doc_id int64 0 8.44k | abstract stringlengths 32 2.7k | keywords stringlengths 0 56 ⌀ |
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0 | Wave turbulence is a regime of interacting nonlinear waves occurring in most physical systems. Kelvin waves are helical distortions that propagate along vortex filaments and are believed to play a central role in quantum turbulence up to atmospheric vortices. Yet, Kelvin wave turbulence has remained inaccessible to dir... | turbulence, vortex, wave |
1 | Super-resolution estimates a high-resolution image from a low-resolution image and has been used for downscaling and resolution enhancement of observations in meteorology. Super-resolution Gaussian process regression with a steering kernel (SRGP-SK) generates more accurate high-resolution images than super-resolution G... | meteorology, radar, resolution, downscaling |
2 | Salt-finger plume forests in a finite layer can differ in strength and in the route by which interfacial activity becomes vertically connected. We use direct three-dimensional simulations to test whether such a route is a short-lived realization-specific transient or a persistent route family under an added mean-shear ... | diffusivity, roughness, shear, salinity |
3 | Global Navigation Satellite Systems (GNSS), best known for positioning, also serve weather science, as atmospheric water vapour delays their signals. This delay, the Zenith Wet Delay (ZWD), is a direct, all-weather measure of column moisture. Although assimilated into numerical weather prediction for decades, ZWD is no... | precipitation, prediction, atmospheric, satellite |
4 | Deep-learning-based climate downscaling aims to learn relationships from historical low-resolution (LR) and high-resolution (HR) climate data to generate HR climate projections. However, this setting faces a temporal out-of-distribution (OOD) challenge: models trained on historical data are commonly applied to future p... | downscaling, climate |
5 | Data-driven models now rival numerical weather prediction in the medium range, but extending them to sub-seasonal lead times raises challenges absent at shorter horizons. Errors accumulate over long autoregressive rollouts, systematic biases grow with lead time, and several years of data must be held out for independen... | ecmwf, prediction, model, cyclone |
6 | This study proposes a real-time remote-sensing-guided decision-support framework for cloud-seeding operations using high frequency geostationary satellite and ground weather radar observations. The framework integrates cloud assessment, human-in-the-loop decision support, and aircraft operation to translate high-freque... | radar, satellite, cloud, precipitation |
7 | Modern machine-learning weather prediction (MLWP) has largely inherited the initial-value-problem (IVP) framing of numerical weather prediction (NWP). This inheritance leads to a dominant paradigm of learned autoregressive time-stepping and constrains how the learning problem is defined and architectures are favored. I... | model, prediction, weather |
8 | Ensemble Kalman filters (EnKFs) are widely used for data assimilation in geophysical systems. Among various implementations, the local ensemble transform Kalman filter (LETKF) has gained popularity because of its computational efficiency. However, the deterministic EnKF such as the LETKF is known to be less robust than... | ensemble, optimization, stochastic, analysis |
9 | This study proposes a precipitation control framework integrating a realistic Numerical Weather Prediction (NWP) model with model predictive control (MPC). At each control instant in MPC, a finite-difference sensitivity matrix is constructed from the NWP model and used as a local linear model of how perturbations to th... | precipitation, prediction, optimization, model |
10 | Foundation models for Earth systems have so far been trained primarily on physical climate and weather data, with limited representation of the human systems that both drive and respond to environmental change. The lack of a unified global training resource that combines climate, land, ocean, cryosphere, infrastructure... | reanalysis, dataset, climate, global |
11 | We develop a Wigner-based phase-space framework for mean paraxial wave propagation in random media. Starting from the random parabolic wave equation, we derive the exact evolution of the realization-dependent Wigner distribution and identify the ensemble-averaged Wigner function as the natural second-order state variab... | turbulence, propagation, diffusion, gaussian |
12 | We study the importance of surface characteristics when forecasting near-surface variables with a data-driven weather prediction model. To target the challenge of predicting small-scale weather conditions at high resolution, we introduce a range of surface descriptors in the training of a state-of-the-art data-driven m... | prediction, model, weather, surface |
