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Physics Physics Atmospheric and Oceanic Physics

physics.ao-ph Watch Atmospheric and Oceanic Physics

Saturday 26 September

Atmospheric and Oceanic Physics · Cross-listed from cond-mat.other

Physics-based logarithmic description of electrostatic field enhancement in hemisphere-on-cylindrical-post structures for high-field applications

Thiago A. de Assis, Fernando F. Dall-Agnol and Richard G. Forbes

Electrostatic (ES) field enhancement at sharp conducting structures plays a central role in lightning protection, corona discharge, electrical breakdown in vacuum (for example in particle accelerators), and more generally in technological applications of field electron emitters. A canonical geometry for studying this ES effect is the hemisphere-on-cylindrical-post (HCP) model, in the regime where the structure stands on a planar conductor of large lateral extent, and a large gap exists between the structure and the counter-electrode. A parameter of major interest is the apex field enhancement factor (AFEF) (apex-ES-field/background-ES-field). For this (and other) structures, a formula for the AFEF can be written in the form AFEF = ASC x ASR, where the apex sharpness ratio (ASR) is given by the ratio (post-height/apex-radius-of-curvature), and the apex sharpness coefficient (ASC) depends on the post shape. For the HCP model, no exact analytical formulas for the ASC or the AFEF are currently known. (Quite possibly none exist.) This paper develops a compact analytical approximation for the ASC and hence for the AFEF. This compact formula has a logarithmic-style structure, rather than the power-style structure used in previous approximations. When compared with precise finite-element analyses of the HCP model, over the computationally accessible range 1 to 1000 for the ASR, this logarithmic-style formula has a maximum error-magnitude of 0.15 percent, which is significantly better than older approximations.
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Atmospheric and Oceanic Physics · Cross-listed from physics.flu-dyn

Spall Failure of Coral Skeleton beneath Gas-Laden Canopies: An Idealized Blast-Fishing Model

Sandy Hardian Susanto Herho, Agus Wahyu Jatmiko, Rizki Dimas Permana et al. (+7 more)

Improvised explosives used for fishing on shallow Indonesian reefs shatter coral skeleton, yet the damage they cause has been described largely through empirical radii and ecological surveys. We formulate an idealized model of how free gas held within a coral canopy modifies the shock loading that such a charge delivers to skeletal plates. The canopy is treated as a relaxed bubbly mixture whose shock impedance follows from the conservation of mass and momentum, and the reef is represented as a layered column of water, canopy, skeletal plate, and canopy struck at normal incidence. Three closed-form results emerge. Above a crossover pressure set by the void fraction and the stiffness of seawater, the canopy becomes nearly transparent to the shock. The impulse transmitted through any lossless layered stack is independent of the canopy, so gas redistributes the pulse in time without changing its total push. A plate carries tension after reflection from its lower face only when the canopy impedance falls below a threshold fixed by the plate thickness and the pulse duration, which defines a critical thickness. For a one-kilogram charge directly overhead, a gas-rich canopy more than triples the standoff at which a twelve-centimeter plate spalls while shortening the standoff at which it is crushed. A prescribed daily cycle of photosynthetic gas makes the same charge markedly more damaging at noon than at night. Bubble dynamics show that the canopy does not reach equilibrium within the pulse, so the results are best read as upper bounds.
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All authors Sandy Hardian Susanto Herho, Agus Wahyu Jatmiko, Rizki Dimas Permana, Iwan Pramesti Anwar, Alfita Puspa Handayani, Faruq Khadami, Karina Aprilia Sujatmiko, Rusmawan Suwarman, Deny Juanda Puradimaja, Dasapta Erwin Irawan

Friday 25 September

Atmospheric and Oceanic Physics

Learning Aerosol Coagulation Dynamics in Latent Space with a Scale-Covariant Neural ODE

Wenhan Tang, Ruqi Yang, Jeffrey H. Curtis et al. (+5 more)

Particle-resolved aerosol models preserve the joint size-composition structure that governs cloud activation, optical properties, and freezing, but their computational cost limits their use in large-scale atmospheric models. We extend AeroMELD from a compact representation of aerosol populations to a prognostic model of coagulation. The resulting AeroMELD-Coag advances nine learned coordinates for population shape and one for total particle number through a scale-covariant neural ordinary differential equation that incorporates the known concentration dependence of coagulation. Across 2,000 held-out trajectories spanning 48 h, median symmetric errors are 3.5% for latent shape and 0.52% for total number. This ten-coordinate state also retains the evolving size-resolved composition and associated cloud-condensation-nuclei (CCN) activation, optical properties, and frozen fraction. Compared with the evaluated 20-bin sectional model with 320 prognostic coordinates, AeroMELD-Coag lowers pooled mean CCN activation error from 1.98% to 1.38% and frozen-fraction error from 1.72% to 0.56%. In a matched integration benchmark, GPU integration time per trajectory, amortized over a batch, is more than three orders of magnitude lower than for both single-core CPU references. These coagulation results provide a proof of concept for advancing aerosol microphysics efficiently in a compact learned state while retaining information about aerosol mixing state. They establish a foundation for future large-scale atmospheric models to represent aerosol microphysics with greater mixing-state detail within practical computational budgets.
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All authors Wenhan Tang, Ruqi Yang, Jeffrey H. Curtis, Ehsan Saleh, Lekha Patel, Peter A. Bosler, Nicole Riemer, Matthew West

Atmospheric and Oceanic Physics · Cross-listed from physics.optics

Distributed laser energy deposition enables fog-resilient optical communications

Malte C. Schroeder, Victor Moreno, Christophe Coreixas et al. (+2 more)

Fog and clouds remain major obstacles to free-space optical (FSO) communications because micron-sized droplets can impose tens of decibels of attenuation. Ultrashort laser filamentation can transiently clear fog in laboratory-scale experiments, but its extension to atmospheric-scale propagation has remained uncertain. Here we demonstrate laser-assisted fog clearing under atmospheric-scale propagation conditions over a 140-m optical path using a high-energy, high-average-power picosecond laser and weak focusing to sustain extended multifilamentation. A steady-state transparent channel approximately 2 cm in diameter restores up to 12 dB of transmission for an independent optical beam. Increasing the clearing-laser pulse energy primarily extends the multifilament region while maintaining a nearly constant longitudinally averaged deposited energy per unit length of ~2 mJ/m over the measured bundle length. This low average deposition, together with self-sustained filament propagation through dense fog, provides a favorable scaling route toward weather-resilient optical links.
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All authors Malte C. Schroeder, Victor Moreno, Christophe Coreixas, Jonas Latt, Jean-Pierre Wolf

