of modern numerical weather prediction by establishing accurate initial conditions. However, unobservable regions are common in observation data, particularly those derived from remote sensing. The nonlinear relationship between data from observable regions and the physical state of unobservable regions may impede DA efficiency. As a result, we propose that deep learning be used to improve data assimilation in such cases by reconstructing a spatially complete first guess of the physical state with deep learning and then applying data assimilation to the reconstructed field. Such deep learning augmentation is found effective in improving the accuracy of data assimilation, especially for sparse observation and small ensemble size.
Abstract
Remote sensing data play a critical role in improving numerical weather prediction (NWP). However, the physical principles of radiation dictate that data voids frequently exist in physical space (e.g., subcloud area for satellite infrared radiance or no-precipitation region for radar reflectivity). Such data gaps impair the accuracy of initial conditions derived from data assimilation (DA), which has a negative impact on NWP. We use the barotropic vorticity equation to demonstrate the potential of deep learning augmented data assimilation (DDA), which involves reconstructing spatially complete pseudo-observation fields from incomplete observations and using them for DA. By training a convolutional autoencoder (CAE) with a long simulation at a coarse “forecast” resolution (T63), we obtained a deep learning approximation of the “reconstruction operator,” which maps spatially incomplete observations to a model state with full spatial coverage and resolution. The CAE was applied to an incomplete streamfunction observation (∼30% missing) from a high-resolution benchmark simulation and demonstrated satisfactory reconstruction performance, even when only very sparse (1/16 of T63 grid density) observations were used as input. When only spatially incomplete observations are used, the analysis fields obtained from ensemble square root filter (EnSRF) assimilation exhibit significant error. However, in DDA, when EnSRF takes in the combined data from the incomplete observations and CAE reconstruction, analysis error reduces significantly. Such gains are more pronounced with sparse observation and small ensemble size because the DDA analysis is much less sensitive to observation density and ensemble size than the conventional DA analysis, which is based solely on incomplete observations.
Significance Statement
Data assimilation plays a critical role in improving the skills of modern numerical weather prediction by establishing accurate initial conditions. However, unobservable regions are common in observation data, particularly those derived from remote sensing. The nonlinear relationship between data from observable regions and the physical state of unobservable regions may impede DA efficiency. As a result, we propose that deep learning be used to improve data assimilation in such cases by reconstructing a spatially complete first guess of the physical state with deep learning and then applying data assimilation to the reconstructed field. Such deep learning augmentation is found effective in improving the accuracy of data assimilation, especially for sparse observation and small ensemble size.
Abstract
In a prior study, GOES-R Geostationary Lightning Mapper (GLM) flash extent density (FED) data were assimilated using ensemble Kalman filter into a convection-allowing model for a mesoscale convective system (MCS) and a supercell storm. The FED observation operator based on a linear relation with column graupel mass was tuned by multiplying a factor to avoid large FED forecast bias. In this study, new observation operators are developed by fitting a third-order polynomial to GLM FED observations and the corresponding FED forecasts of graupel mass of the MCS and/or supercell cases. The new operators are used to assimilate the FED data for both cases, in three sets of experiments called MCSFit, SupercellFit, and CombinedFit, and their performances are compared with the prior results using the linear operator and with a reference simulation assimilating no FED data. The new nonlinear operators reduce the frequency biases (root-mean-square innovations) in the 0–4-h forecasts of the FED (radar reflectivity) relative to the results using the linear operator for both storm cases. The operator obtained by fitting data from the same case performs slightly better than fitting to data from the other case, while the operator obtained by fitting forecasts of both cases produce intermediate but still very similar results, and the latter is considered more general. In practice, a more general operator can be developed by fitting data from more cases.
