distribution of tropical cyclone size (msu capstone presentation)
TRANSCRIPT
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An Investigation of the Spatial Distribution of Tropical Cyclone Sizes
Tyler BurnsMississippi State University
Applied Meteorology Program
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Introduction- A Source of Uncertainty -
Aqua/MODIS 29 October, 2012 ~1800Z
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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
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• 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 -
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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 -
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2014: Three storms crossed into the CPHC’s Area of Responsibility and (sort of) disappeared.
Methods- A Bug in the System -
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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
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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 -
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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-
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Results- The Gale Force Graphics -
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Results- Some Storm Force Figures -
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Results- Hurricane Force History -
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Results- Sub-basin Sorting -
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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
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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
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- 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 -
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- 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-
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- 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 -
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Questions?- (I’ve Got Answers) -