13 | Traditionally, midlatitude storm tracks are viewed as being driven by meridional temperature gradients maintained by differential solar heating. Yet in the Southern Hemisphere, storm activity remains strong even when the summertime insolation gradient nearly vanishes. Here, we show that storm-track cloud radiative effe... | albedo, seasonal |
14 | The Earth's gravitational field exerts a significant influence on atmospheric dynamics, including the behavior of seasonal wind flux, defined by periodic variations in wind speed and direction. While temperature gradients and Earth's rotation are established drivers of wind patterns, the role of gravitational forces in... | meteorological, seasonal, atmospheric |
15 | Suitability scoring for outdoor activities (kitesurfing, paragliding, ski touring) maps environmental conditions to a go/no-go verdict via expert-defined curves. These curves conflate two distinct quantities: the intrinsic difficulty of a condition and the skill of the person facing it. We introduce Inverse Suitability... | meteorological, model, distribution |
16 | The limited predictive skill of forecasts makes it difficult for decision-makers to act decisively. Advance assessment of real-time forecast credibility can strengthen decision-makers' resolve and confidence to act. Such an assessment can draw on real-time observations of large-scale background signals. This study eval... | forecast, predictability, temperature |
17 | With the data-driven artificial intelligence/machine learning (AI/ML) models having demonstrated their ability to extend the prediction horizon of large-scale weather at a fraction of computational cost of numerical weather prediction models, a pertinent question is, could these models do the same for sub-seasonal to s... | monsoon, model, prediction, simulation |
18 | Lightning is a surprisingly poorly understood phenomena. It consists of a wide variety of complex processes such as initiation, propagation, connection to ground, even emission of high-energy radiation. However, due to the extreme challenges in observing lightning at fast time scales, small spatial scales, and behind o... | lightning, plasma, radiation, radio |
19 | Argo profiling floats measure seawater temperature and salinity in the upper 2000 meters of the ocean. These floats are uniquely capable of measuring the global Ocean Heat Content (OHC), a quantity that is of central importance for understanding Earth Energy Imbalance. Yet, producing Argo-based OHC estimates with relia... | seawater, ocean, estimation, salinity |
20 | Kilometer-scale convection shapes precipitation extremes, tropical organization, and cloud feedbacks, but most global atmospheric models approximate these processes at 25-100 km resolution. Global storm-resolving physics models resolve convective systems explicitly, but at a cost -- roughly one MWh per simulated day on... | convective, model, emulation, atmospheric |
21 | We couple Forward Flux Sampling (FFS), a non-equilibrium rare-event technique from statistical mechanics, to a neural weather emulator (SDL-WXFormer, 1° grid spacing) to estimate conditional tropical cyclogenesis rates, or how often a tropical cyclone achieves a hurricane-level central pressure, without modifying model... | cyclone, hurricane, flux, ensemble |
22 | Extreme weather events are difficult to understand for the same reason that they are dangerous: they happen rarely, catching victims unprepared when they do occur and scientists unable to assess risks confidently, given such limited precedent to learn from in the real world and high computational expense to simulate mo... | simulation, extreme, chaos, analysis |
23 | Machine learning (ML) has emerged as a cost-effective approach to complement dynamical downscaling for producing high-resolution regional climate projections. However, the absence of standardised training and evaluation protocols, applied consistently across multiple domains, continues to hinder meaningful model interc... | downscaling, model |
24 | Observed distributions of atmospheric temperature are non-Gaussian. Therefore, moments beyond variance are necessary in determining the frequency of extreme temperature events. Here we propose a simple kinematic model for atmospheric mid-latitude temperature variability based on symmetric advection from a non-symmetric... | temperature, climate, variability, advection |
25 | Data assimilation blends model forecasts with observations to estimate the evolving state of complex dynamical systems, but sparse observing networks remain challenging because unobserved state variables are not directly constrained by observations. In this work, we introduce the Ensemble Score Filter with Linear Regre... | ensemble, linear, analysis, regression |
