CyberSec Research

A disordered quasi-liquid layer of water is thought to cover the ice surface, but many issues, such as its onset temperature, its thickness, or its actual relation to bulk liquid water have been a matter of unsettled controversy for more than a century. In this perspective article, current computer simulations and experimental results are discussed under the light of a suitable theoretical framework. It is found that using a combination of wetting physics, the theory of intermolecular forces, statistical mechanics and out of equilibrium physics a large number of conflicting results can be reconciled and collected into a consistent description of the ice surface. This helps understand the crucial role of surface properties in a range of important applications, from the enigmatic structure of snow crystals to the slipperiness of ice.

The dispersion of Lagrangian particle pairs is a fundamental process in turbulence, with implications for mixing, transport, and the statistical properties of particles in geophysical and environmental flows. While classical theories describe pair dispersion through scaling laws related to energy cascades, extreme events in turbulent flows can significantly alter these dynamics. This is especially important in stratified flows, where intermittency manifests itself also as strong updrafts and downdrafts. In this study, we investigate the influence of extreme events on the relative dispersion of particle pairs in stably stratified turbulence. Using numerical simulations we analyze the statistical properties of pair separation across different regimes, and quantify deviations from classical Richardson scaling. Our results highlight the role of extreme drafts in accelerating dispersion. These findings have important implications for turbulent mixing in natural systems, including atmospheric and oceanic flows, as well as applications in cloud microphysics and pollutant transport.

Atmospheric rivers (ARs) are extreme weather events that play a crucial role in the global hydrological cycle. As a key mechanism of latent heat transport (LHT), they help maintain energy balance in the climate system. While an AR is characterized by a long, narrow corridor of water vapor associated with a low-level jet stream, there is no unambiguous definition of an AR grounded in geophysical fluid dynamics. Therefore, AR identification is currently performed by a large array of expert-defined, threshold-based algorithms. The variety of algorithms introduces uncertainty in the estimated contribution of ARs to LHT. We calculate the instantaneous eddy LHT from moist, poleward anomalies. Based on the dynamics of the large-scale atmospheric circulation, this quantity is a physics-based upper bound that constrains AR projections from the variety of detection algorithms. We quantify the contribution of ARs to transient eddies, stationary eddies, and transient-stationary eddy interactions, and we show the relative contribution of ARs and other processes, such as dry, equatorward transport. We use this upper bound to quantify ARs' frequency, intensity, and temporal variability. In the historical climate, we find that ARs transport 2.21 PW at the latitude of peak meridional transport in Northern Hemisphere winter, with approximately 0.47 PW of temporal variability. By the end of the century in a future climate projection, at this latitude, we find that AR-induced LHT will increase by 0.5 PW and the corresponding temporal variability will increase by 0.14 PW.

This paper presents a novel approach for implementing frequency-dependent hydrodynamic coefficients in Morison's equation, which is widely used in hydrodynamics modeling. Accurate hydrodynamic predictions using Morison's equation necessitate the incorporation of frequency-dependent drag coefficients due to their variation with wave frequency. To address this, the proposed method segments the frequency domain into different regions, such as low-frequency (resonance) and high-frequency (wave) regions. Instead of using a constant drag coefficient across the entire spectrum, different drag coefficients are assigned to these regions. To implement this, a fifth-order low-pass Butterworth velocity filter is applied for the resonance zone, while a first-order high-pass Butterworth velocity filter is applied for the wave-dominated zone. The approach is validated using the INO WINDMOOR 12MW semisubmersible offshore wind turbine, comparing the simulation results against the experimental data. By incorporating frequency-dependent drag coefficients, the model shows improved agreement with experimental surge motion data across both frequency regions, demonstrating the effectiveness of the proposed method.

Traditional numerical global climate models simulate the full Earth system by exchanging boundary conditions between separate simulators of the atmosphere, ocean, sea ice, land surface, and other geophysical processes. This paradigm allows for distributed development of individual components within a common framework, unified by a coupler that handles translation between realms via spatial or temporal alignment and flux exchange. Following a similar approach adapted for machine learning-based emulators, we present SamudrACE: a coupled global climate model emulator which produces centuries-long simulations at 1-degree horizontal, 6-hourly atmospheric, and 5-daily oceanic resolution, with 145 2D fields spanning 8 atmospheric and 19 oceanic vertical levels, plus sea ice, surface, and top-of-atmosphere variables. SamudrACE is highly stable and has low climate biases comparable to those of its components with prescribed boundary forcing, with realistic variability in coupled climate phenomena such as ENSO that is not possible to simulate in uncoupled mode.

