Classification and forecast of heavy rainfall in northern Kyushu during Baiu season using weather pattern recognition
Classification and forecast of heavy rainfall in northern Kyushu during Baiu season using weather pattern recognition
复制标题
DOI：
10.1002/asl.759
复制
发表时间：
2017-07
影响因子：
3
通讯作者：
Dzung Nguyen‐Le;T. Yamada;Duc Tran-Anh
中科院分区：
文献类型：
--
作者：
Dzung Nguyen‐Le;T. Yamada;Duc Tran-Anh
本研究利用自组织地图结合K均值聚类技术，对1979-2010年日本西南部九州北部白雨季节（6 - 7月）诱发100 mm以上日−1强降雨的天气模式进行了分类。结果表明，这些局地极端降水事件可归结为4种聚集型，主要与白球锋和温带/热带气旋/低气压活动有关，以暖湿空气的侵入伴随低空急流或气旋环流为代表。然后利用日本气象厅（JMA）全球谱模式（GSM）的预报天气场，将分类结果与模拟方法应用于2011-2016年6 - 7月局地强降雨日数的发生（是/否）。总的来说，与仅使用GSM预测降雨强度的传统方法相比，通过公平威胁评分评估的方法在7天提前期内的可预测性有了显著提高。虽然误报率仍然很高，但预计新方法将为天气预报员或从事防灾和水管理活动的最终用户的决策和准备提供有用的指导，特别是在超过2天的范围内。
In this study, the Self‐Organizing Maps in combination with K‐means clustering technique are used for classification of synoptic weather patterns inducing heavy rainfall exceeding 100 mm day−1 during the Baiu season (June–July) of 1979–2010 over northern Kyushu, southwestern Japan. It suggests that these local extreme rainfall events are attributed to four clustered patterns, which are primarily related to the Baiu front and the extratropical/tropical cyclone/depression activities and represented by the intrusion of warm and moist air accompanied by the low‐level jet or cyclonic circulation. The classification results are then implemented with the analogue method to predict the occurrence (yes/no) of local heavy rainfall days in June–July of 2011–2016 by using the prognostic synoptic fields from the operational Japan Meteorological Agency (JMA) Global Spectral Model (GSM). In general, the predictability of our approach evaluated by the Equitable Threat Score up to 7‐day lead times is significantly improved than that from the conventional method using only the predicted rainfall intensity from GSM. Although the false alarm ratio is still high, it is expected that the new method will provide a useful guidance, particularly for ranges longer than 2 days, for decision‐making and preparation by weather forecasters or end‐users engaging in disaster‐proofing and water management activities.