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Windstorms affecting Europe are among the natural hazards with the largest socio-economic impacts. Therefore, many sectors like society, the economy, or the insurance industry are highly interested in reliable information on associated impacts and losses. In this study, we compare – for the first time – estimated windstorm losses using a simplified meteorological loss index (LI) with losses obtained from a complex insurance loss (catastrophe) model, namely the European Windstorm Model of Aon Impact Forecasting. To test the sensitivity of LI to different meteorological input data, we furthermore contrast LI based on the reanalysis dataset ERA5 and its predecessor ERA-Interim. We focus on similarities and differences between the datasets in terms of loss values and storm rank for specific historical storm events in the common reanalysis period across 11 European countries. Our results reveal higher LI values for ERA5 than for ERA-Interim for all of Europe (by roughly a factor of 10), coming mostly from the higher spatial resolution in ERA5. The storm ranking is comparable for western and central European countries for both reanalyses, confirmed by high correlation values between 0.6 and 0.89. Compared to the Aon Impact Forecasting model, LI ERA5 shows comparable storm ranks, with correlation values ranging between 0.45 and 0.8. In terms of normalized loss, LI exhibits overall lower values and smaller regional differences. Compared to the market perspective represented by the insurance loss model, LI seems to have particular difficulty in distinguishing between high-impact events at the tail of the wind gust distribution and moderate-impact events. Thus, the loss distribution in LI is likely not steep enough, and the tail is probably underestimated. Nevertheless, it is an effective index that is suitable for estimating the impacts of storm events and ranking storm events, precisely because of its simplicity. [ABSTRACT FROM AUTHOR]

Southern Africa has been strongly affected by ongoing climate change in recent decades. Rainfall variability is modulated by regional patterns of moisture advection and convergence in the lower troposphere. Using reanalysis, ground and satellite‐based rainfall products and observations from 10 weather stations, we perform a synoptic and climatological analysis focussing on atmospheric circulation, moisture transport and their relationship with rainfall anomalies over the Angolan and Namibian Plateau region. Results clearly show that a stronger (weaker) Zambezi low level jet (LLJ) magnitudes are associated with above (below) normal rainfalls over the main Angolan and Namibian plateaus. With lower confidence, a stronger Limpopo LLJ may also lead to enhanced rainfall over Namibia and southeast Angola. The Zambezi LLJ moisture fluxes are moderately controlled by Mozambique Chanel Trough and Angola Low intensities, while the Limpopo LLJ intensities have very low influence from the Mozambique Chanel Trough and Angola Low, respectively. The Angola Low in its tropical phase is associated with deeper moisture convergence and stronger vertical velocities, leading to higher amounts of precipitable water within the air column, thus enhancing precipitation over the region. It is shown that the major moisture source of rainfall, which is advected to via the Zambezi LLJ, is the Indian Ocean. Meanwhile, the Atlantic Ocean plays a minor role. Given the current lack of observations and projected climate change, further research and investments are urgently needed in the region, for example, regarding the expansion of the surface data network. [ABSTRACT FROM AUTHOR]

Landslides are complex phenomena that cause important impacts in vulnerable areas, including the destruction of infrastructure, environmental damage, and loss of life. The occurrence of landslide events is often triggered by rainfall episodes, single and intense ones or multiple ones occurring in sequence, i.e., clustered in time. Landslide prediction is typically obtained via process-based or empirical thresholds. Here, we develop a new approach that uses information on the temporal clustering of rainfall to detect landslide events and compare it with the use of classical empirical rainfall thresholds. In addition, we evaluate the performance of the two approaches combined together as a case study in the region of Lisbon in Portugal. We consider a dataset that categorizes landslides into shallow and deep events and a review of empirical rainfall thresholds that makes a good benchmark for testing our novel method. We show that the new approach based on temporal clustering overall has a good power of detecting landslide events but has a skill comparable with the classic rainfall threshold method. While there is no clear outperformance of one method, the novel clustering-based method has a higher sensitivity despite a lower precision than the threshold-based method. For all approaches, the potential detection is better for deep landslides than for shallow ones. The results of this study could help to improve the prediction of rainfall-triggered landslides. [ABSTRACT FROM AUTHOR]

Extreme precipitation events (EPEs) present potentially an enormous societal risk and often lead to major human and economic impacts. In the mid-latitudes, such EPEs are often triggered by intense extratropical cyclones and their associated frontal systems. Over the last decade, several studies have shown the important and specific role played by Atmospheric Rivers (ARs) in the occurrence of EPEs in western Europe, particularly in the Iberia Peninsula. In this study we analyze the all-time 24 h record-breaking precipitation (120.3 mm) recorded in the historical Dom Luiz Observatory (since 1863) in Lisbon, Portugal, between 12 and 13 December 2022. A synoptic evaluation of surface and upper-level fields from 5 to 14 December is performed using ERA-5 reanalysis. The week before the EPE, there was a combined effect of a large-scale SLP gradient resembling the NAO negative phase, a southerly position of the jet stream and an above normal positive SST anomalies over the North Atlantic leading to the development of several low-pressure systems at relatively low latitudes, all travelling along the same mean path towards western Europe. The atmospheric river associated with this event was first detected on late 10 December associated with a deep extratropical cyclone. The combination of high Integrated Vapor Transport (and moisture inflow by the warm conveyor belt), with a dynamical component characterized by a suitable uplift motion, allowed the system to evolve and maintain its AR characteristics for 72 h. The extratropical cyclone and associated AR moved northeast towards Iberia, making landfall in Portugal, on 12 December, as an extreme AR event, leading to the 24 h precipitation record breaking event. [ABSTRACT FROM AUTHOR]