The Response of Tropical Cyclone Intensity to Temperature Profile Change

Theory indicates that tropical cyclone intensity should respond to changes in the vertical temperature profile. While the sensitivity of tropical cyclone intensity to sea surface temperature is well understood, less is known about sensitivity to the temperature profile. In this paper, we combine historical data analysis and idealised modelling to explore the extent to which historical tropospheric warming and lower stratospheric cooling can explain observed trends in the tropical cyclone intensity distribution. Observations and modelling agree that historical global temperature profile changes coincide with higher lifetime maximum intensities. But observations suggest the response depends on the tropical cyclone intensity itself. Historical lower- and upper-tropospheric temperatures in hurricane environments have warmed significantly faster than the tropical mean. In addition, hurricane-strength storms have intensified at twice the rate of weaker storms per unit warming at the surface and at 300-hPa. Idealized simulations respond in the expected sense to various imposed changes in the temperature profile and agree with tropical cyclones operating as heat engines. Yet lower stratospheric temperature changes have little influence. Idealised modelling further shows an increasing altitude of the TC outflow but little change in outflow temperature. This enables increased efficiency for strong tropical cyclones despite the warming upper troposphere. Observed sensitivities are generally larger than modelled sensitivities, suggesting that observed tropical cyclone intensity change responds to a combination of the temperature profile change and other environmental factors.

Satellite-based monitoring and prediction of tropical cyclone intensity and movement

ABSTRACT. Capabilities of meteorological satellites have gone a long way in meeting requirements of synoptic analysis and forecasting of tropical cyclones. This paper shows the impact made by the satellite data in the intensity estimation and track prediction of tropical cyclones in the Indian Seas and also reviews the universally applied Dvorak algorithm for performing tropical cyclone intensity analysis. Extensive use of Dvorak's intensity estimation scheme has revealed many of its limitations and elements of subjectivity in the analysis of tropical cyclones over the Arabian Sea and the Bay of Bengal, which, like cyclones in other ocean basins, also exhibit wide structural variability as seen in the satellite imagery. Satellite-based cyclone tracking techniques include: (i) use of satellite-derived mean wind flow, (ii) animation of sequence of satellite images and extrapolation of the apparent motion of the cloud system and (iii) monitoring changes in the upper level moisture patterns in the water vapour absorption channel imagery. Satellite-based techniques on tropical cyclone intensity estimation and track prediction have led to very significant improvement in disaster warning and consequent saving of life and property.

The Performance of Three Exponential Decay Models in Estimating Tropical Cyclone Intensity Change After Landfall Over China

In this study, the performance of three exponential decay models in estimating intensity change of tropical cyclones (TCs) after landfall over China is evaluated based on the best-track TC data during 1980–2018. Results indicate that the three models evaluated can reproduce the weakening trend of TCs after landfall, but two of them (M1 and M2) tend to overestimate TC intensity and one (M3) tends to overestimate TC intensity in the first 12 h and underestimate TC intensity afterwards. M2 has the best performance with the smallest errors among the three models within 24 h after landfall. M3 has better performance than M1 in the first 20 h after landfall, but its errors increase largely afterwards. M1 and M2 show systematic positive biases in the southeastern China likely due to the fact that they have not explicitly included any topographic effect. M3 has better performance in the southeastern China, where it was originally attempted, but shows negative biases in the eastern China. The relative contributions of different factors, including landfall intensity, translational speed, 850-hPa moist static energy, and topography, to model errors are examined based on classification analyses. Results indicate that the landfall intensity contributes about 18%, translational speed, moist static energy and topography contribute equally about 15% to the model errors. It is strongly suggested that the TC characteristics and the time-dependent decay constant determined by environmental conditions, topography and land cover properties, should be considered in a good exponential decay model of TC weakening after landfall.

Comparative analysis of subjective/advanced objective technique of tropical cyclone intensity estimation

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Impact of the Madden–Julian Oscillation on North Indian Ocean Cyclone Intensity

The influence of the Madden–Julian Oscillation (MJO) on the intensity of the Tropical Cyclones in the North Indian Ocean is investigated through a machine learning algorithm. The magnitude of wind, considered as a proxy for the intensity, is taken from the Joint Typhoon Warning Centre (JTWC), and the MJO information for 1974–2019 is from Australia’s Bureau of Meteorology. These two observations have been collocated and the influence of MJO indices on the wind speed was studied using an artificial neural network technique. The scatter index, defined as the root mean square error (RMSE) normalized to the input data mean, varies from 0.45 for depressions to 0.03 for the super cyclonic storms, indicating that the MJO index is another parameter that should be investigated in cyclone activity studies.