Atmospheric and Oceanic Physics · Cross-listed from cs.LG

Mechanism-Aware Ensemble Conditioning for Data-Limited Emulation of Extreme Events

Isabella S. Thiel, Juan Bello-Rivas, Yannis G. Kevrekidis et al. (+1 more)

Extreme events in chaotic systems are difficult to learn from short trajectories because they are controlled by transient finite-time instability rather than by frequently observed bulk dynamics. We propose a mechanism-aware conditioning plug-in framework that turns a nudged coarse ensemble into a non-intrusive sensor of local instability geometry. In the small-noise regime, the ensemble covariance aggregates the same finite-time deformation kernels that govern local instability, providing a Jacobian-free proxy for the local amplification structure around a synchronized coarse trajectory. A small FiLM module injects statistics of this ensemble geometry into an otherwise unchanged backbone while leaving the coarse simulator unchanged. We demonstrate this interface in two distinct pipelines: a Transformer-style residual-attention corrector for a controlled low-dimensional chaotic system and a probabilistic recurrent STORN corrector for topographic two-layer quasi-geostrophic (QG) flow. In the low-dimensional benchmark, ensemble covariance directions co-activate with OTD modes and FiLM conditioning improves 99th-percentile exceedance-frequency errors over an identical no-context Transformer baseline. In QG, a fixed ensemble-conditioned FiLM-STORN model trained on only \(50\) time units substantially improves long-horizon rare-event statistics in the data-limited regime, including density-tail errors, exceedance frequencies, and spatial exceedance-area distributions relative to an unconditioned STORN trained on the same data; on averaged high-threshold exceedance diagnostics, it also outperforms the baseline STORN trained with $20$ times more high-resolution data. These results show that local instability geometry is not merely interpretable post hoc, but an actionable conditioning signal for data-efficient rare-event emulation.
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All authors Isabella S. Thiel, Juan Bello-Rivas, Yannis G. Kevrekidis, Themistoklis P. Sapsis

Atmospheric and Oceanic Physics

On the Limits of Univariate Deep Learning for Significant Wave Height Forecasting

Yilin Zhai, Hongyuan Shi and Zaijin You

This study conducts a systematic hyperparameter search across five deep learning architectures, DLinear, LSTM, PatchTST, ResAttLstm, and Mamba2, and nine context lengths (1-168 h) for single-station significant wave height (Hs) forecasting on NDBC buoy 41009, followed by re-evaluation of the best configurations on a 47-buoy, 37-year corpus. The five families converge to a common performance level on the multi-buoy evaluation (between-family SD = 0.0014 m^2, 0.8% of the grand mean), a spread dwarfed by the 4.83x cross-dataset MSE shift between buoy corpora. All multi-buoy trials beat persistence (mean skill +0.062), but no architecture consistently outperforms the others. On the single-buoy experiment, skill peaks at 12-24 h where five trials fall below persistence, per-family Q4/Q3 test MSE ratios range from 2.4 to 2.6, and deep models underperform persistence for the most extreme 1% of waves. These findings are consistent with the interpretation that persistence already captures the dominant linear-inertial signal in univariate Hs, and that architecture engineering under this univariate input setting has reached diminishing returns: cross-buoy variance, not model class, dominates forecast error. Future work should prioritise atmospheric covariates, zero-shot cross-buoy transfer, and decomposition of Hs into swell and wind-sea components. By establishing a rigorous reference baseline for what univariate Hs models can and cannot achieve, this study provides a benchmark against which future multivariate and physics-informed approaches can be calibrated, and offers practical guidance for lightweight buoy-level forecasting in mid-latitude storm-dominated and swell-mixed environments.
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Thursday 24 September

Atmospheric and Oceanic Physics

LoRA Adaptation Strength in Aurora-WRF: Trade-offs in Regional Heavy-Rainfall Forecasting

Boyan Liu and Xiaoyuan Zhang

We investigate how parameter-efficient adaptation of an atmospheric foundation model affects downstream regional precipitation in a controlled Aurora-WRF coupling experiment over the Beijing-Tianjin-Hebei region. A single-step, precipitation-weighted LoRA adaptation of AuroraPretrained is trained on May-September 2020-2021 and selected using 2022 validation data. Five inference-time adaptation strengths are coupled to an unchanged 9-km WRF configuration and compared with a GFS-driven control under common ERA5 initialization and auxiliary-field rules. Evaluation uses 17 initializations in 2023, hourly ERA5 precipitation, and conservative remapping to a fixed 356-cell, 0.25-degree verification grid. Heavy rainfall is defined exclusively as at least 50 mm in a continuous 24-hour window, advanced hourly. For window starts from 0 to 48 hours, unadapted Aurora attains the highest mean critical success index (CSI), 0.216, compared with 0.144 for the GFS control. An intermediate LoRA strength of 0.75 reduces the Aurora-driven 24-hour precipitation mean squared error by 30.7% and brings pooled frequency Bias from 1.510 to 1.024, but lowers CSI to 0.182. GFS retains the lowest whole-domain error. These results characterize a trade-off between rainfall detection, frequency calibration, and intensity error, showing the importance of evaluating adaptation strength against multiple downstream criteria within a common coupling framework.
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Atmospheric and Oceanic Physics · Cross-listed from stat.AP

Suitable Measures for the Potential Operational Utility of AI NWP Rainfall Forecasts Over Africa

Shruti Nath, Docko Sow, Koomi Toussaint Amoussouvi et al. (+5 more)

