Abstract
This study describes an automated analysis of real-time tropical cyclone (TC) aircraft reconnaissance observations to estimate TC surface winds. The wind analysis uses an iterative, objective, data-weighted analysis approach with different smoothing constraints in the radial and azimuthal directions. Smoothing constraints penalize the data misfit when the solutions deviate from smoothed analyses and extend the aircraft information into areas not directly observed. The analysis composites observations following storm motion taken within five hours prior and three hours after analysis time and makes use of prescribed methods to move observations to a Common Flight Level (CFL; 700-hPa) for analysis and reduce reconnaissance observations to the surface. Comparing analyses to several observed and simulated wind fields shows that analyses fit the observations while extending observational information to poorly observed regions. However, resulting analyses tend toward greater symmetry as observational coverage decreases, and show sensitivity to the first guess information in unobserved radii. Analyses produce reasonable and useful estimates of operationally important characteristics of the wind field. But, due to the radial and azimuthal smoothing and the under-sampling of typical aircraft reconnaissance flights, wind maxima are underestimated, and the radii of maximum wind are slightly overestimated. Varying observational coverage using model-based synthetic aircraft observations, these analyses improve as observational coverage increases, and for a typical observational pattern (two transects through the storm) the root-mean-square error deviation is < 10 kt (< 5 m s−1).
Abstract
This study describes an automated analysis of real-time tropical cyclone (TC) aircraft reconnaissance observations to estimate TC surface winds. The wind analysis uses an iterative, objective, data-weighted analysis approach with different smoothing constraints in the radial and azimuthal directions. Smoothing constraints penalize the data misfit when the solutions deviate from smoothed analyses and extend the aircraft information into areas not directly observed. The analysis composites observations following storm motion taken within five hours prior and three hours after analysis time and makes use of prescribed methods to move observations to a Common Flight Level (CFL; 700-hPa) for analysis and reduce reconnaissance observations to the surface. Comparing analyses to several observed and simulated wind fields shows that analyses fit the observations while extending observational information to poorly observed regions. However, resulting analyses tend toward greater symmetry as observational coverage decreases, and show sensitivity to the first guess information in unobserved radii. Analyses produce reasonable and useful estimates of operationally important characteristics of the wind field. But, due to the radial and azimuthal smoothing and the under-sampling of typical aircraft reconnaissance flights, wind maxima are underestimated, and the radii of maximum wind are slightly overestimated. Varying observational coverage using model-based synthetic aircraft observations, these analyses improve as observational coverage increases, and for a typical observational pattern (two transects through the storm) the root-mean-square error deviation is < 10 kt (< 5 m s−1).
Abstract
Modification of grasslands into irrigated and non-irrigated agriculture in the Great Plains results in significant impacts on weather and climate. However, there has been lack of observational data-based studies solely focused on impacts of irrigation on the PBL and convective conditions. The Great Plains Irrigation Experiment (GRAINEX) during the 2018 growing season collected data over irrigated and non-irrigated land uses over Nebraska to understand these impacts. Specifically, the objective was to determine whether the impacts of irrigation are sustained throughout the growing season.
The data analyzed include latent and sensible heat flux, air temperature, dew point temperature, equivalent temperature (moist enthalpy), PBL height, lifting condensation level (LCL), level of free convection (LFC), and PBL mixing ratio. Results show increased partitioning of energy into latent heat compared to sensible heat over irrigated areas while average maximum air was decreased and dewpoint temperature was increased from the early to peak growing season. Radiosonde data suggest reduced planetary boundary layer (PBL) heights at all launch sites from the early to peak growing season. However, reduction of PBL height was much greater over irrigated areas compared to non-irrigated croplands. Compared to the early growing period, LCL and LFC heights were also lower during the peak growing period over irrigated areas. Results note, for the first time, that the impacts of irrigation on PBL evolution and convective environment can be sustained throughout the growing season and regardless of background atmospheric conditions. These are important findings and applicable to other irrigated areas in the world.
Abstract
Modification of grasslands into irrigated and non-irrigated agriculture in the Great Plains results in significant impacts on weather and climate. However, there has been lack of observational data-based studies solely focused on impacts of irrigation on the PBL and convective conditions. The Great Plains Irrigation Experiment (GRAINEX) during the 2018 growing season collected data over irrigated and non-irrigated land uses over Nebraska to understand these impacts. Specifically, the objective was to determine whether the impacts of irrigation are sustained throughout the growing season.
