Synthetic Cyclone Research Articles

Simulating sea level extremes from synthetic low-pressure systems

Abstract. In this article we present a method for numerical simulations of extreme sea levels using synthetic low-pressure systems as atmospheric forcing. Our simulations can be considered to be estimates of the high sea levels that may be reached when a low-pressure system of high intensity and optimal track passes the studied region. We test the method using sites located along the Baltic Sea coast and simulate synthetic cyclones with various tracks. To model the effects of the cyclone properties on sea level, we simulate internal Baltic Sea water level variations with a numerical two-dimensional hydrodynamic model, forced by an ensemble of time-dependent wind and air-pressure fields from synthetic cyclones. The storm surges caused by the synthetic cyclones come on top of the mean water level of the Baltic Sea, for which we used a fixed upper estimate of 100 cm. We find high extremes in the northern Bothnian Bay and in the eastern Gulf of Finland, where the sea level extreme due to the synthetic cyclone reaches up to 3.5 m. In the event that the mean water level of the Baltic Sea has a maximal value (1 m) during the cyclone, the highest sea levels of 4.5 m could thus be reached. We find our method to be suitable for use in further studies of sea level extremes.

Combining uncertain machine learning predictions and numerical simulation results for the extreme value analysis of cyclone-induced wave heights – Application in Guadeloupe

Assessing return level RL (with return period typically ranging from 100 to 500 years) for extreme waves in cyclone-prone regions is often made problematic by the lack of sufficient representative samples to properly fit the extreme value probability distributions. Motivated by the Guadeloupe context (French West Indies), we address this problem when the wave numerical model is too expensive to be run a large number of times. A possible option is to use a machine learning (ML) model in place of the long-running numerical mode. By construction, ML predictions are however uncertain, because they are learned from a limited number of pre-calculated wave simulation results. We propose to account for this type of error along the inference of the parameters of the extreme value probability distribution within an Approximate Bayesian Computation (ABC) scheme. Using a subset of the database of numerically computed HS for Guadeloupe (for approximately 700 synthetic cyclones representative of 1000 years of cyclonic activity), we train a random forest (RF) regression model to relate the cyclone characteristics (radius, atmospheric pressure, distance to cyclone eye) to HS. The quantile RF model is then used to model the prediction error within the ABC scheme. By comparison to the 100-year and 500-year RL reference solutions (calculated using the entire database), we show that the ML-based approach achieves low bias and high reliability of the RL estimates at locations both along Guadeloupe coasts and in deep ocean environments for low sample size (down to ∼70). Provided that the prediction error is low-to-moderate, integrating this type of uncertainty improves the performance of the ML-based approach, and decreases the computational burden of the return level estimation.

Using Synthetic Cyclone Models for High Wind GNSS-R Calibration, Validation, and Algorithm Development: A CYGNSS Case Study

Using Synthetic Cyclone Models for High Wind GNSS-R Calibration, Validation, and Algorithm Development: A CYGNSS Case Study

Assessment of coastal vulnerability for extreme events

The necessity to protect coastal regions from sea-level rise (SLR) and extreme events demands a rigorous assessment of coastal vulnerability. The intergovernmental panel for climate change (IPCC) predicts that climate change will severely impact the coastal region, river systems, and urban infrastructures. The changing climate is observed to be increasing the frequency and magnitude of extreme hydrometeorological conditions, such as storm surges that adversely affect coastal areas by flood inundation. This study emphasizes the need to use extreme events and socio-economic data to evaluate the Coastal Vulnerability Index (CVI) for planning and adaptation measures. Therefore, the present study utilizes spatially varying surge heights for improved estimation of the CVI, unlike the constant surge heights considered in the previous studies. For coastal adaptation measures, synthetic cyclones of different return periods, such as 20-, 50- and 100-year, are simulated using a hydrodynamic model to represent a range of risk and vulnerability in the estimated CVI. The proposed methodology for calculating the CVI is demonstrated by considering the Chennai coast in the state of Tamil Nadu, India. The use of spatial distribution of storm surges from hydrodynamic simulations makes the CVI more realistic. Such thorough assessments utilizing high-resolution CVI maps can help policymakers suggest appropriate measures for specific coastal zones, which are more devastatingly affected by shoreline erosion, SLR and storm surge. The vulnerability assessment indicates that great care is needed in the planning and adaptation of the coastal ecosystem to extreme events for the safety and well-being of coastal populations and infrastructures.

