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Abstract
A CIMSS vertical wind shear (VWS-C) dataset based on reprocessed GOES-East atmospheric motion vectors (AMVs) at 15-min intervals has a −0.36 correlation with the CIMSS Satellite Consensus (SATCON) intensity changes at 30-min intervals over the life cycle of Hurricane Joaquin (2015). Correlations are then calculated for four intensity change events including two rapid intensifications (RIs) and two decays, and four intensity change segments immediately before or after these events. During the first RI, the peak intensity increase of 16 kt (6 h)−1 (1 kt ≈ 0.51 m s−1) follows a small VWS-C decrease to a moderate 8 m s−1 value (negative correlation). A 30-h period of continued RI following the first peak RI occurred under moderate magnitude VWS-C (negative correlation), but with a rotation of the VWS-C direction to become more aligned with the southwestward heading of Joaquin. During the second RI, the peak intensity increase of 15 kt (6 h)−1 leads the rapid VWS-C increase (positive correlation), which the horizontal plots of VWS-C vectors demonstrate is related to an upper-tropospheric cyclone to the northeast of Joaquin. A conceptual model of ocean cooling within the anticyclonic track loop is proposed to explain a counterintuitive decreasing intensity when the VWS-C was also decreasing (positive correlation) during the Joaquin track reversal. These alternating negative and positive correlations during the four events and four segments of intensity change demonstrate the nonlinear relationships between the VWS-C and intensity changes during the life cycle of Joaquin that must be understood, analyzed, and modeled to improve tropical cyclone intensity forecasts, and especially RI events.
Abstract
A CIMSS vertical wind shear (VWS-C) dataset based on reprocessed GOES-East atmospheric motion vectors (AMVs) at 15-min intervals has a −0.36 correlation with the CIMSS Satellite Consensus (SATCON) intensity changes at 30-min intervals over the life cycle of Hurricane Joaquin (2015). Correlations are then calculated for four intensity change events including two rapid intensifications (RIs) and two decays, and four intensity change segments immediately before or after these events. During the first RI, the peak intensity increase of 16 kt (6 h)−1 (1 kt ≈ 0.51 m s−1) follows a small VWS-C decrease to a moderate 8 m s−1 value (negative correlation). A 30-h period of continued RI following the first peak RI occurred under moderate magnitude VWS-C (negative correlation), but with a rotation of the VWS-C direction to become more aligned with the southwestward heading of Joaquin. During the second RI, the peak intensity increase of 15 kt (6 h)−1 leads the rapid VWS-C increase (positive correlation), which the horizontal plots of VWS-C vectors demonstrate is related to an upper-tropospheric cyclone to the northeast of Joaquin. A conceptual model of ocean cooling within the anticyclonic track loop is proposed to explain a counterintuitive decreasing intensity when the VWS-C was also decreasing (positive correlation) during the Joaquin track reversal. These alternating negative and positive correlations during the four events and four segments of intensity change demonstrate the nonlinear relationships between the VWS-C and intensity changes during the life cycle of Joaquin that must be understood, analyzed, and modeled to improve tropical cyclone intensity forecasts, and especially RI events.
Abstract
Observations from High-Definition Sounding System (HDSS) dropsondes, collected for Hurricane Joaquin during the Office of Naval Research Tropical Cyclone Intensity (TCI) field experiment in 2015, are assimilated into the NCEP Hurricane Weather Research and Forecasting (HWRF) Model. The Gridpoint Statistical Interpolation (GSI)-based hybrid three-dimensional and four-dimensional ensemble–variational (3DEnVar and 4DEnVar) data assimilation configurations are compared. The assimilation of HDSS dropsonde observations can help HWRF initialization by generating consistent analysis between wind and pressure fields and can also compensate for the initial maximum surface wind errors in the absence of initial vortex intensity correction. Compared with GSI–3DEnVar, the assimilation of HDSS dropsonde observations using GSI–4DEnVar generates a more realistic initial vortex intensity and reproduces the rapid weakening (RW) of Hurricane Joaquin, suggesting that the assimilation of high-resolution inner-core observations (e.g., HDSS dropsonde data) based on an advanced data assimilation method (e.g., 4DEnVar) can potentially outperform the vortex initialization scheme currently used in HWRF. Additionally, the assimilation of HDSS dropsonde observations can improve the simulation of vortex structure changes and the accuracy of the vertical motion within the TC inner-core region, which is essential to the successful simulation of the RW of Hurricane Joaquin with HWRF. Additional experiments with GSI–4DEnVar in different configurations also indicate that the performance of GSI–4DEnVar can be further improved with a high-resolution background error covariance and a denser observational bin.
