Sciences de l'atmosphère, des océans et du climat.
Language-model agents are increasingly asked to carry out open-ended scientific research, yet their results are usually graded against a known answer, a rubric,...
arXiv
Rapid and accurate prediction of urban wind and temperature fields is important for urban microclimate design and climate adaptation. Large-eddy simulation (LES...
arXiv
Transient supplies of nutrients to the surface ocean, both natural and artificial, stimulate blooms of phytoplankton and the formation of organic matter, drivin...
arXiv
Assessing compound weather risks requires forecasts representing dependence between variables. CLARA (Calibrated Advection-Routing Attention) learns joint Gauss...
arXiv
Accurate precipitation estimation at fine spatial scales is critical for hydrology, agriculture, and climate studies. Infrared brightness temperatures from geos...
arXiv
Deep learning has made rapid advances in weather forecasting: autoregressive models trained on atmospheric reanalyses now rival dynamical models across nowcasti...
arXiv
We introduce EC-EarthFlow, a generative flow matching model that emulates simulations from the physical climate model EC-Earth3. The model is trained on transie...
arXiv
The Global Ocean Observing System is key to understanding oceanic variability and climate change. The ocean is vast and often sparsely and irregularly sampled i...
arXiv
Atmospheric warming raises evaporative demand, but actual evapotranspiration (ETa) may track demand or decouple under water limitation. Here, we quantified dema...
arXiv
A combination of remote sensing techniques, open space data and field testing was developed to derive astronomical and meteorological parameters and assess Dark...
arXiv
Dissolved oxygen is important for the ocean's ecosystems and biogeochemical cycles. Yet, Earth System Models (ESMs) vary in their simulations of the present-day...
arXiv
The production of adipic acid generates nitrous oxide as a by-product, which, if emitted, very effectively warms the climate and depletes the stratospheric ozon...
arXiv
Data assimilation (DA) is the process of combining forecasts from a model with observations in order to optimally estimate the state of a system. This is critic...
arXiv
Deep learning has revolutionised weather forecasting in recent years, especially through atmospheric foundation models, which offer competitive skill for a frac...
arXiv
Despite substantial progress in short-to-medium-range weather forecasting, predicting high-impact events such as flash droughts remains a key challenge for both...
arXiv
We develop structure-preserving methods for the compressible Euler equations with gravity in vector-invariant form and potential temperature as a prognostic var...
arXiv
Analytical greenhouse models traditionally rely on the gray approximation, simplifying radiative transfer but treating optical depth as an unconstrained paramet...
arXiv
We present ClimateBench v2, a standardized protocol for evaluating climate models on diagnostics expected to be informative for their skill in projecting mid-ce...
arXiv
The Gálvez Davison Index (GDI) is a thermodynamic diagnostic designed to summarize convective favorability in the tropical regions, yet its transferability acro...
arXiv
Climate oscillations are intrinsically out-of-equilibrium phenomena, yet the connection between their predictability and nonequilibrium thermodynamic properties...
arXiv
Earth system models (ESMs) have grown tremendously in realism, yet key uncertainties persist in the climate response to greenhouse-gas forcing, particularly due...
arXiv
Near the ocean surface, tropical cyclone winds change rapidly with height, but direct measurements are limited because aircraft dropsondes provide only sparse a...
arXiv
Skillful prediction of biomass-burning activity several days in advance is important for air-quality forecasting and aerosol prediction. Two operational constra...
arXiv
The intermittent and variable nature of precipitation makes its accurate estimation over extended domains difficult, yet its spatiotemporal structure suggests t...
arXiv
We develop a unified description of convolution on the sphere by lifting to the rotation group. A number of definitions of spherical convolution have been propo...
arXiv
Tidally-locked planets express a range of climate regimes distinct from those found in our solar system. Their atmospheres are often dominated by planetary-wide...
arXiv
Currently available methods for processing atmospheric wave disturbances are often based on integral wavelet transform techniques, which efficiently estimate sp...
arXiv
In this report, 15 variants of nine parametric models for non-directional ocean wave spectra are reviewed. They are compared against observation-based spectra c...
arXiv
Legacy Fortran physics packages in atmospheric models such as CESM, WRF, and E3SM pose a barrier to GPU modernization, while manual porting requires accelerator...
arXiv
Cloud droplets respond to turbulent motion with finite inertia and settle under gravity, while condensation and evaporation change their sizes. The nonuniform a...
arXiv
Fronts, eddies, jets, and meanders populate the ocean surface and play a key role in organizing transport and mixing across a wide range of scales. From a Lagra...
arXiv
Rainfall forecasts over the Western Himalaya remain poor, and the relative contributions of the cumulus and microphysics parameterizations to that error are not...
arXiv
We present HiRO-ACEv2 (High Resolution Output Ai2 Climate Emulator), a two-stage framework for AI emulation of the 3~km resolution X-SHiELD global storm-resolvi...
arXiv
The Hybrid forecast covariance model is an operationally successful way to build a background error covariance matrix. It is constructed as a weighted sum, with...
arXiv
Many approaches have recently been proposed to combine data assimilation with machine learning for improved forecasts with geophysical models. Here, we focus on...
