Recent technological advancements have made real-time observation and forecasting of physical and biological processes in coastal marine environments possible.

Our focus is on research and development of multi-component models using the data streams from a coastal observing system both for validation of models describing conditions in the past, and for direct assimilation into forecast models.

Our aim is to develop the capability for real-time forecasting of physical, chemical and biological conditions in the coastal marine environment.
Through the collection of high resolution data with a network of automated environmental sensors, long-term records with continuity that cannot be obtained by conventional sampling are generated.

These continuous observations provide the means to initialize forecast models, to rapidly check model results for accuracy and can also be used to guide targeted sampling of environmental events, such as algal blooms.

• Seahorse
• 6.6b-Biological Forecast Model
• 6.6c Simulations of plankton dynamics on the Scotian shelf
• 6.6c Box Model Simulations of plankton dynamics on the Scotian shelf