最佳子集多元线性回归模型在热带气旋风圈变化预报中的应用

基于最佳路径（IBTrACS）数据集和欧洲中期天气预报中心（ECMWF）的再分析（ERA-Interim）数据，建立了西北太平洋上（Western North Pacific, WNP）热带气旋（Tropical Cyclone, TC）的七级风圈（R17）变化的最佳子集多元线性回归（bs-MLR）模型。首先根据2001～2014年6～11月TC初始半径（R_{17_0}）的第1～25、26～50、51～75、76～100个百分位点将TC分为4类，建立针对各类TC的bs-MLR模型，再利用2015年6～11月的全部TC对模型的预报效果进行检验。结果表明:对TC生命周期中任意时刻的未来12小时R17（R_{17_12}）进行预报时，当R_{17_0}小于92.6 km及R_{17_0} 在111.1～138.9 km范围内时，模型对于 R_{17_12}的趋势预报和大小预报均具有较好的效果；对TC生命周期中任意时刻未来24小时R17（R_{17_24}）进行预报时，当R_{17_0}在111.1～138.9 km范围内时，模式对R_{17_24}的趋势预报的效果较好。整体而言，bs-MLR模型对于R_{17_12}的预报准确性高于对R_{17_24}。

Application of Best-Subsets Multiple Linear Regression Models in Forecasting the Gale-Force Wind Radii of Tropical Cyclones

Based on the International Best Track Archive for Climate Stewardship dataset and European Centre for Medium-Range Weather Forecasts reanalysis data, the best-subsets multiple linear regression (bs-MLR) models were established by forecasting the gale-force wind radii (R17) of Tropical Cyclones (TCs) in the western North Pacific region. First, TCs from June to November 2001–2014 were divided into four categories according to the 1?25, 26?50, 51?75, and 76?100 percentiles of the initial sizes (R_{17_0}), and the bs-MLR models for TCs in each category were established. Then all TCs from June to November 2015 were used to test the estimated effectiveness of the bs-MLR models. The results showed that, when R_{17_0} was less than 92.6 km or R_{17_0} was between 111.1 km and 138.9 km, the models had better performances in forecasting the values and changing tendencies of R17 in the next 12 h (R_{17_12}) for any moment of TC life cycle. When R_{17_0} was between 111.1 km and 138.9 km, the models had better performances in forecasting the values and changing tendencies of R17 in the next 24 h (R_{17_24}) for any moment of TC life cycle. Overall, bs-MLR models had higher accuracy in forecasting R_{17_12} than R_{17_24}.