Climatological Analysis of Tropical Cyclones over the North Indian Ocean: 1980–2023

Department of Meteorology

University of Dhaka

Presented By:

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• Sadia Afrin Sayfa Negaban
• Mohammad Fahimul Islam
• Jannatul Ferdous Jerin

Literature Review

Research Gap

• Data Gaps – Limited high-resolution cyclone records before 2000 hinder long-term trend analysis.

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• Regional Relevance – NIO especially BoB cyclone dynamics remain understudied despite being a global hotspot.

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• Intensification – Observed in multiple past cyclones but still poorly understood in North Indian basin.

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• Policy & Preparedness – Without clear evidence, strategies stay reactive instead of anticipatory.

Research Objectives

• To analyze the spatial and temporal distribution of tropical cyclones over the North Indian Ocean, identifying annual, seasonal and monthly variation patterns.

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• To explore the seasonal and monthly spatial variation of genesis locations of tropical cyclones in the North Indian Ocean.

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• To understand the decadal frequency and trends of tropical cyclones across different seasons, categories, and sub-basins.

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• To examine trends in cyclone frequency highlighting any significant changes.

• To assess the relation between Maximum Sustained Wind (MSW), Mean Sea Level Pressure (MSLP).

Coriolis

Parameter

Weak Vertical

Wind Shear

Pre-existing Disturbance

Sea Surface Temperature Above 26°C

Middle Troposphere Relative Humidity

Low-level Relative Vorticity

FACTORS

Factors

BMD Classification of Tropical Cyclone

Significance of the Study

The table summarizes the human, economic, and infrastructural losses caused by selected cyclones in the Bay of Bengal, highlighting their intensity, affected areas, and overall impact.

Methodology

• Genesis Location Distribution Analysis

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• Annual Frequency Analysis

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• Seasonal Frequency Analysis

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• Monthly Frequency Analysis

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• Decadal Frequency Analysis

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• Duration Analysis

Data & Tools

Data Sources

The Joint Typhoon Warning Centre (JTWC), USA, has the best track data for the North Indian Ocean. From 1980, JTWC began using satellites to detect TCs in the North Indian Ocean. In this study, NIO TCs from 1980 to 2023 (44 years) were analyzed using JTWC best track data, which can be found at https://www.metoc.navy.mil/jtwc/jtwc.html?north-indian-ocean.

This JTWC dataset is appropriate and reliable for the long-term climatological study of NIO TCs. At 6-hour intervals (0000, 0600, 1200, and 1800UTC), the dataset includes TCs' name, position (latitude and longitude), MSLP, and 1-min MSW speed.

Tools Used

Statistical Analysis

Of

Climatological Study

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Genesis Location

Distribution Analysis

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Genesis Location

• Genesis points of all the cyclones of 44 years

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• Genesis more condensed over the BoB than AS.

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• Bob is more suitable for cyclone formation than AS

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• Genesis in western part of BoB is more concentrated than the eastern side

Seasonal & Monthly Genesis Location

Latitudinal & Longitudinal Distribution of Genesis

Kernel Density Estimation (KDE) of Genesis

• KDE shows Genesis location density for TCs.

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• Two distinct cores of high probability in BoB.

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• Weaker but elongated density in the AS

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• Sharp, intense density peak in Bob while more diffuse and spread out in AS

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Annual

Frequency Analysis

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Cyclone Time Series - North Indian Ocean

• Slight Upward Trend: The data shows a weak but positive increase in cyclone frequency over time in the North Indian Ocean.
• Low Predictive Power: The trend line is a poor fit for the data, indicating high year-to-year variability.
• Dominance of Natural Variability: Factors like inter-annual climate patterns (e.g., ENSO, IOD) likely have a stronger influence on cyclone count than the observed long-term trend.
• Significant Uncertainty: The low R² value means we cannot be confident in using this trend to predict future cyclone counts.

Annual Frequency in Sub-Basin

• Sub-Basin Comparison: The Bay of Bengal (BB) is consistently more active than the Arabian Sea (AS).
• High Variability: Significant year-to-year variation in both basins.
• Notable Extremes:
• BB's peak activity in 1992 with 8 cyclones.
• The rare year of 2018 where both basins were equally active.
• Dominant Basin: BB is the primary generator of TCs in the NIO.

Intensity-wise Total(44years) Frequency

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Monthly

Frequency Analysis

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Monthly Frequency – North Indian Ocean

• Main Peak: November is the most active month by far (60 cyclones).

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• Other Active Months: October, (47 cyclones), and May, (34 cyclones) also have many cyclones.

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• Very Quiet Period: Activity falls sharply during July, and August (only 3-4 cyclones each).

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• Consistently Low: The start of the year (Jan, Feb, Mar) also has very few cyclones.

Monthly Frequency in Sub-Basin

• Bay of Bengal Dominance: The Bay of Bengal (BB) has more cyclones than the Arabian Sea (AS) in every month of the year.
• Peak Season: Both basins are most active in the same core months:  November, October, and May.