Artificial intelligence (AI)-based weather prediction is approaching the skill of physical numerical weather prediction (NWP) systems at a fraction of the computational cost. This is particularly promising for Africa, where rainfall extremes are intensifying and many forecasting centres lack the infrastructure to run physical models at extended lead times. We present a calibrated comparison of GraphCast, GenCast and the Functional Generative Network (FGN) against the physical NWP model IFS for rainfall prediction across Africa. Deterministic and probabilistic forecasts are postprocessed using Isotonic Distributional Regression and evaluated with the Continuous Ranked Probability Score against IMERG, RFEv2 and CHIRPS across seasons, wet and dry regimes, elevation zones and lead times. All models retain skill beyond climatology across most seasons and at extended lead times. AI models generally outperform IFS in wet regions, whereas IFS performs better in dry, high-elevation areas, where its finer resolution better represents orographic controls on rainfall. Across observational datasets and seasons, AI models achieve a median improvement of approximately 5% over IFS. GraphCast achieves calibrated skill comparable to the ensemble-based FGN, although FGN provides greater significant skill at longer lead times. These results highlight the potential of calibrated AI weather prediction to provide accessible and computationally efficient rainfall forecasts across Africa, while demonstrating the continuing importance of spatial resolution, ensemble design and regional characteristics.
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All authors Shruti Nath, Docko Sow, Koomi Toussaint Amoussouvi, Fenwick Cooper, Josiah Kiarie Kimani, John Bagiliko, Florian Pappenberger, Rendani Mbuvha

Atmospheric and Oceanic Physics

Weather and Climate Without Fluid Mechanics

Thomas D. DeWitt

Even if it were possible, attempting to model the motion of a fluid in a pipe by tracking each individual molecule would be an extraordinary waste of computation. The emergent laws of fluid mechanics accomplish the same goal on a laptop instead of a datacenter. But a sufficiently complex fluid such as in Earth's atmosphere again requires a datacenter, this time to resolve the many contortions in the fields making up our weather. Might there be some analogous set of emergent laws that would represent the effect of these perturbations without needing their explicit representation on a numerical grid? We argue that the existence of a symmetry principle implies such laws are likely to exist. The atmosphere is scale invariant, implying that the observed structure at one scale is nothing but a stretched version of that at any other scale, at least within the invariant regime. Though some emergent laws built on scale invariance exist, they are built on a challenging ontological foundation, making them hard to develop further. We propose a path forward, first by introducing a new fundamental building block of the atmosphere that we term a “turbulon”, inspired by a lesser-known generalization of turbulence theory that accounts for buoyancy. We then construct the Superposition of Turbulons and Eddies Atmospheric Model (STEAM), and show that simulated atmospheric volumes are broadly plausible when compared to state-of-the-art hydrodynamic simulation output. Some aspects of STEAM clearly need improvement, though other statistics reproduce observations better than hydrodynamic models, and visualizations of simulated clouds are strikingly realistic. We estimate STEAM's computational cost to be up to a million times less than hydrodynamic models, suggesting that the Navier-Stokes equations may not, in fact, be required for simulation and prediction of Earth's weather and climate.
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Atmospheric and Oceanic Physics

Toward Actionable Predictability of Tropical Mesoscale Convective Systems through Large-scale Recurrent Environments in Northern South America

Vanessa Robledo, John F. Mejía, Jhayron S. Pérez-Carrasquilla et al. (+2 more)

Mesoscale convective systems (MCSs) produce much of the extreme precipitation, floods, and landslides that affect northern South America (NOSA), yet remain difficult to anticipate because they are strongly governed by local processes. On the night of 31 March 2017, an MCS triggered a catastrophic flash flood in Mocoa, Colombia —an event whose severity was not foreseen with sufficient lead time for emergency action, which typically requires several hours to a couple of days. Motivated by this disaster, this study investigates whether such events emerge within recurrent environments that provide a source of large-scale predictability, rather than as isolated and inherently unpredictable occurrences. We combine a 21-year (2001–2021) MCS tracking dataset, ERA5 reanalysis, equatorial Rossby (ER) wave diagnostics, and a Self-Organizing Map (SOM) to identify recurrent environments associated with MCS occurrence in the Orinoco–Amazon basin. The Mocoa environment maps onto one of nine recurrent configurations characterized by enhanced low-level moisture, neutral Orinoco low-level jet conditions, favorable upper-level circulation, and strong ER-wave activity. We further trace convective precursors to an upstream source region over the Guiana Highlands up to 24~h before arrival in the Mocoa region. Together, these results show that the Mocoa event was not climatologically exceptional but a high-impact expression of a recurrent, partly predictable large-scale regime, demonstrating that portion of MCS predictability arise from recurring large-scale conditions that can be monitored hours to days in advance, and support hazardous weather forecasting in NOSA and other tropical regions.
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All authors Vanessa Robledo, John F. Mejía, Jhayron S. Pérez-Carrasquilla, Mohamed Abdelkader, Humberto Vergara

Atmospheric and Oceanic Physics

Understanding Perturbed Parameter Ensemble Sensitivities Using A Contrastive Learning Approach

Da Fan, David John Gagne II, Gregory S Elsaesser et al. (+5 more)

Perturbed parameter ensembles (PPEs) reveal how physics parameters affect climate simulations, but interpreting parameter sensitivities across multivariate, spatially structured outputs remains challenging, particularly when calibrating models against observations. We develop an explainable contrastive learning model that maps 5 monthly cloud and radiation fields into a shared representation space. We train the model on the fields of two 100-member Community Atmosphere Model version 6 (CAM6) PPEs, spanning 34 parameters, that only differ in the warm rain microphysics scheme: KK2000, the default bulk microphysics scheme, and TAU-ML, a neural network emulator of a bin microphysics scheme. The learned representations separates two PPEs with over 94% linear classification accuracy while preserving the seasonal variability and ensemble spread due to parameter perturbations. In the shared representation space, the representations of satellite observations occupy the same low-dimensional manifold as the PPEs but are displaced from them most strongly during boreal spring and autumn. TAU-ML PPE has a lower distance to observations compared to KK2000 in the representation space. Integrated Gradients attributions highlights the contributions in subtropical low-cloud regions, Northern and Southern Hemisphere storm track regions, and tropical convection regions to differences between PPEs and observations. Regional attributions correlate most strongly with parameters associated with cloud microphysics, boundary layer turbulence, and deep convection. These results demonstrate that explainable representations of climate fields can attribute model differences to specific variables, regions, seasons, and physical parameters.
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All authors Da Fan, David John Gagne II, Gregory S Elsaesser, Brian Medeiros, Addisu G Semie, Qingyuan Yang, Akila Sampath, Subashree Venkatasubramanian

