arXiv subjects

Olivia Martius

Publications and source records attributed to Olivia Martius.

12 recordsLinked to original sources

arXiv2026

Exploring the role of input data on hail nowcast skill using spatiotemporal neural networks

Hail can cause large financial losses and poses risks to public safety, making reliable nowcasts essential for timely warnings. Deep-learning approaches have emerged as a strong alternative to conventional methods, but how input data choices affect performance has not been deeply explored. We investigate how the skill of a deep-learning hail nowcasting model can be improved without changing the model architecture. Sensitivity experiments assess the impact of training-data volume, random data augmentation (mirroring and rotation) and the number of input timesteps. Increasing the years of training data substantially improves forecast skill, by up to 25 minutes at later lead times. Augmentation improved performance for larger datasets but interestingly degraded performance for smaller ones. Sensitivity to input timesteps was weaker than sensitivity to training years. These findings show that improvements in data selection and preprocessing can yield substantial gains even with a fixed architecture, offering guidance for future deep-learning nowcasting development.

physics.ao-ph↗

arXiv2026

Evaluating local climate in global storm-resolving models with the Köppen-Geiger classification

Global storm-resolving models aspire to become digital twins of the Earth, delivering information at the local scale at which humans experience climate. We evaluated how well two such models, ICON and IFS-FESOM, reproduce the climate as classified by the Köppen-Geiger system, using 30-year (2020-2049) simulations from the nextGEMS project at 9~km global resolution under SSP3-7.0 scenario. Both models capture the global distribution of the five main climate categories, encouraging given the infancy of storm-resolving climate modelling. Substantial regional biases nonetheless remain. Both underestimate tropical rainforest (Af) extent due to insufficient dry-month precipitation in Amazonia and equatorial Africa. ICON almost eliminates hot arid desert (BWh) across Australia through excessive precipitation, while IFS-FESOM reproduces it well. The two models show opposing biases along the temperate--continental boundary: IFS-FESOM winters are too cold in western Europe, ICON winters too warm. Substituting observed temperature or precipitation into the model fields reveals that precipitation errors dominate misclassification, while temperature biases play a secondary role confined to mid-latitude climate zone boundaries. Under climate change, the two models and CMIP6 projections agree on the direction of climate zone shifts: expansion of tropical savanna and hot desert at the expense of subarctic, tundra, and ice cap zones. However, inter-model differences in present-day climate exceed the 30-year climate change signal for many zones, calling for caution in regional projections and adaptation planning. Our results expose where local-scale climate representation still falls short of the digital twin ambition, while confirming that storm-resolving models already perform well across many regions. We propose Köppen-Geiger classification as a standard diagnostic to help track further progress.

physics.ao-ph↗

arXiv2025

Using spatial extreme-value theory with machine learning to model and understand spatially compounding weather extremes

When extreme weather events affect large areas, their regional to sub-continental spatial scale is important for their impacts. We propose a novel machine learning (ML) framework that integrates spatial extreme-value theory to model weather extremes and to quantify probabilities associated with the occurrence, intensity, and spatial extent of these events. Our approach employs new loss functions adapted to extreme values, enabling our model to prioritize the tail rather than the bulk of the data distribution. Applied to a case study of Western European summertime heat extremes, we use daily 500-hPa geopotential height fields and local soil moisture as predictors to capture the complex interplay between local and remote physical processes. Our generative model reveals that different facets of heat extremes are influenced by individual circulation features, such as the relative position of upper-level ridges and troughs that are part of a large-scale wave pattern. This enriches our process understanding from a data-driven perspective. Our approach can extrapolate beyond the range of the data to make risk-related probabilistic statements. It applies more generally to other weather extremes and offers an alternative to traditional physical and ML-based techniques that focus less on the extremal aspects of weather data.

stat.AP↗

arXiv2025

European supercell thunderstorms -- an underestimated current threat and an increasing future hazard

Supercell thunderstorms are the most hazardous thunderstorm category and particularly impactful to society. Their monitoring is challenging and often confined to the radar networks of single countries. By exploiting kilometer-scale climate simulations, a first-of-its-kind characterization of supercell occurrence in Europe is derived for the current and a warmer climate. Despite previous notions of supercells being uncommon in Europe, the model shows ~700 supercells per convective season. Occurrence peaks are co-located with complex topography e.g. the Alps. The absolute frequency maximum lies along the southern Alps with minima over the oceans and flat areas. Contrasting a current-climate simulation with a pseudo-global-warming +3$^\circ$C global warming scenario, the future climate simulation shows an average increase of supercell occurrence by 11 %. However, there is a spatial dipole of change with strong increases in supercell frequencies in central and eastern Europe and a decrease in frequency over the Iberian Peninsula and southwestern France.