Understanding controls on Mesoscale Convective Systems (MCSs) is critical for predicting rainfall extremes across scales. Spatial variability of soil moisture (SM) presents such a control, with $\sim$200km dry patches in the Sahel observed to intensify mature MCSs. Here we test MCS sensitivity to spatial scales of surface heterogeneity using a framework of experiments initialised from scale-filtered SM. We demonstrate the control of SM heterogeneity on MCS populations, and the mechanistic chain via which spatial variability propagates through surface fluxes to convective boundary layer development and storm environments. When all sub-synoptic SM variability is homogenised, peak MCS counts drop by 23%, whereas maintaining small-scale variability yields a weaker decrease due to higher primary initiation. In sensitivity experiments, boundary layer development prior to MCSs is similar to that over mesoscale dry SM anomalies, but instead driven by diffuse cloud-free slots of increased shortwave radiation. This reduces storm numbers and potential predictability.

Land-atmosphere coupling is an important process for correctly modelling near-surface temperature profiles, but it involves various uncertainties due to subgrid-scale processes, such as turbulent fluxes or unresolved surface heterogeneities, suggesting a probabilistic modelling approach. We develop a copula Bayesian network (CBN) to interpolate temperature profiles, acting as alternative to T2m-diagnostics used in numerical weather prediction (NWP) systems. The new CBN results in (1) a reduction of the warm bias inherent to NWP predictions of wintertime stable boundary layers allowing cold temperature extremes to be better represented, and (2) consideration of uncertainty associated with subgrid-scale spatial variability. The use of CBNs combines the advantages of uncertainty propagation inherent to Bayesian networks with the ability to model complex dependence structures between random variables through copulas. By combining insights from copula modelling and information entropy, criteria for the applicability of CBNs in the further development of parameterizations in NWP models are derived.

In a warming climate with more frequent severe weather, artificial intelligence (AI) weather models have the potential to provide cheaper, faster, and more accurate forecasts of high-impact weather events. To realize this potential, there is a need for more research on how models learn extreme events and how that learning might be improved. We investigate how a spherical Fourier neural operator model (SFNO) learns extreme weather by saving every checkpoint throughout training and analyzing a collection of 9 extreme weather events including heatwaves, atmospheric rivers, and tropical cyclones. The SFNO learns heatwaves similarly to other weather days, but we find evidence that the model learns information about atmospheric river and tropical cyclone forecasts that it loses later in training. We propose a possible training strategy to improve the forecasting of extreme events by retaining information from earlier training checkpoints, and provide initial evidence of its utility.

Lightning plays a crucial role in the Earth's climate system, yet existing parameterizations for use in forecasting and earth system models show room for improvement in capturing spatial and temporal variations in its frequency. This study develops deep learning-based parameterizations of lightning stroke density using meteorological variables from the ERA and IMERG datasets. Convolutional neural networks (CNNs) with U-Net architectures are trained using World Wide Lightning Location Network (WWLLN) data from 2010 to 2021 and evaluated on WWLLN lightning observations from 2022 and 2023. The CNNs reduce the average domain mean bias by an order of magnitude and produce significantly higher Fractions Skill Score (FSS) values across all lightning regimes compared to the multiplicative product of CAPE and precipitation. The CNNs show skill relative to previously published parameterizations over the oceans especially, with r2 values as high as 0.93 achieved between the best performing modeled and observed lightning stroke density climatologies. The CNNs are also able to accurately capture the 12-hourly evolution of lightning spatial patterns on an event-scale with high skill. These results show the potential for deep learning to improve on lightning parameterizations in weather and earth system models.

We develop structure-preserving numerical methods for the compressible Euler equations, employing potential temperature as a prognostic variable. We construct three numerical fluxes designed to ensure the conservation of entropy and total energy within the discontinuous Galerkin framework on general curvilinear meshes. Furthermore, we introduce a generalization for the kinetic energy preservation property and total energy conservation in the presence of a gravitational potential term. To this end, we adopt a flux-differencing approach for the discretization of the source term, treated as non-conservative product. We present well-balanced schemes for different constant background states for both formulations (total energy and potential temperature) on curvilinear meshes. Finally, we validate the methods by comparing the potential temperature formulation with the traditional Euler equations formulation across a range of classical atmospheric scenarios.

