文章摘要
黄海近岸海雾的初始场择优集合预报试验——个例研究
Ensemble Forecast Experiment with Initial Condition Optimization on Coastal Sea Fog over the Yellow Sea: A Case Study
投稿时间:2022-04-18  修订日期:2022-04-26
DOI:
中文关键词: 黄海  山东半岛  近岸海雾  集合预报  初始场择优
英文关键词: the Yellow Sea  the Shandong Peninsula  coastal sea fog  ensemble forecast  initial condition optimization
基金项目:国家自然科学基金项目(42075069,黄海平流海雾演变中平流效应的深究);山东省重点研发计划项目(2019GSF111066,黄渤海海雾短临近数值集合预报关键技术研究)
作者单位邮编
谷文梁 中国海洋大学海洋与大气学院 266100
高山红 中国海洋大学海洋与大气学院 266100
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中文摘要:
   摘要:选取2014年4月发生的一次黄海近岸海雾个例,利用WRF(Weather Research and Forecasting)模式开展了集合预报试验研究。依据每个集合成员初始场中海平面气压(SLP, sea level pressure)、2-m温度(T)、2-m水汽混合比(Qv, water vapor mixing ratio)与2-m相对湿度(RH, relative humidity)4个变量的RMSE(root mean square error)与RMSE集合平均值的相对大小,以剔除高于者而保留低于者的原则,设计了4种不同的初始场集合体择优方案。实施了一系列数值预报试验,比较了不同择优方案的集合预报效果。研究结果表明:(1)Monte Carlo方法所生成的集合体中存在不少海雾预报效果较差的成员,这会降低集合预报效果,因此初始场择优十分必要。(2)以RH作为择优变量的择优方案(记为RH-RMSE方案),集合预报效果明显优于其它3种方案。(3)对比不择优集合预报,采用RH-RMSE方案的择优集合预报效果不仅节省了50%左右的计算时间,并且ETS(Equitable Threat Score)评分改进率高达36%左右。本研究提出的RH-RMSE方案,具有业务化应用前景。
英文摘要:
   Abstract In this paper, a case of coastal fog over the Yellow Sea in April 2014 is selected to carry out ensemble forecast experiment using the Weather Research and Forecasting (WRF) model. According to SLP (sea level pressure), 2-m temperature (T), 2-m water vapor mixing ratio (Qv) and 2-m relative humidity (RH) in the initial field of each ensemble member, and the relative size of RMSE (root mean Square Error) and RMSE ensemble average of four variables, four different optimal schemes for initial condition are designed. In order to eliminate the ones higher than RMSE and retain the ones lower than RMSE. The results are as follows. 1) In the ensemble generated by Monte Carlo method, there are many members with poor sea fog forecast performance (poor members), resulting in a decrease in ensemble forecast effect, and it is necessary for the initial condition optimization; 2) Relative humidity (RH) was taken as the optimal factor, and the members whose root mean square error (RMSE) of RH was higher than the aggregate level were removed as the RH-RMSE scheme. Similar to the other three schemes, sea level pressure, water vapor mixing ratio and temperature were taken as the optimal factors. RH-RMSE scheme has better ensemble forecast performance than the other three schemes; 3) Compared with the non-optimized ensemble forecast, the optimized ensemble forecast using RH-RMSE scheme not only saves about 50% of the calculation time, but also improves the score of Equitable Threat Score (ETS) by 36%. The RH scheme proposed in this study has the prospect of commercial application.
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