physics.ao-ph↗

arXiv2024

Convective environments within Mediterranean cyclones

Understanding convective processes leading to severe weather hazards within Mediterranean cyclones is relevant for operational forecasters, insurance industry, and enhancing societal preparedness. In this work we examine the climatological link between Mediterranean cyclones and atmospheric conditions conducive to the formation of severe convection and convective hazards (convective precipitation, lightning and hail potential). Using ATDnet lightning detections we find that, from autumn to spring, 20 to 60% of lightning hours over the Mediterranean basin and adjacent land regions are associated with the presence of a nearby cyclone. Based on reanalysis data, severe convective environments, deep, moist convection (i.e., lightning potential) and related hazards are frequent in the warm sector of Mediterranean cyclones and to the north-east of their centres. In agreement with previous literature, convective processes and hazards peak approximately six hours prior to the time of minimum pressure of the cyclone centre. Moreover, severe convective environments are often detected in cyclone categories typical of transition seasons (especially autumn) and summer, while they are rarer in deep baroclinic cyclones with peak occurrence during winter. Finally, we show that dynamical cyclone features distinguish regions favourable to deep, moist convection. Warm conveyor belts of Mediterranean cyclones, characterised by large-scale ascent and located in regions of high thermodynamic instability, have the largest lightning potential. The potential is only half as intense along the cyclones' cold fronts.

physics.ao-ph↗

arXiv2024

Differential reflectivity columns and hail -- linking C-band radar-based estimated column characteristics to crowdsourced hail observations in Switzerland

Differential reflectivity columns (ZDRC) have been shown to provide information about a storm's updraft intensity and size. The updraft's characteristics, in turn, influence a severe storm's propensity to produce hail and the size of said hail. Consequently, there is the potential to use ZDRC for the detection and sizing of hail. In this observational study, we investigate the characteristics of ZDRC (volume, height, area, maximum ZDR within) automatically detected on an operational C-band radar network in Switzerland and relate them to hail on the ground using 173'000 crowdsourced hail reports collected over a period of 3.5 years. We implement an adapted version of an established ZDRC detection algorithm on a 3D composite of ZDR data derived from five Swiss weather radars. The composite, in combination with the dense network of radars located on differing altitudes up to 3000 m.a.s.l, helps to counteract the effects of the complex topography of the study region. The alpine region presents visibility and data quality challenges, which are especially crucial for measuring ZDRC. Our analysis finds ZDRC present in most hail-producing storms, with higher frequencies in storms producing severe hail. Further, when looking at lifetime maximum values, we find significant differences in various ZDRC characteristics between hail-producing and non-hail-producing storms. We also attempt to determine thresholds to differentiate between storm types. The temporal evolution of the ZDRC proves challenging to investigate due to their intermittent nature. Nevertheless, the peak values of the ZDRC characteristics are most often measured 5-10 minutes before the first hail reports on the ground, highlighting the potential for ZDRC to be used in warning applications.

physics.ao-ph↗

arXiv2024

Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments with Global AI-based Weather Models

Severe convective storms are among the most dangerous weather phenomena and accurate forecasts mitigate their impacts. The recently released suite of AI-based weather models produces medium-range forecasts within seconds, with a skill similar to state-of-the-art operational forecasts for variables on single levels. However, predicting severe thunderstorm environments requires accurate combinations of dynamic and thermodynamic variables and the vertical structure of the atmosphere. Advancing the assessment of AI-models towards process-based evaluations lays the foundation for hazard-driven applications. We assess the forecast skill of three top-performing AI-models for convective parameters at lead-times of up to 10 days against reanalysis and ECMWF's operational numerical weather prediction model IFS. In a case study and seasonal analyses, we see the best performance by GraphCast and Pangu-Weather: these models match or even exceed the performance of IFS for instability and shear. This opens opportunities for fast and inexpensive predictions of severe weather environments.

physics.ao-ph↗

arXiv2022

Weighted verification tools to evaluate univariate and multivariate forecasts for high-impact weather events

