采用POMgcs(Princeton Ocean Model with generalized coordinate system)和MITgcm(MIT General Circulation Model)两个海洋数值模式,研究了M-Y2.0、基于固壁近似假定的M-Y2.5、基于波浪破碎作用的M-Y2.5和KPP 4种垂向混合参数化方案对模拟黄海夏季上层温度结构的影响。结果表明,M-Y2.0和基于固壁近似假定的M-Y2.5方案低估了黄海上层的湍动能,模拟的黄海夏季温度上混合层的效果与实测相比均偏浅,不能够很好地重构黄海夏季温度的垂直结构。而基于波浪破碎作用的M-Y2.5和KPP方案均可以增加海洋上层湍动能的输入量,模拟的黄海夏季温度上混合层的效果与实测较为一致。故推测黄海夏季的上层结构是受波浪混合和流场剪切等物理机制共同调节的,若通过合理的垂向混合参数化方案将这些物理机制的作用加以体现,将会较真实地模拟和重构出黄海夏季海温上层结构。
Sea surface winds （SSWs） are vital to many meteorological and oceanographic applications, especially for regional study of short-range forecasting and Numerical Weather Prediction （NWP） assimilation. Spaceborne seatterometers can provide global ocean surface vector wind products at high spatial resolution. However, given the limited spatial coverage and revisit time for an individual sensor, it is valuable to study improvements of multiple microwave scatterometer observations, including the advanced scatterometer onboard parallel satellites MetOp-A （ASCAT-A） and MetOp-B （ASCAT-B） and microwave scatterometers aboard Oceansat-2 （OSCAT） and HY-2A （HY2-SCAT）. These four scatterometer-derived wind products over the China Seas （0°-40°N, 105°-135°E） were evaluated in terms of spatial coverage, revisit time, bias of wind speed and direction, after comparison with ERA-Interim forecast winds from the European Centre for Medium-Range Weather Forecasts （ECMWF） and spectral analysis of wind components along the satellite track. The results show that spatial coverage of wind data observed by combination of the four sensors over the China Seas is about 92.8% for a 12-h interval at 12：00 and 90.7% at 24：00, respectively. The analysis of revisit time shows that two periods, from 5：30-8：30 UTC and 17：00-21：00 UTC each day, had no observations in the study area. Wind data observed by the four sensors along satellite orbits in one month were compared with ERA-Interim data, indicating that bias of both wind speed and direction varies with wind speed, especially for speeds less than 7 m/s. The bias depends on characteristics of each satellite sensor and its retrieval algorithm for wind vector data. All these results will be important as guidance in choosing the most suitable wind product for applications and for constructing blended SSW products.