Significance Statement
Prior studies found that assimilation of satellite lightning observation can benefit storm forecasts for up to 4 h. A linear lightning observation operator originally developed for assimilating pseudo-satellite lightning observations was tuned earlier through sensitivity experiments when assimilating real lightning data. However, the linear relation does not fit the model and observational data well and significant bias can exist. This study develops new lightning observation operators by fitting a high-order polynomial to satellite lightning observations and model-predicted quantities that directly relate to lightning. The new operator was found to reduce the frequency biases and root-mean-square innovations for lightning and radar reflectivity forecasts, respectively, up to several hours relative to the linear operator. The methodology can be applied to larger data samples to obtain a more general operator for use in operational data assimilation systems.
Abstract
In a prior study, GOES-R Geostationary Lightning Mapper (GLM) flash extent density (FED) data were assimilated using ensemble Kalman filter into a convection-allowing model for a mesoscale convective system (MCS) and a supercell storm. The FED observation operator based on a linear relation with column graupel mass was tuned by multiplying a factor to avoid large FED forecast bias. In this study, new observation operators are developed by fitting a third-order polynomial to GLM FED observations and the corresponding FED forecasts of graupel mass of the MCS and/or supercell cases. The new operators are used to assimilate the FED data for both cases, in three sets of experiments called MCSFit, SupercellFit, and CombinedFit, and their performances are compared with the prior results using the linear operator and with a reference simulation assimilating no FED data. The new nonlinear operators reduce the frequency biases (root-mean-square innovations) in the 0–4-h forecasts of the FED (radar reflectivity) relative to the results using the linear operator for both storm cases. The operator obtained by fitting data from the same case performs slightly better than fitting to data from the other case, while the operator obtained by fitting forecasts of both cases produce intermediate but still very similar results, and the latter is considered more general. In practice, a more general operator can be developed by fitting data from more cases.
Significance Statement
Prior studies found that assimilation of satellite lightning observation can benefit storm forecasts for up to 4 h. A linear lightning observation operator originally developed for assimilating pseudo-satellite lightning observations was tuned earlier through sensitivity experiments when assimilating real lightning data. However, the linear relation does not fit the model and observational data well and significant bias can exist. This study develops new lightning observation operators by fitting a high-order polynomial to satellite lightning observations and model-predicted quantities that directly relate to lightning. The new operator was found to reduce the frequency biases and root-mean-square innovations for lightning and radar reflectivity forecasts, respectively, up to several hours relative to the linear operator. The methodology can be applied to larger data samples to obtain a more general operator for use in operational data assimilation systems.
Abstract
While considerable attention has been given to how convectively coupled Kelvin waves (CCKWs) influence the genesis of tropical cyclones (TCs) in the Atlantic Ocean, less attention has been given to their direct influence on African easterly waves (AEWs). This study builds a climatology of AEW and CCKW passages from 1981 to 2019 using an AEW-following framework. Vertical and horizontal composites of these passages are developed and divided into categories based on AEW position and CCKW strength. Many of the relationships that have previously been found for TC genesis also hold true for non-developing AEWs. This includes an increase in convective coverage surrounding the AEW center in phase with the convectively enhanced (“active”) CCKW crest, as well as a buildup of relative vorticity from the lower to upper troposphere following this active crest. Additionally, a new finding is that CCKWs induce specific humidity anomalies around AEWs that are qualitatively similar to those of relative vorticity. These modifications to specific humidity are more pronounced when AEWs are at lower latitudes and interacting with stronger CCKWs. While the influence of CCKWs on AEWs is mostly transient and short lived, CCKWs do modify the AEW propagation speed and westward-filtered relative vorticity, indicating that they may have some longer-term influences on the AEW life cycle. Overall, this analysis provides a more comprehensive view of the AEW–CCKW relationship than has previously been established, and supports assertions by previous studies that CCKW-associated convection, specific humidity, and vorticity may modify the favorability of AEWs to TC genesis over the Atlantic.
