An automatic lake-model application using near-real-time data forcing: development of an operational forecast workflow (COASTLINES) for Lake Erie

Lin, Shuqi; Boegman, Leon; Shan, Shiliang; Mulligan, Ryan

doi:https://doi.org/10.5194/gmd-15-1331-2022

Articles | Volume 15, issue 3

https://doi.org/10.5194/gmd-15-1331-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Modelling inland waters in a changing climate (GMD/ESD/TC...

https://doi.org/10.5194/gmd-15-1331-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 3

Model description paper

|

16 Feb 2022

Model description paper |

| 16 Feb 2022

An automatic lake-model application using near-real-time data forcing: development of an operational forecast workflow (COASTLINES) for Lake Erie

Shuqi Lin, Leon Boegman, Shiliang Shan, and Ryan Mulligan

Data sets

An automatic lake-model application using near real-time data forcing: Development of an operational forecast model for Lake Erie Shuqi Lin, Leon Boegman, Shiliang Shan, and Ryan Mulligan https://doi.org/10.5683/SP2/VTN7WC

Short summary

An operational hydrodynamics forecast system, COASTLINES, using the Windows Task Scheduler, Python, and MATLAB scripts, to automate application of a 3-D model (AEM3D) in Lake Erie was developed. The system predicted storm-surge and up-/downwelling events that are important for flood water and drinking water/fishery management. This example of the successful development of an operational forecast system can be adapted to simulate aquatic systems as required for management and public safety.