Ensemble prediction of rainfall during the 2000–2002 Mei‐Yu seasons: Evaluation over the Taiwan area
Ensemble prediction of rainfall during the 2000–2002 Mei‐Yu seasons: Evaluation over the Taiwan area
复制标题
2000-2002年梅雨季降雨量集合预报：台湾地区评估
DOI：
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发表时间：
2004
期刊：
影响因子：
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通讯作者：
Hui‐Chuan Lin
中科院分区：
文献类型：
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作者：
Ming;B. Jou;Shih;Jing;P. Lin;Jen;Hui‐Chuan Lin
[1]本文首次对2000-2002年台湾地区梅雨期（5 - 6月）降水进行实时集合预报。六名成员被包括在内，每个使用第五代宾夕法尼亚州立大学-国家大气研究中心中尺度模式（MM 5）嵌套到15公里的网格大小，不同的组合积云和微物理参数化。降雨预报的公平威胁评分（ETS）和偏见评分（BS）进行了评估。经三个梅雨季节15公里网格点检验，发现没有一个成员持续具有12-24小时和24-36小时累积的最小均方根误差。对于降雨发生，大多数成员对东北山区、西北沿海平原、中部山坡、西南沿海平原和西南山区有较好的预测。这些区域也对应着三个梅雨季节中积累较多的积雨云。合奏预测，使用多元线性回归（MLR）的方法，进行了最小二乘拟合之间的预测和观察到的季后赛分析，有最好的ETS和BS技能。MLR集合预报优于平均预报（所有六个成员），积云（四个成员）和微物理（三个成员）集合的平均预报，以及高分辨率（5公里）预报;然而，高分辨率预报仍然有更好的技能，为暴雨事件。MLR集合预报，使用以前的梅雨季节确定的权重，仍然有类似的ETS趋势与今年的梅雨季节确定的权重，虽然技术较差。
[1] This paper reports the first effort on real-time ensemble predictions of precipitation during the 2000–2002 Mei-Yu seasons (May to June) over the Taiwan area. Six members were included, each using the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) nesting down to 15-km grid size, with different combinations of cumulus and microphysics parameterizations. Rainfall forecasts were evaluated with the equitable threat score (ETS) and bias score (BS). On the basis of verifications on 15-km grid points over three Mei-Yu seasons, it was found that no one member persistently had the least root mean square error of 12–24 hours and 24–36 hours accumulated rainfalls. For rainfall occurrence, most members had better predictions over the northeastern mountainous area, the northwestern coastal plain, the central mountain slope, the southwestern coastal plan, and the southwestern mountainous area. These regions also corresponded to areas of more accumulated rainfalls during three Mei-Yu seasons. An ensemble prediction, using a multiple linear regression (MLR) method which performed a least-square fit between the predicted and observed rainfalls in postseason analysis, had the best ETS and BS skill. The MLR ensemble forecast outperformed the average forecast (for all six members), the average forecasts of cumulus (four-member) and microphysics (three-member) ensembles, and also a high-resolution (5-km) forecast; however, a high-resolution forecast still had better skill for heavy rainfall events. The MLR ensemble forecast, using the weightings determined from previous Mei-Yu seasons, still had similar ETS trend to that with weightings determined by current-year Mei-Yu season, albeit with less skill.