Abstract
While considerable attention has been given to how convectively coupled Kelvin waves (CCKWs) influence the genesis of tropical cyclones (TCs) in the Atlantic Ocean, less attention has been given to their direct influence on African easterly waves (AEWs). This study builds a climatology of AEW and CCKW passages from 1981 to 2019 using an AEW-following framework. Vertical and horizontal composites of these passages are developed and divided into categories based on AEW position and CCKW strength. Many of the relationships that have previously been found for TC genesis also hold true for non-developing AEWs. This includes an increase in convective coverage surrounding the AEW center in phase with the convectively enhanced (“active”) CCKW crest, as well as a buildup of relative vorticity from the lower to upper troposphere following this active crest. Additionally, a new finding is that CCKWs induce specific humidity anomalies around AEWs that are qualitatively similar to those of relative vorticity. These modifications to specific humidity are more pronounced when AEWs are at lower latitudes and interacting with stronger CCKWs. While the influence of CCKWs on AEWs is mostly transient and short lived, CCKWs do modify the AEW propagation speed and westward-filtered relative vorticity, indicating that they may have some longer-term influences on the AEW life cycle. Overall, this analysis provides a more comprehensive view of the AEW–CCKW relationship than has previously been established, and supports assertions by previous studies that CCKW-associated convection, specific humidity, and vorticity may modify the favorability of AEWs to TC genesis over the Atlantic.
Abstract
As rainfed agriculture remains India’s critical source of livelihood, improving our understanding of rainy season onset timing in the region is of great importance for a better prediction. Using a new gridded dataset of rainy season characteristics, we found a clear phase relationship between the Madden–Julian oscillation (MJO) and the onset timing of the rainy season over the Indian subcontinent. A significantly high probability of rainy season onset is observed when the MJO convection stays over the western-central Indian Ocean. On the other hand, the rainy season onset is infrequent when the MJO is over the Maritime Continent and western Pacific. The MJO-associated convective instability with anomalous warm and moist air in the lower troposphere appears and grows during the period 10 days prior to the onset of rainy season, and drops substantially after the start of rainy season, suggesting its role as a trigger of rainy season onset. In contrast, the low-frequency background state (LFBS) with a period > 90 days favors a convectively unstable stratification even after the onset of the rainy season, supporting the succeeding precipitation during the entire rainy season. Based on the scale-decomposed moisture budget diagnosis, we further found that the key processes inducing the abrupt transition from a dry to a wet condition come mainly from two processes: 1) convergence of LFBS moisture by MJO-related circulation perturbations and 2) advection of MJO moisture anomalies by the background cross-equatorial flow toward the Indian subcontinent. The results may help provide a better and longer lead-time prediction of the rainy season onset over the Indian subcontinent.
Abstract
As rainfed agriculture remains India’s critical source of livelihood, improving our understanding of rainy season onset timing in the region is of great importance for a better prediction. Using a new gridded dataset of rainy season characteristics, we found a clear phase relationship between the Madden–Julian oscillation (MJO) and the onset timing of the rainy season over the Indian subcontinent. A significantly high probability of rainy season onset is observed when the MJO convection stays over the western-central Indian Ocean. On the other hand, the rainy season onset is infrequent when the MJO is over the Maritime Continent and western Pacific. The MJO-associated convective instability with anomalous warm and moist air in the lower troposphere appears and grows during the period 10 days prior to the onset of rainy season, and drops substantially after the start of rainy season, suggesting its role as a trigger of rainy season onset. In contrast, the low-frequency background state (LFBS) with a period > 90 days favors a convectively unstable stratification even after the onset of the rainy season, supporting the succeeding precipitation during the entire rainy season. Based on the scale-decomposed moisture budget diagnosis, we further found that the key processes inducing the abrupt transition from a dry to a wet condition come mainly from two processes: 1) convergence of LFBS moisture by MJO-related circulation perturbations and 2) advection of MJO moisture anomalies by the background cross-equatorial flow toward the Indian subcontinent. The results may help provide a better and longer lead-time prediction of the rainy season onset over the Indian subcontinent.
