A New Prediction Model for Tropical Storm Frequency over the Western North Pacific Using Observed Winter-Spring Precipitation and Geopotential Height at 500 hPa
  • 【DOI】

    10.1007/s13351-011-0302-6

  • 【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... 展开>>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 ERA40 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. 收起<<

  • 【作者】

  • 【作者单位】

    Institute of Atmospheric Physics

  • 【刊期】

    气象学报(英文版) SCI 2011年3期

  • 【关键词】

    tropical storm  frequency  western North Pacific  seasonal prediction 

  • 【基金项目】

    the National Basic Research Program of China National Natural Science Foundation of China