Perspectives of Tropical Cyclone Intensification

CYCLONE: “A natural system of winds rotating inwards to an area of low barometric pressure, with the clockwise (southern hemisphere) or an anticlockwise (northern hemisphere) or with a circulation, and depression.”

Tropical Cyclones (TC) are detected with the probability of their location, track, speed, and impacted link to anthropogenic climate change and their related analysis can be illustrated with observed scientific data, previous prediction, and satellite support over the global nature. TC motion is a central issue for the prediction and mitigation efforts, as it affects the location of storm-related damages. When TCs move slowly, the accumulated damage by TCs can be intensifying with a responsible one for a specific location. Global researcher's care in the new technical development of TC-permitting global and suitable regional climate simulations gives an opportunity for the research study to TC activities under altering climate conditions. The dynamical models (in simulation) are used to identify these issues in real-world TC motions with the best quantitative results.

Figure 1: Nivar Tropical Cyclone(top) and Tropical cyclone structure (bottom) (Image credit to SNB).

The collective analysis of tropical cyclone trends are discussed below:

(A)Tropical Cyclone Trends:

The TC respect to understanding a climatic change, Kossin has reported his research work (about the period of 1949–2016) was expressed the 10 % decreasing trend of tropical-cyclone translation speed (TCS). Further, their team have stated about the expected climatic changes by anthropogenic warming scenarios.

The following themes are discussed,

• Latitude of the TC,
• Sensitive to the bias of TC detection,
• Systematic biases in the detection.

Finally, Kossin expressed, that the motion of TCS stated results may not be a real-time climate signal or it may also, be exaggerated in 2018 [1].

TC Trends Analysis (1949–2016): During the pre-satellite era (1949–1965), TC experimental results have pointed out the inhomogeneity issues. Kossin was argued that the observations of TC position and translation speed are almost insensitive to the inhomogeneity of TC track data [1]. However, Il-Ju Moon et al., have calculated the annual mean TCS globally and insists on the sensitivity to the inhomogeneity issues, which relates to the TC trend analysis. In general, TCS showed increasing trends with latitude and changes with concern location [2]. On average, the TCS in the North Atlantic (~ latitude 38°) is double than the North Indian Ocean (~ latitude 15°).

The scientific team (Typhoon Research Center, South Korea, and Guy Carpenter Asia-Pacific Climate Impact Centre, China) was helpful to demonstrate the issues in Kossin statement with TC statistical data (like pre-satellite era, post-satellite era, post geostationary, climatological TCS, and climatological latitude) and drawbacks at the time of the pre-satellite era. The researcher insisted to discuss the following concepts are an inhomogeneous TC data, TC position, TCS, intensity (lifetime maximum) and frequency [3].

Uncertainties in TC Translation Speed: Lanzante et al., suggested on the scientific literature of anthropogenic climate change (ACC), which may lead to mild or slower migration of TC, and reporting in more intense with their path. This explains the ACC concept and their controversy based upon the Kossin report on TCS. Previously recorded data have suggested that decreasing trend in TCS, due to integration of internal climate variability and sudden changes in actual measurement practices. Around the 1980s, it can be observed specifically in the Northern (Southern) Hemisphere regions, rather than symptomatic climate changes in tropical trends. Above preferred results with the following fact changes in TC are step-like, long term in TCS, and finally argues against an ACC effect [4].  

Scientific Reply to TC Issues: The main problem that was claimed by the scientist team (Moon et al. and Lanzante et al.) is the vital potential for systematic temporal biases in the mean latitude of the TC tracks. The concern bias could be supported via a systematic meridional shift in the TC tracks and also inter-basin frequency trends. Due to uncertainties in the represented data, the potential evidence for impacts on lives given here in Kossin report is very essential to the measurements “attribution without formal detection” by controlled numerical simulations, which have highlighted a vital need for further studies. Murakami et al., have represented the influence of anthropogenic forcing and variability on hurricane season, to found that the TCS of Atlantic hurricanes is diminished, which one is physically link to human life activities and global warming scenarios [5].