Monitoring the abundance of greenhouse gases (GHGs) such as carbon dioxide (CO$_2$) and methane (CH$_4$) is necessary to quantify their impact on global warming and climate change. Although a number of satellites and ground-based networks measure the total column volume mixing ratio (VMR) of these gases, they rely on sunlight, and column measurements at night are comparatively scarce. We present a new algorithm, Astroclimes, that hopes to complement and extend nighttime CO$_2$ and CH4 column measurements. Astroclimes can measure the abundance of GHGs on Earth by generating a model telluric transmission spectra and fitting it to the spectra of telluric standard stars in the near-infrared taken by ground-based telescopes. A Markov Chain Monte Carlo (MCMC) analysis on an extensive dataset from the CARMENES spectrograph showed that Astroclimes was able to recover the long term trend known to be present in the molecular abundances of both CO$_2$ and CH$_4$, but not their seasonal cycles. Using the Copernicus Atmosphere Monitoring Service (CAMS) global greenhouse gas reanalysis model (EGG4) as a benchmark, we identified an overall vertical shift in our data and quantified the long term scatter in our retrievals. The scatter on a 1 hour timescale, however, is much lower, and is on par with the uncertainties on individual measurements. Although currently the precision of the method is not in line with state of the art techniques using dedicated instrumentation, it shows promise for further development.

The winter Arctic Oscillation (AO) modulates East Asian climate and the East/Japan Sea (EJS), yet local, scale-dependent air-sea couplings linking atmosphere, ocean and sea-surface temperature anomalies (SSTA) remain unclear. Using 30 years of daily fields (1993--2022), we compute detrended fluctuation/cross-correlation metrics over 5--50-day scales at every grid: the Hurst exponent ($H$), the cross-Hurst exponent ($\lambda$), and the DCCA coefficient ($\rho_{DCCA}$). Significance is assessed with iterative-AAFT surrogates and Benjamini--Hochberg false-discovery-rate control. Three robust features emerge. (1) During AO+ winters, the EKB--SPF corridor exhibits high SSTA variance and near-ballistic persistence ($H \approx 1.4$--$1.5$), indicating increased susceptibility to marine heatwaves. (2) SSTA co-fluctuates positively with near-surface air-temperature anomalies, whereas turbulent heat-flux anomalies are largely anti-phased and show negligible cross-persistence, consistent with fast damping. (3) Oceanic fields impart persistent coupling: sea-surface height anomalies display basin-wide positive links with SSTA; meridional geostrophic velocity imprints advective cross-coupling along EKWC/SPF pathways, while zonal flow and vorticity yield patchy signatures. Winter SST variability in the EJS thus reflects a two-tier process in which mesoscale structure and along-front advection organize persistence, while synoptic forcing and turbulent heat exchange supply strong but non-persistent tendencies. The FDR-controlled, grid-point DFA/DCCA framework is transferable to other marginal seas.

The coupling of weather, sea-ice, ocean, and wave forecasting systems has been a long-standing research focus to improve Arctic forecasting systems and their realism and is also a priority of international initiatives such as the WMO research project PCAPS. The goal of the Svalbard Marginal Ice Zone 2025 Campaign (SvalMIZ-25) was to observe and better understand the complex interplay between atmosphere, waves, and sea-ice in the winter Marginal Ice Zone (MIZ) in order to advance predictive skill of coupled Arctic forecasting systems. The main objective has been to set up a network of observations with a spatial distribution that allows for a representative comparison between in situ observations and gridded model data. The observed variables include air and surface temperature, sea-ice drift, and wave energy spectra. With the support of the Norwegian Coast Guard, we participated in the research cruise with KV Svalbard from 22.April - 11.May 2025. In total 21 buoys were deployed in the Marginal Ice Zone north of the Svalbard Archipelago.

The winter climate of the East/Japan Sea (EJS) is strongly affected by the Arctic Oscillation (AO), yet how AO polarity reshapes the memory, coupling patterns, and predictability of sea-surface temperature anomalies (SSTA) remains poorly quantified. Using 30 winters (1993--2022) of daily OISST and ERA5 fields, we combine multivariate Maximum Covariance Analysis (MCA) with an Ornstein--Uhlenbeck (OU)-like integration of atmospheric principal components (PCs). The leading coupled mode explains 87% (+AO) and 75% (-AO) of squared covariance, with SSTA hot spots in East Korea Bay and along the subpolar front. Zero-lag correlations between the SSTA PC and OU-integrated atmospheric PCs reveal characteristic memory timescales ($\tau$) of $\sim$18--25 days for wind-stress curl (CurlTau), $\sim$15--30 days for near-surface air temperature (ATMP) and zonal winds, and $\sim$30--50 days for sea-level pressure (SLP) and meridional winds -- longer under -AO. Detrended Fluctuation Analysis (DFA) shows SSTA persistence $H \approx 1.3$--$1.4$ and that integrated atmospheric responses acquire ocean-like persistence, validating Hasselmann's stochastic framework for winter EJS. AO-phase contrasts align with a curl$\rightarrow$Ekman pumping$\rightarrow$eddy/SSH$\rightarrow$SST pathway: +AO favors anticyclonic/downwelling responses and warmer SSTA, whereas -AO favors cyclonic/upwelling and cooler SSTA. These diagnostics identify phase-specific predictor windows (e.g., 3-week OU-integrated CurlTau/ATMP; 4--7-week SLP/V-wind under -AO) to initialize subseasonal extremes prediction (marine heatwaves and cold-surge-impacted SST). The approach quantifies memory scales and spatial coupling that were not explicitly resolved by previous composite analyses, offering a tractable foundation for probabilistic forecast models.