arXiv
Global reanalyses are too coarse to resolve the convective systems that produce most West African monsoon rainfall. In this work, we use U-Net based architectur...
arXiv
A potentially super El Niño is forecasted in 2026, raising the need to anticipate land carbon-cycle risks before its impacts unfold. We develop a prospective ea...
arXiv
This work investigates the incidence of rapid ice-loss events in the Arctic, in which a substantial fraction of September sea-ice is lost in only a few years. S...
arXiv
Ensemble forecasts from the ECMWF's Integrated Forecasting System (IFS) and the Artificial Intelligence Forecasting System (AIFS-ENS) are compared using a multi...
arXiv
Aim. We try to understand the interaction of eccentricity tide and convection in icy satellites. Method. We use the numerical calculation of rotating convection...
arXiv
We present two and three dimensional direct numerical simulations of planar intrusions injected from a continuously supplied, narrow source into a linearly stra...
arXiv
A long-standing challenge in climate science since the Charney Report in 1979 is to determine how much the Earth's climate warms in response to a unit external...
arXiv
Deep-learning models increasingly rival numerical simulation in Earth-system forecasting, yet forecast error alone cannot determine whether their learned input-...
arXiv
The notion of tipping points and related terminology have so far had inconsistent and sometimes confusing definitions in both scientific and popular literature....
arXiv
Conventional deep convection schemes remain a primary source of uncertainty in global climate models, yet replacing them with data-driven alternatives while mai...
arXiv
Measurements of optical turbulence are essential in both ground-based astronomy and free-space optical communication systems contexts for multiple reasons inclu...
arXiv
High-resolution precipitation datasets are essential for weather forecasting, hydrological modeling, and climate risk assessment, yet observation-based gridded...
arXiv
Inferring climate fields at higher spatio-temporal resolutions from coarse representations is essential for accessing socioeconomic and environmental impacts. T...
arXiv
This work introduces a Process Additive Neural Network (PANN) framework to disentangle compensating biases in atmospheric models, where errors in different phys...
arXiv
Under anthropogenic greenhouse forcing, climate models project a decrease in year-to-year temperature variability in the mid-to-high latitudes and an increase i...
arXiv
We present the RemoTAP-Hybrid algorithm for global aerosol products retrieved from Multi-Angle Polarimeters (MAP), and apply it to one-year aerosol retrievals f...
arXiv
Over 4 billion years, Earth's atmosphere has been transformed from a composition devoid of molecular oxygen (O$_2$) to one where O$_2$ is now 21% of our modern...
arXiv
Accurate forecasting of optical turbulence (OT) is useful to optimise telescope scheduling and maximising observing efficiency, as the performance of adaptive o...
arXiv
A possible mechanism of tornado generation is associated with the Schwarzschild criterion for convective instability. The supercooled cloud-air mixture near the...
arXiv
In partially-observed dynamical systems, distinct hidden mechanisms can produce similar observable summary statistics while explaining individual events differe...
arXiv
Weather prediction is undergoing an important shift, with data-driven AI models outperforming leading numerical weather prediction systems on average at a fract...
arXiv
Multi-aperture reception converts spatial diversity into a coherent-combining gain, but only if the relative phase and polarization of the collected field repli...
arXiv
Tropical cyclones can produce extreme winds in the lowest portion of the boundary layer, posing high risks to critical energy infrastructure, including wind ene...
arXiv
The unimolecular dynamics of Criegee intermediates depend strongly on their internal energy, which can differ substantially between nascent atmospheric species...
arXiv
In the recent AI Model Intercomparison Project (AIMIP) benchmark, the ACE2.1-ERA5 emulator was shown to underestimate the true near-surface warming trend both d...
arXiv
Utilities request mutual aid crews to restore power after severe storms, and requesting early secures more crews but relies on less accurate forecasts. We exami...
arXiv
Urban hydrodynamic flood models typically rely on a surveyed terrain model, an explicit pipe drainage network, and rain gauge measurements, none of which were a...
arXiv
The ACSIOM workshop was held in Cambridge from 10th-12th November 2025. Its primary aims were twofold: 1) to gather UK researchers involved in Mars atmospheric...
arXiv
Earth system models underpin the evidence that informs net-zero policy, yet their energy demands are rarely considered when judging model quality. Using archive...
arXiv
Seasonal prediction of Yangtze summer rainfall is challenged by nonstationary oceanic precursors. Here we demonstrate that accounting for the evolution of sprin...
arXiv
Atmospheric composition and chemistry-climate interactions are central to predicting future climate change. Yet simulating chemical and aerosol processes is typ...
arXiv
Generative models for precipitation nowcasting are trained and evaluated predominantly on wet-biased datasets, which over-represent precipitation events to emph...
arXiv
Effectively preparing for changes in extreme flooding requires understanding present and future rainfall extremes with return periods up to $\sim$100-1000 years...
arXiv
Accurate real-time forecasting of atmospheric pollutants is essential for reducing exposure risks and improving public health. Traditional numerical forecasting...