(B) Large Ensemble Simulation:

Gan Zhang et al., have expressed the model simulation (large ensemble) which was conducted by Meteorological Research Institute (Atmospheric General Circulation Model Version 3.2H). The integrated result of (1951-2010) simulation and AGCM 3.2H provides the relation between TC motion and the large-scale circulation using a unique set of high-resolution and large-ensemble simulations. They have handled with the simulation model and the experimental settings were available in these methods, which leads actually realistic climate and TC response. During notable TC motion, for analyzing the facts of historical environmental boundary conditions (like sea surface temperature SST variability, greenhouse gases, ozone, and aerosols) and an unharmed atmospheric variability was determined. These kinds of simulations can be helpful to highlight on (i) multi-decadal SST variability and its trends, (ii) anthropogenic warming, can change TC motion in Asia and North America (mid-latitude regions) via the large-scale circulation [6].

(C) Environmental with Deep learning:

TC motion has an essential one on human lives and their environmental infrastructure. Identifying and probable assumption of TC intensity is crucial, especially within the 24 h warning time. TC intensity of prediction change can be regarded as a problem of both regression and classification. Previously, researchers are in trouble with an accurate prediction of TC intensity via statistical forecasting methods (which is based on traditional numerical prediction and empirical relationship method).

Xin Wang et al., have detailed about the detection algorithm of TC (intensity changes), which is supported via proposal of the deep learning process by expressing the three dimensional (3D) environmental conditions (with atmosphere and ocean variables). A new 3D convolutional neural network under the impact of collective results between the TC intensity and spatial distribution features was determined. The concerned scientist expresses the detailed deep hybrid features from image pattern analysis within 24 h TC intensity changes. The collective nature of Mean Absolute Error (MAE) and experimental results are helpful to confirm the accurate TC classification towards weakening or intensifying one. A combined result of spatial layers (low and high layers) and 3D environmental variables were provided in-depth of the TC evolution process [7].

(D) Satellite Measurement and Predications:

In the global TC data analysis (during 2020-2014), which consists of 5019 Atmospheric Infrared Sounder (AIRS) overpasses in Tropical storms (1061) to find out the relationship between warm core structure and TC intensity changes. Xiang Wang et al. was expressed their direction towards the nature of rapid intensification (RI) using the comparative result (with 13-year dataset) of Atmospheric Infrared Sounder (AIRS) + Advanced Microwave Sounding Unit (AMUS). The AIRS TC intensity overpasses may be classified under the following are: (i) slowly intensifying (SI), (ii) neutral (N), (iii) weakening (W) categories.

They have inter-related the concept of warm-core structure (maximum temperature), convective available potential energy (CAPE) and RI were provided the following findings: (i) Identified the composite warm-core maximum temperature anomaly is the strongest in RI storms (~7 K), followed by W (~6 K), SI (~5 K), and N (~ 4 K) storms. (ii) The warm-core height is also positively correlated with the TC intensification rate at a high confidence level. (iii) A proper investigation of TC intensity changes categories are carried out with weakening group (in excluded and included) were leads to understanding the real-time rapidly intensification (RI) and practical RI forecasts rapidly [8].

Our SNB team has emphasized this research article to help the reader to know and realize about the cyclone and their perspectives. During this pandemic situation, a new way of climate issues was started with tropical cyclones over Indian Ocean locations. So we need to be aware of COVID-19 issues as well as to protect us from the environmental cyclone scenarios. Scientist’s predictions and technological tools will be more supportable during this “Nivar Tropical Cyclone” and their position, track, and motion with respect to the environmental issues. The above conclusions and suggestions in this content will be guiding us from the prediction of TC for future challenges.

References:

1. J. P. Kossin, Nature 2018, 558, 104; Author Correction Nature, 2018 564, E11.
2. I. J. Moon et al., Nature 2019, 570, E1.
3. J. R. Lanzante, Nature 2019, https://doi.org/10.1038/s41586-019-1223-2.
4. J. P. Kossin, Nature 2019, https://doi.org/10.1038/s41586-019-1224-1.
5. Murakami et al. Bull. Am. Meteorol.Soc. 2015, 96, S115.
6. Zhang et al., Sci. Adv. 2020, 6, 1.
7. X. Wang et al, Water 2020, 12, 2685.
8. X. Wang et al., Atmospheric Research 2020, 240, 104931.

 

Dr. K. Rajkumar

The Director

V.E.T. Group of Institutions

Villupuram, Tamil Nadu, India.

Email:drcrystalphd@gmail.com

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