We present a deep learning emulator for stochastic and chaotic spatio-temporal systems, explicitly conditioned on the parameter values of the underlying partial differential equations (PDEs). Our approach involves pre-training the model on a single parameter domain, followed by fine-tuning on a smaller, yet diverse dataset, enabling generalisation across a broad range of parameter values. By incorporating local attention mechanisms, the network is capable of handling varying domain sizes and resolutions. This enables computationally efficient pre-training on smaller domains while requiring only a small additional dataset to learn how to generalise to larger domain sizes. We demonstrate the model's capabilities on the chaotic Kuramoto-Sivashinsky equation and stochastically-forced beta-plane turbulence, showcasing its ability to capture phenomena at interpolated parameter values. The emulator provides significant computational speed-ups over conventional numerical integration, facilitating efficient exploration of parameter space, while a probabilistic variant of the emulator provides uncertainty quantification, allowing for the statistical study of rare events.

Nor'easters frequently impact the North American East Coast, bringing hazardous precipitation, winds, and coastal flooding. Accurate simulation of their pressure and wind fields is essential for forecasting, risk assessment, and infrastructure planning, yet remains challenging due to their complex, asymmetric structure. This study introduces a novel hybrid analytical-data-driven model designed to efficiently simulate Nor'easter pressure and boundary layer wind fields. The pressure field is modeled using an adapted Holland-type formulation, with azimuthally varying parameters estimated through Kriging surrogate models informed by sensitivity analysis of reanalysis data. The wind field is then derived analytically from the momentum equations by decomposing the wind flow into gradient and frictional components. Model performance is assessed against ERA-Interim reanalysis data and surface wind observations from a historical event. Results show that the proposed pressure model accurately reproduces elliptical isobars and key asymmetrical patterns, while the wind model captures the fundamental structure and intensity of the boundary layer flow, including the presence of supergradient winds. Owing to its physical basis, computational efficiency, and ability to represent critical storm asymmetries, the model offers a valuable alternative to computationally expensive numerical simulations for hazard assessment and scenario analysis of extreme Nor'easters.

Data-driven weather models have recently achieved state-of-the-art performance, yet progress has plateaued in recent years. This paper introduces a Mixture of Experts (MoWE) approach as a novel paradigm to overcome these limitations, not by creating a new forecaster, but by optimally combining the outputs of existing models. The MoWE model is trained with significantly lower computational resources than the individual experts. Our model employs a Vision Transformer-based gating network that dynamically learns to weight the contributions of multiple "expert" models at each grid point, conditioned on forecast lead time. This approach creates a synthesized deterministic forecast that is more accurate than any individual component in terms of Root Mean Squared Error (RMSE). Our results demonstrate the effectiveness of this method, achieving up to a 10% lower RMSE than the best-performing AI weather model on a 2-day forecast horizon, significantly outperforming individual experts as well as a simple average across experts. This work presents a computationally efficient and scalable strategy to push the state of the art in data-driven weather prediction by making the most out of leading high-quality forecast models.

Accurately tracking the global distribution and evolution of precipitation is essential for both research and operational meteorology. Satellite observations remain the only means of achieving consistent, global-scale precipitation monitoring. While machine learning has long been applied to satellite-based precipitation retrieval, the absence of a standardized benchmark dataset has hindered fair comparisons between methods and limited progress in algorithm development. To address this gap, the International Precipitation Working Group has developed SatRain, the first AI-ready benchmark dataset for satellite-based detection and estimation of rain, snow, graupel, and hail. SatRain includes multi-sensor satellite observations representative of the major platforms currently used in precipitation remote sensing, paired with high-quality reference estimates from ground-based radars corrected using rain gauge measurements. It offers a standardized evaluation protocol to enable robust and reproducible comparisons across machine learning approaches. In addition to supporting algorithm evaluation, the diversity of sensors and inclusion of time-resolved geostationary observations make SatRain a valuable foundation for developing next-generation AI models to deliver more accurate, detailed, and globally consistent precipitation estimates.