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Physics Physics Atmospheric and Oceanic Physics

physics.ao-ph Watch Atmospheric and Oceanic Physics

Tuesday 11 August

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

Outdoor 100-m-scale air waveguides

Andrew Goffin, Gregory Babic, Andrew DeRusha et al. (+3 more)

Optical power densities for standoff spectroscopy, remote sensing, directed energy, and free-space optical communications are limited by diffraction and adverse atmospheric conditions such as turbulence, fog, and wind. Air waveguides, generated by ultrashort-pulsed laser filamentation, are a promising approach for transmission and collection of optical signals over long distances, overcoming beam diffraction and, for point-like sources, inverse square signal falloff with distance. In this work, we demonstrate outdoor guiding for the first time over a record length of ~100 m, exceeding the prior (indoor) record of 42 m. We correlate air waveguide performance to a range of real atmospheric conditions including turbulent refractive index structure parameter ($C_n^2$) values up to ~$2.5\times10^{-14} m^{-2/3}$, crosswind speeds up to ~2 m/s, as well as temperature, pressure, and humidity variations. Accompanying propagation simulations provide insight into the real effects of these environmental perturbations, particularly crosswind, on waveguide performance and lifetime. Our results pave the way for quasi-continuous air waveguiding with kHz-scale repetition rate filaments in challenging outdoor environments.
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All authors Andrew Goffin, Gregory Babic, Andrew DeRusha, Maksim Livshits, Howard Milchberg, Patrick Skrodzki

Monday 10 August

Atmospheric and Oceanic Physics · Cross-listed from physics.ins-det

Anemometer-free indoor calibration of Doppler lidar using fiber optics and Monte Carlo simulation

Andrew Black, Klaus Franke, Davide Trabucci et al. (+3 more)

Existing methods for calibrating and classifying Doppler lidars for wind energy applications are time-consuming and overestimate lidar measurement uncertainty. These shortcomings are due to the reference instruments: anemometers on meteorological masts. The input signals in field calibrations are real wind conditions, and include uncertainties associated with the met mast and the terrain. This research presents validation of a new method for lidar calibration. The new approach has two steps. First, the uncertainty of the lidar line of sight velocities (LOS) are measured. Second, those intermediate uncertainties are used to derive the uncertainty of the horizontal wind speed using a simulated wind field. LOS uncertainties are assessed using a fiber optic bench as a synthetic wind tunnel called Simulation of the Atmosphere with Fiber Optics Multi-Velocity (SAFO-MV). Using SAFO-MV, the lidar LOS is shifted using an acousto-optic modulator (AOM) and backscattered from a fiber spool, mimicking backscatter from a uniform, low turbulence wind field. The reference signals are SI-traceable via calibration of the AOM's RF-driver. Sources of uncertainty in the bench and the laboratory process are examined and found to be small. Wind speed uncertainties are derived via Monte Carlo uncertainty propagation. Wind fields are simulated using PyConTurb, an implementation of the KSEC turbulence model. A virtual lidar in the simulation replicates lidar sensitivities derived by IEC 61400-50-2 classification campaigns. Replication of device sensitivities and the traceability of the approach comprise a validation of the measurement model, enabling its use for lidar calibration. The proposed method yields uncertainties comparable to IEC lidar calibration, while greatly reducing calibration time. This new technology can streamline lidar deployment and reduce uncertainty in wind resource assessment.
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All authors Andrew Black, Klaus Franke, Davide Trabucci, Pierre E. Allain, Lisa Lopez, Axel Albers

Atmospheric and Oceanic Physics

Stochastic Emulation of a Fully Coupled Preindustrial E3SMv3 Simulation

Elynn Wu, James P. C. Duncan, Troy Arcomano et al. (+11 more)