The data analyzed include latent and sensible heat flux, air temperature, dew point temperature, equivalent temperature (moist enthalpy), PBL height, lifting condensation level (LCL), level of free convection (LFC), and PBL mixing ratio. Results show increased partitioning of energy into latent heat compared to sensible heat over irrigated areas while average maximum air was decreased and dewpoint temperature was increased from the early to peak growing season. Radiosonde data suggest reduced planetary boundary layer (PBL) heights at all launch sites from the early to peak growing season. However, reduction of PBL height was much greater over irrigated areas compared to non-irrigated croplands. Compared to the early growing period, LCL and LFC heights were also lower during the peak growing period over irrigated areas. Results note, for the first time, that the impacts of irrigation on PBL evolution and convective environment can be sustained throughout the growing season and regardless of background atmospheric conditions. These are important findings and applicable to other irrigated areas in the world.
Abstract
As one of the most prominent weather systems over the Indian subcontinent, the Indian summer monsoon low pressure systems (MLPSs) have been studied extensively over the past decades. However, the processes that govern the growth of the MLPSs are not well understood. To better understand these processes, we created an MLPS index using bandpass-filtered precipitation data. Lag regression maps and vertical cross sections are used to document the distribution of moisture, moist static energy (MSE), geopotential, and horizontal and vertical motions in these systems. It is shown that moisture governs the distribution of MSE and is in phase with precipitation, vertical motion, and geopotential during the MLPS cycle. Examination of the MSE budget reveals that longwave radiative heating maintains the MSE anomalies against dissipation from vertical MSE advection. These processes nearly cancel one another, and it is variations in horizontal MSE advection that are found to explain the growth and decay of the MSE anomalies. Horizontal MSE advection contributes to the growth of the MSE anomalies in MLPSs prior to the system attaining a maximum amplitude and contributes to decay thereafter. The horizontal MSE advection is largely due to meridional advection of mean state MSE by the anomalous winds, suggesting that the MSE anomalies undergo a moisture–vortex instability (MVI)-like growth. In contrast, perturbation kinetic energy (PKE) is generated through barotropic conversion. The structure, propagation, and energetics of the regressed MLPSs are consistent with both barotropic and moisture–vortex growth.
Abstract
As one of the most prominent weather systems over the Indian subcontinent, the Indian summer monsoon low pressure systems (MLPSs) have been studied extensively over the past decades. However, the processes that govern the growth of the MLPSs are not well understood. To better understand these processes, we created an MLPS index using bandpass-filtered precipitation data. Lag regression maps and vertical cross sections are used to document the distribution of moisture, moist static energy (MSE), geopotential, and horizontal and vertical motions in these systems. It is shown that moisture governs the distribution of MSE and is in phase with precipitation, vertical motion, and geopotential during the MLPS cycle. Examination of the MSE budget reveals that longwave radiative heating maintains the MSE anomalies against dissipation from vertical MSE advection. These processes nearly cancel one another, and it is variations in horizontal MSE advection that are found to explain the growth and decay of the MSE anomalies. Horizontal MSE advection contributes to the growth of the MSE anomalies in MLPSs prior to the system attaining a maximum amplitude and contributes to decay thereafter. The horizontal MSE advection is largely due to meridional advection of mean state MSE by the anomalous winds, suggesting that the MSE anomalies undergo a moisture–vortex instability (MVI)-like growth. In contrast, perturbation kinetic energy (PKE) is generated through barotropic conversion. The structure, propagation, and energetics of the regressed MLPSs are consistent with both barotropic and moisture–vortex growth.