Gauging mixed climate extreme value distributions in tropical cyclone regions

In tropical cyclone (TC) regions, tide gauge or numerical hindcast records are usually of insufficient length to have sampled sufficient cyclones to enable robust estimates of the climate of TC-induced extreme water level events. Synthetically-generated TC populations provide a means to define a broader set of plausible TC events to better define the probabilities associated with extreme water level events. The challenge is to unify the estimates of extremes from synthetically-generated TC populations with the observed records, which include mainly non-TC extremes resulting from tides and more frequently occurring atmospheric-depression weather and climate events. We find that extreme water level measurements in multiple tide gauge records in TC regions, some which span more than 100 years, exhibit a behaviour consistent with the combining of two populations, TC and non-TC. We develop an equation to model the combination of two populations of extremes in a single continuous mixed climate (MC) extreme value distribution (EVD). We then run statistical simulations to show that long term records including both historical and synthetic events can be better explained using MC than heavy-tailed generalised EVDs. This has implications for estimating extreme water levels when combining synthetic cyclone extreme sea levels with hindcast water levels to provide actionable information for coastal protection.

Statistical estimation of spatial wave extremes for tropical cyclones from small data samples: validation of the STM-E approach using long-term synthetic cyclone data for the Caribbean Sea

Abstract. Occurrences of tropical cyclones at a location are rare, and for many locations, only short periods of observations or hindcasts are available. Hence, estimation of return values (corresponding to a period considerably longer than that for which data are available) for cyclone-induced significant wave height (SWH) from small samples is challenging. The STM-E (space-time maximum and exposure) model was developed to provide reduced bias in estimates of return values compared to competitor approaches in such situations and realistic estimates of return value uncertainty. STM-E exploits data from a spatial neighbourhood satisfying certain conditions, rather than data from a single location, for return value estimation. This article provides critical assessment of the STM-E model for tropical cyclones in the Caribbean Sea near Guadeloupe for which a large database of synthetic cyclones is available, corresponding to more than 3000 years of observation. Results indicate that STM-E yields values for the 500-year return value of SWH and its variability, estimated from 200 years of cyclone data, consistent with direct empirical estimates obtained by sampling 500 years of data from the full synthetic cyclone database; similar results were found for estimation of the 100-year return value from samples corresponding to approximately 50 years of data. In general, STM-E also provides reduced bias and more realistic uncertainty estimates for return values relative to single-location analysis.

Understanding hazards: Probabilistic cyclone modelling for disaster risk to the Eastern Coast in Bangladesh

Cyclones and their associated wind and water hazards result in the largest number of fatalities. Coastal inundations due to cyclonic storm surges are an increasing threat to the lives and livelihoods of people in low-lying, highly populated coastal areas. Understanding hazards and the probability and intensity of of their occurrence are essential for determining the risk to life and infrastructure, as it allows the relevant agencies to plan for and respond to disasters. This paper developes a synthetic cyclone modelling (Category 4) to understand probable maximum impacts of a tropical cyclone and its cascading and compounding hazards in the Cox's Bazar area in Bangladesh. These models are used to develop contingency plans and strategies for disaster response. The probabilistic cyclone hazard modelling provides scenarios based hazards information that could benefit humanitarian agencies and future infrastructure risk assessments.

Measuring and Pricing Cyclone-Related Physical Risk Under Changing Climate

We propose a statistical methodology to quantify the financial implications of tropical cyclone-related physical risks implied by climate change. To address the sensitivity of disaster intensity to climate change, we provide a Monte Carlo methodology to generate synthetic cyclones consistent with climate scenarios of the Couple Model Intercomparison Project (CMIP5). Sovereign exposure and vulnerability assessments in principal tropical cyclone basins are based on projections of population densities in shared socioeconomic pathways coupled with downscaled physical asset values constructed using mixed data along with locally calibrated damage functions. Finally, we compute the direct climate impact on emerging countries’ bond spreads using the spread sensitivity to the debt to GDP ratio, assuming that damage costs are financed by issuing new government debt. We find that the ‘business as usual’ RCP8.5 concentration scenario coupled with the ‘middle road’ shared socioeconomic pathway (SSP2) leads to global average annual damages 142% larger than in the concentration scenario RCP2.6 allowing to remain under 2°C warming. In terms of emerging market impact, we estimate that in 2070-2100, the impact of extreme cyclones on the bond spread of most vulnerable countries will up to 200 bps higher in the RCP8.5 pathway than in the 2°C baseline. In every step of our assessment, we quantify the impact of model uncertainty on our results using 7 different climate models from the CMIP5 database.

