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- Author or Editor: Y. Peter Sheng x
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Abstract
State-of-the-art coupled hydrodynamic and wave models can predict the inundation threat from an approaching hurricane with high resolution and accuracy. However, these models are not highly efficient and often cannot be run sufficiently fast to provide results 2 h prior to advisory issuance within a 6-h forecast cycle. Therefore, to produce a timely inundation forecast, coarser grid models, without wave setup contributions, are typically used, which sacrifices resolution and physics. This paper introduces an efficient forecast method by applying a multidimensional interpolation technique to a predefined optimal storm database to generate the surge response for any storm based on its landfall characteristics. This technique, which provides a “digital lookup table” to predict the inundation throughout the region, is applied to the southwest Florida coast for Hurricanes Charley (2004) and Wilma (2005) and compares well with deterministic results but is obtained in a fraction of the time. Because of the quick generation of the inundation response for a single storm, the response of thousands of possible storm parameter combinations can be determined within a forecast cycle. The thousands of parameter combinations are assigned a probability based on historic forecast errors to give a probabilistic estimate of the inundation forecast, which compare well with observations.
Abstract
State-of-the-art coupled hydrodynamic and wave models can predict the inundation threat from an approaching hurricane with high resolution and accuracy. However, these models are not highly efficient and often cannot be run sufficiently fast to provide results 2 h prior to advisory issuance within a 6-h forecast cycle. Therefore, to produce a timely inundation forecast, coarser grid models, without wave setup contributions, are typically used, which sacrifices resolution and physics. This paper introduces an efficient forecast method by applying a multidimensional interpolation technique to a predefined optimal storm database to generate the surge response for any storm based on its landfall characteristics. This technique, which provides a “digital lookup table” to predict the inundation throughout the region, is applied to the southwest Florida coast for Hurricanes Charley (2004) and Wilma (2005) and compares well with deterministic results but is obtained in a fraction of the time. Because of the quick generation of the inundation response for a single storm, the response of thousands of possible storm parameter combinations can be determined within a forecast cycle. The thousands of parameter combinations are assigned a probability based on historic forecast errors to give a probabilistic estimate of the inundation forecast, which compare well with observations.
Abstract
A prototype of an efficient and accurate rapid forecasting and mapping system (RFMS) of storm surge is presented. Given a storm advisory from the National Hurricane Center, the RFMS can generate a coastal inundation map on a high-resolution grid in 1 min (reference system Intel Core i7–3770K). The foundation of the RFMS is a storm surge database consisting of high-resolution simulations of 490 optimal storms generated by a robust storm surge modeling system, Curvilinear-Grid Hydrodynamics in 3D (CH3D-SSMS). The RFMS uses an efficient quick kriging interpolation scheme to interpolate the surge response from the storm surge database, which considers tens of thousands of combinations of five landfall parameters of storms: central pressure deficit, radius to maximum wind, forward speed, heading direction, and landfall location. The RFMS is applied to southwest Florida using data from Hurricane Charley in 2004 and Hurricane Irma in 2017, and to the Florida Panhandle using data from Hurricane Michael in 2018 and validated with observed high water mark data. The RFMS results agree well with observation and direct simulation of the high-resolution CH3D-SSMS. The RFMS can be used for real-time forecasting during a hurricane or “what-if” scenarios for mitigation planning and preparedness training, or to produce a probabilistic flood map. The RFMS can provide more accurate surge prediction with uncertainties if NHC can provide more accurate storm forecasts in the future. By incorporating storms for future climate and sea level rise, the RFMS could be used to generate future flood maps for coastal resilience and adaptation planning.
Abstract
A prototype of an efficient and accurate rapid forecasting and mapping system (RFMS) of storm surge is presented. Given a storm advisory from the National Hurricane Center, the RFMS can generate a coastal inundation map on a high-resolution grid in 1 min (reference system Intel Core i7–3770K). The foundation of the RFMS is a storm surge database consisting of high-resolution simulations of 490 optimal storms generated by a robust storm surge modeling system, Curvilinear-Grid Hydrodynamics in 3D (CH3D-SSMS). The RFMS uses an efficient quick kriging interpolation scheme to interpolate the surge response from the storm surge database, which considers tens of thousands of combinations of five landfall parameters of storms: central pressure deficit, radius to maximum wind, forward speed, heading direction, and landfall location. The RFMS is applied to southwest Florida using data from Hurricane Charley in 2004 and Hurricane Irma in 2017, and to the Florida Panhandle using data from Hurricane Michael in 2018 and validated with observed high water mark data. The RFMS results agree well with observation and direct simulation of the high-resolution CH3D-SSMS. The RFMS can be used for real-time forecasting during a hurricane or “what-if” scenarios for mitigation planning and preparedness training, or to produce a probabilistic flood map. The RFMS can provide more accurate surge prediction with uncertainties if NHC can provide more accurate storm forecasts in the future. By incorporating storms for future climate and sea level rise, the RFMS could be used to generate future flood maps for coastal resilience and adaptation planning.
