Dataset: 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.

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