Open AccessA soft‐computing cyclone intensity prediction scheme for the Western North Pacific Ocean
Author(s) -
Sharma Neerja,
Ali M. M.,
Knaff John A.,
Chand Purna
Publication year - 2013
Publication title -
atmospheric science letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.951
H-Index - 45
ISSN - 1530-261X
DOI - 10.1002/asl2.438
Subject(s) - tropical cyclone , environmental science , climatology , intensity (physics) , cyclone (programming language) , artificial neural network , meteorology , linear regression , computer science , geology , machine learning , geography , physics , quantum mechanics , field programmable gate array , computer hardware
A soft‐computing cyclone intensity prediction scheme ( SCIPS ) is introduced using an artificial neural network ( ANN ) approach and adding ocean heat content, as an additional predictor to the normally used atmospheric parameters, to predict tropical cyclone intensity change in the western north Pacific Ocean. We used 1997–2004 data to develop and validate this scheme. The ANN ‐based estimations have been compared with observations and estimations using the multiple linear regression ( MLR ). SCIPS performance improves upon MLR as the lead hour increases from 12 to 120 h and also for high intensifying cyclones.