To mitigate the impacts associated with adverse weather conditions, meteorological services issue weather warnings to the general public. These warnings rely heavily on forecasts issued by underlying prediction systems. When deciding which prediction system(s) to utilise to construct warnings, it is important to compare systems in their ability to forecast the occurrence and severity of extreme weather events. However, evaluating forecasts for extreme events is known to be a challenging task. This is exacerbated further by the fact that high-impact weather often manifests as a result of several confounding features, a realisation that has led to considerable research on so-called compound weather events. Both univariate and multivariate methods are therefore required to evaluate forecasts for high-impact weather. In this paper, we discuss weighted verification tools, which allow particular outcomes to be emphasised during forecast evaluation. We review and compare different approaches to construct weighted scoring rules, both in a univariate and multivariate setting, and we leverage existing results on weighted scores to introduce weighted probability integral transform (PIT) histograms, allowing forecast calibration to be assessed conditionally on particular outcomes having occurred. To illustrate the practical benefit afforded by these weighted verification tools, they are employed in a case study to evaluate forecasts for extreme heat events issued by the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss).

stat.AP↗

arXiv2022

Increasing countries financial resilience through global catastrophe risk pooling

Extreme weather events can have severe impacts on national economies, leading the recovery of low- to middle-income countries to become reliant on foreign financial aid. Foreign aid, however, is slow and uncertain. Therefore, the Sendai Framework and the Paris Agreement advocate for more resilient financial instruments like sovereign catastrophe risk pools. Existing pools, however, might not fully exploit financial resilience potentials because they were not designed with the goal of maximizing risk diversification and they pool risk only regionally. To address this, we introduce a method that forms pools maximizing risk diversification and which selects countries with low bilateral correlations or low shares in the pool risk. We apply the method to explore the benefits of global pooling with respect to regional pooling. We find that global pooling increases risk diversification, it lowers countries shares in the pool risk and it increases the number of countries profiting from risk pooling.

q-fin.RM↗

arXiv2021

High return level estimates of daily ERA-5 precipitation in Europe estimated using regionalised extreme value distributions

Accurate estimation of daily rainfall return levels associated with large return periods is needed for a number of hydrological planning purposes, including protective infrastructure, dams, and retention basins. This is especially relevant at small spatial scales. The ERA-5 reanalysis product provides seasonal daily precipitation over Europe on a 0.25 x 0.25 grid (about 27 x 27 km). This translates more than 20,000 land grid points and leads to models with a large number of parameters when estimating return levels. To bypass this abundance of parameters, we build on the regional frequency analysis (RFA), a well-known strategy in statistical hydrology. This approach consists in identifying homogeneous regions, by gathering locations with similar distributions of extremes up to a normalizing factor and developing sparse regional models. In particular, we propose a step-by-step blueprint that leverages a recently developed and fast clustering algorithm to infer return level estimates over large spatial domains. This enables us to produce maps of return level estimates of ERA-5 reanalysis daily precipitation over continental Europe for various return periods and seasons. We discuss limitations and practical challenges and also provide a git hub repository. We show that a relatively parsimonious model with only a spatially varying scale parameter can compete well against statistical models of higher complexity.

stat.AP↗

arXiv2021

On Computationally-Scalable Spatio-Temporal Regression Clustering of Precipitation Threshold Excesses

Focusing on regression based analysis of extremes in a presence of systematically missing covariates, this work presents a data-driven spatio-temporal regression based clustering of threshold excesses. It is shown that in a presence of systematically missing covariates the behavior of threshold excesses becomes nonstationary and nonhomogenous. The presented approach describes this complex behavior by a set of local stationary Generalized Pareto Distribution (GPD) models, where the parameters are expressed as regression models, and a latent spatio-temporal switching process. The spatio-temporal switching process is resolved by the nonparametric Finite Element Methodology for time series analysis with Bounded Variation of the model parameters (FEM-BV). The presented FEM-BV-GPD approach goes beyond strong a priori assumptions made in standard latent class models like Mixture Models and Hidden Markov Models. In addition, it provides a pragmatic description of the underlying dependency structure. The performance of the framework is demonstrated on historical precipitation data for Switzerland and compared with the results obtained by the standard methods on the same data.

stat.ME↗

arXiv2018

Modeling non-stationary extreme dependence with stationary max-stable processes and multidimensional scaling

Modeling the joint distribution of extreme weather events in multiple locations is a challenging task with important applications. In this study, we use max-stable models to study extreme daily precipitation events in Switzerland. The non-stationarity of the spatial process at hand involves important challenges, which are often dealt with by using a stationary model in a so-called climate space, with well-chosen covariates. Here, we instead chose to warp the weather stations under study in a latent space of higher dimension using multidimensional scaling (MDS). The advantage of this approach is its improved flexibility to reproduce highly non-stationary phenomena, while keeping a tractable stationary spatial model in the latent space. Two model fitting approaches, which both use MDS, are presented and compared to a classical approach that relies on composite likelihood maximization in a climate space. Results suggest that the proposed methods better reproduce the observed extremal coefficients and their complex spatial dependence.

stat.ME↗