Impact of Tropical Cyclone Landfall Angle on Storm Surge Along the Mid‐Atlantic Bight

AbstractStorm surge impact depends on coastal geographical and bathymetric features as well as various tropical cyclone characteristics including the size, intensity, and impact angle of the storm. Although the factors contributing to storm surge are well studied, uncertainties remain regarding the level of sensitivity to these parameters. This work seeks to contribute to the current knowledge of storm surge by studying the sensitivity to tropical cyclone landfall angle. We perform an ensemble of synthetic tropical cyclones using a newly developed modeling capability derived from the Weather Research and Forecasting (WRF) model, the Hybrid WRF Cyclone Model. Wind and atmospheric pressure field outputs from 200 synthetic cyclones are used as atmospheric forcing for the Advance Circulation (ADCIRC) model. We study the sensitivity of storm surge offshore extent and inundation to tropical cyclone impact angle. The extent of the impact area around the landfall location is sensitive to the cyclone landfall angle. Cyclones with tracks perpendicular to the coast are shown to produce the highest water levels and broadest inland and offshore extent. Results also indicate a heterogeneity in the sensitivity to landfall angle along the coast, highlighting the importance of both cyclone impact angle and location.

Near-reef and nearshore tropical cyclone wave climate in the Great Barrier Reef with and without reef structure

The Great Barrier Reef (GBR) coral coverage is in rapid decline from severe and sustained pressures from lagoon water quality, crown-of-thorns starfish (COTS), coral bleaching, tropical cyclones, pollution and diseases. The two recent GBR coral bleaching events (2016–2017) lead to Great Barrier Reef Marine Park Authority (GBRMPA) shifting their focus from passive management to active intervention (Great Barrier Reef Blueprint for resilience by GBRMPA). These active interventions, potentially able to increase GBR resilience, as there are reefs that, due to their physical location relative to all other reefs, river and estuary entrances, ocean currents, have favourable coral growth conditions. To undertake such interventions, various information is required including tropical cyclone wave climates. This paper develops tropical cyclone wave climates for the entire GBR. These wave climates were developed by simulating several thousand synthetic cyclones derived from the “HadGEM” general circulation model with RCP8.5 climate change scenario. The synthetic cyclones adopted herein include the following climate changes assessed by comparing averages of key forcing parameters between 1950 to 1999 and 2050 to 2099. Their average arrival rate increases from 2.25 to 2.41 cyclones/year and their average maximum wind speed increases from 24 to 28 m/s. Their average radius to maximum winds remains constant at 51 km. Two key challenges were resolved, namely, long runtimes and large files (600 m grid increment covering 1800 km by 280 km). Runtimes were reduced by excluding cyclones where their wind speeds over the entire event never exceeded 10 m/s within GBR itself or within 100 km of the GBR over water. Maximum wave heights were compared with an extended fetch empirical expression, which was based on satellite data of tropical cyclones in open waters, when cyclones were outside the GBR lagoon. These comparisons indicate that predicted wave heights have a lower bias using default wave generation parameters when compared with the extended fetch empirical expression. Prediction uncertainty was estimated at no more than 10% from various cyclonic wind-field models. The existing GBR reefs reduce nearshore wave or runup height by between 1.5 and 2 times compared to the no reef case. The reduction in wave or runup height was found to be minimal for 1 m sea level rise. These two findings indicate that there is more flooding potential from coral removal than SLR within the GBR lagoon.

