IMPROVEMENT IN HURRICANE
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1 IMPROVEMENT IN HURRICANE INTENSITY FORECAST USING NEURAL NETWORKS Tirthankar Ghosh and TN Krishnamurti Florida State University Tallahassee, FL-32306, USA HFIP Annual Review Meeting, Jan 11-12, 2017 National Hurricane Center, Miami Acknowledgement: HFIP, NOAA Award No. NA15OAR
2 Human brain takes best possible decision from past experience Information from the environment is taken by the sensory organs & passed to the brain through neurons (nerve cells) 10 billion nerves with synapses (meeting point of two nerves) 2
3 Input branch (Dendrites) Output branch (Axon) Dendrites sends the received information through the cell body to the action * Axon passes it to dendrite of the next neuron via synapse 3
4 IDEA IS FOLLOWED TO APPROXIMATE OUTPUT FOR A GIVEN SET OF INPUTS w 0 x 1 x 2 f Y x 3 Y k = f w + 0 i= 1 w i x i 4
5 ACTIVATION FUNCTION Linear Logistic Hyper tangent 5
6 APPLICATIONS Classification Discrimination Estimation (time series prediction) Process identification Process control Etc 6
7 TYPES WE CONSIDER Multilayer Perceptron (MLP) Generalized Regression Neural Network Information flows from input to output 7
8 LEARNING Previous observations on input (s) as well as output are provided repeatedly to estimate the neuron parameters (supervised learning) Modification of parameters for better performance (desired output) 8
9 CHOICE OF WEIGHTS { x, y }, k = 1,2,..., N; j = 1,2, p k k Let given observations j..., be a set of Estimate y which minimizes the square error loss ESS = 1 2 [ ( )] k k y f x, w k = 1 The weights (here model weights) are so chosen that ESS would be minimum N 2 9
10 CHOICE OF WEIGHTS CONTD ESS w i = = N k = 1 ( y k k ε x k w x i k ) x k 10
11 MLP Input Hidden layers Output layer layer 11
12 TASKS Numbers of hidden layers (developer provided) Determining the learning rate (developer provided) Train the network Evaluate the performance Repeat the above process if not satisfied (iterative) 12
13 GRNN: BASED ON STANDARD STATISTICAL THEORY 13
14 14
15 15
16 GRNN: SCHEMATIC PRESENTATION Input layer Pattern layer Summation layer Output layer 16
17 ADVANTAGES No user choice for the network architecture Only one parameter to be estimated Does not get trapped into the local optima Requires less number of data for training Useful for continuous data 17
18 RESULTS: SEASON 2012 INTENSITY ERRORS MAE(kt) LGEM HWFI GHMI DSHP AVNI OFCI IVCN EM MMSE MM_ANN 0 12(306) 24(292) 36(285) 48(266) 60(257) 72(243) 84(229) 96(215) 108(198) 120(177) Forecast leads hours (Cases) 18
19 INTENSITY ERRORS: SEASON MAE (kt) AVNI DSHP GHMI HWFI IVCN OFCI SHIP LGEM EM MMSE MMSE_ann 0 12(89) 24(80) 36(77) 48(71) 60(64) 72(56) 84(49) 96(40) 108(32) 120(24) Forecast lead in hours (cases) 19
20 SEASON MAE (kt) OCD5 AVNI DSHP GHMI LGEM HWFI OFCI IVCN MMSE MMSE_ann 0 12(105) 24(103) 36(99) 48(93) 60(87) 72(80) 84(72) 96(61) 108(53) 120(46) Forecast lead hours (cases) 20
21 80 Model Skills: 2015 Season 60 Skill relative to Decay-SHIFOR5 %) (105) 24(103) 36(99) 48(93) 60(87) 72(80) 84(72) 96(61) 108(53) 120(46) Forecast lead (#cases) 21 AVNI DSHP GHMI LGEM HWFI OFCI IVCN MMSE MMSE_ann
22 SEASON 2016: INTENSITY ERRORS MAE (kt) (231) 24 (221) 36(208) 48(193) 60 (168) 72(157) 84(148) 96(138) 108(123) 120(114) Forecast lead (#cases) 22 AEMI AVNI OFCI GHMI HWFI DSHP LGEM MMSE1 MMSE2
23 SKILLS: 2016 SEASON % of Skill (195) 24(191) 36(182) 48(171) 60(156) 72(147) 84(140) 96(132) 108(123) 120(113) AEMI AVNI OFCI GHMI HWFI DSHP LGEM MMSE1 MMSE Forecast leads (#cases) 23
24 Gaston MAE (kt) (44) 24(42) 36(40) 48(38) 60(36) 72(34) 84(32) 96(30) 108(28) 120(25) Forecast lead (#cases) AEMI AVNI OFCI GHMI HWFI DSHP LGEM IVCN MMSE1 MMSE2 24
25 Nicole MAE (kt) (52) 24(50) 36(48) 48(46) 60(44) 72(42) 8440) 96(38) 108(36) 120(34) Forecast lead (#cases) 25 AEMI AVNI OFCI GHMI HWFI DSHP LGEM MMSE1 MMSE2
26 Matthew MAE (kt) (44) 24(42) 36(40) 48(38) 60(36) 72(34) 84(32) 96(30) 108(28) 120(26) Forecast leads (#cases) AEMI AVNI OFCI GHMI HWFI DSHP LGEM IVCN MMSE1 MMSE2 26
27 CONCLUDING REMARKS Seasonal summaries indicate that the improved MMSE carries, consistently, least intensity forecast errors For longer forecast leads, beyond 60hrs, Neural Networked based MMSE performs better than the earlier forecast leads. It is very useful for government planning and evacuation, if needed Individual storm forecast errors show that none of the models is consistently best 27
28 CONCLUDING REMARKS CONTD Improved MMSE is the best or the second best performer for individual storms as well Proposed method is providing consistent consensus forecasts having least forecast errors which be depended upon Ensemble forecasts based on neural networks may be considered for real-time forecast guidance in case of hurricane and tropical storms Forecasting of tracks may also be examined 28
29 Thank you 29
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