A soft-computing cyclone intensity prediction scheme for the Western North Pacific Ocean

被引：18

作者：

Sharma, Neerja ^{[1
]}

Ali, M. M. ^{[1
]}

Knaff, John A. ^{[2
]}

Chand, Purna ^{[1
]}

机构：

[1] Natl Remote Sensing Ctr, Indian Space Res Orginisat, Dept Space, Hyderabad, Andhra Pradesh, India

[2] NOAA Ctr Satellite Applicat & Res, Ft Collins, CO USA

来源：

ATMOSPHERIC SCIENCE LETTERS | 2013年 / 14卷 / 03期

关键词：

cyclone intensity prediction change; artificial neural network approach; ocean heat content; SENSOR MICROWAVE IMAGER; MIXED-LAYER DEPTH; NEURAL-NETWORK; HURRICANE INTENSITY; SURFACE PARAMETERS; TYPHOON INTENSITY; HEAT-CONTENT; MODEL; FORECASTS; ATLANTIC;

D O I：

10.1002/asl2.438

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

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.

引用

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页码：187 / 192

页数：6

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