Forecast And A Three-dimensional Numerical Study On Sea Ice In The Bohai Sea

The water temperature in the Bohai Sea is low in winter and the sea ice occursevery year. In heavy ice years, the sea ice disaster would be induced and be harmful tothe safety of human life and property in the sea. Therefore, it is of great importancefor preventing and reducing sea disaster to develop prediction models of sea icethrough obtaining the sea ice information by satellite remote sensing, investigating theprocesses of sea ice formation and disappearance using meteorological data,simulating the sea ice evolution applying the mumerical model.To obtain basic data with high accuracy for sea ice forecasting, the methodologyof CART decision tree was utilized to retrieve sea ice from MODIS satallite remotingsensing data. The multi-bands, including visible light, near infrared light and thermalinfrared imaging were automatically used to classify sea ice and other category, andthe misjudgment of sea ice was effectively eliminated comparing with the traditionalthreshold methods when retrieving sea ice from MODIS data with special seaenvironment such as high sediment suspension. The retrieved results are verified bythe Small Satellite Constellation for Environment and Disaster Monitoring andForecasting(HJ-1A/1B)with high spatial resolution.Based on the sea ice acreage of6winters in the Liaodong Bay retrieved by remotesensing, combined with the analysis of measured data by meteorological stations inJinzhou and Yingkou, it is concluded that the accruement of sea ice area in theLiaodong Bay is mainly affected by the low air temperature, and especially theaccumulated times of strong wind offshore in days with lower air temperature directlyinfluences the annual maximum area of sea ice. It is found that the accumulatedfreezing air temperature lower than-5°C has significant linear correlation with seaice acreage in the frozen phase.The formulae for long term and short term forecasting of sea ice acreage in theLiaodong Bay were developed based on the least-square-method. The long termforecasting formula can run just on the given weather conditions. The short termforecasting formula should run on the given weather conditions and the last measuredvalue of sea ice acreage. The fitting and forecasting results show that both formulaecan rather accurately forecast the sea ice acreage. The BP neural networks model withstronger non-linear approximation capabilities was further used to develop the sea iceforecast model, and the accuracy is improved.In the end, a three-dimensional unstructered grid FVCOM model based on thefinite-volume solver was used for numerical simulation of sea ice in2003~2004winter in the Bohai Sea. The parameters of the model were adjusted to be suitable for the Bohai Sea, and the dynamical processes and thermodynamic processes of sea icewere fully incorporated through coupling of ocean and ice model. The simulatedresults show good agreement with measured data such as hydrodynamics, sea watertemperature, sea water salinity, sea ice area and thickness. It is indicated that thedeveloped3D sea ice model in the present dissertation is reasonable for parametersand suitable for long-term numerical simulation of sea ice with high accuracy. Theunstructured triangular meshes used in FVCOM can accurately describe the complexshoreline and be locally refined for the interesting region, therefore, combined withthe weather foarcast, the model can be applied in the refined simulation and forecastof sea ice in the future.