Abstract
Spatial patterns of tropical cyclone tornadoes (TCTs), and their relationship to patterns of mesoscale predictors within U.S. landfalling tropical cyclones (LTCs) are investigated using multicase composites from 27 years of reanalysis data (1995–2021). For 72 cases of LTCs with wide-ranging TC intensities at landfall, daytime TCT frequency maxima are found in the northeast, right-front, and downshear-right quadrants when their composites are constructed in ground-relative, TC-heading relative, and environmental shear relative coordinates, respectively. TCT maxima are located near maxima of 10-m–700-hPa bulk wind difference (BWD), which are enhanced by the TC circulation. This proxy for bulk vertical shear in roughly the lowest 3 km is among the best predictors of maximum TCT frequency. Relative to other times, the position of maximum TCT frequency during the afternoon shifts ∼100 km outward from the LTC center toward larger MLCAPE values. Composites containing the strongest LTCs have the strongest maximum 10-m–700-hPa and 10-m–500-hPa BWDs (∼20 m s−1) with nearby maximum frequencies of TCTs. Corresponding composites containing weaker LTCs but still many TCTs, had bulk vertical shear values that were ∼20% smaller (∼16 m s−1). Additional composites of cases having similarly weak average LTC strength at landfall, but few or no TCTs, had both maximum bulk vertical shears that were an additional ∼20% lower (∼12 m s−1) and smaller MLCAPE. TCT environments occurring well inland are distinguished from others by having stronger westerly shear and a west–east-oriented baroclinic zone (i.e., north–south temperature gradient) that enhances mesoscale ascent and deep convection on the LTC’s east side.
Abstract
Spatial patterns of tropical cyclone tornadoes (TCTs), and their relationship to patterns of mesoscale predictors within U.S. landfalling tropical cyclones (LTCs) are investigated using multicase composites from 27 years of reanalysis data (1995–2021). For 72 cases of LTCs with wide-ranging TC intensities at landfall, daytime TCT frequency maxima are found in the northeast, right-front, and downshear-right quadrants when their composites are constructed in ground-relative, TC-heading relative, and environmental shear relative coordinates, respectively. TCT maxima are located near maxima of 10-m–700-hPa bulk wind difference (BWD), which are enhanced by the TC circulation. This proxy for bulk vertical shear in roughly the lowest 3 km is among the best predictors of maximum TCT frequency. Relative to other times, the position of maximum TCT frequency during the afternoon shifts ∼100 km outward from the LTC center toward larger MLCAPE values. Composites containing the strongest LTCs have the strongest maximum 10-m–700-hPa and 10-m–500-hPa BWDs (∼20 m s−1) with nearby maximum frequencies of TCTs. Corresponding composites containing weaker LTCs but still many TCTs, had bulk vertical shear values that were ∼20% smaller (∼16 m s−1). Additional composites of cases having similarly weak average LTC strength at landfall, but few or no TCTs, had both maximum bulk vertical shears that were an additional ∼20% lower (∼12 m s−1) and smaller MLCAPE. TCT environments occurring well inland are distinguished from others by having stronger westerly shear and a west–east-oriented baroclinic zone (i.e., north–south temperature gradient) that enhances mesoscale ascent and deep convection on the LTC’s east side.
Abstract
The surface and air temperature gradient (T S00-T air) drives the development of the convective boundary layer and the occurrence of clouds and precipitation. However, its variability is still poorly understood due to the lack of high-quality observations. This study fills in this gap by investigating the diurnal to decadal variability in T S00-T air from 2002 to 2022 based on hourly observations collected at over 100 stations of the U.S. Climate Reference Network. It is found that T S00-T air reaches its maximum at noon with an average of 6.85°C over the Contiguous United States, which decreases to 4.28°C when the soil moisture exceeds 30%. The daily minimum of T S00-T air has an average of −2.08°C, which generally occurs in the early evening but is postponed as the cloud fraction decreases. Moreover, while existing studies have used the near-surface soil temperature, such as the 5-cm soil temperature (T S05), to calculate T S05-T air, we find that T S00-T air and T S05-T air have opposite diurnal cycles, and their amplitudes differed drastically. The daily minimum of T S00-T air has a significant decreasing trend (−0.50±0.007°C/decade) from 2002 to 2022 due to T air increasing at a higher rate than T S00 during the nighttime. The occurrence frequency of near surface stable condition (T S00-T air<0) increases significantly, and the frequency of unstable condition (T S00-T air>0) decreases notably throughout the year except for winter. When it is stable, the magnitude of T S00-T air tends to decrease while the T S00-T air tends to increase when it is unstable, which is consistent with the drying condition caused by precipitation deficit. This study provides the first observational evidence on how T S00-T air responds to warming.
