基于气象大数据的台风强度预测统计模型研究
基于气象大数据的台风强度预测统计模型研究
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广东省自然基金自由申请项目(2015A030313742);深圳市科技创新委员会重点项目(JCYJ20120617115926138)

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

    台风是一种破坏力极强的灾害性天气系统,做好台风路径和强度预报是防灾减灾的关键。除了气候性因子、台风持续性因子以及环境背景场因子,文章还考虑了在近海时,受陆地影响下,台风强度演变的情况,引入了新变量,即海陆比。将 2000—2014 年西北太平洋的所有台风样本分成海盆样本和近海样本,研究它们在 12、24、36 和 48 小时间隔的强度演变规律。本研究利用 1°×1°美国国家环境预报中心/美国国家大气研究中心提供的 FNL 全球再分析资料(Final Operational Global Analysis)数据,采用逐步回归和主成分分析法的多元统计回归模型预测台风强度,并比较了两种模型在台风强度预测上的表现。综合深海盆和近海台风强度的预测结果可以看出,文章提供的近海台风强度预报方法,比国内外的其他研究更具有防台减灾的实际应用价值。

    Abstract:

    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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引文格式
汤婷婷,李晴岚,李广鑫,彭玉龙.基于气象大数据的台风强度预测统计模型研究 [J].集成技术,2016,5(2):73-84

Citing format
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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  • 在线发布日期: 2016-04-01
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