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- Author or Editor: J. M. Lewis x
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Abstract
The return of tropical air from the Gulf of Mexico is examined in the autumnal cool season. Results from the thermodynamic equilibrium model of Betts and Ridgway are used to calculate the equilibrium equivalent potential temperature (θ e ) over the gulf and the northwestern Caribbean Sea. With a climatological study as a backdrop, a case of severe weather outbreak in mid-November 1988 is analyzed with emphasis on the analysis of low-level θ e that flowed into the storm region from the Gulf of Mexico.
The primary results of the study are the following:
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The climatological distribution of equilibrium θ e over the gulf and the Caribbean in November serves as a useful tool for the analysis of the 1988 case study.
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Between 5 and 15 November 1988, equilibrium in the marine layer was established over the gulf due to the absence of any deep cold-air penetrations during this period.
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The high-valued θ e that streamed into the severe storm region on 15 November 1988 tracked from the Yucatán straits and the northwestern Caribbean over a three-day period.
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This air was able to maintain its high-θ e property because of an anomalously warm gulf.
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Significant increases in available energy for deep convection could have been anticipated by means of the upper bounds on coastal θ e predicted by the Betts and Ridgway formulation, which was supported by observations along the Texas coast.
Abstract
The return of tropical air from the Gulf of Mexico is examined in the autumnal cool season. Results from the thermodynamic equilibrium model of Betts and Ridgway are used to calculate the equilibrium equivalent potential temperature (θ e ) over the gulf and the northwestern Caribbean Sea. With a climatological study as a backdrop, a case of severe weather outbreak in mid-November 1988 is analyzed with emphasis on the analysis of low-level θ e that flowed into the storm region from the Gulf of Mexico.
The primary results of the study are the following:
-
The climatological distribution of equilibrium θ e over the gulf and the Caribbean in November serves as a useful tool for the analysis of the 1988 case study.
-
Between 5 and 15 November 1988, equilibrium in the marine layer was established over the gulf due to the absence of any deep cold-air penetrations during this period.
-
The high-valued θ e that streamed into the severe storm region on 15 November 1988 tracked from the Yucatán straits and the northwestern Caribbean over a three-day period.
-
This air was able to maintain its high-θ e property because of an anomalously warm gulf.
-
Significant increases in available energy for deep convection could have been anticipated by means of the upper bounds on coastal θ e predicted by the Betts and Ridgway formulation, which was supported by observations along the Texas coast.
Abstract
A feed forward neural network (FFNN) is developed for tropical cyclone (TC) intensity prediction, where intensity is defined as the maximum 1-min average 10-m wind speed. This deep learning model incorporates a real-time operational estimate of the current intensity and predictors derived from Hurricane Weather Research and Forecasting (HWRF; 2017 version) Model forecasts. The FFNN model is developed with the operational constraint of being restricted to 6-h-old HWRF data. Best track intensity data are used for observational verification. The forecast training data are from 2014 to 2016 HWRF reforecast data and cover a wide variety of TCs from both the Atlantic and eastern Pacific Ocean basins. Cross validation shows that the FFNN increasingly outperforms the operational observation-adjusted HWRF (HWFI) in terms of mean absolute error (MAE) at forecast lead times from 3 to 57 h. Out-of-sample testing on real-time data from 2017 shows the HWFI produces lower MAE than the FFNN at lead times of 24 h or less and similar MAEs at later lead times. On the other hand, the 2017 data indicate significant potential for the FFNN in the prediction of rapid intensification (RI), with RI defined here as an intensification of at least 30 kt (1 kt ≈ 0.51 m s−1) in a 24-h period. The FFNN produces 4 times the number of hits in HWFI for RI. While the FFNN has more false alarms than the HWFI, Brier skill scores show that, in the Atlantic, the FFNN has significantly greater skill than the HWFI and probabilistic Statistical Hurricane Intensity Prediction System RI index.
Abstract
A feed forward neural network (FFNN) is developed for tropical cyclone (TC) intensity prediction, where intensity is defined as the maximum 1-min average 10-m wind speed. This deep learning model incorporates a real-time operational estimate of the current intensity and predictors derived from Hurricane Weather Research and Forecasting (HWRF; 2017 version) Model forecasts. The FFNN model is developed with the operational constraint of being restricted to 6-h-old HWRF data. Best track intensity data are used for observational verification. The forecast training data are from 2014 to 2016 HWRF reforecast data and cover a wide variety of TCs from both the Atlantic and eastern Pacific Ocean basins. Cross validation shows that the FFNN increasingly outperforms the operational observation-adjusted HWRF (HWFI) in terms of mean absolute error (MAE) at forecast lead times from 3 to 57 h. Out-of-sample testing on real-time data from 2017 shows the HWFI produces lower MAE than the FFNN at lead times of 24 h or less and similar MAEs at later lead times. On the other hand, the 2017 data indicate significant potential for the FFNN in the prediction of rapid intensification (RI), with RI defined here as an intensification of at least 30 kt (1 kt ≈ 0.51 m s−1) in a 24-h period. The FFNN produces 4 times the number of hits in HWFI for RI. While the FFNN has more false alarms than the HWFI, Brier skill scores show that, in the Atlantic, the FFNN has significantly greater skill than the HWFI and probabilistic Statistical Hurricane Intensity Prediction System RI index.