26 | Accurate parameterizations of ocean wave spectra are necessary in a wide array of disciplines including coastal, ocean, and naval engineering as well as in the study of wave interactions and ocean-atmosphere momentum flux. Many such applications use spectrum parameterizations based on temporal data collected well over ... | wavenumber, spectrum, wave, spectra |
27 | Atmospheric carbon dioxide (CO2) growth rates reflects the combined influence of anthropogenic emissions, biospheric carbon exchange, and climate variability. While climate mitigation is primarily evaluated using bottom-up emission inventories within political boundaries, there is a need to validate these emission redu... | atmospheric, reanalysis, climate |
28 | Sea surface temperature (SST) gradients associated with western boundary currents affect the atmospheric circulation across a range of spatial and temporal scales. Yet, several aspects of ocean-atmosphere interactions linked to oceanic fronts remain unclear. This PhD thesis analyses such interactions for the Gulf Strea... | reanalysis, gulf, sea, climate |
29 | The Garrett-Munk (GM) spectrum has long provided a canonical model of the oceanic internal gravity wave field. However, it relies on hydrostatic assumptions and idealized stratification that limit its applicability where non-hydrostatic dynamics, vertical boundary effects, or non-monotonic stratification are important.... | wavenumber, wave, spectrum, spectra |
30 | Designing efficient observation networks for reducing forecast errors is a fundamental challenge in numerical weather prediction. Data-driven sparse sensor placement (SSP) and ensemble-based data assimilation via the Ensemble Kalman Filter (EnKF) have each addressed this challenge independently, yet their mathematical ... | forecast, ensemble, prediction, analysis |
31 | Diffusion models are increasingly applied to climate emulation, but whether they capture the correct modes of variability remains unclear, a concern amplified by data scarcity at longer timescales. We investigate this using synthetic tropical Pacific SST fields from Linear Inverse Models (LIMs), whose known low-order s... | diffusion, model, emulation, climate |
32 | Order parameters are difficult to define in high-dimensional nonequilibrium systems that lack a Hamiltonian, a thermodynamic limit or an observed control coordinate. Here we show that such transitions can be diagnosed from the spectrum of occupations over data-derived eigen-microstates. We combine Eigen Microstate Theo... | entropy, thermodynamic, vortex, hamiltonian |
33 | Machine learning is emerging as a valuable tool for convection-related applications such as post-processing numerical weather prediction output, improving understanding of convective storm climatology and potentially improving existing convective parameterization schemes. In a rapidly developing field, it is vital to a... | convective, prediction, predictor, model |
34 | Sea state prediction is essential for operational maritime applications and coupled earth system modeling, yet current spectral wave models remain computationally prohibitive for many use cases, including online coupling to climate simulations and making probabilistic (ensemble-based) predictions. While deep learning h... | model, prediction, diffusion, estimate |
35 | A general formulation of metriplectic dynamics is presented, where the metriplectic four-bracket is constructed by multiplying two skew-symmetric brackets. The new formulation is then used to introduce irreversibility in a generalized two-dimensional (2D) quasigeostrophic (QG) upper-ocean model involving advected quant... | vorticity, entropy, dissipation, formulation |
36 | ML foundation models are able to emulate atmospheric dynamics accurately and efficiently but operate as opaque ``black boxes''. We investigate the internal representations of the Aurora model using spatially pooled PCA and layer-wise relevance propagation (LRP). We find evidence that Aurora's latent space is primarily ... | model, meteorological, atmospheric |
37 | We introduce a new energy-conserving, moist shallow water model with thermal stratification and rotation. The model is derived from a variational principle, using a Lagrangian expressed in terms of enthalpy. In this model, the latent heat from phase transitions modifies the buoyancy dynamics, which in turn feeds back t... | moisture, moist, lagrangian, model |
38 | Accurate forecasting of high-impact weather, specifically extreme precipitation and lightning, remains a significant challenge in numerical weather prediction (NWP) due to the complexity of atmospheric microphysics. While deep-learning models have shown promise in large-scale forecasting, they often suffer from systema... | convective, prediction, meteorological, precipitation |
