文章摘要
谷文梁,高山红.黄海近岸海雾的初始场择优集合预报试验:个例研究[J].海洋气象学报,2022,42(3):23-37
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 field optimization on coastal sea fog over the Yellow Sea: a case study
  
DOI:10.19513/j.cnki.issn2096-3599.2022.03.003
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  Shandong Peninsula  coastal sea fog  ensemble forecast  initial field optimization
基金项目:国家自然科学基金项目(42075069);山东省重点研发计划项目(2019GSF111066)
作者单位
谷文梁 中国海洋大学海洋与大气学院,山东 青岛 266100 中国海洋大学物理海洋教育部重点实验室,山东 青岛 266100 
高山红 中国海洋大学海洋与大气学院,山东 青岛 266100 中国海洋大学物理海洋教育部重点实验室,山东 青岛 266100 
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中文摘要:
   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、2 m温度、2 m水汽混合比与2 m相对湿度(relative humidity, RH)4个变量的均方根误差(root mean square error, RMSE)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)蒙特卡罗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他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,并且公正预兆评分(equitable threat score,ETS)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:
   A case of coastal sea fog over the Yellow Sea in April 2014 is selected to carry out ensemble forecast experiment using the WRF (Weather Research and Forecasting) model. Based on RMSE (root mean square error) of SLP (sea-level pressure), 2-m temperature, 2-m water vapor mixing ratio, and 2-m relative humidity (RH) in the initial field of each ensemble member, as well as RMSE of ensemble mean, 4 different schemes of ensemble optimization are designed by eliminating/retaining the members whose RMSE values are higher/lower than the RMSE of ensemble mean. A series of numerical forecast experiments are conducted to compare the effects of ensemble forecast with different optimization schemes. The results are as follows. (1) In the ensemble generated by Monte Carlo method, there are many members with poor performance in forecasting sea fog, degrading the skill of ensemble forecast. Therefore, initial field optimization is necessary in sea fog ensemble forecast. (2) The RH-RMSE scheme, which takes RH as the optimization reference variable, performs better 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 ETS (equitable threat score) by 36%. The RH-RMSE scheme proposed in this study has the prospect of operational application.
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