This study evaluates the potential of generative models, trained on historical ERA5 reanalysis data, for simulating windstorms over the UK. Four generative models, including a standard GAN, a WGAN-GP, a U-net diffusion model, and a diffusion-GAN were assessed based on their ability to replicate spatial and statistical characteristics of windstorms. Different models have distinct strengths and limitations. The standard GAN displayed broader variability and limited alignment on the PCA dimensions. The WGAN-GP had a more balanced performance but occasionally misrepresented extreme events. The U-net diffusion model produced high-quality spatial patterns but consistently underestimated windstorm intensities. The diffusion-GAN performed better than the other models in general but overestimated extremes. An ensemble approach combining the strengths of these models could potentially improve their overall reliability. This study provides a foundation for such generative models in meteorological research and could potentially be applied in windstorm analysis and risk assessment.

Windstorms significantly impact the UK, causing extensive damage to property, disrupting society, and potentially resulting in loss of life. Accurate modelling and understanding of such events are essential for effective risk assessment and mitigation. However, the rarity of extreme windstorms results in limited observational data, which poses significant challenges for comprehensive analysis and insurance modelling. This dissertation explores the application of generative models to produce realistic synthetic wind field data, aiming to enhance the robustness of current CAT models used in the insurance industry. The study utilises hourly reanalysis data from the ERA5 dataset, which covers the period from 1940 to 2022. Three models, including standard GANs, WGAN-GP, and U-net diffusion models, were employed to generate high-quality wind maps of the UK. These models are then evaluated using multiple metrics, including SSIM, KL divergence, and EMD, with some assessments performed in a reduced dimensionality space using PCA. The results reveal that while all models are effective in capturing the general spatial characteristics, each model exhibits distinct strengths and weaknesses. The standard GAN introduced more noise compared to the other models. The WGAN-GP model demonstrated superior performance, particularly in replicating statistical distributions. The U-net diffusion model produced the most visually coherent outputs but struggled slightly in replicating peak intensities and their statistical variability. This research underscores the potential of generative models in supplementing limited reanalysis datasets with synthetic data, providing valuable tools for risk assessment and catastrophe modelling. However, it is important to select appropriate evaluation metrics that assess different aspects of the generated outputs. Future work could refine these models and incorporate more ...

Descartes Underwriting, the Paris-based parametric insurtech, has formed a partnership with Reask, the tropical-cyclone modeling firm. The partnership aims to expand the availability and advancement of parametric cyclone insurance products by combining Descartes' ability to incorporate new technology into parametric insurance product design with wind data provided by Sydney-headquartered insurtech Reask. This partnership also seeks to address the insurance protection gap by expanding global cyclone parametric coverage, Descartes said, explaining that the consistent global coverage of Reask's tropical cyclone product, Metryc, enables the expansion of parametric insurance policies into regions and geographies where data limitations impeded previous coverage. Furthermore, Reask's ability to augment scarcely available ground-level observations and deliver high-resolution wind hazard intensity metrics within days following an event greatly supports the deployment of Descartes' parametric products. As natural catastrophe and extreme weather risks evolve due to climate change, the inherent difficulties in obtaining accurate data due to the destructive nature of cyclone activity are also likely to be accentuated.

Build Change, a foundation dedicated to preventing housing loss caused by natural disasters such as earthquakes and windstorms, is announcing the "Intelligence Supervision Assistant for Construction" (ISAC-SIMO) app. The tool utilizes machine learning to help people ensure they're using the best materials and construction methods to ensure buildings are disaster-ready. ISAC-SIMO has amazing potential to radically improve construction quality and ensure that homes are built or strengthened to a resilient standard, especially in areas affected by earthquakes, windstorms, and climate change. The Linux Foundation, building on the support of IBM over these past three years, will help us build this community. Putting an app in people's hands that will let them know things such as whether the brick and mortar in their walls is safe, down to whether the proper rebar and brackets are in use ahead of a build, will definitely save time, money, and lives.

"It's not an earthquake that kills people, but the collapse of a poorly built building." Build Change, a foundation dedicated to preventing housing loss caused by natural disasters such as earthquakes and windstorms, is announcing the "Intelligence Supervision Assistant for Construction" (ISAC-SIMO) app. And it could save countless lives. Attend the tech festival of the year and get your super early bird ticket now! The tool utilizes machine learning to help people ensure they're using the best materials and construction methods to ensure buildings are disaster-ready.

When observing a phenomenon, severe cases or anomalies are often characterised by deviation from the expected data distribution. However, non-deviating data samples may also implicitly lead to severe outcomes. In the case of unsupervised severe weather detection, these data samples can lead to mispredictions, since the predictors of severe weather are often not directly observed as features. We posit that incorporating external or auxiliary information, such as the outcome of an external task or an observation, can improve the decision boundaries of an unsupervised detection algorithm. In this paper, we increase the effectiveness of a clustering method to detect cases of severe weather by learning augmented and linearly separable latent representations.We evaluate our solution against three individual cases of severe weather, namely windstorms, floods and tornado outbreaks.