Abstract
Observations from High-Definition Sounding System (HDSS) dropsondes, collected for Hurricane Joaquin during the Office of Naval Research Tropical Cyclone Intensity (TCI) field experiment in 2015, are assimilated into the NCEP Hurricane Weather Research and Forecasting (HWRF) Model. The Gridpoint Statistical Interpolation (GSI)-based hybrid three-dimensional and four-dimensional ensemble–variational (3DEnVar and 4DEnVar) data assimilation configurations are compared. The assimilation of HDSS dropsonde observations can help HWRF initialization by generating consistent analysis between wind and pressure fields and can also compensate for the initial maximum surface wind errors in the absence of initial vortex intensity correction. Compared with GSI–3DEnVar, the assimilation of HDSS dropsonde observations using GSI–4DEnVar generates a more realistic initial vortex intensity and reproduces the rapid weakening (RW) of Hurricane Joaquin, suggesting that the assimilation of high-resolution inner-core observations (e.g., HDSS dropsonde data) based on an advanced data assimilation method (e.g., 4DEnVar) can potentially outperform the vortex initialization scheme currently used in HWRF. Additionally, the assimilation of HDSS dropsonde observations can improve the simulation of vortex structure changes and the accuracy of the vertical motion within the TC inner-core region, which is essential to the successful simulation of the RW of Hurricane Joaquin with HWRF. Additional experiments with GSI–4DEnVar in different configurations also indicate that the performance of GSI–4DEnVar can be further improved with a high-resolution background error covariance and a denser observational bin.
Abstract
A dynamic initialization assimilation scheme is demonstrated utilizing rapid-scan atmospheric motion vectors (AMVs) at 15-min intervals to simulate the real-time capability that now exists from the new generation of geostationary meteorological satellites. The impacts of these AMVs are validated with special Tropical Cyclone Intensity Experiment (TCI-15) datasets during 1200–1800 UTC 4 October leading up to a NASA WB-57 eyewall crossing of Hurricane Joaquin. Incorporating the AMV fields in the Spline Analysis at Mesoscale Utilizing Radar and Aircraft Instrumentation (SAMURAI) COAMPS Dynamic Initialization (SCDI) means there are 30 and 90 time steps on the 15- and 5-km grids, respectively, during which the mass fields are adjusted to these AMV-based wind increments during each 15-min assimilation period. The SCDI analysis of the three-dimensional vortex structure of Joaquin at 1800 UTC 4 October closely replicates the vortex tilt analyzed from the High-Definition Sounding System (HDSS) dropwindsondes. Vertical wind shears based on the AMVs at 15-min intervals are well correlated with the extreme rapid decay, an interruption of that rapid decay, and the subsequent period of constant intensity of Joaquin. Utilizing the SCDI analysis as the initial conditions for two versions of the COAMPS-TC model results in an accurate 72-h prediction of the interruption of the rapid decay and the period of constant intensity. Upscaling a similar SCDI analysis based on the 15-min interval AMVs provides a more realistic intensity and structure of Tropical Storm Joaquin for the initial conditions of the Navy Global Environmental Model (NAVGEM) than the synthetic TC vortex used operationally. This demonstration for a single 6-h period of AMVs indicates the potential for substantial impacts when an end-to-end cycling version is developed.
