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Development and performance of a high-resolution surface wave and storm surge forecast model: application to a large lake
by
Mulligan, Ryan P
, Shan, Shiliang
, Boegman, Leon
, Swatridge, Laura L
in
Accuracy
/ Buoys
/ Climate change
/ Coastal storms
/ Coastal zone
/ Coasts
/ Fluid mechanics
/ Forecasting models
/ High resolution
/ Hurricanes
/ Hydrodynamics
/ Lakes
/ Meteorological data
/ Open data
/ Performance evaluation
/ Predictions
/ Real time
/ Root-mean-square errors
/ Significant waves
/ Simulation
/ Source code
/ Storm forecasting
/ Storm surge forecasting
/ Storm surge prediction
/ Storm surges
/ Storms
/ Structured grids (mathematics)
/ Surface water waves
/ Surface waves
/ Tidal waves
/ Water
/ Water levels
/ Wave buoys
/ Wave height
/ Weather forecasting
/ Wind
2024
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Development and performance of a high-resolution surface wave and storm surge forecast model: application to a large lake
by
Mulligan, Ryan P
, Shan, Shiliang
, Boegman, Leon
, Swatridge, Laura L
in
Accuracy
/ Buoys
/ Climate change
/ Coastal storms
/ Coastal zone
/ Coasts
/ Fluid mechanics
/ Forecasting models
/ High resolution
/ Hurricanes
/ Hydrodynamics
/ Lakes
/ Meteorological data
/ Open data
/ Performance evaluation
/ Predictions
/ Real time
/ Root-mean-square errors
/ Significant waves
/ Simulation
/ Source code
/ Storm forecasting
/ Storm surge forecasting
/ Storm surge prediction
/ Storm surges
/ Storms
/ Structured grids (mathematics)
/ Surface water waves
/ Surface waves
/ Tidal waves
/ Water
/ Water levels
/ Wave buoys
/ Wave height
/ Weather forecasting
/ Wind
2024
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Development and performance of a high-resolution surface wave and storm surge forecast model: application to a large lake
by
Mulligan, Ryan P
, Shan, Shiliang
, Boegman, Leon
, Swatridge, Laura L
in
Accuracy
/ Buoys
/ Climate change
/ Coastal storms
/ Coastal zone
/ Coasts
/ Fluid mechanics
/ Forecasting models
/ High resolution
/ Hurricanes
/ Hydrodynamics
/ Lakes
/ Meteorological data
/ Open data
/ Performance evaluation
/ Predictions
/ Real time
/ Root-mean-square errors
/ Significant waves
/ Simulation
/ Source code
/ Storm forecasting
/ Storm surge forecasting
/ Storm surge prediction
/ Storm surges
/ Storms
/ Structured grids (mathematics)
/ Surface water waves
/ Surface waves
/ Tidal waves
/ Water
/ Water levels
/ Wave buoys
/ Wave height
/ Weather forecasting
/ Wind
2024
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Development and performance of a high-resolution surface wave and storm surge forecast model: application to a large lake
Journal Article
Development and performance of a high-resolution surface wave and storm surge forecast model: application to a large lake
2024
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Overview
A real-time forecast model of surface hydrodynamics in Lake Ontario (Coastlines-LO) was developed to automatically predict storm surges and surface waves. The system uses a dynamically coupled Delft3D–SWAN model with a structured grid to generate 48 h predictions for the lake that are updated every 6 h. The lake surface is forced with meteorological data from the High Resolution Deterministic Prediction System (HRDPS). The forecast model has been running since May 2021, capturing a wide variety of storm conditions. Good agreement between observations and modelled results is achieved, with root mean squared errors (RMSEs) for water levels and waves under 0.02 and 0.26 m, respectively. During storm events, the magnitude and timing of storm surges are accurately predicted at nine monitoring stations (RMSE <0.05 m), with model accuracy either improving or remaining consistent with decreasing forecast length. Forecast significant wave heights agree with observed data (1 %–12 % relative error for peak wave heights) at four wave buoys in the lake. Coastlines-LO forecasts for storm surge prediction for two consecutive storm events were compared to those from the Great Lakes Coastal Forecasting System (GLCFS) to further evaluate model performance. Both systems achieved comparable results with average RMSEs of 0.02 m. Coastlines-LO is an open-source wrapper code driven by open data and has relatively low computational requirements compared to GLCFS, making this approach suitable for forecasting marine conditions in other coastal regions.
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