Relationship Between Cyclone Intensities and Sea Surface Temperature in the Tropical Indian Ocean

Ali, M. Masoom; Swain, Debadatta; Kashyap, Tina; McCreary, Julian P.; Nagamani, P. V.

doi:10.1109/lgrs.2012.2226138

Cited by 35 publications

(28 citation statements)

References 18 publications

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“…One of the metrics for tropical cyclone‐ocean interaction is the tropical cyclone heat potential (TCHP) [ Leipper and Volgenau , ; Shay et al ., ]. It represents the integrated ocean heat content between the sea surface and the depth of 26°C isotherm [ Goni et al ., ; Shay et al ., ; Goni and Trinanes , ; Pun et al ., ; Ali et al ., ]. Oceanic regions with deep mixed layers of temperature above 26°C and hence large TCHP can fuel storm intensification by making more ocean heat energy available for uptake by the cyclone.…”

Section: Introductionmentioning

confidence: 99%

Variability of upper‐ocean characteristics and tropical cyclones in the South West Indian Ocean

Mawren

Reason

2017

JGR Oceans

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Track and intensity are key aspects of tropical cyclone behavior. Intensity may be impacted by the upper‐ocean heat content relevant for TC intensification (known as Tdy) and barrier layer thickness (BLT). Here the variability of Tdy and BLT in the South West Indian Ocean and their relationships with tropical cyclones are investigated. It is shown that rapid cyclone intensification is influenced by large Tdy values, thick barrier layers and the presence of anticyclonic eddies. For TC generation in the South West Indian Ocean, the parameter Tdy was found to be important. Large BLT values overlay with large Tdy values during summer. Both fields are modulated by the westward propagation of Rossby waves, which are often associated with ENSO. For example, the 1997–1998 El Niño shows a strong signal in Tdy, SST, and BLT over the South West Indian Ocean. After this event, an increasing trend in Tdy occurred over most of the basin which may be associated with changes in atmospheric circulation. Increasing SST, Power Dissipation Index and frequency of Category 5 tropical cyclones also occurred from 1980 to 2010. To further examine the links between tropical cyclones, Tdy, and BLT, the ocean response to Category 5 Tropical Cyclone Bansi that developed near Madagascar during January 2015 was analyzed. Its unusual track was found to be linked with the strengthening of the monsoonal north westerlies while its rapid intensification from Category 2 to Category 4 was linked to a high‐Tdy region, associated with a warm core eddy and large BLT.

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Section: Introductionmentioning

confidence: 99%

Variability of upper‐ocean characteristics and tropical cyclones in the South West Indian Ocean

Mawren

Reason

2017

JGR Oceans

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“…They reported that out of 75 cyclones occurred in the tropical Indian Ocean during 1998-2011; more than 50% of the cyclones have no significant correlation between CI and SST. The number of cyclones with significant negative (positive) correlation are 31(3), 13(10) and 17 (14) with SST leading CI by one, two and three days respectively Figure 3 of Ali et al [15,16]. In these cases SST of previous days is correlated with CI.…”

Section: Ssha Sst and CImentioning

confidence: 91%

“…Several other studies also reported that CIs are positively (negatively) correlated with positive (negative) SSHAs [3,13,14]. Ali et al [15,16] further analyzed TRMM Microwave Imager (TMI) daily SST and CI, defined as the maximum sustained winds from Joint Typhoon Warning Center (JTWC) best track data. They reported that out of 75 cyclones occurred in the tropical Indian Ocean during 1998-2011; more than 50% of the cyclones have no significant correlation between CI and SST.…”

Section: Ssha Sst and CImentioning

confidence: 99%

Importance of Ocean Heat Content for Cyclone Studies

Modave¹,

Shokar²

2014

J Oceanogr Mar Res

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The physical process involved from formation to dissipation of any cyclone is very complex in nature. Thus, accurate and timely prediction of cyclone intensity and track still remain as challenging scientific problems. Though presently available cyclone prediction models have improved our understanding about many physical processes from cyclone formation to dissipation, some are still unresolved. The input parameters that used into the models as a source of energy for the cyclones need to be reconsidered. Model estimate the energy for cyclones as latent heat release through evaporation computed using only Sea Surface Temperature (SST). However, the energy in the ocean for cyclone is available in the upper layers of the oceans rather than in the skin layer represented by SST. Therefore we examined the importance of heat content of the upper ocean in estimating four important aspects of cyclones (i) pressure drop, (ii) track change, (iii) intensity and (iv) storm surge height. The results obtained during the series of analysis emphasized that the thermal structure of the upper ocean is more critical and sensitive predictor for cyclones compared to SST, which need to be incorporated in numerical models for better and accurate forecasting.

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“…MLP approach has been used by Ali et al . (, , , , ) to estimate the tropical cyclone heat potential and the ocean windstress. We tried all the models and found that an MLP network provided the least error and best results.…”

Section: Approachmentioning

confidence: 99%

“…Ali et al . (, , ) reported that more than 50% of the cyclone intensities in the north Indian Ocean have a significant (at 95% level) negative correlation with SST. Thus it is the deep surface layer, not simply the surface itself that is important to cyclone intensification.…”

Section: Introductionmentioning

confidence: 99%

A soft‐computing cyclone intensity prediction scheme for the Western North Pacific Ocean

Sharma

Ali

Knaff

et al. 2013

Atmospheric Science Letters

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A soft-computing cyclone intensity prediction scheme (SCIPS) is introduced using an artificial neural network (ANN) approach and adding ocean heat content, as an additional predictor to the normally used atmospheric parameters, to predict tropical cyclone intensity change in the western north Pacific Ocean. We used 1997-2004 data to develop and validate this scheme. The ANN-based estimations have been compared with observations and estimations using the multiple linear regression (MLR). SCIPS performance improves upon MLR as the lead hour increases from 12 to 120 h and also for high intensifying cyclones.

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Relationship Between Cyclone Intensities and Sea Surface Temperature in the Tropical Indian Ocean

Cited by 35 publications

References 18 publications

Variability of upper‐ocean characteristics and tropical cyclones in the South West Indian Ocean

Variability of upper‐ocean characteristics and tropical cyclones in the South West Indian Ocean

Importance of Ocean Heat Content for Cyclone Studies

A soft‐computing cyclone intensity prediction scheme for the Western North Pacific Ocean

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