39 | The performance of two machine learning (ML) atmosphere models - GraphCast and FourCastNetV2 - is evaluated in the context of sub-seasonal prediction, including their ability to represent key climate drivers of variability, namely the Madden-Julian Oscillation and the Southern Annular Mode. Model skill is assessed over... | meteorology, prediction, model, seasonal |
40 | Following the success of machine learning in producing weather predictions with competitive skill compared to complex traditional systems, this article shifts attention from forecast output to the working practices that make prediction systems possible. We argue that machine learning and recent digital technologies wil... | forecast, prediction, computing, climate |
41 | A novel stochastic parametrization scheme representing organized convection is described. The effects of mesoscale convective systems (MCSs) are represented in an observationally constrained manner, by probabilistically triggering an MCS scheme in regions of enhanced environmental total column water vapour. In combinat... | convective, mesoscale, precipitation, convection |
42 | The Earth's climate is an ensemble of interacting, spatially extended oscillatory media ('climate systems') whose slow-changing averages coexist with chaotic, high-frequency weather fluctuations in quasi-equilibrium. The limit of deterministic predictability (LDP) for any climate system is determined by its fastest-gro... | predictability, predictor, prediction, monsoon |
43 | The representation of cloud microphysics and its nonlinear character and scale-dependence is a remaining source of uncertainty in Earth system models (ESMs). Here, we develop and couple online a machine learning (ML)-based cloud microphysics parameterization with the Icosahedral non-hydrostatic modeling framework (ICON... | model, climate, atmospheric, simulation |
44 | Open research data services have matured to the point where the cost of sustaining them at scale has become a primary design constraint, driving providers to make deliberate choices that may reduce user convenience to keep the service viable. The FAIR (Findable, Accessible, Interoperable, Reuseable) principles describe... | dataset, analysis, research |
45 | We adapt the \textit{magnetohydrodynamic} wave phase-mixing paradigm [Tsiklauri et al. (2003)] to investigate \textit{acoustic} wave propagation and damping in media where transverse sound speed gradients exist. Using an analytical model, we recover previous harmonic wave and Gaussian pulse evolution solutions now cont... | wave, amplitude, dissipation, propagation |
46 | This paper uses ADT v9.0 HURSAT v07b data to measure tropical cyclone (TC) activity in four basins from 1990-2024. It is only in the North Atlantic basin that relative sea-surface temperature contrasts and the number of extreme (category 3+) tropical cyclones increased. And it is only there that the complete chain held... | cyclone, tropical, atlantic, temperature |
47 | The atmospheric tropical belt is believed to be more predictable than the extratropics. This question is revisited here by exploring the emergence of chaos in reduced-order model versions of the vorticity equation under the weak temperature gradient hypothesis, which provides a good description of the large-scale tropi... | predictability, vorticity, chaos, tropical |
48 | Some numerical simulations of very warm climates suggest that the Earth's atmosphere may undergo a transition to a state of equatorial superrotation, where the zonal-mean zonal wind in the tropics is westerly. However, major uncertainties remain about the circumstances under which such a transition could happen. A natu... | tropical, equatorial, temperature |
49 | In recent years a weak but robust response of mean midlatitude circulation to Arctic amplification (AA) has emerged from modeling experiments. However, open questions remain about the mechanisms linking such circulation differences to weather extremes in the midlatitudes. In this study we investigate such mechanisms an... | arctic, circulation, atmospheric |
50 | Satellite observations in the solar spectrum (including visible and near-infrared channels) offer high-resolution information on clouds and atmospheric properties valuable for data assimilation. While forward operators for a direct assimilation of solar images have become available recently and a first visible channel ... | satellite, solar, atmospheric |
51 | Subgrid-scale orography remains a leading source of uncertainty in numerical modeling because terrain spectra must be recovered from irregularly sampled elevation data and then reduced to a flow-dependent launch budget for parameterizations. Existing approaches are limited either by assuming regular samples on rectangu... | fourier, spectrum, numerical, spectra |
