Probabilistic models for lightning and large hail were developed across Europe using lightning observations and hail reports. These models accurately predict the occurrence of lightning and large hail several days in advance. In addition, the hail model was shown to perform significantly better than the state-of-the-art forecasting methods. These results suggest that the models developed in this study may help improve forecasting of convective hazards and eventually limit the associated risks.

Global navigation satellite system signals are used to measure the perturbations induced in the ionosphere by earthquakes related to volcanic eruptions. The study uses data from ground stations and satellites measuring the signals reflected on the ocean or during radio occultation. The results shows a small correlation, but given the small magnitude of the earthquakes, it is difficult to apply this concept to any practical application that finds earthquake proxies in ionospheric perturbations.

Efficient adaptation planning for coastal flooding caused by extreme sea levels requires accurate assessments of the underlying hazard. Tide-gauge data alone are often insufficient for providing the desired accuracy but may be supplemented with historical information. We estimate extreme sea levels along the German Baltic coast and show that relying solely on tide-gauge data leads to underestimations. Incorporating historical information leads to improved estimates with reduced uncertainties.

Cold fronts are often associated with areas of intense precipitation (cells) and sometimes with hazards such as flooding, hail and lightning. We find that cold-frontal cell days are associated with higher cell frequency and cells are typically more intense. We also show both spatially and temporally where cells are most frequent depending on their cell-front distance. These results are an important step towards a deeper understanding of cold-frontal storm climatology and improved forecasting.

Landslides are one of the major weather-related geohazards. To assess their potential impact and design mitigation solutions, a detailed understanding of the slope is required. We tested if the use of machine learning, combined with satellite remote sensing data, would allow us to forecast deformation. Our results on the Vögelsberg landslide, a deep-seated landslide near Innsbruck, Austria, show that the formulation of such a machine learning system is not as straightforward as often hoped for.

Lightning activity in China is analyzed. Low latitudes, undulating terrain, seaside, and humid surfaces are beneficial for lightning occurrence. Summer of the year or afternoon of the day is the high period. Large cloud-to-ground lightning frequency always corresponds to a small ratio and weak intensity of positive cloud-to-ground lightning on either a temporal or spatial scale. Interestingly, the discharge intensity difference between the two types of lightning shrinks on the Tibetan Plateau.

This work documents the impact of Hurricane Irma (2017) on the Codrington barrier and lagoon on Barbuda Island. Irma caused two wide breaches in the sandy barrier, which remained unopened for 250 years. The thick and extensive sand sheet at the top of the lagoon fill was attributed to Irma. This unique deposit in a 3700-year record confirms Irma's exceptional character. This case study illustrates the consequences of high-intensity hurricanes in low-lying islands in a global warming context.

This paper divides preparedness behavior into minimal and adequate preparedness. In addition to studying the main factors that promote families' disaster preparedness, we also study the moderating effects of response efficacy and self-efficacy on preparedness actions by vulnerable families. Based on the findings of this study, policymakers can target interventions and programs that can be designed to remedy the current lack of disaster preparedness education for vulnerable families.

The Probabilistic Hydrological Estimation of LandSlides (PHELS) model combines ensembles of landslide susceptibility and of hydrological predictor variables to provide daily, global ensembles of hazard for hydrologically triggered landslides. Testing different hydrological predictors showed that the combination of rainfall and soil moisture performed best, with the lowest number of missed and false alarms. The ensemble approach allowed the estimation of the associated prediction uncertainty.

Hail and tornadoes are devastating hazards responsible for significant property damage and economic losses in the United States. Quantifying the connection between hazard events and mesoscale convective systems (MCSs) is of great significance for improving predictability, as well as for better understanding the influence of the climate-scale perturbations. A 14-year statistical dataset of MCS-related hazard production is presented.

The paper deals with wind speeds of extreme wind events in Poland and the descriptions of their effects. Two recent estimations developed by the Institute of Meteorology and Water Management in Warsaw and by Halina Lorenc are presented and briefly described. The 37 annual maximum gusts of wind speeds measured between 1971 and 2007 are analysed. Based on the measured and estimated wind speeds, the authors suggest new estimations for extreme winds that may occur in Poland.

This research presents a model for estimating extreme gusts associated with European windstorms. Using observed storm footprints we are able to calculate the return level of events at the 200-year return period. The largest gusts are found across NW Europe, and these are larger when the North Atlantic Oscillation is positive. Using theoretical future climate states we find that return levels are likely to increase across NW Europe to levels that are unprecedented compared to historical storms.

This study uses a machine learning technique, the multivariate regression tree approach, to assess the hydroclimatic characteristics that govern agricultural and hydrological drought severity. The results show that the employed technique successfully identified the primary drivers of droughts and their critical thresholds. In addition, it provides relevant information to identify the areas most vulnerable to droughts and design strategies and interventions for drought management.

Extreme flooding occurred in Slovenia in August 2023. This brief communication examines the main causes, mechanisms and effects of this event. The flood disaster of August 2023 can be described as relatively extreme and was probably the most extreme flood event in Slovenia in recent decades. The economic damage was large and could amount to well over 5 % of Slovenia's annual gross domestic product; the event also claimed three lives.

Total water levels (TWLs) induced by tropical cyclones (TCs) are among the leading hazards faced by coastal communities. Using numerical models, we examined how TWL components (surge and wave runup) along the South Atlantic Bight varied during hurricanes Matthew (2016), Dorian (2019), and Isaias (2020). Peak surge and peak wave runup were dominated by wind speeds and relative positions to TCs. The exceedance time of TWLs was controlled by normalized distances to TC and TC translation speeds.

A new approach for the deployment of landslide early warning systems (LEWSs) is proposed. We combine data-driven landslide susceptibility mapping and population maps to identify exposed locations. We estimate the cost of monitoring sensors and demonstrate that LEWSs could be installed with a budget ranging from EUR 5 to EUR 41 per person in Medellín, Colombia. We provide recommendations for stakeholders and outline the challenges and opportunities for successful LEWS implementation.