arXiv
Meteorological downscaling is crucial for high-resolution regional climate forecasting and disaster early warning. While neural operators have emerged as a prom...
arXiv
A mechanistic interpretation for baroclinic normal mode instability with exponential growth is the interaction arising from Counter-propagating Rossby Waves (CR...
arXiv
Radiance-based retrievals and data assimilation require radiative transfer models (RTMs) to simulate radiances and quantify their sensitivities to atmospheric a...
arXiv
AI weather prediction models are often regarded as black boxes. Despite their impressive skill, there is little understanding of how the input data is processed...
arXiv
Attribution of observed historical climate evolution to externally forced change and internal variability is fundamental to understanding climate change and eva...
arXiv
Uncertainty in the terrestrial carbon cycle remains a major constraint in climate projections, partly due to imperfect land surface representations in Earth sys...
arXiv
Filtering by spatial convolution underlies coarse-graining and Large Eddy Simulation, the analysis of multiscale dynamics, and data processing across the scienc...
arXiv
Titan's equatorial superrotation is associated with climatological retrograde (westward) near-surface winds at low latitudes, as consistently simulated by Titan...
arXiv
Airborne transmission of biological matter is likely to cause numerous respiratory distress in coastal populations during certain harmful algal blooms. While we...
arXiv
This paper describes an approach to constructing nowcast and forecast predictive models for the occurrence of high-latitude decameter ionospheric irregularities...
arXiv
Land use regression models relate intra-urban air temperature to spatial predictors such as building volume, tree canopy cover and topography, and are increasin...
arXiv
Short-range hindcasts of measured wave spectra are evaluated using a recurrent model initialized by one hourly buoy spectrum and supplied with local wind, fetch...
arXiv
Satellite precipitation products face a persistent trade-off: real-time estimates are timely but less accurate, whereas the most reliable products are often rel...
arXiv
Artificial intelligence (AI) weather models now forecast weather events skillfully, but how they represent these events internally remains unknown. Sparse autoe...
arXiv
AI weather models perform competitively with traditional physics-based models at a fraction of the computational cost, and they are thus already used operationa...
arXiv
Air quality policy will cut aerosol and ozone precursor emissions, and studies disagree on how the tropical Pacific circulation and the El Nino Southern Oscilla...
arXiv
Computational Fluid Dynamics (CFD) is increasingly used to resolve wind, heat, and pollution patterns in cities with high fidelity but high computational cost....
arXiv
Long-term environmental IoT/LPWAN deployments often aggregate heterogeneous data streams from diverse devices without systematic auditing of each variable's act...
arXiv
Background-error covariance models are central to atmospheric data assimilation because they spread partial observational information across space, variables, a...
arXiv
Machine learning Earth-system models are becoming increasingly important, and so is their development. Graph-based models, one of the most prominent approaches,...
arXiv
Antarctic ice shelves play a key role in controlling the mass loss of the Antarctic Ice Sheet. Increasing evidence from regional studies suggests the possibilit...
arXiv
Godin (\textit{J. Fluid Mech.}, vol. 767, 2015, pp. 52--64) showed that compressible fluids in a uniform gravity field support finite-amplitude incompressible w...
arXiv
Porting a regional weather model in its entirety to a graphics processing unit (GPU) requires complete implementation of the dynamical core and all physical pro...
arXiv
Forecast-error growth in multiscale chaotic systems involves both advancement toward saturation and evolution of the error's scale distribution. We introduce Lo...
arXiv
We analyze scale-by-scale budgets of velocity and buoyancy second-order structure functions using Large Eddy Simulation of the BOMEX trade-wind cumulus layer. T...
arXiv
Satellite-derived bathymetry (SDB) is often proposed as a low-cost complement to multibeam surveys of ports, but its accuracy is seldom measured on an area the...
arXiv
Venus is home to vivid sulfur chemistry, with SO$_2$ as the major sulfur gas species and a global cloud layer between 47 and~70 km composed of H$_2$SO$_4$ and H...
arXiv
An input skip can reduce reconstruction error while leaving a model's distribution of new samples unchanged. For a Gaussian variational autoencoder (VAE) with c...
arXiv
Preserving fine-scale structure without degrading spatial accuracy is a central challenge for kilometre-scale machine-learning weather prediction (MLWP). We int...
arXiv
Simulating extreme monsoon rainfall over the Konkan coast and Western Ghats remains challenging at 9 km resolution, where convection is partly resolved and part...
arXiv
Existing noise mitigation strategies for wind farms usually consist in reducing turbines power output, without accounting for the wake interactions between them...
arXiv
The long range propagation of acoustic waves in Earth's atmosphere is sensitive to winds and temperature variations in the upper atmosphere. Thus, infrasound ob...
arXiv
Significant wave height (SWH) retrieval from high-frequency (HF) radar data conventionally relies on Doppler-spectrum processing and interpretation of the weak...
arXiv
Coupled climate simulations are computationally expensive, largely because of the atmosphere. We introduce FASST, a coupled climate model that retains the progn...
arXiv
Improving convective-scale skill in data-driven regional weather models requires better representing rare high-impact events, training at native resolution, bro...
arXiv