王会军. 2012. 基于前期观测降水和500 hPa高度场的西北太平洋热带风暴生成频数的新预测方案[J]. 气象学报, 70(2):165-173, doi:10.11676/qxxb2012.016
基于前期观测降水和500 hPa高度场的西北太平洋热带风暴生成频数的新预测方案
A new prediction model for tropical storm frequency over the western North Pacific using observed winter-spring precipitation and geopotential height at 500 hPa
投稿时间:2010-03-19  修订日期:2011-03-22
DOI:10.11676/qxxb2012.016
中文关键词:  热带风暴,频数,西北太平洋,季节预测
英文关键词:Tropical storm, Frequency, Western North Pacific, Seasonal prediction
基金项目:国家973项目(2009CB421406)及公益行行业(气象)科研专项(GYHY200906018)
作者单位
王会军 中国科学院大气物理研究所北京100029 
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中文摘要:
      利用前期1—2月和4—5月平均的东半球格点降水与500 hPa高度场资料,通过多元线性逐步回归,建立了预测西北太平洋年热带风暴生成频数的预测方案。由于分别使用了欧洲中期数值预报中心和美国国家环境预测中心的大气再分析资料,建立了两个预测模型,对1979—2002年的预测交叉检验的距平相关系数分别为0.78和0.74。预测的多年平均绝对误差是3.0和3.2,即多年平均西北太平洋年热带风暴生成频数的10%左右。进一步指出:实际预测中可以把两个模型的预测结果平均作为最后预测结果,这样的话,多年交叉检验的距平相关系数是0.88,多年平均的预测绝对误差是1.92个。这样就可能得到更加准确的预测。本文结果还只是该方案的交叉检验结果,尚需在实际预测中进一步检验其能力。
英文摘要:
      A new seasonal prediction model for annual tropical storm numbers (ATSNs) over the western North Pacific was developed using the preceding January-February (JF) and April-May (AM) grid point data at a resolution of 2.5°× 2.5°. The JF and AM mean precipitation and the AM mean 500 hPa geopotential height in the Northern Hemisphere, together with the JF mean 500 hPa geopotential height in the Southern Hemisphere, were employed to compose the ATSN forecast model via the stepwise multiple linear regression technique. All JF and AM mean data were confined to the Eastern Hemisphere. We established two empirical prediction models for ATSN using the ERA-40 reanalysis and NCEP reanalysis datasets, respectively, together with the observed precipitation. The performance of the models was verified by cross-validation. Anomaly correlation coefficients (ACC) at 0.78 and 0.74 were obtained via comparison of the retrospective predictions of the two models and the observed ATSNs from 1979 to 2002. The multi year mean absolute prediction errors were 3.0 and 3.2 for the two models respectively, or roughly 10% of the average ATSN. In practice, the final prediction was made by averaging the ATSN predictions of the two models. This resulted in a higher score, with ACC being further increased to 0.88, and the mean absolute error reduced to 1.92, or 6.13% of the average ATSN.
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