52 | The invariant-imbedding T-matrix method (IITM) is a standard tool for light scattering by large, sharply faceted, non-axisymmetric particles (atmospheric ice crystals and mineral dust) where the surface-based extended boundary condition method loses accuracy. Its accuracy is limited by "staircasing": the dielectric con... | resolution, particle |
53 | Turbulence closure models are essential for solving the equations of motion in realistic systems, where fully resolving all relevant scales of motion is computationally infeasible. Developing turbulence closures remains one of the most challenging problems in fluid dynamics. Specifically, the Navier-Stokes equations, w... | turbulence, mesoscale, fluid, ocean |
54 | Comprehensive information on cloud microphysical process rates from numerical simulations allows for better understanding of precipitation formation pathways and aerosol-cloud interactions. However, resource limitations often make it impractical to include all microphysical process rates in the model output, limiting i... | precipitation, prediction, model, regression |
55 | Increased upper-ocean stratification is an unavoidable consequence of global warming and will strongly impact the structure of ocean currents. Using a high-resolution ocean model, we show that intensification of stratification leads to the loss of coherence of the Gulf Stream Extension, replacing its steady eastward pa... | turbulence, stratification, ocean, circulation |
56 | Hyperspectral infrared observations are an important data source for numerical weather prediction (NWP) because they provide rich information on the vertical structure of atmospheric temperature and humidity. However, most existing deep learning methods mainly focus on one-way retrieval from radiances to atmospheric pr... | reanalysis, atmospheric, radiance, reconstruction |
57 | We investigate the seasonal phase locking of the El Niño-Southern Oscillation (ENSO) in a linear stochastic recharge oscillator (SRO), a damped oscillator with additive noise and a time-dependent growth rate. Phase locking is reflected in the seasonality of the variance of the sea surface temperature anomaly (SSTA). In... | entropy, seasonality, thermodynamic, stochastic |
58 | Probabilistic weather forecasting is undergoing rapid transformation with artificial intelligence (AI). In traditional numerical weather prediction, computing power can limit how well ensemble forecasts approximate the unknown statistical distribution of future states. AI models facilitate larger ensembles and are trai... | prediction, model, calibration, ensemble |
59 | Spatial patterns in observed climate trends remain poorly understood. Here we argue that a warming of land relative to ocean has shaped observed surface temperature and atmospheric circulation trends, including the negative Inter-Decadal Pacific Oscillation (IPO)-like tendency across the Pacific basin. Observed and mod... | climate, pacific, ocean |
60 | As deep learning for physical systems continues to grow in popularity, efforts to improve generalizability have primarily focused on designing architectures that embed physical constraints. However, for machine-learning surrogate climate models (emulators), we show that the low structural diversity in existing scenario... | emulation, model, emulator, dataset |
61 | We introduce the Artificial Intelligence Forecasting System for Direct Observation Prediction (AIFS-DOP). AIFS-DOP is trained on a 40-year harmonized dataset of gridded observations, without using numerical weather prediction (NWP) reanalysis or model data. The resulting model is competitive with ECMWF's Integrated For... | reanalysis, ecmwf, prediction, model |
62 | Forecast errors in high-resolution numerical weather prediction (NWP) systems are often linked to unresolved planetary boundary layer (PBL) processes, convection, terrain-induced circulations, and other vertically structured atmospheric phenomena. Previous work demonstrated that Long Short-Term Memory (LSTM) networks c... | forecast, prediction, transformer, model |
63 | The Tropical Atlantic exhibits complex sea surface temperature (SST) variability driven by internal ocean-atmosphere interactions and remote climate forcing. We perform a comparative multifractal analysis of three SST indices, South Atlantic Tropical (SAT), Tropical Southern Atlantic (TSA), and the Tropical Atlantic SS... | tropical, atlantic, correlation, variability |
64 | Machine-learning emulators are increasingly used for weather prediction and have the potential to extend skill on subseasonal-to-seasonal timescales by learning dynamically important sources of predictability. A key challenge is whether the models can exploit predictability anchors, such as stratospheric variability, t... | emulation, stratosphere, model, prediction |
