An Investigation of the Spatial Distribution of Tropical Cyclone Sizes

Tyler BurnsMississippi State University

Applied Meteorology Program

Introduction- A Source of Uncertainty -

Aqua/MODIS 29 October, 2012 ~1800Z

Introduction- Does Size Matter? -

Cyclone Tracy (1974), a small storm with a big impact

Hurricane Patricia (2015), a small storm with a small impact

Super Typhoon Tip (1979), a big storm with a (relatively) small impact

Sandy (2012), a big storm with a big impact

• Objectives:

– Create a graphical depiction of storm size

– Order regions of the Northern Hemisphere by storm size

– Develop a model using latitude to predict storm size

Introduction- Let’s Figure This Out -

International Best Track Archive for Climate Stewardship (IBTrACS)

- National Hurricane Center (NHC)- Central Pacific Hurricane Center (CPHC)- Joint Typhoon Warning Center (JTWC)

Methods- You Have to Start Somewhere -

2014: Three storms crossed into the CPHC’s Area of Responsibility and (sort of) disappeared.

Methods- A Bug in the System -

Methods- Everybody Loves Pictures -

Use MATLAB to resample the wind radii data onto a 5 by 5 degree mesh grid

Split data into Eastern and Western Hemispheres

Fix the color bar range

Place the data into one of eight different sub-basins

Bootstrap the data to obtain a normal distribution of mean sizes for each sub-basin

Compare 95% CI and medians of the resampled data to identify any statistically significant trends

Methods- Block Party -

Train linear regression models with storm size as a function of latitude using 80% of the total data

Plot the data and the model’s fit line

Obtain the models’ R2 values for the quality of the fit

Test the model on the remaining data

Examine the box plot of the residuals of the tested data

Methods- Moving On Up-

Results- The Gale Force Graphics -

Results- Some Storm Force Figures -

Results- Hurricane Force History -

Results- Sub-basin Sorting -

Results- Regional Ranking -

Sub-basin34 kt Radii 50 kt Radii 64 kt Radii

Lower CI Upper CI Range Median Rank Lower CI Upper CI Range Median Rank Lower CI Upper CI Range Median Rank

Open Atlantic 86.7 90.5 3.9 88.6 2 28.1 30.4 2.2 29.3 1 9.8 11.0 1.2 10.4 3

Gulf of Mexico 76.1 82.4 6.4 79.2 3 26.2 30.8 4.6 28.4 2 11.4 14.4 3.0 12.8 1

Eastern East Pacific 57.8 60.1 2.3 58.9 5 16.9 18.5 1.7 17.7 5 6.1 7.0 0.9 6.5 5

Central Pacific 59.3 63.0 3.7 61.1 6 14.9 17.0 2.1 16.0 6 5.8 6.9 1.1 6.3 6

Open West Pacific 89.2 91.7 2.5 90.5 1 24.2 25.6 1.4 24.9 3 10.8 11.6 0.8 11.2 2

South China Sea 65.8 69.9 4.2 67.8 4 17.4 19.7 2.3 18.6 4 6.5 7.8 1.3 7.1 4

Bay of Bengal 50.9 56.2 5.3 53.5 7 10.6 13.5 2.9 12.0 7 3.9 5.5 1.6 4.7 7

Arabian Sea 47.5 55.0 7.6 51.2 8 8.6 12.7 4.1 10.6 8 2.9 5.2 2.3 4.0 8

Results- Latitude Lackluster-

Wind RadiiFull Dataset Zeros Removed

β0 (Intercept) β1 (Slope) R2 Tested Residuals Median β0 (Intercept) β1 (Slope) R2

34 kt 40.144 1.723 0.0591 -1.94 49.702 1.888 0.0906

50 kt 10.302 0.477 0.0156 -15.94 28.109 0.895 0.0601

64 kt 5.590 0.130 0.0037 -7.62 21.118 0.479 0.0389

- In general, storm size increases as one moves west across a basin and poleward- North Indian storms are the exception; they are all

pretty small- Storms in the Atlantic and Western Pacific

Oceans are typically the largest- Predicting storm size becomes more difficult

as storms gain latitude - Gulf of Mexico storms vary wildly in size

Discussion- What It All Means -

- Latitude by itself is a very poor predictor of storm size

- Small sub-basins require many more factors to be considered

- The variations in activity of different basins results in inconsistent data quality

- Aircraft reconnaissance needs to resume in the West Pacific!

Discussion- What It Doesn’t Show-

- This study demonstrated the wildcard nature of predicting storm size

- Further studies should use larger datasets, include the Southern Hemisphere, and consider more potential factors

- The results of this study, and those that build on it should be used in conjunction with studies of how storm size affects impact

- The combined data would allow decision makers to estimate the impact of a tropical cyclone by simply knowing its size and location

Conclusions- For the Future -

Questions?- (I’ve Got Answers) -