Atmospheric and Oceanic Physics · Cross-listed from cs.LG

Generative Atmospheric Super-Resolution from Heterogeneous In Situ Observations through Composable Interfaces

Yang Xu, Dibyajyoti Chakraborty, Haiwen Guan et al. (+2 more)

Atmospheric observations are sparse, heterogeneous, and unevenly distributed, whereas many generative atmospheric models learn distributions over regularly gridded multivariate states. Once pretrained, diffusion models can supply atmospheric priors that can be combined with observation-derived likelihood factors in a Bayesian formulation. However, these observation sources differ substantially in geometry and sampling density, complicating the consistent use of their observations within a common inference framework. Here, we formulate this reconstruction problem as generative atmospheric super-resolution and introduce composable observation interfaces for conditioning a single pretrained 13-variable atmospheric diffusion model. The interfaces convert sparse radiosonde (R), clustered aircraft (A), and dense irregular surface-station (S) observations into source-specific likelihood factors that specify where observations constrain the gridded state, how residuals are counted under uneven sampling, and how strongly each source guides posterior sampling. We developed the aircraft and surface observation interfaces using 2019 observations and evaluated the selected interfaces throughout 2020 without further tuning. Compared with reconstructions conditioned only on radiosonde observations, the composed R+A+S interface reduces RMSE evaluated against ERA5 by $9.24\%$ across all 13 state variables over the CONUS domain. The aircraft and surface factors provide complementary improvements in upper-air and surface variables. The R+A+S combination also lowers the Continuous Ranked Probability Score (CRPS), while evaluations at held-out aircraft and surface-station observations show reduced prediction errors. Together, these results demonstrate a modular route for conditioning a pretrained atmospheric generative prior on heterogeneous in situ observations without retraining the underlying model.
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All authors Yang Xu, Dibyajyoti Chakraborty, Haiwen Guan, Sen Wang, Romit Maulik

Wednesday 23 September

Atmospheric and Oceanic Physics

Black-Scholes for the land: modelling savannah response to rain as a dynamic world model

Thomas Pitts

Carbon credits, offsets and index insurance rest on counterfactuals: what the land would have done under the same weather without management, or what a bad season cost. Current baselines fail in two ways: some ignore rain, others condition on rain linearly and annually. But the true response lags the rain, depends on hidden state such as moisture from other sources, and responds differently from pixel to pixel. Focussing on grassland pasture as a proof of concept, we fit a temporal world model of MODIS greenness conditioned on CHIRPS rain over two 45 km^2 sites in Laikipia, Kenya: a transition density map for greenness given recent and current rainfall. We use it, conceptually, as Black-Scholes is used. Inverting the model on observed greenness it yields an implied residual, everything the land did that the rain does not explain. Two capabilities drop out of this solution: 1) attribution, what the land did beyond the rain; 2) sensitivities, derivatives of the rollout with respect to rain and state, close analogues of the Greeks. On Laikipia test sites, our learned model recovers three-month greenness with 23% less error than a history-only baseline and 13% less than the best hand-written model on unseen years. In the 2022 drought, the land ended 0.17 NDVI below normal; the rain that failed explains roughly half of that, and under normal rain with the same residual it would still have ended 0.10 below. Delta, the derivative of greenness with respect to rain, is computed at every pixel and averages 0.075 NDVI per extra 100 mm of rain. A Delta at every pixel is an exposure to rain that can be hedged, allowing efficient pricing of contracts and accurate attribution.
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Atmospheric and Oceanic Physics · Cross-listed from cs.LG

Evaluating Cross-region Generalization for Wavelet-Diffusion Precipitation Downscaling

Weikang Qian, Yixin Wen, Chugang Yi et al. (+3 more)

Diffusion models have shown strong potential for kilometer-scale precipitation downscaling, but their performance in geographically unseen regions and event regimes remains insufficiently understood. Building on the wavelet diffusion model (WDM) framework, this study evaluates cross-region and cross-event generalization. Six 3 x 3 deg U.S. regions represent convective, winter, tropical, and atmospheric-river precipitation regimes. Low-resolution inputs are generated by block averaging NOAA Multi-Radar/Multi-Sensor (MRMS) composite reflectivity fields. A WDM trained only on Oklahoma (OK) samples and a WDM trained on all six regions are compared with nearest-neighbor and Bicubic interpolation. Model performance is evaluated using three metric families that measure image-domain reconstruction, spectral and distributional fidelity, and bin-wise precipitation detection. The OK-trained WDM remains competitive outside OK. Although the all-region WDM delivers the best and most consistent overall image-domain and detection performance, its gains are uneven across precipitation intensities. Bin-wise critical success index (CSI) over 5-dBZ reflectivity bins shows that WDM improvements concentrate in localized higher-reflectivity structures, which image-domain metrics partly obscure. In addition, the performance differences among samples are strongly associated with the spatial organization of the precipitation field, quantified by Moran's I as the spatial autocorrelation of each reflectivity bin. The sample-level Moran's I-CSI correlation stratified by sample intensity reaches 0.901 in all six regions, including regions unseen during training. Overall, these findings support future efforts to transfer downscaling models to regions with limited local training data and to generate globally consistent, high-resolution precipitation products.
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All authors Weikang Qian, Yixin Wen, Chugang Yi, Zhi Li, Lingcheng Li, Haizhao Yang

Atmospheric and Oceanic Physics

HClimRep-Ocean: A Global Ocean Emulator on an Unstructured Mesh

Kacper Nowak, Aleksei Koldunov, Nikolay Koldunov et al. (+7 more)

