Statistical Models for Tropical Cyclone Intensity Forecasting Based on Meteorological Big Data


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    Tropical cyclone (TC) is a destructive weather system. Accurate and timely forecast of the TC’s intensity and track is vital for disaster prevention and mitigation. This study proposed statistical regression methods to forecast the TC’s intensity change for 12, 24, 36 and 48 hours in the future over the Northwest Pacific Ocean. In addition to the conventional factors of climatology and persistence, this study took into account the land effect on the TC’s intensity change by introducing a new factor, i.e. the ratio of sea to land, into the statistical regression models. Three sets of TC samples, ocean basin samples, offshore samples, and total TC samples for the years 2000—2014 were applied to develop the intensity forecasting models. Final operational global analysis proposed by 1°×1° National Centers for Environmental Prediction/National Centre for Atmospheric Research were used as the predictors for the environmental effects. Two methods, stepwise regression and principal component analysis, were employed to develop the TC intensity forecasting models. Due to the consideration of the ratio of sea to land, the intensity forecasting performance for offshore TCs was significantly improved. Therefore, the proposed models are valuable for practical disaster prediction.

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TANG Tingting, LI Qinglan, LI Guangxin, PENG Yulong. Statistical Models for Tropical Cyclone Intensity Forecasting Based on Meteorological Big Data[J]. Journal of Integration Technology,2016,5(2):73-84

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  • Online: April 01,2016
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