65 | The properties of candidate particles for solar radiation management (SRM) through stratospheric aerosol injection (SAI) should comply with safety, controllability, and functionality requirements. Following the proposal in arXiv:2604.02283 of safety and controllability requirements, we define here a set of functionalit... | aerosol, radiation, solar, atmospheric |
66 | Biofouling -- the occupation of microplastic (MP) surfaces by marine microbes -- alters particles' buoyancy and transport, yet its effect on the global distribution of MPs has not been well quantified. We present the first three-dimensional global Eulerian model to fully couple MP transport with biofouling, by augmenti... | buoyancy, subsurface, density |
67 | Characterizing the Marine Atmospheric Boundary Layer (MABL) requires understanding the coupling between ocean waves and the turbulent atmospheric boundary layer above them. This coupling controls momentum exchange between the atmosphere and the ocean; it is of practical importance in the global climate, flow of ocean c... | turbulence, ocean, wave, atmospheric |
68 | Greenland iceberg discharge exhibits complex nonlinear dynamics with limited observability, challenging traditional predictive models. We present a Hybrid NARX-LLM framework that combines a nonlinear autoregressive model with exogenous inputs (NARX) and a large language model (LLM) for residual correction. We further p... | model, prediction, climate |
69 | An ensemble study using the CESM climate model is used to investigate the response of the atmosphere to changes in sea surface temperatures along the path of the North Equatorial Counter Current. The results support those of a previous study which concentrated on a single run, confirming the sensitivity of deep convect... | ecmwf, reanalysis, convection, equatorial |
70 | Emulators provide a cost-effective alternative to regional climate models (RCMs) by capturing their dynamical downscaling function. They link large-scale predictors simulated by global climate models (GCMs) to RCM-simulated high-resolution fields of the target variable, here precipitation. Machine learning methods, typ... | diffusion, emulation, model, ensemble |
71 | The open ocean lacks systematic in situ wind observations, and satellite scatterometer calibration depends on collocated surface measurements largely absent away from coastlines. Compact wave-sensing drifters already retrieve the open-ocean wind vector, but at modest accuracy -- about 1-2 m/s in speed and with unreliab... | satellite, wind, ocean |
72 | Non-trivial alignments between vorticity and the strain-rate tensor play an important role in the evolution of velocity gradients and the energy cascade in isotropic turbulence. Here we explore how alignments between the fluctuating and mean density gradients impact the mechanisms governing the turbulent kinetic energy... | turbulence, vorticity, stability, isotropic |
73 | Accurately representing convection and precipitation remains a persistent challenge for Numerical Weather Prediction (NWP) models due to biases in convective initiation, storm organization, and rainfall distribution, particularly in subtropical/tropical environments. This study evaluated how microphysics parameterizati... | convective, precipitation, convection, rainfall |
74 | The Coupled Model Intercomparison Project Phase 6 (CMIP6) has generated thousands of peer-reviewed publications documenting model configurations, evaluation procedures, emergent constraints, and projection uncertainties. As the community transitions toward CMIP7, efficiently extracting and operationalizing this unstruc... | model, analysis, research |
75 | As part of the international research programme TEAMx (multi-scale transport and exchange processes in the atmosphere over mountains - programme and experiment) a one-year long measurement campaign, the TEAMx Observational Campaign (TOC), was conducted between 2024 and 2025 in a north-south transect through the Alps. B... | atmospheric, experiment, atmosphere, convection |
76 | Uncertainty in the terrestrial carbon cycle remains a major constraint in climate projections, partly driven by the uncertainties affecting the land surface representation and variability in Earth system models. To address this limitation, we present a data-driven framework AI4Land, for generating high-resolution histo... | climate, carbon, surface |
77 | Urban heat is amplified by impermeable surfaces and heterogeneous built environments, yet street-level variability remains difficult to quantify because multi-sensor observations are rarely available in consistent, analysis-ready form at the necessary spatiotemporal scales. We present "Urban Heat MiniCubes," a publicly... | dataset, temperature, heat, radar |
