Research
A Novel Method of Assessing Storm Surge Multi-Scenarios based on Ensemble Tropical Cyclone Forecasting [Link]
A Novel Method of Assessing Storm Surge Multi-Scenarios based on Ensemble Tropical Cyclone Forecasting [Link]
Ensemble forecasting is a promising tool to aid in making informed decisions against risks of coastal storm surges. Although tropical cyclone (TC) ensemble forecasts are commonly used in operational numerical weather prediction systems, their potential for disaster prediction has not been maximized. Here we present a novel, efficient, and practical method to utilize a large ensemble forecast of 1000 members to analyze storm surge scenarios toward effective decision making such as evacuation planning and issuing surge warnings. We perform the simulation of TC Hagibis (2019) using the Japan Meteorological Agency’s (JMA) non-hydrostatic model. The simulated atmospheric predictions were utilized as inputs for a statistical surge model named the Storm Surge Hazard Potential Index (SSHPI) to estimate peak surge heights along the central coast of Japan. We show that Pareto optimized solutions from an ensemble storm surge forecast can describe potential worst (maximum) and optimum (minimum) storm surge scenarios while exemplifying a diversity of trade-off surge outcomes among different coastal places. For example, some of the Pareto optimized solutions that illustrate worst surge scenarios for inner bay locations are not necessarily accountable for bringing severe surge cases in open coasts. We further emphasize that an in-depth evaluation of Pareto optimal solutions can shed light on how meteorological variables such as track, intensity, and size of TCs influence the worst and optimum surge scenarios, which is not clearly quantified in current multi-scenario assessment methods such as those used by JMA/National Hurricane Center in the United States.
Ensemble forecasting is a promising tool to aid in making informed decisions against risks of coastal storm surges. Although tropical cyclone (TC) ensemble forecasts are commonly used in operational numerical weather prediction systems, their potential for disaster prediction has not been maximized. Here we present a novel, efficient, and practical method to utilize a large ensemble forecast of 1000 members to analyze storm surge scenarios toward effective decision making such as evacuation planning and issuing surge warnings. We perform the simulation of TC Hagibis (2019) using the Japan Meteorological Agency’s (JMA) non-hydrostatic model. The simulated atmospheric predictions were utilized as inputs for a statistical surge model named the Storm Surge Hazard Potential Index (SSHPI) to estimate peak surge heights along the central coast of Japan. We show that Pareto optimized solutions from an ensemble storm surge forecast can describe potential worst (maximum) and optimum (minimum) storm surge scenarios while exemplifying a diversity of trade-off surge outcomes among different coastal places. For example, some of the Pareto optimized solutions that illustrate worst surge scenarios for inner bay locations are not necessarily accountable for bringing severe surge cases in open coasts. We further emphasize that an in-depth evaluation of Pareto optimal solutions can shed light on how meteorological variables such as track, intensity, and size of TCs influence the worst and optimum surge scenarios, which is not clearly quantified in current multi-scenario assessment methods such as those used by JMA/National Hurricane Center in the United States.
Variability in storminess, storm surge, and mean sea level (MSL) can substantially alter coastal hazards associated with extreme sea levels (ESL). However, the detection and attribution of past changes in tropical cyclone (TC) and associated storm surges are hampered by inhomogeneous TC records. Here we investigate spatiotemporal changes in storm surge levels in Japan from 1980–2019, a period when observational platforms including tide gauges and storm records are highly consistent. We find statistical evidence of the increase of surge annual maxima in several places including the bay area of Tokyo since 1980. This rate of change is comparable to that observed for MSL rise over the same period. These findings doubt the current hypothesis on the flood adaptation plan in which future surge extremes will remain the same and MSL changes are only considered. We demonstrate that the changes in ESL in the last 40 years cannot be explained by the rise of MSL alone, rather, the northeastward shifting of TC landfall location as well as intensifying and widening of TC might have altered the likelihood of ESL including surge extremes. The substantial influence of these TC meteorological variables on surge levels coupled with the rise of MSL suggests that current coastal planning practices including critical heights for flood defenses might be inadequate in the future.
Variability in storminess, storm surge, and mean sea level (MSL) can substantially alter coastal hazards associated with extreme sea levels (ESL). However, the detection and attribution of past changes in tropical cyclone (TC) and associated storm surges are hampered by inhomogeneous TC records. Here we investigate spatiotemporal changes in storm surge levels in Japan from 1980–2019, a period when observational platforms including tide gauges and storm records are highly consistent. We find statistical evidence of the increase of surge annual maxima in several places including the bay area of Tokyo since 1980. This rate of change is comparable to that observed for MSL rise over the same period. These findings doubt the current hypothesis on the flood adaptation plan in which future surge extremes will remain the same and MSL changes are only considered. We demonstrate that the changes in ESL in the last 40 years cannot be explained by the rise of MSL alone, rather, the northeastward shifting of TC landfall location as well as intensifying and widening of TC might have altered the likelihood of ESL including surge extremes. The substantial influence of these TC meteorological variables on surge levels coupled with the rise of MSL suggests that current coastal planning practices including critical heights for flood defenses might be inadequate in the future.
An Improved Statistical Storm Surge Model: Integration of Coastal Geometry, Bathymetry, and Storm Information [Link]
An Improved Statistical Storm Surge Model: Integration of Coastal Geometry, Bathymetry, and Storm Information [Link]
This study presents a new storm surge hazard potential index (SSHPI) for estimating tropical cyclone (TC) induced peak surge levels at a coast. The SSHPI incorporates parameters that are often readily available at real-time: intensity in 10-min maximum wind speed (Vmax), radius of 50-kt wind (R50), translation speed (S), coastal geometry (a), and bathymetry information (L30). The inclusion of translation speed and coastal geometry information lead to improvements of the SSHPI to other existing surge indices. A retrospective analysis of SSHPI using data from 1978–2019 in Japan suggests that this index captures historical events reasonably well. In particular, it explains ~ 66% of the observed variance and ~ 74% for those induced by TCs whose landfall intensity was larger than 79-kt. The performance of SSHPI is not sensitive to the type of coastal geometry (open coasts or semi-enclosed bays) and can be utilized in any TC basin. Such a prediction methodology can decrease numerical computation requirements, improve public awareness of surge hazards, and may also be useful for communicating surge risk.
This study presents a new storm surge hazard potential index (SSHPI) for estimating tropical cyclone (TC) induced peak surge levels at a coast. The SSHPI incorporates parameters that are often readily available at real-time: intensity in 10-min maximum wind speed (Vmax), radius of 50-kt wind (R50), translation speed (S), coastal geometry (a), and bathymetry information (L30). The inclusion of translation speed and coastal geometry information lead to improvements of the SSHPI to other existing surge indices. A retrospective analysis of SSHPI using data from 1978–2019 in Japan suggests that this index captures historical events reasonably well. In particular, it explains ~ 66% of the observed variance and ~ 74% for those induced by TCs whose landfall intensity was larger than 79-kt. The performance of SSHPI is not sensitive to the type of coastal geometry (open coasts or semi-enclosed bays) and can be utilized in any TC basin. Such a prediction methodology can decrease numerical computation requirements, improve public awareness of surge hazards, and may also be useful for communicating surge risk.