Abstract
A dynamic initialization assimilation scheme is demonstrated utilizing rapid-scan atmospheric motion vectors (AMVs) at 15-min intervals to simulate the real-time capability that now exists from the new generation of geostationary meteorological satellites. The impacts of these AMVs are validated with special Tropical Cyclone Intensity Experiment (TCI-15) datasets during 1200–1800 UTC 4 October leading up to a NASA WB-57 eyewall crossing of Hurricane Joaquin. Incorporating the AMV fields in the Spline Analysis at Mesoscale Utilizing Radar and Aircraft Instrumentation (SAMURAI) COAMPS Dynamic Initialization (SCDI) means there are 30 and 90 time steps on the 15- and 5-km grids, respectively, during which the mass fields are adjusted to these AMV-based wind increments during each 15-min assimilation period. The SCDI analysis of the three-dimensional vortex structure of Joaquin at 1800 UTC 4 October closely replicates the vortex tilt analyzed from the High-Definition Sounding System (HDSS) dropwindsondes. Vertical wind shears based on the AMVs at 15-min intervals are well correlated with the extreme rapid decay, an interruption of that rapid decay, and the subsequent period of constant intensity of Joaquin. Utilizing the SCDI analysis as the initial conditions for two versions of the COAMPS-TC model results in an accurate 72-h prediction of the interruption of the rapid decay and the period of constant intensity. Upscaling a similar SCDI analysis based on the 15-min interval AMVs provides a more realistic intensity and structure of Tropical Storm Joaquin for the initial conditions of the Navy Global Environmental Model (NAVGEM) than the synthetic TC vortex used operationally. This demonstration for a single 6-h period of AMVs indicates the potential for substantial impacts when an end-to-end cycling version is developed.
Abstract
The objective in this study is to demonstrate how two unique datasets from the Tropical Cyclone Intensity (TCI-15) field experiment can be used to diagnose the environmental and internal factors contributing to the interruption of the rapid decay of Hurricane Joaquin (2015) and then a subsequent 30-h period of constant intensity. A special CIMSS vertical wind shear (VWS) dataset reprocessed at 15-min intervals provides a more precise documentation of the large (~15 m s−1) VWS throughout most of the rapid decay period, and then the timing of a rapid decrease in VWS to moderate (~8 m s−1) values prior to, and following, the rapid decay period. During this period, the VWS was moderate because Joaquin was between large VWSs to the north and near-zero VWSs to the south, which is considered to be a key factor in how Joaquin was able to be sustained at hurricane intensity even though it was moving poleward over colder water. A unique dataset of High Definition Sounding System (HDSS) dropwindsondes deployed from the NASA WB-57 during the TCI-15 field experiment is utilized to calculate zero-wind centers during Joaquin center overpasses that reveal for the first time the vortex tilt structure through the entire troposphere. The HDSS datasets are also utilized to calculate the inertial stability profiles and the inner-core potential temperature anomalies in the vertical. Deeper lower-tropospheric layers of near-zero vortex tilt are correlated with stronger storm intensities, and upper-tropospheric layers with large vortex tilts due to large VWSs are correlated with weaker storm intensities.
Abstract
The objective in this study is to demonstrate how two unique datasets from the Tropical Cyclone Intensity (TCI-15) field experiment can be used to diagnose the environmental and internal factors contributing to the interruption of the rapid decay of Hurricane Joaquin (2015) and then a subsequent 30-h period of constant intensity. A special CIMSS vertical wind shear (VWS) dataset reprocessed at 15-min intervals provides a more precise documentation of the large (~15 m s−1) VWS throughout most of the rapid decay period, and then the timing of a rapid decrease in VWS to moderate (~8 m s−1) values prior to, and following, the rapid decay period. During this period, the VWS was moderate because Joaquin was between large VWSs to the north and near-zero VWSs to the south, which is considered to be a key factor in how Joaquin was able to be sustained at hurricane intensity even though it was moving poleward over colder water. A unique dataset of High Definition Sounding System (HDSS) dropwindsondes deployed from the NASA WB-57 during the TCI-15 field experiment is utilized to calculate zero-wind centers during Joaquin center overpasses that reveal for the first time the vortex tilt structure through the entire troposphere. The HDSS datasets are also utilized to calculate the inertial stability profiles and the inner-core potential temperature anomalies in the vertical. Deeper lower-tropospheric layers of near-zero vortex tilt are correlated with stronger storm intensities, and upper-tropospheric layers with large vortex tilts due to large VWSs are correlated with weaker storm intensities.