Tropical Cyclone Hazard to Mumbai in the Recent Historical Climate

Abstract The hazard to the city of Mumbai, India, from a possible severe tropical cyclone under the recent historical climate is considered. The authors first determine, based on a review of primary sources, that the Bombay Cyclone of 1882, documented in a number of print and Internet sources and claimed to have caused 100 000 or more deaths, did not occur. Two different tropical cyclone hazard models, both of which generate large numbers of synthetic cyclones using environmental data—here taken from reanalyses in the satellite era—as input, are then used to quantify the hazard, in conjunction with historical observations. Both models indicate that a severe cyclone landfall at or near Mumbai is possible, though unlikely in any given year. Return periods for wind speeds exceeding 100 kt (1 kt = 0.5144 m s−1) (the threshold for category 3 in the Saffir–Simpson hurricane wind scale) at Mumbai itself are estimated to be in the range of thousands to greater than 10 000 years, while the return period for a storm with maximum wind speed of 100 kt or greater passing within 150 km of Mumbai (possibly close enough to generate a substantial storm surge at the city) is estimated to be around 500 years. Return periods for winds exceeding 65 kt (hurricane intensity on the Saffir–Simpson hurricane wind scale) are estimated to be around 200 years at Mumbai itself, and 50–90 years within 150 km. Climate change is not explicitly considered in this study, but the hazard to the city is likely to be increasing because of sea level rise as well as changes in storm climatology.

Impact of Tidal Phase on Inundation and Thrust Force Due to Storm Surge

Impact of storm surge largely varies depending on the tidal phase during the landfall of a tropical cyclone. This study investigates comparative variance in inundation condition and thrust force for an identical cyclone during low tide and high tide by applying a numerical model (Delft3D) and a semi-analytical model (DFM). A moderate strength cyclone, Mora, which made landfall on Bangladesh coast in May 2017 is selected to study its impact on land during low tide and high tide. Actual landfall time of Mora was during low tide. To study the impact of storm surge during high tide, a synthetic cyclone is created which has similar strength and track to that of Mora but makes landfall during high tide. The results show that inundation depth, inundation extent, and thrust force increase when a cyclone makes landfall during high tide compared to the condition when the cyclone makes landfall during low tide. But the relation between storm surge impact and tidal phase is not linear. It depends on the land topography of the location, direction of cyclone movement, direction and magnitude of water velocity and wind velocity, gradients of water surface and wind velocity, and proximity and position of the location with respect to cyclone track.

Temporal clustering of tropical cyclones on the Great Barrier Reef and its ecological importance

Tropical cyclones have been a major cause of reef coral decline during recent decades, including on the Great Barrier Reef (GBR). While cyclones are a natural element of the disturbance regime of coral reefs, the role of temporal clustering has previously been overlooked. Here, we examine the consequences of different types of cyclone temporal distributions (clustered, stochastic or regular) on reef ecosystems. We subdivided the GBR into 14 adjoining regions, each spanning roughly 300 km, and quantified both the rate and clustering of cyclones using dispersion statistics. To interpret the consequences of such cyclone variability for coral reef health, we used a model of observed coral population dynamics. Results showed that clustering occurs on the margins of the cyclone belt, being strongest in the southern reefs and the far northern GBR, which also has the lowest cyclone rate. In the central GBR, where rates were greatest, cyclones had a relatively regular temporal pattern. Modelled dynamics of the dominant coral genus, Acropora, suggest that the long-term average cover might be more than 13 % greater (in absolute cover units) under a clustered cyclone regime compared to stochastic or regular regimes. Thus, not only does cyclone clustering vary significantly along the GBR but such clustering is predicted to have a marked, and management-relevant, impact on the status of coral populations. Additionally, we use our regional clustering and rate results to sample from a library of over 7000 synthetic cyclone tracks for the GBR. This allowed us to provide robust reef-scale maps of annual cyclone frequency and cyclone impacts on Acropora. We conclude that assessments of coral reef vulnerability need to account for both spatial and temporal cyclone distributions.

Statistics-based Models of Flood-causing Cyclones for the Baltic Sea Region

Coastal floods are natural extreme phenomena that have rare occurrence and small duration. For reconstruction of different scenarios of these events synthetic cyclones can be used. In this paper, statistics-based approaches for synthetic cyclone creation are applied for reconstruction of coastal floods with rare occurrence in Saint-Petersburg. The model developed in the frame of this research is based on analysis and clusterization of historical 50-year dataset for cyclones over Baltic Sea. As a result, realistic pressure profile (RPP) model of cyclones with specified parameters was proposed. It can be used for reconstruction of historical coastal floods and estimation of potential flood parameters for different cases of cyclone movement.