78 | Large language models (LLMs) can translate and modify source code, and have been shown to do so for codes of different complexity. Whether they can port a complete, production geophysical model to a different language without degrading its physics has not been established. We demonstrate that LLM-assisted code translat... | model, simulation |
79 | Machine learning weather prediction (MLWP) models have achieved impressive forecasting performance at a small fraction of the computational costs required for traditional physics-based methods. However, they are primarily (1) data-driven and (2) evaluated using pixel-wide error metrics (e.g., RMSE), so there are no gua... | prediction, weather, evaluation |
80 | Tropospheric ozone (O3) is a key greenhouse gas and atmospheric oxidant, yet its sources in the remote troposphere remain strongly debated. Observation-based tracer analyses suggest that O3 attributed to biomass burning is much greater than that from fossil fuel sources (by a factor of ~2-10), contradicting state-of-th... | ozone, troposphere, greenhouse |
81 | Kling-Gupta efficiency ($\mathrm{KGE}$) is a model performance evaluation metric widely used in hydrology, but its properties as a statistical estimator have remained unexplored. We formalize the Kling-Gupta loss $L_\mathrm{KG} = (1 - \mathrm{KGE})^2$ in an extremum estimation framework (maximizing $\mathrm{KGE}$) for ... | regression, predictor, estimation |
82 | Tropical Pacific sea-surface-temperature (SST) variability spans interacting timescales, with the ENSO as its dominant interannual expression. Yet the dynamical structure organizing this variability and underpinning extended-range predictability remains difficult to extract from high-dimensional observations. Koopman o... | predictability, variability, pacific, tropical |
83 | Urban populations are increasingly exposed to extreme heat events such as heatwaves, which can be exacerbated in cities due to the urban heat island (UHI) effect. With the aim of developing adaptation strategies, recent years have seen a growing interest in deploying high-resolution measurement networks using low-cost ... | temperature, heat, climate |
84 | The Atlantic Meridional Overturning Circulation (AMOC) has been identified as a tipping element in the Earth system. Under the current climate change scenarios, it is urgent to develop robust methods for determining the probability of future AMOC transitions. Recent studies using an Earth System Model of Intermediate C... | climate, atlantic, equator, network |
85 | While global data-driven models excel at predicting continuous atmospheric variables, three-dimensional hydrometeor forecasting remains challenging due to the zero-inflated, long-tailed distributions of these variables. Standard deep learning optimization often yields overly smooth forecasts, attenuating extreme events... | forecast, prediction, atmospheric, hurricane |
86 | Accurate oceanic forecasting is critical for climate monitoring and disaster early warning. However, ocean spatiotemporal forecasting encounters the double challenges of modeling complex dynamical systems and ensuring computational efficiency. We present Koopman Fourier Time-Differentiable (KFTD) Network, a time contin... | forecast, diffusion, fourier, prediction |
87 | While previous versions of the Ai2 Climate Emulator (ACE) have been trained with CO$_2$ as a forcing, they are only accurate within a narrow range of scenarios, for example climate over the last 80 years forced by observed sea surface temperature (SST), sea ice, and CO$_2$ (AMIP), or equilibrium or near-equilibrium cli... | model, climate, emulator, temperature |
88 | How much does the wind affect the path of meteorite falls? We finely model the lower ~30 km of the atmosphere using Weather Research and Forecasting open source tools at 1 km spatial resolution. Models initialised at different times give different results, which can be used as a proxy for uncertainty. We find that in m... | wind, model, uncertainty, weather |
89 | As atmospheric environmental prediction continues to improve, interpretable validation of pollution mechanisms and feedback processes has become a main challenge in atmospheric chemistry. Yet mechanism validation based on complex numerical models still relies heavily on expert knowledge: mechanistic hypotheses must be ... | atmospheric, ozone, aerosol, mechanism |
90 | The Loop Current is the dominant circulation feature of the Gulf of Mexico and exhibits pronounced variability associated with northward extension, retraction, and eddy shedding. Despite decades of study, the extent to which this variability admits a reduced dynamical description remains unclear.
