Identifying limitations when deriving probabilistic views of North Atlantic hurricane hazard from counterfactual ensemble NWP re-forecasts

Downward counterfactual analysis – or quantitatively estimating how our observed history could have been worse – is increasingly being used by the re/insurance industry to identify, quantify, and mitigate against as-yet-unrealised “grey-swan” catastrophic events. While useful for informing site-specific adaptation strategies, the extraction of probabilistic information remains intangible from such downside-only focused analytics. We hypothesise that combined upward and downward counterfactual analysis (i.e., how history could have been either better or worse) may allow us to obtain probabilistic information from counterfactual research if it can be applied objectively and without bias. Here we test this concept of objective counterfactual analysis by investigating how initial-condition-driven track variability of events in our North Atlantic Hurricane (NAHU) record may affect present-day probabilistic views of US landfall risk. To do this, we create 10,000 counterfactual NAHU histories from NCEP GEFS v2 initial-condition ensemble reforecast data for the period 1985-2016 and compare the statistics of these counterfactual histories to a model-based version of our single observational history. While the methodology presented herein attempts to produce the histories as objectively as possible, there is clear – and, ultimately, intuitively understandable – systematic underprediction of US NAHU landfall frequency in the counterfactual histories. This limits the ability to use the data in real-world applications at present. However, even with this systematic under-prediction, it is interesting to note both the magnitude of volatility and spatial variability in hurricane landfalls in single cities and wider regions along the US coastline, which speaks to the potential value of objective counterfactual analysis once methods have evolved

Investigation of climate change impact on hurricane wind and freshwater flood risks using machine learning techniques

Hurricane causes severe damage along with the U.S. coastal states. With the potential increase in hurricane intensity in changing climate conditions, the impacts of hurricanes are expected to be severer. Current hurricane risk management practices are based on the hurricane risk assessment without considering climate impact, which would result in a higher level of risk for the built environment than intended. For the development of proper hurricane risk management strategies, it is crucial to investigate the climate change impact on hurricane risk. However, investigation of future hurricane risk can be very time-consuming because of the high resolution of the models for climate-dependent hazard simulation and regional loss assessment. This study aims at investigating the climate change impact on hurricane wind and rain-ingress risk and freshwater flood risk on residential buildings across the southeastern U.S. coastal states. To address the challenge of computational inefficiency, surrogate models are developed using machine learning techniques for evaluating wind and freshwater flood losses of simulated climate-dependent hurricane scenarios. It is found that climate change impact varies by region and has a more significant influence on wind and rain-ingress damage, while both increases in wind and flood risks are not negligible

Hydroclimatology of Extreme Precipitation and Floods Originating from the North Atlantic Ocean

This study explores seasonal patterns and structures of moisture transport pathways from the North Atlantic Ocean and the Gulf of Mexico that lead to extreme large-scale precipitation and floods over land. Storm tracks, such as the tropical cyclone tracks in the Northern Atlantic Ocean, are an example of moisture transport pathways. In the first part, North Atlantic cyclone tracks are clustered by the moments to identify common traits in genesis locations, track shapes, intensities, life spans, landfalls, seasonal patterns, and trends. The clustering results of part one show the dynamical behavior differences of tropical cyclones born in different parts of the basin. Drawing on these conclusions, in the second part, statistical track segment model is developed for simulation of tracks to improve reliability of tropical cyclone risk probabilities. Moisture transport pathways from the North Atlantic Ocean are also explored though the specific regional flood dynamics of the U.S. Midwest and the United Kingdom in part three of the dissertation. Part I. Classifying North Atlantic Tropical Cyclones Tracks by Mass Moments. A new method for classifying tropical cyclones or similar features is introduced. The cyclone track is considered as an open spatial curve, with the wind speed or power information along the curve considered as a mass attribute. The first and second moments of the resulting object are computed and then used to classify the historical tracks using standard clustering algorithms. Mass moments allow the whole track shape, length and location to be incorporated into the clustering methodology. Tropical cyclones in the North Atlantic basin are clustered with K-means by mass moments producing an optimum of six clusters with differing genesis locations, track shapes, intensities, life spans, landfalls, seasonality, and trends. Even variables that are not directly clustered show distinct separation between clusters. A trend analysis confirms recent conclusions of increasing tropical cyclones in the basin over the past two decades. However, the trends vary across clusters. Part II: Tropical cyclone Intensity and Track Simulator (HITS) with Atlantic Ocean Applications for Risk Assessment. A nonparametric stochastic model is developed and tested for the simulation of tropical cyclone tracks. Tropical cyclone tracks demonstrate continuity and memory over many time and space steps. Clusters of tracks can be coherent, and the separation between clusters may be marked by geographical locations where groups of tracks diverge due to the physics of the underlying process. Consequently, their evolution may be non-Markovian. Markovian simulation models, as often used, may produce tracks that potentially diverge or lose memory quicker than nature. This is addressed here through a model that simulates tracks by randomly sampling track segments of varying length, selected from historical tracks. For performance evaluation, a spatial grid is imposed on the domain of interest. For each grid box, long-term tropical cyclone risk is assessed through the annual probability distributions of the number of storm hours, landfalls, winds, and other statistics. Total storm length is determined at birth by local distribution, and movement to other tropical cyclone segments by distance to neighbor tracks, comparative vector, and age of track. An assessment of the performance for tropical cyclone track simulation and potential directions for the improvement and use of such model are discussed. Part III: Dynamical Structure of Extreme Floods in the U.S. Midwest and the United Kingdom. Twenty extreme spring floods that occurred in the Ohio Basin between 1901 and 2008, identified from daily river discharge data, are investigated and compared to the April 2011 Ohio River flood event. Composites of synoptic fields for the flood events show that all these floods are associated with a similar pattern of sustained advection of low-level moisture and warm air from the tropical Atlantic Ocean and the Gulf of Mexico. The typical flow conditions are governed by an anomalous semi-stationary ridge situated east of the US East Coast, which steers the moisture and converges it into the Ohio Valley. Significantly, the moisture path common to all the 20 cases studied here as well as the case of April 2011 is distinctly different from the normal path of Atlantic moisture during spring, which occurs further west. It is shown further that the Ohio basin moisture convergence responsible for the floods is caused primarily by the atmospheric circulation anomaly advecting the climatological mean moisture field. Transport and related convergence due to the covariance between moisture anomalies and circulation anomalies are of secondary but non-negligible importance. The importance of atmospheric circulation anomalies to floods is confirmed by conducting a similar analysis for a series of winter floods on the River Eden in northwest England