Application of a synthetic cyclone method for assessment of tropical cyclone storm tides in Samoa

Tropical cyclone-induced storm surges cause damaging impacts in coastal regions. The present study uses a stochastic cyclone modelling approach to evaluate the likelihoods of storm tides, the combination of storm surges and astronomical tides, for Samoa. Cyclones that occurred in the vicinity of Samoa from 1969 to 2009 are used to build a stochastic tropical cyclone data set, and an analytic cyclone model and hydrodynamic model are used to model storm tides under average, La Nina and El Nino cyclone and sea level conditions for present climate conditions as well as cyclone and sea level conditions relevant for 2055, and storm tide return periods are estimated. We find that extreme storm tides exhibit relatively modest variation around the coastline of Samoa owing to the uniform width of the shelf surrounding the coastlines of two main islands of Savai’i and Upolu. The frequency of cyclones and hence storm tides during El Nino conditions is similar to the frequency for all seasons, but is considerably lower in La Nina conditions. For the future, tropical cyclones are assumed to undergo decreased frequency and increased intensity. This is found to lower the storm tide height for return periods <100 years and increase it for return periods greater than about 200 years. Sea level rise is shown to have a larger influence on storm tides than future changes to tropical cyclones. Considering the aggregated probabilities of storm tides occurring at the national scale, we find that the likelihood of a storm tide occurring that locally exceeds a 1-in-100-year level (i.e. an event with a 1 % annual exceedance probability) has a 6 % probability of occurring somewhere along the entire coastline of Samoa. Such information may be useful for those involved in coastal management and disaster response for which there may be a need to consider the overall likelihood that a nation may have to respond to such a disaster.

Synthetic storms reconstruction for coastal floods risks assessment

Coastal surge floods are extreme phenomena with low frequency of occurrence. In this paper, a combination of two approaches based on the stochastic model for multivariate extremes and the synthetic storm model was applied for coastal floods reconstruction in St. Petersburg. The stochastic model is based on multivariate distributions of cyclone’ parameters with the use of an equal probability contours method. The synthetic storm model was developed taking into account probability distributions of cyclone parameters. As a result, the synthetic cyclone generation mechanism (as an implementation) was proposed. Finally, reconstruction of the extreme coastal floods was used to investigate related risks based on the two stage ensemble of plan generation for protection barrier gate maneuvering as well as for the plan generation quality estimation.

Stochastic modelling of tropical cyclone tracks

A stochastic model for the tracks of tropical cyclones that allows for the computerised generation of a large number of synthetic cyclone tracks is introduced. This will provide a larger dataset than previously available for the assessment of risks in areas affected by tropical cyclones. To improve homogeneity, the historical tracks are first split into six classes. The points of cyclone genesis are modelled as a spatial Poisson point process, the intensity of which is estimated using a generalised version of a kernel estimator. For these points, initial values of direction, translation speed, and wind speed are drawn from histograms of the historical values of these variables observed in the neighbourhood of the respective points, thereby generating a first 6-h segment of a track. The subsequent segments are then generated by drawing changes in theses variables from histograms of the historical data available near the cyclone’s current location. A termination probability for the track is determined after each segment as a function of wind speed and location. In the present paper, the model is applied to historical cyclone data from the western North Pacific, but it is general enough to be transferred to other ocean basins with only minor adjustments. A version for the North Atlantic is currently under preparation.

Markov Models and Polygon Diagrams for Climatological Interpretations of Cyclones and Anticyclones over the Northern Hemisphere

Annual cyclone and anticyclone numbers in addition to the temperature data for the northern hemisphere are processed through simple models leading to meaningful interpretations. Historical climatological characteristics of the cyclone and anticyclone numbers are modeled by a first order Markov process. Statistically indistinguishable synthetic cyclone and anticyclone numbers are generated. Polygon diagrams which show the mutual relationships between average cyclone (anticyclone) numbers and the temperature are employed for finding possible relevant climatological changes during the record period. A polygon diagram concept is proposed and applied in order to identify sub-period warming and cooling spells of any desired short or long term durations and their relationship to the annual number of cyclone (anticyclone) occurrence numbers. The results indicate a decrease (increase) in the cyclone and anticyclone numbers during cooling (warming) periods. The methodologies proposed in this paper can be easily adopted for cyclone and anticyclone numbers modeling in any part of the world.