Abstract
Hurricane Patricia (2015) broke records in both peak intensity and rapid intensification (RI) rate over the eastern Pacific basin. All of the then-operational models predicted less than half of its extraordinary intensity and RI rate, leaving a challenge for numerical modeling studies. In this study, a successful 42-h simulation of Patricia is obtained using a quintuply nested-grid version of the Weather Research and Forecast (WRF) Model with the finest grid size of 333 m. Results show that the WRF Model, initialized with the Global Forecast System Final Analysis data only, could reproduce the track, peak intensity, and many inner-core features, as verified against various observations. In particular, its simulated maximum surface wind of 92 m s−1 is close to the observed 95 m s−1, capturing the unprecedented RI rate of 54 m s−1 (24 h)−1. In addition, the model reproduces an intense warm-cored eye, a small-sized eyewall with a radius of maximum wind of less than 10 km, and the distribution of narrow spiral rainbands. A series of sensitivity simulations is performed to help understand which model configurations are essential to reproducing the extraordinary intensity of the storm. Results reveal that Patricia’s extraordinary development and its many inner-core structures could be reasonably well simulated if ultrahigh horizontal resolution, appropriate model physics, and realistic initial vortex intensity are incorporated. It is concluded that the large-scale conditions (e.g., warm sea surface temperature, weak vertical wind shear, and the moist intertropical convergence zone) and convective organization play important roles in determining the predictability of Patricia’s extraordinary RI and peak intensity.
Abstract
Hurricane Patricia (2015) broke records in both peak intensity and rapid intensification (RI) rate over the eastern Pacific basin. All of the then-operational models predicted less than half of its extraordinary intensity and RI rate, leaving a challenge for numerical modeling studies. In this study, a successful 42-h simulation of Patricia is obtained using a quintuply nested-grid version of the Weather Research and Forecast (WRF) Model with the finest grid size of 333 m. Results show that the WRF Model, initialized with the Global Forecast System Final Analysis data only, could reproduce the track, peak intensity, and many inner-core features, as verified against various observations. In particular, its simulated maximum surface wind of 92 m s−1 is close to the observed 95 m s−1, capturing the unprecedented RI rate of 54 m s−1 (24 h)−1. In addition, the model reproduces an intense warm-cored eye, a small-sized eyewall with a radius of maximum wind of less than 10 km, and the distribution of narrow spiral rainbands. A series of sensitivity simulations is performed to help understand which model configurations are essential to reproducing the extraordinary intensity of the storm. Results reveal that Patricia’s extraordinary development and its many inner-core structures could be reasonably well simulated if ultrahigh horizontal resolution, appropriate model physics, and realistic initial vortex intensity are incorporated. It is concluded that the large-scale conditions (e.g., warm sea surface temperature, weak vertical wind shear, and the moist intertropical convergence zone) and convective organization play important roles in determining the predictability of Patricia’s extraordinary RI and peak intensity.
