Estimation of global tropical cyclone wind speed probabilities using the STORM dataset

Tropical cyclones (TC) are one of the deadliest and costliest natural disasters. To mitigate the impact of such disasters, it is essential to know extreme exceedance probabilities, also known as return periods, of TC hazards. In this paper, we demonstrate the use of the STORM dataset, containing synthetic TCs equivalent of 10,000 years under present-day climate conditions, for the calculation of TC wind speed return periods. The temporal length of the STORM dataset allows us to empirically calculate return periods up to 10,000 years without fitting an extreme value distribution. We show that fitting a distribution typically results in higher wind speeds compared to their empirically derived counterparts, especially for return periods exceeding 100-yr. By applying a parametric wind model to the TC tracks, we derive return periods at 10 km resolution in TC-prone regions. The return periods are validated against observations and previous studies, and show a good agreement. The accompanying global-scale wind speed return period dataset is publicly available and can be used for high-resolution TC risk assessments

STORM tropical cyclone wind speed return periods

Datasets containing tropical cyclone maximum wind speed (in m/s) return periods, generated using the STORM datasets (see https://www.nature.com/articles/s41597-020-0381-2). Return periods were empirically calculated using Weibull&#39;s plotting formula. The STORM_FIXED_RETURN_PERIOD dataset contains maximum wind speeds for a fixed set of return periods at 10 km resolution in every ocean basin. The STORM_FIXED_WIND_SPEED dataset contains return periods for a fixed set of maximum wind speeds at 10 km resolution in every ocean basin. The STORM_CITIES dataset contains return periods at fixed wind speeds and wind speeds at fixed return periods (on two seperate sheets), occurring within 100 km from a selection of 18 coastal cities. The STORM_ISLANDS contains return periods at fixed wind speeds and wind speeds at fixed return periods (on two seperate sheets), occurring within 100 km from the capital city of an island. We included the Small Island Developing States and a set of other islands.</span

Generation of a global synthetic tropical cyclone hazard dataset using STORM

Over the past few decades, the world has seen substantial tropical cyclone (TC) damages, with the 2017 Hurricanes Harvey, Irma and Maria entering the top-5 costliest Atlantic hurricanes ever. Calculating TC risk at a global scale, however, has proven difficult given the limited temporal and spatial information on TCs across much of the global coastline. Here, we present a novel database on TC characteristics on a global scale using a newly developed synthetic resampling algorithm we call STORM (Synthetic Tropical cyclOne geneRation Model). STORM can be applied to any meteorological dataset to statistically resample and model TC tracks and intensities. We apply STORM to extracted TCs from 38 years of historical data from IBTrACS to statistically extend this dataset to 10,000 years of TC activity. We show that STORM preserves the TC statistics as found in the original dataset. The STORM dataset can be used for TC hazard assessments and risk modeling in TC-prone regions

Political affiliation affects adaptation to climate risks : Evidence from New York City

Research reveals that liberals and conservatives in the United States diverge about their beliefs regarding climate change. We show empirically that political affiliation also matters with respect to climate related risks such as flooding from hurricanes. Our study is based on a survey conducted 6 months after Superstorm Sandy in 2012 of over 1,000 residents in flood-prone areas in New York City. Democrats’ perception of their probability of suffering flood damage is significantly higher than Republicans’ and they are also more likely to invest in individual flood protection measures. However, 50% more Democrats than Republicans in our sample expect to receive federal disaster relief after a major flood. These results highlight the importance of taking into account value-based considerations in designing disaster risk management policies

Improving the classification of flood tweets with contextual hydrological information in a multimodal neural network

While text classification can classify tweets, assessing whether a tweet is related to an ongoing flood event or not, based on its text, remains difficult. Inclusion of contextual hydrological information could improve the performance of such algorithms. Here, a multilingual multimodal neural network is designed that can effectively use both textual and hydrological information. The classification data was obtained from Twitter using flood-related keywords in English, French, Spanish and Indonesian. Subsequently, hydrological information was extracted from a global precipitation dataset based on the tweet's timestamp and locations mentioned in its text. Three experiments were performed analyzing precision, recall and F1-scores while comparing a neural network that uses hydrological information against a neural network that does not. Results showed that F1-scores improved significantly across all experiments. Most notably, when optimizing for precision the neural network with hydrological information could achieve a precision of 0.91 while the neural network without hydrological information failed to effectively optimize. Moreover, this study shows that including hydrological information can assist in the translation of the classification algorithm to unseen languages