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Genesis Location

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Seasonal

Frequency Analysis

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Seasonal Frequency - Sub-Basin

• Post-monsoon season: Highest activity observed in the Bay of Bengal (73) compared to the Arabian Sea (34)
• Pre-monsoon season: Greater number of cyclone observed in Bay of Bengal(32) than Arabian Sea(11)
• Monsoon season: Unexpectedly Arabian Sea(23) slightly exceeds the Bay of Bengal (19).
• Winter season: Due to atmospheric condition lesser number of cyclone in both Arabian Sea and Bay of Bengal
• Overall: Bay of Bengal remains the primary cyclone hotspot, with peak activity in the post-monsoon season

Intensity wise Seasonal Frequency

• Cyclonic Storms (CS) are consistently the most frequent category in every season.
• Very Severe Cyclonic Storms (VSCS) occur regularly, especially high in pre- and post-monsoon periods.
• Super Cyclonic Storms (SuCS) are rare, appearing only in pre- and post-monsoon, and absent in winter.
• Monsoon season shows overall lower storm intensity, with CS dominating but fewer severe storms compared to transitional seasons.

Post-Monsoon is the peak season for cyclonic activity, showing the highest frequency across all storm categories

Seasonal Distribution of Accumulated Hours, Intensity Wise

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Decadal

Frequency Analysis

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Decadal Frequency of TC

• The total frequency of tropical cyclones on a decadal basis has increased from 36 in 1984–1993 to 43 in 2014–2023.

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• In the Bay of Bengal, the frequency shows only a slight increase from 36 to 38, with a drop to 33 during 1994–2003.

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• Notably, the Arabian Sea experienced a more pronounced rise, with cyclone frequency increasing from 18 to 26 over the last four decades.

Seasonal-Decadal Frequency

Decade-by-Decade Seasonal Trends

• Cyclone frequency rose from 31 (1984–1993) to 38 (2004–2013), then slightly dropped to 35 (2014–2023).

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• Post-monsoon cyclones dominated each decade.

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• Pre-monsoon cyclones gradually increased from 7 to 12

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Intensity-wise-Decadal Frequency

Decadal frequency by intensity, we observe that all cyclone categories have generally increased over time, from Cyclonic Storm to Super Cyclonic Storm.

• Cyclonic Storms (CS): Increased from 19 in 1984 to 29 in 2013, but decreased to 22 in the last decade.
• Severe Cyclonic Storms (SCS): Decreasing trend, from 19 to 8.
• Very Severe Cyclonic Storms (VSCS): Fluctuating trend, no clear increase or decrease.
• Super Cyclonic Storms (SuCS): Slight increasing trend, but not statistically significant

Additional Parameters

Landfall Location

Cyclone Landfall Highlights:

• Bay of Bengal Focus: Most cyclones make landfall along Bangladesh, West Bengal, Odisha, and Andhra Pradesh, Tamil Nadu, with Bangladesh-West Bengal as the most affected hotspot.
• Arabian Sea & Others: Fewer landfalls occur in the Arabian Sea and along western India; no landfalls are recorded along Sri Lanka, eastern Pakistan, or Iran.

Wind Speed and Pressure - Relationship

Inverse Relationship: Higher wind speeds correspond to lower central pressures, showing that more intense cyclones have lower pressures.

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Strong Correlation: Data points closely follow the line of best fit, confirming a strong negative correlation between wind speed and pressure, which can be used to estimate cyclone intensity.

Duration Intensity Wise

Summary of Findings

Summary of Findings

Limitations

• Temporal Resolution: Observations in JTWC datasets are typically available every 3–6 hours. Rapid intensification or weakening events occurring within shorter timeframes may not be fully resolved.

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• Data Homogeneity: JTWC data are primarily based on satellite observations and model estimates may have changed over time, affecting consistency in wind speed, pressure, and track data across different years.

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• Intensity Estimates: Maximum sustained wind speeds and central pressures are derived from remote sensing and modeling, which can introduce uncertainties compared to direct in-situ measurements.

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• Limited Environmental Variables: JTWC primarily provides cyclone tracks and intensity. It does not include detailed environmental data (e.g., SST, vertical wind shear, or atmospheric moisture), which are important for understanding cyclone formation and rapid intensification.

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• Time Constraints: Due to limited time, the study could not include more extensive analyses, such as finer-scale environmental interactions or additional statistical modeling of cyclones.

References

• Balaguru, K. et al. (2014) ‘Increase in the intensity of postmonsoon Bay of Bengal tropical cyclones’, Geophysical Research Letters, 41(10), pp. 3594–3601. Available at: https://doi.org/10.1002/2014GL060197.

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• Bhardwaj, P. and Singh, O. (2020) ‘Climatological characteristics of Bay of Bengal tropical cyclones: 1972–2017’, Theoretical and Applied Climatology, 139(1–2), pp. 615–629. Available at: https://doi.org/10.1007/s00704-019-02989-4.

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• Evan, A.T. et al. (2011) ‘Arabian Sea tropical cyclones intensified by emissions of black carbon and other aerosols’, Nature, 479(7371), pp. 94–97. Available at: https://doi.org/10.1038/nature10552.

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• Murakami, H. et al. (2015) Simulation and Prediction of Category 4 and 5 Hurricanes in the High-Resolution GFDL HiFLOR Coupled Climate Model. Available at: http://www.gfdl.noaa.gov/cms-filesystem-action/user_files/gav/publications/draft_v20150319.pdf.

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• Bhardwaj, P. and Singh, O., 2020. Climatological characteristics of Bay of Bengal tropical cyclones: 1972–2017: P. Bhardwaj, O. Singh. Theoretical and Applied Climatology, 139(1), pp.615-629. Available at: https://link.springer.com/article/10.1007/s00704-019-02989-4

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