Machine-learning (ML) emulators for atmospheric processes have advanced rapidly in recent years, transforming weather forecasting. Although early ML ocean forecasting models now exist, they remain less developed than their atmospheric counterparts. Unlike the atmosphere, much of the ocean's kinetic energy resides in mesoscale eddies whose characteristic spatial scales are approximately an order of magnitude smaller than those of comparable atmospheric features. Moreover, complex coastlines, narrow straits, and ice-covered seas make boundary representation a central challenge that atmospheric models do not face. Consequently, numerical ocean simulations commonly use locally refined or even completely unstructured meshes. However, their data-driven counterparts have so far been built around latitude-longitude grids. We present HClimRep-Ocean, an ocean emulator that operates directly on the native unstructured mesh of FESOM2. The emulator is trained on a 209-year AWI-CM3 control integration and is run without atmospheric forcing, receiving the atmospheric state only at initialisation time, which isolates the predictability carried by the ocean state itself. Skill is strongly field-dependent: for currents, HClimRep-Ocean outperforms every reference at 30 day forecast, whereas for temperature and salinity a damped-anomaly persistence forecast remains the more accurate estimator. This behaviour is physically interpretable: current variability is largely geostrophic and internally generated, whereas sea-surface temperature and salinity fluctuations are driven by atmospheric forcing through weather state. Evaluated independently on the OceanBench benchmark, a reanalysis-trained variant of HClimRep-Ocean achieves the lowest RMSE against GLORYS reanalysis among all assessed systems, confirming the competitiveness of the native-mesh approach.
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All authors Kacper Nowak, Aleksei Koldunov, Nikolay Koldunov, Savvas Melidonis, Ankit Patnala, Simon Grasse, Julius Polz, Christian Lessig, Martin Schultz, Thomas Jung

Tuesday 22 September

Atmospheric and Oceanic Physics

MLS and CALIPSO Evidence for a Warm-Pool Transition in Tropical Convective Moistening and Cloud Ice

Isabel Nucera, Jonathan H. Jiang, Yiyang Zhang et al. (+4 more)

Upper-tropospheric water vapor and cloud ice associated with tropical deep convection are central to water-vapor and high-cloud feedbacks. We investigate a warm-pool transition in tropical convective moistening using 20 years of Aura Microwave Limb Sounder (MLS) Version 5 H2O and cloud-ice observations (August 2004-December 2024), together with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission (June 2006-June 2023). The combined day-plus-night CALIPSO product (CALIPSO-A) provides an independent ice-cloud constraint; the H2O column shown on the CALIPSO grid is derived from Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), not from CALIOP. We combine objective sea surface temperature (SST) breakpoint fitting, cross-sensor ice water path (IWP) comparisons, Clouds and the Earth's Radiant Energy System (CERES) longwave, shortwave, and net cloud radiative effect (CRE), and Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation diagnostics from the Global Precipitation Measurement (GPM) mission. MLS and CALIPSO-A show cloud-ice maxima over deep-convective warm-pool regions and identify warm-regime SST breakpoints near 299-300 K. Normalized MLS and CALIPSO-A IWP vary coherently in the western Pacific and tropical mean. CERES indicates that stronger longwave CRE in the warm regime is partly offset by stronger shortwave cooling, while GPM IMERG confirms that MLS IWP increases with convective rain rate. These results show that convective moistening and cloud-ice production across the tropical warm-pool transition are persistent cross-sensor features of the tropical atmosphere.
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All authors Isabel Nucera, Jonathan H. Jiang, Yiyang Zhang, King-Fai Li, Bin Zhao, Hwayon Choi, Yong-Sang Choi

Atmospheric and Oceanic Physics

Deep Learning Denoising of Real SWOT Sea Surface Height Observations

Gaétan Meis, Anaëlle Tréboutte, Maxime Ballarotta et al. (+2 more)

The SWOT (Surface Water Ocean Topography) mission is currently providing unpreceded high-resolution measurements of Sea Surface Height (SSH), revealing ocean features at finer scales. Nevertheless, the two-dimensional observations of KaRIn altimeter of SWOT suffer from instrumental errors. This noise degradation is altering the high frequencies of SWOT signal, and the small to sub-mesoscale dynamics of interest for some oceanographers. For this reason, Tréboutte et al. (2023) have developed a convolutional neural network (CNN) based on U-Net architecture to separate the noise from the physical signals contained in the SSH. Their approach has demonstrated great potential on simulated SWOT measurements, and Dibarboure et al. (2024) report a positive influence on actual flight data from SWOT. However, degraded denoising performance, and occasional negative side-effects have been observed in atypical conditions (e.g. very high surface waves, internal tides solitons). In this study, we illustrate some of these limitations and we present an improved approach of the CNN-based denoising: we modified the training procedure to obtain a more robust version of the algorithm, to avoid biases and artifacts in the denoised SSH. This paper also presents a more complete validation process with a robust and standardized evaluation benchmark: these metrics could be of interest to assess other SWOT filtering and denoising algorithms.
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All authors Gaétan Meis, Anaëlle Tréboutte, Maxime Ballarotta, Marie-Isabelle Pujol, Gérald Dibarboure

Atmospheric and Oceanic Physics · Cross-listed from physics.space-ph

The Localized 12-hour Wave Over Alaska: Leveraging Meridional Wind Measurements From the Sodium Lidar

Sophie R. Phillips, Katrina Bossert, Komal Kumari et al. (+3 more)

The 12-h wave in meridional winds in the mesosphere and lower thermosphere (MLT) during the solar minimum 2018-2019 Arctic winter is investigated using sodium lidar observations at Poker Flat Research Range (PFRR), Chatanika, Alaska (64N,147W). Nightly 12-h wave amplitudes increased significantly during December-January, with amplitudes exceeding 130 m/s above 97 km on several days. This was more than double the 12-h wave amplitudes observed outside this time period. Meteor radar winds over Chatanika also showed significant increase in meridional wind 12-h wave amplitudes at altitudes between 82 and 97 km during this time period with lower amplitudes than lidar measurements. The strong variation in 12-h wave amplitudes was not correlated with SME index, though the largest amplitudes coincided with the sudden stratospheric warming (SSW) in early January. Measurements were compared to a seasonal WACCM-X model run and four days of HIAMCM. For the four dates of available HIAMCM data, 12-h wave amplitudes over Chatanika were found to be similar between both HIAMCM and WACCM-X and the lidar below 97 km, with amplitudes measured by lidar exceeding the models at altitudes between 97-105 km. All measurements followed a similar seasonal trend with increasing amplitudes at the end of December/early January. Fits of SW2 from WACCM-X show the SW2 tidal amplitude following similar seasonal trends to 12-h wave measurements. These high-resolution lidar measurements indicate that localized 12-h wave amplitudes are larger than previously reported by studies using meteor radar measurements.
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All authors Sophie R. Phillips, Katrina Bossert, Komal Kumari, Erich Becker, Richard L. Collins, Nicholas Pedatella

