Open AccessTrack and intensity forecast of tropical cyclones over the North Indian Ocean with multilayer feed forward neural nets
Author(s) -
Chaudhuri Sutapa,
Dutta Debashree,
Goswami Sayantika,
Middey Anirban
Publication year - 2015
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.1488
Subject(s) - tropical cyclone , environmental science , climatology , meteorology , tropical cyclone forecast model , intensity (physics) , track (disk drive) , cyclone (programming language) , bengal , computer science , geology , bay , oceanography , geography , physics , quantum mechanics , operating system , field programmable gate array , computer hardware
Precise forecasting of the track and intensity of tropical cyclones remains one of the top priorities for the meteorological community. In the present study multilayer feed forward neural nets with different architectures are developed to identify the best neural net for forecasting the track and intensity of tropical cyclones over the North Indian Ocean ( NIO ) with 6, 12 and 24 h lead time. Forecast errors are estimated with each neural net. The result reveals that the neural net architecture 1 ( NNA 1) with 10 input layers, 2 hidden layers, 5 hidden nodes and 2 output layers provides the best forecast for both the track and the intensity of the tropical cyclones over NIO . Two cyclones of the same category in Saffir Simpson Hurricane Scale, namely Nargis and Phet, that occurred over the Bay of Bengal and the Arabian Sea of the NIO basin are considered in the present study for validation. The result reveals that the prediction errors (%) with NNA 1 model in estimating the intensity of the cyclones Nargis and Phet during the validation are 3.37, 8.29 and 9.74 as well as 6.38, 11.26 and 18.72 with 6, 12 and 24 h lead time, respectively. The mean track errors for 6, 12 and 24 h forecasts are observed to be 45, 69 and 89 km for cyclone Nargis and 54, 87 and 98 km for cyclone Phet. NNA 1 model is observed to perform better than NNA 2 and NNA 3 models and the existing numerical models.