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Atmosphere 2018, 9(6), 224; https://doi.org/10.3390/atmos9060224

Application of Intelligent Dynamic Bayesian Network with Wavelet Analysis for Probabilistic Prediction of Storm Track Intensity Index

College of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, China
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Received: 3 May 2018 / Revised: 3 June 2018 / Accepted: 8 June 2018 / Published: 11 June 2018
(This article belongs to the Section Climatology and Meteorology)
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Abstract

The effective prediction of storm track (ST) is greatly beneficial for analyzing the development and anomalies of mid-latitude weather systems. For the non-stationarity, nonlinearity, and uncertainty of ST intensity index (STII), a new probabilistic prediction model was proposed based on dynamic Bayesian network (DBN) and wavelet analysis (WA). We introduced probability theory and graph theory for the first time to quantitatively describe the nonlinear relationship and uncertain interaction of the ST system. Then a casual prediction network (i.e., DBN) was constructed through wavelet decomposition, structural learning, parameter learning, and probabilistic inference, which was used for expression of relation among predictors and probabilistic prediction of STII. The intensity prediction of the North Pacific ST with data from 1961–2010 showed that the new model was able to give more comprehensive prediction information and higher prediction accuracy and had strong generalization ability and good stability. View Full-Text
Keywords: storm track; probabilistic prediction; dynamic Bayesian network; wavelet analysis storm track; probabilistic prediction; dynamic Bayesian network; wavelet analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, M.; Liu, K. Application of Intelligent Dynamic Bayesian Network with Wavelet Analysis for Probabilistic Prediction of Storm Track Intensity Index. Atmosphere 2018, 9, 224.

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