We present a stochastic coupled emulator of E3SM version 3, built on the SamudrACE framework, which couples an atmosphere emulator (ACE2) with a full-depth ocean emulator (Samudra). We replace the deterministic atmosphere emulator with its stochastic counterpart, ACE2S, and fine-tune the coupled system with a probabilistic objective, so that the atmosphere acts as a source of internal variability for the ocean. Trained on 105 years of a pre-industrial control simulation and evaluated on an independent 400 years, the emulator reproduces E3SMv3's mean climate state with biases much smaller than existing model-to-observation differences. Relative to a deterministic baseline, stochastic training maintains internal variability across timescales, most notably in the ENSO power spectrum, eddy-rich SST anomalies, and sea ice variability in the marginal ice zone. The emulator captures daily precipitation accurately up to the 99.99th percentile, but underestimates the rarest tropical extremes. These results show that stochastic coupled emulators can reproduce long-timescale variability with high fidelity, while extrapolation to unseen extremes remains a key challenge.
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All authors Elynn Wu, James P. C. Duncan, Troy Arcomano, Jeremy McGibbon, Oliver Watt-Meyer, Christopher S. Bretherton, Naser Mahfouz, Claudia Tebaldi, Luke Van Roekel, Andrew Roberts, Wuyin Lin, Finn Rebassoo, Jean-Christophe Golaz, Peter M. Caldwell

Atmospheric and Oceanic Physics · Cross-listed from hep-ph

Mysterious Transients in the Palomar Observatory Sky Survey (POSS-1) as profound manifestation of the Dark Matter physics

Ariel Zhitnitsky

Transient star-like objects of unknown origin have been identified in the first Palomar Observatory Sky Survey (POSS-1) as part of the Vanishing and Appearing Sources during a Century of Observations (VASCO) project. The source of the transients recorded by POSS-1 remains unknown, which is the warrant to coin the observed phenomena as Mysterious Transients (MT). We advocate an idea that the dark matter (DM) in form of the axion quark nuggets (AQN) made of standard model quarks (or antiquarks) and gluons, similar to the old idea of the Witten's strangelets, could simultaneously explain all the observed MT signals (including very short time scale for flash itself, association with nuclear test timing, observed alignments of several MT events, correlation with UAP reports, etc) collected or recorded for many years. Essentially we argue that the MT is a cousin of Ball Lightning (BL) events, also observed for centuries, without commonly accepted physics explanation. The basic parameters of this model (such as the typical baryon charge of the nuggets) had been fixed long ago by explaining the observed excess of radiation at variety of scales: from galactic to the solar, to local Earth's environments. In this work we use the same framework with the same set of parameters to study the observed MT phenomena. We also suggest several tests which substantiate or refute our proposal. We also present some suggestions on type of instruments required to study this specific (and well defined) type of the UAP events representing the cousins of BL and MT events in the AQN framework.
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Atmospheric and Oceanic Physics · Cross-listed from cs.LG

Deep Learning Imputation of Missing Radius of Maximum Winds (Rmax) Values in Tropical Cyclone Best-Track Data

Swastik Agrawal, Nishkal Hundia, Ziyue Liu et al. (+1 more)

Probabilistic coastal hazard assessments require accurate characterization of tropical cyclone (TC) parameters, yet datasets often contain missing records for the radius of maximum winds (Rmax), a key variable in Joint Probability Method analyses. This study evaluates data-driven approaches for Rmax imputation, including one-dimensional Convolutional Neural Networks (1DCNNs), Long Short-Term Memory (LSTM) networks, and conventional machine learning models. We examine physics-informed input augmentation, temporal modeling, and transfer learning using synthetic RAFT and STORM datasets for pre-training and observational IBTrACS data for fine-tuning. Including the radius of 34-knot winds (R34) substantially improves performance across all model types. Temporal models achieve higher average correlations than non-temporal models despite using approximately an order of magnitude fewer samples, indicating better preservation of relative Rmax variability across storms. This advantage is more pronounced when R34 is unavailable, suggesting temporal information can partially compensate for missing storm-size predictors. Transfer learning does not improve performance, likely because synthetic datasets have lower and less variable Rmax distributions than IBTrACS. These findings demonstrate the potential of temporal deep learning for reconstructing incomplete TC records and highlight the importance of physics-informed inputs, observational data availability, and distributional consistency in coastal hazard assessment.
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All authors Swastik Agrawal, Nishkal Hundia, Ziyue Liu, Michelle Bensi

Atmospheric and Oceanic Physics

Identifying the large-scale synoptic drivers contributing to the Kerala floods using multivariate feature-based analysis