Atmospheric and Oceanic Physics · Cross-listed from cs.LG

PR-Smoother: Simulator-Preserving Non-Gaussian Smoothing for Data Assimilation

Yuta Tarumi

Many physical data assimilation (DA) workflows require smoothing methods that represent non-Gaussian posteriors over physical state variables, scale to high-dimensional simulators, train from observation windows alone, and remain compatible with calibration of the prescribed simulator. We introduce PR-Smoother, a simulator-preserving amortized smoother designed for this prescribed-simulator DA regime. Its key design principle is to keep the prescribed simulator explicit in both the evidence lower bound and the variational family: rather than learning replacement dynamics or a learned trajectory prior, PR-Smoother learns only future-conditioned corrections around the prescribed rollout. This yields an explicit non-Gaussian smoothing distribution over physical trajectories and supports joint state, parameter, and sensor-bias learning from observations alone. The variational family contains the exact smoother in deterministic and linear-Gaussian limits. Empirically, PR-Smoother captures multimodal posteriors in 4-dimensional Lorenz-96, remains accurate under ambiguous nonlinear observations and process noise in 40-dimensional Lorenz-96, and scales to joint state-parameter-bias inference in 16,384-dimensional Kolmogorov flow.
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Atmospheric and Oceanic Physics · Cross-listed from cs.CE

Analysis of trade-offs in urban heat mitigation using a Bayesian Optimization framework for an urban canopy layer model

Rebekka Walter, Johanna Gelhaus, David Anton et al. (+2 more)

To mitigate the challenges of climate change and intensifying heat stress in urban areas, local adaptation strategies are discussed and introduced in cities worldwide. To understand processes and potential trade-offs of these strategies a Bayesian optimization and surrogate modeling framework was employed to investigate urban parameter ranges of heat mitigation strategies with focus on three thermal metrics: daytime air temperature, Universal Thermal Climate Index (UTCI), and nighttime air temperature. Based on an urban street canyon configuration, it was shown that heat mitigation measures that reduce daytime air temperature and UTCI are often associated with higher nighttime temperatures. This results in a curved Pareto front that reflects the trade-off between daytime and nighttime thermal comfort. Under identical forcing conditions, different urban configurations, varying in geometry, vegetation, and surface characteristics, are shown to alter peak canyon air temperature by up to 5.2~$^\circ$C during the day and 2.6~$^\circ$C at night, while UTCI varies by up to 7.9~$^\circ$C, demonstrating that favorable urban configurations can substantially mitigate microclimatic heat stress. These findings suggest that combining multi-objective Bayesian optimization and surrogate modeling can help bridge the gap between computationally intensive climate simulations and practical decision-making in urban planning. An interactive visualization tool was developed to explore these trade-offs, making the often opposing relationships between urban parameters and the three thermal metrics directly accessible to planners.
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All authors Rebekka Walter, Johanna Gelhaus, David Anton, Henning Wessles, Stephan Weber

Atmospheric and Oceanic Physics

A dataset of one-dimensional idealized probabilistic fields

Gregor Skok and Romain Pic

Verification of probabilistic weather forecasts remains a crucial aspect of numerical weather prediction, as new AI-based models become more widely used alongside the more traditional physics-based ensemble forecasting systems that continue to be developed and improved. We present a first-of-its-kind idealized probabilistic dataset composed of one-dimensional cases aimed at analyzing the behavior and properties of verification methods for probabilistic forecasts and comparing their behavior. It covers a wide range of probabilistic cases, such as constant, localized events, gradients, fronts, noisy, bimodal, and limiting cases. Moreover, the code associated with the dataset provides great flexibility for customizing the experiments it covers. The dataset represents the first building block of the more extensive comparison dataset of the Bridging The Gap project, which aims to facilitate the development and comparison of spatial verification methods for probabilistic forecasts.
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Atmospheric and Oceanic Physics

Precursors, Pathways, and State-Dependent Reliability of Long-range ENSO Prediction in the CESM2 Seasonal-to-Multiyear Large Ensemble

Yudi Mao, Gan Zhang, Hui Li et al. (+3 more)

Understanding the sources of ENSO prediction skill and error is essential for improving long-range climate prediction. Here we examine ENSO precursors in CESM2 Seasonal-to-Multiyear Large Ensemble (SMYLE) hindcasts initialized in February and August during 1970-2019, using ensemble sensitivity analysis and an extended nonlinear recharge oscillator (XRO) benchmark. SMYLE exhibits meaningful long-lead skill, with February-initialized predictions remaining skillful through the following winter, and August-initialized predictions retaining useful skill at the December target sixteen months ahead. However, the predictions are under-dispersive in a state-dependent manner. Rank histograms reveal overconfidence issues for strong ENSO events and a warm bias for weak events. February predictability arises from canonical Bjerknes feedback dynamics reinforced by subtropical-tropical interactions resembling the North Pacific Meridional Mode (NPMM). The best February-initialized predictions feature warm western Pacific subsurface anomalies and La Nina anomalies at initialization, with equatorially confined anomalies and relatively weak sensitivities to extratropical influences thereafter. Second-year ENSO predictability in August-initialized predictions instead emerges through a delayed pathway characterized by cross-basin preconditioning. Skillful cases begin with a cold tropical equatorial South Atlantic and a warmer tropical western Indian Ocean, while Pacific processes play a reduced role until the following year. SMYLE matches the skill of the cross-validated XRO at both December targets, and its sensitivities agree with the observationally fitted benchmark, though the Atlantic couplings appear misrepresented. These findings suggest that improving the initial Pacific state and cross-basin coupling fidelity in GCMs may help improve ENSO prediction.
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All authors Yudi Mao, Gan Zhang, Hui Li, Dillon J. Amaya, Stephen G. Yeager, Sen Zhao

Atmospheric and Oceanic Physics

Hydrological Constraints on Temperature Sensitivities of Precipitation Frequency and Intensity