Abstract
The surface and air temperature gradient (T S00-T air) drives the development of the convective boundary layer and the occurrence of clouds and precipitation. However, its variability is still poorly understood due to the lack of high-quality observations. This study fills in this gap by investigating the diurnal to decadal variability in T S00-T air from 2002 to 2022 based on hourly observations collected at over 100 stations of the U.S. Climate Reference Network. It is found that T S00-T air reaches its maximum at noon with an average of 6.85°C over the Contiguous United States, which decreases to 4.28°C when the soil moisture exceeds 30%. The daily minimum of T S00-T air has an average of −2.08°C, which generally occurs in the early evening but is postponed as the cloud fraction decreases. Moreover, while existing studies have used the near-surface soil temperature, such as the 5-cm soil temperature (T S05), to calculate T S05-T air, we find that T S00-T air and T S05-T air have opposite diurnal cycles, and their amplitudes differed drastically. The daily minimum of T S00-T air has a significant decreasing trend (−0.50±0.007°C/decade) from 2002 to 2022 due to T air increasing at a higher rate than T S00 during the nighttime. The occurrence frequency of near surface stable condition (T S00-T air<0) increases significantly, and the frequency of unstable condition (T S00-T air>0) decreases notably throughout the year except for winter. When it is stable, the magnitude of T S00-T air tends to decrease while the T S00-T air tends to increase when it is unstable, which is consistent with the drying condition caused by precipitation deficit. This study provides the first observational evidence on how T S00-T air responds to warming.
Abstract
Water vapor supersaturation in clouds is a random variable that drives activation and growth of cloud droplets. The Pi Convection-Cloud Chamber generates a turbulent cloud with a microphysical steady-state that can be varied from clean to polluted by adjusting the aerosol injection rate. The supersaturation distribution and its moments, e.g., mean and variance, are investigated for varying cloud microphysical conditions. High-speed and co-located Eulerian measurements of temperature and water vapor concentration are combined to obtain the temporally resolved supersaturation distribution. This allows quantification of the contributions of variances and covariances between water vapor and temperature. Results are consistent with expectations for a convection chamber, with strong correlation between water vapor and temperature; departures from ideal behavior can be explained as resulting from dry regions on the warm boundary, analogous to entrainment. The saturation ratio distribution is measured under conditions that show monotonic increase of liquid water content and decrease of mean droplet diameter with increasing aerosol injection rate. The change in liquid water content is proportional to the change in water vapor concentration between no-cloud and cloudy conditions. Variability in the supersaturation remains even after cloud droplets are formed, and no significant buffering is observed. Results are interpreted in terms of a cloud microphysical Damköhler number (Da), under conditions corresponding to Da ≲ 1, i.e., the slow-microphysics regime. This implies that clouds with very clean regions, such that Da ≲ 1 is satisfied, will experience supersaturation fluctuations without them being buffered by cloud droplet growth.
Abstract
Water vapor supersaturation in clouds is a random variable that drives activation and growth of cloud droplets. The Pi Convection-Cloud Chamber generates a turbulent cloud with a microphysical steady-state that can be varied from clean to polluted by adjusting the aerosol injection rate. The supersaturation distribution and its moments, e.g., mean and variance, are investigated for varying cloud microphysical conditions. High-speed and co-located Eulerian measurements of temperature and water vapor concentration are combined to obtain the temporally resolved supersaturation distribution. This allows quantification of the contributions of variances and covariances between water vapor and temperature. Results are consistent with expectations for a convection chamber, with strong correlation between water vapor and temperature; departures from ideal behavior can be explained as resulting from dry regions on the warm boundary, analogous to entrainment. The saturation ratio distribution is measured under conditions that show monotonic increase of liquid water content and decrease of mean droplet diameter with increasing aerosol injection rate. The change in liquid water content is proportional to the change in water vapor concentration between no-cloud and cloudy conditions. Variability in the supersaturation remains even after cloud droplets are formed, and no significant buffering is observed. Results are interpreted in terms of a cloud microphysical Damköhler number (Da), under conditions corresponding to Da ≲ 1, i.e., the slow-microphysics regime. This implies that clouds with very clean regions, such that Da ≲ 1 is satisfied, will experience supersaturation fluctuations without them being buffered by cloud droplet growth.
