东海海洋预报的评价

Xiaochun Wang, Yingjun Zou, Xianqiang He
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引用次数: 1

摘要

利用2015年冬、2016年夏东海现场温度、盐度和卫星盐度观测资料,对全球海洋预报系统初始条件的精度及其预报能力进行了评价。该系统对长江口和东海的冬季预报能力优于夏季预报能力。在冬季,盐度系统初始场的均方根误差(RMSE)为1.90 psu,相关性为0.56。该模型的盐偏差为0.29 psu。盐度均方根误差随离海岸距离的增加而减小。相比之下,温度的RMSE为0.76°C,相关性高达0.95。模型温度和观测值之间没有偏差。在夏季,全球海洋预报系统的精度和预报能力都很差。盐度的RMSE为3.14 psu,相关系数为0.28。该模型的盐偏差为0.95 psu。温度的RMSE为7.22°C,模型的热偏差高达5.52°C。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of Ocean Forecasting in the East China Sea
The accuracy of the initial condition of a global ocean forecasting system and its prediction skill was evaluated against in situ temperature, salinity and satellite salinity observations during the winter of 2015 and the summer of 2016 for the East China Sea. The ocean forecasting system demonstrates better skill for the Yangtze River estuary and the East China Sea during winter time than during summer time. During winter time, the rootmean-square error (RMSE) of the initial fields of the system for salinity is 1.90 psu, and the correlation is 0.56. The model has a salty bias of 0.29 psu. The salinity RMSE reduces with increasing distance from the coast. In contrast, the RMSE for temperature is 0.76°C, and the correlation is as high as 0.95. There is no bias between model temperature and observation. During summer time, the accuracy and forecast skill of the global ocean forecasting system are very poor. The RMSE for salinity is 3.14 psu, and the correlation is 0.28. The model has a salty bias of 0.95 psu. The RMSE for temperature is 7.22°C, and the model has a warm bias as high as 5.52°C.
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