What Will the Weather Do? Forecasting Flood Losses Based on Oscillation Indices

Atmospheric oscillations are known to drive the large-scale variability of hydrometeorological extremes in Europe, which can trigger flood events and losses. However, to date there are no studies that have assessed the combined influence of different large-scale atmospheric oscillations on the probabilities of flood losses occurring. Therefore, in this study we examine the relationship between five indices of atmospheric oscillation and four classes of flood losses probabilities at subregional European scales. In doing so, we examine different combinations of atmospheric oscillations, both synchronous and seasonally lagged. By applying logistic regressions, we aim to identify regions and seasons where probabilities of flood losses occurring can be estimated by indices of atmospheric oscillation with higher skill than historical probabilities. We show that classes of flood losses can be predicted by synchronous indices of atmospheric oscillation and that in some seasons and regions lagged relationships may exist between the indices of atmospheric oscillation and the probability of flood losses. Furthermore, we find that some models generate increased (or decreased) probability of flood losses occurring when the indices are at their extreme positive or negative phases. A better understanding of the effects of atmospheric oscillations on the likelihood of flood losses occurring represents a step forward in achieving flood resilience in Europe. For instance, improved early predictions of the indices that represent such atmospheric oscillations, or the evidence of a lagged relationship between their teleconnections and floods, can significantly contribute to mitigating the socioeconomic burden of floods

STORM tropical cyclone wind speed return periods

Datasets containing tropical cyclone maximum wind speed (in m/s) return periods, generated using the STORM datasets (see https://www.nature.com/articles/s41597-020-0381-2). Return periods were empirically calculated using Weibull's plotting formula. The STORM_FIXED_RETURN_PERIOD dataset contains maximum wind speeds for a fixed set of return periods at 10 km resolution in every ocean basin. The STORM_FIXED_WIND_SPEED dataset contains return periods for a fixed set of maximum wind speeds at 10 km resolution in every ocean basin. The STORM_CITIES dataset contains return periods at fixed wind speeds and wind speeds at fixed return periods (on two seperate sheets), occurring within 100 km from a selection of 18 coastal cities. The STORM_ISLANDS contains return periods at fixed wind speeds and wind speeds at fixed return periods (on two seperate sheets), occurring within 100 km from the capital city of an island. We included the Small Island Developing States and a set of other islands

STORM IBTrACS present climate synthetic tropical cyclone tracks

Datasets consisting of 10,000 years of synthetic tropical cyclone tracks, generated using the Synthetic Tropical cyclOne geneRation Model (STORM) algorithm (see Bloemendaal et al, Generation of a Global Synthetic Tropical cyclone Hazard Dataset using STORM, in review). The dataset is generated using historical data from IBTrACS and resembles present-climate conditions. The data can be used to calculate tropical cyclone risk in all (coastal) regions prone to tropical cyclones. VERSION UPDATE (30 Sept 2020): The Saffir-Simpson category thresholds were wrongly calculated in the previous version, this has now been corrected. VERSION UPDATE (18 March 2021): The old version of STORM contained some duplicate cyclone tracks. These have now been removed. VERSION UPDATE (22 July 2022): The last version accidentally still had the old wrong categories ... This version has the right categories AND the duplicates removed! :-)</span

How the U.S. can benefit from risk-based premiums combined with flood protection

Flood risk management in the USA is largely embedded in the National Flood Insurance Program (NFIP). Climate change and increasing exposure in flood plains pose a challenge to flood risk managers and make it vital to reduce risk in the future. The proposed reforms are steering the NFIP to risk-based premiums, but it is uncertain if the reforms will result in unaffordability and incentivize risk-reduction investments or how the NFIP is affected by large-scale adaptation efforts. Using an agent-based model approach for current and future scenarios, we demonstrate that risk-based premiums will yield a positive societal benefit (US$10 billion) because they will incentivize household risk-reduction investments. Moreover, our results show that proactive investment in large-scale adaptation measures complements a transition to risk-based premiums to yield a higher overall societal benefit (US$26 billion). We suggest that transitioning the NFIP to risk-based premiums can only be secured by additional investments in large-scale flood protection infrastructure

STORM EC-Earth present climate synthetic tropical cyclone tracks

Datasets consisting of 10,000 years of synthetic tropical cyclone tracks, generated using the Synthetic Tropical cyclOne geneRation Model (STORM) algorithm (see Bloemendaal et al, Generation of a Global Synthetic Tropical cyclone Hazard Dataset using STORM, in prep.). The dataset is generated using data the EC-Earth model and resembles present-climate conditions. The data can be used to calculate tropical cyclone risk in all (coastal) regions prone to tropical cyclones.</span