Assimilation of Satellite Observations into Coastal Biogeochemical Models

This thesis investigates the improvement of forecasting water temperature in a coastal embayment through the assimilation of satellite sea surface temperature (SST). Port Phillip Bay (PPB) in southeastern Australia was used as a case study, where temperature forecasts could be compared against in situ temperature measurements. Over the long term satellite derived SST observations were found to have negligible bias, however a strong diurnal bias was apparent. The model of PPB replicated the main features of PPB well, although the temperature prediction was warm biased.

The actual assimilation of SST data was contrasted against a climatology forecast of PPB temperature. The assimilation of SST, without any specific accounting for the diurnal bias improved the forecast, although errors due to observational bias were noted. Attempts to remove this bias using diurnal correction algorithms failed, owing to a larger than expected cool skin. Conditional merging, which combines spatial and in situ observations, was applied to the SST observations and improved forecast accuracy by reducing the observation bias. This work demonstrates that forecasting models can be improved through the assimilation of satellite derived observations. An examination of the assimilation innovations indicated where the forecast accuracy could be further improved.

本论文通过同化卫星海表温度（sea surface temperature, SST）数据，探究如何提升海岸海湾的水温预报精度。研究选取澳大利亚东南部的菲利普港湾（Port Phillip Bay, PPB）作为案例研究区域，其水温预报结果可与原位（in situ）实测数据进行对比。长期来看，卫星反演得到的SST观测数据偏差可忽略不计，但存在显著的日周期偏差。菲利普港湾的数值模型能够较好复现该海湾的主要水文特征，但水温预报结果整体偏暖。
本研究将实际开展的SST数据同化实验，与菲利普港湾水温的气候学预报进行了对比。未针对日周期偏差进行校正的SST数据同化，仍可提升预报精度，但观测偏差带来的误差依然存在。尝试使用日周期校正算法消除该偏差的工作均告失败，原因是实际存在的冷皮效应（cool skin）比预期更强。将空间观测与原位观测相结合的条件合并法被应用于SST观测数据，通过校正观测偏差提升了预报精度。本研究表明，通过同化卫星反演观测数据，可有效改进水温预报模型性能。对同化增量（assimilation innovations）的分析表明，仍存在可进一步提升预报精度的方向。