Jun Yin and Amilcare Porporato

To improve understanding of how precipitation frequency and intensity change with warming, we combine the coupled land–atmosphere water balance with a stochastic hydrological model that retains explicit dependence on precipitation frequency and event depth. We find a linear relationship between co-variations of rainfall frequency and intensity, where the slope is a hydrological sensitivity determined by the dryness and storage indices and the intercept is a forcing term associated with changes in moisture convergence, potential evapotranspiration, and effective water-storage capacity. While a given relative change in either rainfall frequency or intensity produces an identical relative change in the mean rate, rainfall intensity additionally influences the runoff coefficient by altering terrestrial water storage relative to rainfall depth. This means frequency and intensity have different impacts on the water cycle, allowing the frequency–intensity slope to depart from -1. Analysis of long-term MOPEX records shows that the forcing term is dominated by moisture convergence and that the frequency–intensity relation becomes steeper from wet to dry regimes, consistent with the slope predicted by the theory. The framework provides a physical interpretation of the observed negative covariation between precipitation frequency and intensity and clarifies the roles of hydrological partitioning and atmospheric moisture supply in their responses to warming.
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Atmospheric and Oceanic Physics

West-WRF AI 2-km: High-Resolution Prediction of Integrated Vapor Transport and Precipitation

Nazak Rouzegari, Vesta Afzali Gorooh, Agniv Sengupta et al. (+6 more)

We introduce a stretched-grid artificial intelligence (AI) weather forecasting model with 2-km resolution over the western United States and part of the Northeast Pacific and approximately 31-km resolution elsewhere globally. Forecasting over the western U.S. is challenging because complex topography and atmospheric rivers (ARs) strongly influence orographic precipitation. West-WRF AI 2-km builds on a global model pretrained with a 40-year European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) dataset and is fine-tuned with the Center for Western Weather and Water Extremes (CW3E) 2-km regional reanalysis to produce autoregressive 6-hourly forecasts of precipitation and integrated vapor transport (IVT). Forecasts are evaluated over winters 2020-2023 using gridded precipitation observations, rain gauges, and AR Reconnaissance dropsondes and are benchmarked against coarser-resolution AI forecasts and regional and global numerical weather prediction (NWP) systems. West-WRF AI 2-km reproduces observed precipitation-intensity distributions, retains fine-scale spectral variability, and produces sharper narrow coastal precipitation bands and localized, terrain-sensitive extremes. Its broader-scale performance remains comparable to coarser-resolution configurations while preserving large-scale skill despite higher resolution. Dropsonde verification shows lower errors and improved categorical skill at the most extreme IVT threshold. Overall, West-WRF AI 2-km provides its greatest value for localized precipitation extremes and intense AR-related moisture transport.
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All authors Nazak Rouzegari, Vesta Afzali Gorooh, Agniv Sengupta, Phu Nguyen, Kuo-Lin Hsu, Amir AghaKouchak, Soroosh Sorooshian, F. Martin Ralph, Luca Delle Monache

Atmospheric and Oceanic Physics

FAST-ML: A Hybrid Physics-Machine Learning Framework for Tropical Cyclone Intensity Forecasting

Shijie Xiao, Jonathan Lin, Thomas Ehrmann et al. (+1 more)

Rapid intensification (RI) remains one of the most consequential and difficult aspects of tropical cyclone (TC) forecasting. Although full-physics numerical weather prediction models can represent the processes governing RI, resolving storm-environment interactions remains computationally expensive, while purely data-driven approaches often lack physical interpretability. We present FAST-ML, a hybrid framework that bridges data-driven efficiency with physical constraints. A physically informed dual-stream neural parameterization ingests 3D ERA5 fields to diagnose ventilation controls—environmental wind shear and mid-level entropy deficit. By optimizing these parameters end-to-end through a differentiable FAST intensity model, this architecture establishes a robust new paradigm for observation-driven parameter optimization, ensuring storm evolution remains strictly governed by thermodynamic principles. By better capturing the storm's continuous intensity evolution, FAST-ML improves upon its physical baseline, reducing ensemble CRPS across forecast lead times, with a reduction of approximately 31% at 60 h and nearly halving the RI false alarm ratio without sacrificing detection skill. In a 100-member ensemble configuration, FAST-ML produces intensity forecasts comparable to FNV3 for selected storms under the evaluated input configurations. Furthermore, zero-shot tests on selected Eastern Pacific storms provide encouraging evidence of cross-basin transferability. FAST-ML provides a modular intensity forecasting framework that can be coupled with externally supplied storm tracks and environmental fields. It demonstrates that observation-driven parameter learning within physically constrained dynamics simultaneously enhances accuracy, interpretability, and computational efficiency.
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All authors Shijie Xiao, Jonathan Lin, Thomas Ehrmann, Ali Sarhadi

Monday 21 September

Atmospheric and Oceanic Physics · Cross-listed from cs.LG

Learning Prognostic Variables for AI Convective Parameterizations via Symbolic Distillation

Jurij Schönfeld, Tom Beucler, Julien Savre et al. (+2 more)

Hybrid AI-physics climate modeling aims to improve coarse (~100km-resolution) Earth system models by learning to parameterize subgrid processes from high-fidelity data. However, this so far mostly involves local-in-time, diagnostic parameterizations, in which the subgrid state depends only on the current coarse state with no memory of previous states, which is unrealistic for processes such as convection that have intrinsic persistence. To address this, we enhance local-in-time parameterizations by learning prognostic variables that compactly carry important, additional past information where no explicit sub-grid information is available. First we compress past information into a low-dimensional latent space using an autoencoder, which then informs a neural network trained to parameterize targeted subgrid-scale processes. We then replace the autoencoder with symbolic equations that govern the time evolution of the latent variables, yielding additional prognostic memory variables that can be integrated alongside the resolved atmospheric state. We evaluate this approach on two systems: the Lorenz-96 model (online) and surface precipitation from high-resolution atmospheric simulations (offline). A forced multivariate linear ordinary differential equation recovers most of the added value achieved by the autoencoder-based approach in both experiments. Benchmarked against diagnostic parameterizations without memory, our memory-informed approach improves climate statistics and temporal structure, including a realistic diurnal cycle of tropical land precipitation.
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All authors Jurij Schönfeld, Tom Beucler, Julien Savre, Steven Sherwood, Veronika Eyring

Atmospheric and Oceanic Physics · Cross-listed from cs.DC

Toward GPU-Resident Climate Models: A Feasibility Study on Lossy Compression for the Spherical Harmonic Transform's Communication Bottleneck