Abstract
Recent observations and numerical simulations have demonstrated the potential for significant interactions between mesoscale eddies and smaller-scale tidally generated internal waves—also known as internal tides. Here, we develop a simple theoretical model that predicts the one-way upscale transfer of energy from internal tides to mesoscale eddies through a critical level mechanism. We find that—in the presence of a critical level—the internal tide energy flux into an eddy is partitioned according to the wave frequency Ω and local inertial frequency f: a fraction of 1 − f/Ω is transferred to the eddy kinetic energy, while the remainder is viscously dissipated or supports mixing. These predictions are validated by comparison with a suite of numerical simulations. The simulations further show that the wave-driven energization of the eddies also accelerates the onset of hydrodynamical instabilities and the breakdown of the eddies, thereby increasing eddy kinetic energy, but reducing eddy lifetimes. Our estimates suggest that in regions of the ocean with both significant eddy fields and internal tides—such as parts of the Gulf Stream and Antarctic Circumpolar Current—the critical level effect could drive a ∼10% month−1 increase in the kinetic energy of a typical eddy. Our results provide a basis for parameterizing internal tide–eddy interactions in global ocean models where they are currently unrepresented.
Abstract
Recent observations and numerical simulations have demonstrated the potential for significant interactions between mesoscale eddies and smaller-scale tidally generated internal waves—also known as internal tides. Here, we develop a simple theoretical model that predicts the one-way upscale transfer of energy from internal tides to mesoscale eddies through a critical level mechanism. We find that—in the presence of a critical level—the internal tide energy flux into an eddy is partitioned according to the wave frequency Ω and local inertial frequency f: a fraction of 1 − f/Ω is transferred to the eddy kinetic energy, while the remainder is viscously dissipated or supports mixing. These predictions are validated by comparison with a suite of numerical simulations. The simulations further show that the wave-driven energization of the eddies also accelerates the onset of hydrodynamical instabilities and the breakdown of the eddies, thereby increasing eddy kinetic energy, but reducing eddy lifetimes. Our estimates suggest that in regions of the ocean with both significant eddy fields and internal tides—such as parts of the Gulf Stream and Antarctic Circumpolar Current—the critical level effect could drive a ∼10% month−1 increase in the kinetic energy of a typical eddy. Our results provide a basis for parameterizing internal tide–eddy interactions in global ocean models where they are currently unrepresented.
Abstract
This study conducts a thorough investigation into the behaviors of analysis ensemble spreads linked to stratospheric sudden warming (SSW) events. A stratosphere-resolving ensemble data assimilation system is used here to document the evolution of analysis spread leading up to a pair of warming events. Precursory signals of the increased ensemble spreads were found a few days prior to two SSW events that occurred during December 2018 and August–September 2019 in the Northern and Southern Hemispheres, respectively. The signals appeared in the upper and middle stratosphere and did not appear at lower heights. When the signals appeared, it was found that both tendency by forecast and analysis increment in a forecast-analysis (data assimilation) cycle simultaneously became large. An empirical orthogonal function analysis showed that the dominant structures of the precursory signals were equivalent barotropic and were 90° out-of-phase with the analysis ensemble-mean field. Over the same period, the upper and middle stratosphere became more susceptible to barotropic instability than in their previous states. We conclude that the differing growth of barotropically unstable modes across ensemble members can amplify spread during the lead-up to SSW events.
Significance Statement
Winds in the winter stratospheric polar vortex are typically westerly. Occasionally, however, warming over the pole leads to a reversal of the flow through a process known as stratospheric sudden warming. These events are difficult to predict even in state-of-the-art analysis and forecasting systems. In this study, we identify a precursor signal in the form of increased ensemble spread that appears to originate from differing realizations of growing barotropic modes across the ensemble. This signal could serve as a useful forecasting tool by enhancing situational awareness in the lead-up to potential stratospheric sudden warming events.