Abstract
The three-dimensional time-dependent flow in the Western Basin of Lake Erie has been calculated numerically by means of both a two-mode free-surface model and a rigid-lid model. Detailed comparisons of the results from these two models are presented for two wind conditions: 1) a constant wind suddenly imposed, and 2) an actual wind which is variable in both space and time. For relatively short time intervals, significant differences between the results of the two models occur since the seiche motion is eliminated in the rigid-lid model. Long-term time-averaged circulation computed by the two models agree well in periods of strong wind, but differ appreciably in periods of light wind and active seiching.
Abstract
The three-dimensional time-dependent flow in the Western Basin of Lake Erie has been calculated numerically by means of both a two-mode free-surface model and a rigid-lid model. Detailed comparisons of the results from these two models are presented for two wind conditions: 1) a constant wind suddenly imposed, and 2) an actual wind which is variable in both space and time. For relatively short time intervals, significant differences between the results of the two models occur since the seiche motion is eliminated in the rigid-lid model. Long-term time-averaged circulation computed by the two models agree well in periods of strong wind, but differ appreciably in periods of light wind and active seiching.
Abstract
To create more useful storm surge and inundation forecast products, probabilistic elements are being incorporated. To achieve the highest levels of confidence in these products, it is essential that as many simulations as possible are performed during the limited amount of time available. This paper develops a framework by which probabilistic storm surge and inundation forecasts within the Curvilinear Hydrodynamics in 3D (CH3D) Storm Surge Modeling System and the Southeastern Universities Research Association Coastal Ocean Observing and Prediction Program’s forecasting systems are initiated with specific focus on the application of these methods in a limited-resource environment. Ensemble sets are created by dividing probability density functions (PDFs) of the National Hurricane Center model forecast error into bins, which are then grouped into priority levels (PLs) such that each subsequent level relies on results computed earlier and has an increasing confidence associated with it. The PDFs are then used to develop an ensemble of analytic wind and pressure fields for use by storm surge and inundation models. Using this approach applied with official National Hurricane Center (OFCL) forecast errors, an analysis of Hurricane Charley is performed. After first validating the simulation of storm surge, a series of ensemble simulations are performed representing the forecast errors for the 72-, 48-, 24-, and 12-h forecasts. Analysis of the aggregated products shows that PL4 (27 members) is sufficient to resolve 90% of the inundation within the domain and appears to represent the best balance between accuracy and timeliness of computed products for this case study. A 5-day forecast using the PL4 set is shown to complete in 83 min, while the intermediate PL2 and PL3 products, representing slightly less confidence, complete in 14 and 28 min, respectively.
Abstract
To create more useful storm surge and inundation forecast products, probabilistic elements are being incorporated. To achieve the highest levels of confidence in these products, it is essential that as many simulations as possible are performed during the limited amount of time available. This paper develops a framework by which probabilistic storm surge and inundation forecasts within the Curvilinear Hydrodynamics in 3D (CH3D) Storm Surge Modeling System and the Southeastern Universities Research Association Coastal Ocean Observing and Prediction Program’s forecasting systems are initiated with specific focus on the application of these methods in a limited-resource environment. Ensemble sets are created by dividing probability density functions (PDFs) of the National Hurricane Center model forecast error into bins, which are then grouped into priority levels (PLs) such that each subsequent level relies on results computed earlier and has an increasing confidence associated with it. The PDFs are then used to develop an ensemble of analytic wind and pressure fields for use by storm surge and inundation models. Using this approach applied with official National Hurricane Center (OFCL) forecast errors, an analysis of Hurricane Charley is performed. After first validating the simulation of storm surge, a series of ensemble simulations are performed representing the forecast errors for the 72-, 48-, 24-, and 12-h forecasts. Analysis of the aggregated products shows that PL4 (27 members) is sufficient to resolve 90% of the inundation within the domain and appears to represent the best balance between accuracy and timeliness of computed products for this case study. A 5-day forecast using the PL4 set is shown to complete in 83 min, while the intermediate PL2 and PL3 products, representing slightly less confidence, complete in 14 and 28 min, respectively.