Lorenzo Breschi and Flavio Vella

Operational pseudospectral atmospheric models such as the ECMWF Integrated Forecasting System (IFS) run today almost exclusively on CPUs; GPU ports are under active development but not yet used in production. These models rely on the Spherical Harmonic Transform (SHT). Each time-step requires forward and inverse SHTs, and both passes depend on global pencil transposition that redistribute multi-dimensional arrays across compute nodes. At large node counts these global collectives dominate wall-clock time. We investigate GPU-resident lossy compression, using representative fields from the DYAMOND high-resolution operational dataset as input, and combining measured GPU compression throughput with SimGrid network simulation, we show that ZFP at 16 bits per value (rate-16), about the same storage budget as float16, matches the communication-time reduction of float16 truncation while delivering approximately $\mathbf{1600\times}$ lower mean relative error. ZFP at 8 bits per value (rate-8) achieves approximately $\mathbf{1.93\times}$ the speedup of float16 while retaining $\mathbf{4\times}$ lower mean relative error.
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2609.24294 · 21 Sep · Accepted for publication in the 2026 IEEE International Conference for High Performance Computing, Networking, Storage and Analysis Workshops (SC-W) - High-Performance Computing for Environmental and Earth Sciences (HPC4EES). \textcopyright 2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media

Abstract PDF $CLIM ×1$DATA ×1

Atmospheric and Oceanic Physics

Conservation Constraints and Distributed Advective Memory in a Reduced Model of Atlantic Overturning Hysteresis

Sandy Hardian Susanto Herho, Iwan Pramesti Anwar, Mutiara Rachmat Putri et al. (+3 more)

Interbasin exchange through the Indo-Pacific gateway supplies salt to the Atlantic and is widely invoked as a control on the stability of the Atlantic overturning circulation. We ask whether that control can act on the equilibrium structure of a conceptual thermohaline model. A closed five-box model with an exact salt invariant is constructed, comprising North Atlantic, upper-limb, Indian, Pacific, and deep reservoirs, with the return flow split between a warm route through the Indian reservoir and a cold route, together with an Indonesian Throughflow branch and an Agulhas retroflection. Adding the steady-state budgets of the gateway reservoirs shows that every internal exchange cancels, so the salt they export to the Atlantic is fixed by the net Atlantic freshwater export alone. This holds independently of the warm-route fraction, the throughflow, the retroflection, and how the export is apportioned among gateway reservoirs; across a parameter sweep the largest departure is of order ten to the minus eleven. The gateway therefore enters as a purely additive forcing and cannot renormalize the salt-advection feedback. Replacing the discrete transit lag by a gamma memory kernel leaves the equilibria unchanged but yields a closed-form threshold for oscillatory instability depending only on kernel shape. Broad memory is strongly stabilizing, and a discrete lag is the least stable member of the family. Because the instantaneous feedback vanishes at the fold, oscillatory instability always precedes the saddle-node, over an interval widening more than tenfold as memory sharpens. Gateways therefore appear to act on transient rather than equilibrium dynamics
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All authors Sandy Hardian Susanto Herho, Iwan Pramesti Anwar, Mutiara Rachmat Putri, Rusmawan Suwarman, Deny Juanda Puradimaja, Dasapta Erwin Irawan

Saturday 19 September

Atmospheric and Oceanic Physics · Cross-listed from physics.flu-dyn

An Exactly Solvable Ekman Layer with a Fractional-Order Stress Closure

Sandy Hardian Susanto Herho, Rizki Dimas Permana, Iwan Pramesti Anwar et al. (+3 more)

The classical theory of the wind-driven surface layer of a rotating ocean closes the momentum balance with a local flux-gradient law, in which the turbulent stress at a given depth is proportional to the shear at that depth. It predicts a surface current deflected forty-five degrees from the wind, which exceeds most direct measurements. This study asks what follows when locality is relaxed. Beginning from the exact integral relation between turbulent stress and mean shear, and requiring the memory kernel to carry no preferred vertical scale, we obtain a power-law kernel and therefore a stress law of fractional order. The resulting equation cannot be posed on the velocity, because the fractional derivative of a bounded profile vanishes at the surface, so the wind stress cannot be applied, while the alternative definition of the derivative leaves the surface current unbounded. Posed on the stress instead, the problem is solvable in closed form in Mittag-Leffler functions at every order between zero and one. The surface deflection then depends on the closure order alone and is smaller than forty-five degrees throughout, whereas the depth-integrated transport stays exactly normal to the wind, because that constraint follows from the momentum balance and not from the closure. The far field decays as a power law rather than exponentially, and its amplitude vanishes in the local limit, so that limit is singular. Under a suddenly applied stress the surface transient decays algebraically. Three independent algorithms agree closely, and no observational or model data are used.
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All authors Sandy Hardian Susanto Herho, Rizki Dimas Permana, Iwan Pramesti Anwar, Rusmawan Suwarman, Deny Juanda Puradimaja, Dasapta Erwin Irawan

Atmospheric and Oceanic Physics

ClimTip-GML: A global bias-corrected and downscaled dataset for assessing impacts of climate tipping events

Philipp Hess, Sebastian Bathiany, Lucas Ferreira Correa et al. (+3 more)

Assessing the impacts of future climate scenarios including tipping events of major Earth system components such as the Amazon rainforest (ARF) or the Atlantic meridional overturning circulation (AMOC), requires accurate and high-resolution simulations. Here, we present ClimTip-GML, the first globally bias-corrected and downscaled climate dataset for impact assessment of large-scale tipping scenarios, comprising eight key variables at 0.25{\deg} spatial resolution from three general circulation models (GCMs): CESM1-CAM5, HadGEM3-GC31-MM, and MPI-ESM1-2-HR. The dataset includes 100-year-long climate simulations with preindustrial and historical conditions, as well as scenarios at a +2{\deg}C warming level with and without tipping transitions of the AMOC or ARF. We apply generative machine learning (GML) techniques trained on reanalysis data to bias-correct and downscale the GCMs in a manner that is physically consistent across space, time, and all eight variables. Comprehensive validation shows substantially reduced biases, improved small-scale spatial variability, multivariate correlations, and consistent long-term climate responses to the external forcing and tipping events. The results hence permit substantially improved impact assessments of tipping transitions of the ARF and AMOC, directly informing mitigation and adaptation policies.
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All authors Philipp Hess, Sebastian Bathiany, Lucas Ferreira Correa, Laura C. Jackson, Casey R. Patrizio, Niklas Boers