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.本论文围绕通过同化卫星海表温度（Sea Surface Temperature, SST）数据提升近海海湾水温预报精度这一主题展开研究。本研究以澳大利亚东南部的菲利普港湾（Port Phillip Bay, PPB）为案例，能够将该区域的水温预报结果与原位实测水温数据开展对比验证。长期观测结果表明，卫星反演的SST数据整体偏差极小，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数据同化试验与菲利普港湾水温气候学预报方案进行了对比。尽管未针对日周期偏差进行专门校正，SST数据同化仍有效提升了预报精度，但同时也出现了由观测偏差引发的预报误差。由于实际存在的海表冷皮（cool skin）强度超出预期，采用日周期校正算法消除该偏差的尝试未能成功。本研究将融合空间观测与原位观测的条件合并（conditional merging）算法应用于SST观测数据，通过修正观测偏差进一步提升了预报精度。本研究证实，通过同化卫星反演的观测数据，可有效改善水温预报模型的性能。对同化增量（assimilation innovations）的分析结果也指明了后续可进一步提升预报精度的方向。