Abstract
This study conducts a thorough investigation into the behaviors of analysis ensemble spreads linked to stratospheric sudden warming (SSW) events. A stratosphere-resolving ensemble data assimilation system is used here to document the evolution of analysis spread leading up to a pair of warming events. Precursory signals of the increased ensemble spreads were found a few days prior to two SSW events that occurred during December 2018 and August–September 2019 in the Northern and Southern Hemispheres, respectively. The signals appeared in the upper and middle stratosphere and did not appear at lower heights. When the signals appeared, it was found that both tendency by forecast and analysis increment in a forecast-analysis (data assimilation) cycle simultaneously became large. An empirical orthogonal function analysis showed that the dominant structures of the precursory signals were equivalent barotropic and were 90° out-of-phase with the analysis ensemble-mean field. Over the same period, the upper and middle stratosphere became more susceptible to barotropic instability than in their previous states. We conclude that the differing growth of barotropically unstable modes across ensemble members can amplify spread during the lead-up to SSW events.
Significance Statement
Winds in the winter stratospheric polar vortex are typically westerly. Occasionally, however, warming over the pole leads to a reversal of the flow through a process known as stratospheric sudden warming. These events are difficult to predict even in state-of-the-art analysis and forecasting systems. In this study, we identify a precursor signal in the form of increased ensemble spread that appears to originate from differing realizations of growing barotropic modes across the ensemble. This signal could serve as a useful forecasting tool by enhancing situational awareness in the lead-up to potential stratospheric sudden warming events.
Abstract
A scheme for integration of atmospheric equations containing terms with differing time scales is developed. The method employs a filtered leapfrog scheme utilizing a fourth-order implicit time filter with one function evaluation per time step to compute slow-propagating phenomena such as advection and rotation. The terms involving fast-propagating modes are handled implicitly with an unconditionally stable method that permits application of larger time steps and faster computations compared to fully explicit treatment. Implementation using explicit and recurrent formulation is provided. Stability analysis demonstrates that the method is conditionally stable for any combination of frequencies involved in the slow and fast terms as they approach the origin. The implicit filter used in the method damps the computational modes without noticeably sacrificing the accuracy of the physical mode. The O[(Δt 4)] accuracy for amplitude errors achieved by the implicitly filtered leapfrog is preserved in applications where terms responsible for fast propagation are integrated with a semi-implicit method. Detailed formulation of the method for soundproof nonhydrostatic anelastic equations is provided. Procedures for implementation in global spectral shallow-water models are also given. Examples comparing numerical and analytical solutions for linear gravity waves demonstrate the accuracy of the scheme. The performance is also shown in more practical nonlinear applications, where numerical solutions accomplished by the method are evaluated against those computed from a scheme where the slow terms are handled by the third-order Runge–Kutta scheme. It demonstrates that the method is able to accurately resolve fine-scale dynamics of Kelvin–Helmholtz shear instabilities, the evolution of density current, and nonlinear drifts of twin tropical cyclones.
Abstract
A scheme for integration of atmospheric equations containing terms with differing time scales is developed. The method employs a filtered leapfrog scheme utilizing a fourth-order implicit time filter with one function evaluation per time step to compute slow-propagating phenomena such as advection and rotation. The terms involving fast-propagating modes are handled implicitly with an unconditionally stable method that permits application of larger time steps and faster computations compared to fully explicit treatment. Implementation using explicit and recurrent formulation is provided. Stability analysis demonstrates that the method is conditionally stable for any combination of frequencies involved in the slow and fast terms as they approach the origin. The implicit filter used in the method damps the computational modes without noticeably sacrificing the accuracy of the physical mode. The O[(Δt 4)] accuracy for amplitude errors achieved by the implicitly filtered leapfrog is preserved in applications where terms responsible for fast propagation are integrated with a semi-implicit method. Detailed formulation of the method for soundproof nonhydrostatic anelastic equations is provided. Procedures for implementation in global spectral shallow-water models are also given. Examples comparing numerical and analytical solutions for linear gravity waves demonstrate the accuracy of the scheme. The performance is also shown in more practical nonlinear applications, where numerical solutions accomplished by the method are evaluated against those computed from a scheme where the slow terms are handled by the third-order Runge–Kutta scheme. It demonstrates that the method is able to accurately resolve fine-scale dynamics of Kelvin–Helmholtz shear instabilities, the evolution of density current, and nonlinear drifts of twin tropical cyclones.