Abstract
Forecasting tropical storm intensities is a very challenging issue. In recent years, dynamical models have improved considerably. However, for intensity forecasts more improvement is necessary. Dynamical models have different kinds of biases. Considering a multimodel consensus could eliminate some of the biases resulting in improved intensity forecasts as compared to the individual models. Apart from the ensemble mean, the construction of multimodel consensuses has always contributed to somewhat improved forecasts. The Florida State University (FSU) multimodel superensemble is one that, over the years, has systematically provided improved forecasts for hurricanes, numerical weather prediction, and seasonal climate forecasts. The present study considers an artificial neural network (ANN), based on biological principles, for the construction of a multimodel ensemble. ANN has been used for constructing multimodel consensus forecasts for tropical cyclone intensities. This study uses the generalized regression neural network (GRNN) method for the construction of consensus intensity forecasts for the Atlantic basin. Hurricane seasons 2012–16 are considered. Results show that with only five input models improved guidance for tropical storm intensities may be obtained. The consensus using GRNN mostly outperforms all the models included in the study and the ensemble mean. Forecast errors at the longer forecast leads are considerably less for this multimodel superensemble based on the generalized regression neural network. The skill and correlations of different models along with the developed consensus are provided in our analysis. Results suggest that this consensus forecast may be used for operational guidance and for planning and emergency evacuation management. Possibilities for future improvements of the consensus based on new advances in statistical algorithms are also indicated.
Abstract
Forecasting tropical storm intensities is a very challenging issue. In recent years, dynamical models have improved considerably. However, for intensity forecasts more improvement is necessary. Dynamical models have different kinds of biases. Considering a multimodel consensus could eliminate some of the biases resulting in improved intensity forecasts as compared to the individual models. Apart from the ensemble mean, the construction of multimodel consensuses has always contributed to somewhat improved forecasts. The Florida State University (FSU) multimodel superensemble is one that, over the years, has systematically provided improved forecasts for hurricanes, numerical weather prediction, and seasonal climate forecasts. The present study considers an artificial neural network (ANN), based on biological principles, for the construction of a multimodel ensemble. ANN has been used for constructing multimodel consensus forecasts for tropical cyclone intensities. This study uses the generalized regression neural network (GRNN) method for the construction of consensus intensity forecasts for the Atlantic basin. Hurricane seasons 2012–16 are considered. Results show that with only five input models improved guidance for tropical storm intensities may be obtained. The consensus using GRNN mostly outperforms all the models included in the study and the ensemble mean. Forecast errors at the longer forecast leads are considerably less for this multimodel superensemble based on the generalized regression neural network. The skill and correlations of different models along with the developed consensus are provided in our analysis. Results suggest that this consensus forecast may be used for operational guidance and for planning and emergency evacuation management. Possibilities for future improvements of the consensus based on new advances in statistical algorithms are also indicated.
Abstract
A method is developed to calculate the zero-wind center (ZWC) position from a sequence of Yankee High Density Sounding System (HDSS) dropwindsondes deployed during a high-altitude overpass of a tropical cyclone. The approach is similar to the Willoughby and Chelmow technique in that it utilizes the intersections of bearings normal to the wind directions across the center to locate the ZWC position. Average wind directions over 1-km layers are calculated from the accurate global positioning system (GPS) latitude–longitude positions as the HDSS sonde falls from the 60 000-ft flight level of the NASA WB-57 to the ocean surface. An iterative procedure is used to also account for the storm translation, which is necessary to put these high-frequency HDSS observations into a storm-relative coordinate system. The Tropical Cyclone Intensity (TCI-15) mission into Hurricane Joaquin on 4 October 2015 is examined here. The ZWC positions from two center overpasses indicate the vortex tilts from 1- to 10-km elevation and rotates cyclonically with height.
Abstract
A method is developed to calculate the zero-wind center (ZWC) position from a sequence of Yankee High Density Sounding System (HDSS) dropwindsondes deployed during a high-altitude overpass of a tropical cyclone. The approach is similar to the Willoughby and Chelmow technique in that it utilizes the intersections of bearings normal to the wind directions across the center to locate the ZWC position. Average wind directions over 1-km layers are calculated from the accurate global positioning system (GPS) latitude–longitude positions as the HDSS sonde falls from the 60 000-ft flight level of the NASA WB-57 to the ocean surface. An iterative procedure is used to also account for the storm translation, which is necessary to put these high-frequency HDSS observations into a storm-relative coordinate system. The Tropical Cyclone Intensity (TCI-15) mission into Hurricane Joaquin on 4 October 2015 is examined here. The ZWC positions from two center overpasses indicate the vortex tilts from 1- to 10-km elevation and rotates cyclonically with height.