We investigate this ... | forecast, circulation, vorticity, regression |
91 | This study presents a novel framework for estimating the central sea-level pressure ($P_\mathrm{c}$) of tropical cyclones (TCs) using infrared radiometers. We leverage the long-overlooked combination of high spatial resolution and sounding capability of the Moderate Resolution Imaging Spectroradiometer (MODIS) to measu... | estimation, tropical, intensity, regression |
92 | We establish the ultimate precision limit for estimating the optical spatial coherence radius (Fried parameter) within a quantum metrological framework. In the weak field regime, we show that spatial-mode decomposition -- originally introduced for superresolution imaging -- enables substantially more precise estimation... | estimation, quantum, decomposition |
93 | Wildfire monitoring and suppression require timely information on fire behavior, including fire energy release and rate of spread, to support operational decision-making and resource allocation. Active fire products from the Flexible Combined Imager (FCI) aboard the geostationary Meteosat Third Generation (MTG) satelli... | wildfire, seasonal |
94 | The atmosphere is a resonant system and its oscillation spectrum is determined by the spatial distribution of parameters. For example, the frequency of internal gravity waves depends on the vertical temperature gradient. Therefore, the study of the spectra of internal wave processes can be used to estimate the spatial ... | atmospheric, atmosphere, spectrum, spectra |
95 | Global infrasound monitoring provides a persistent means of detecting energetic bolide atmospheric entries, complementing optical observations and extending coverage over remote regions. We present a global assessment of the physical factors governing bolide infrasound detectability by correlating 623 bolide events rep... | detection, atmospheric, frequency |
96 | The detection of mass distributions and mass transport via gravity mapping is a key geophysical tool for understanding the structure and dynamics of the earth. Changes in mass distribution, driven by natural processes and human activity (e.g., extraction of oil, gas, and minerals), contribute to observable phenomena su... | sensor, detection |
97 | Deep learning weather models now match numerical weather prediction accuracy while running orders of magnitude faster, but produce deterministic forecasts without uncertainty estimates, a critical gap for high-stakes decisions during extreme weather events. This paper proposes Neural Tangent Kernel-based uncertainty qu... | prediction, inference, neural, uncertainty |
98 | A fundamental goal in climate attribution is to estimate how forced climate change contributes to observed extreme weather events. The storyline attribution method compares an observed weather event, conditional on its atmospheric dynamic state (i.e., atmospheric circulation), in the current, 'factual' climate to an ev... | climate, heatwave, estimate, model |
99 | Climate change is expected to increase heatwave risk, but exceedance frequency alone cannot explain why some regions show stronger amplification in event persistence. This study develops an integrated event-dynamical workflow to diagnose changes in warm-season heatwaves and link them to coherent, multiscale structures ... | heatwave, climate, temperature |
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