Marion P. Mittermaier

Parts of Kerala were hit by devastating floods three years in a row. This sequence was historically without precedent. This paper uses a multivariate object-based approach to examine the global forecasts of the Met Office Unified Model for the 2018, 2019 and 2020 monsoon seasons to identify and understand the large-scale synoptic drivers that led to the floods. Identifying similarities between synoptic drivers behind these flooding events was a key objective, as was understanding whether one could enhance predictability by using a multivariate approach for post-processing forecast output, using variables other than just precipitation, which is often inherently less predictable on its own. To this end event identification focused on the analyses first, and then on a day 5 forecast. The study found that the multivariate version of the Method for Object-based Diagnostic Evaluation (MvMODE) was able to successfully identify sequences of days in all three seasons corresponding to the event dates, which in combination, had flood-producing potential. This was achieved in both the analyses and in the 5-day forecasts, proving that a) the events share common synoptic drivers and b) there is inherent predictability which can be tapped into. The 5-day forecasts matched the analysed objects on each occasion, proving that the underlying large-scale drivers may be predictable into the medium-range. Based on the results, the paper proposes a conceptual synoptic pattern evolution which can help identify such events in future. The synoptic pattern has some similarities to atmospheric rivers in the mid-latitudes.
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Atmospheric and Oceanic Physics

3D Modeling of a Tethered Autogyro with Articulated Rotors and Attitude Control using Differential Rotor Braking

Tasnia Noboni and Tuhin Das

A tethered autogyro with articulated rotors can operate as an unmanned aerial vehicle capable of energy-efficient, long-duration deployment by utilizing ambient wind energy to sustain flight. This article presents a model-based attitude control technique for such a system using a full-fidelity dynamic model. Using Lagrangian approach combined with Blade Element Momentum Theory and catenary mechanics, a previously developed 2D hybrid model is extended to three dimensions. The new model describes the complete rigid-body motion of the frame, including roll and yaw dynamics, and is augmented with rotor speed and flapping degrees of freedom for each blade. Equilibrium characteristics are examined through steady-state responses and compared with the prior model. The resulting trends of equilibria are found to be consistent with those reported in the literature. A feedback control strategy based on regenerative differential rotor braking is developed to modulate all three attitude angles. Simulations demonstrate effective attitude regulation and stable flight.
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Sunday 9 August

Atmospheric and Oceanic Physics

Deep Learning-Based Statistical Downscaling of Sea Surface Temperature Using a Residual Corrective Neural Network

Onkar Jadhav, Tim French, Ivica Janekovic et al. (+2 more)

The large-scale oceanic and atmospheric forecasts provided by global climate models typically lack sufficient resolution to accurately capture the response of the coastal ocean to atmospheric forcing and coastal circulation that drive fine-scale SST variability. Dynamical downscaling is computationally prohibitive, when applied to extensive coastlines, predictive ensembles, or long time periods. Therefore, this work presents a statistical downscaling of sea surface temperature (SST) from the seasonal coupled ocean-atmosphere forecast system (ACCESS-S2) using machine learning techniques. This study proposes a novel deep learning framework that uses a U-Net to generate an initial high-resolution SST estimate, which is subsequently refined using a residual corrective approach. The target SST fields are derived from the Regional Ocean Modeling System (ROMS). This two step approach called Residual Corrective Neural Network (RCNN) progressively refines initial U-Net predictions by incorporating dynamically scaled residuals at each step, enabling accurate capture of broad patterns and fine-grained features such as eddies and fronts. We also introduce a custom loss-assisted RCNN variant to improve performance during extreme events, which may be absent from training data due to climate-driven shifts in SST extremes. The framework efficiently downscales SST along the west coast of Australia. A 2011 marine heatwave case study shows that the RCNN improves ACCESS-S2 SST predictions by increasing horizontal resolution from 25 km to 2 km, enabling identification of fine-scale anomalies unresolved in the ACCESS-S2 dataset. This balance between computational efficiency and accuracy supports applications in coastal impact assessment and marine ecosystem studies.
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All authors Onkar Jadhav, Tim French, Ivica Janekovic, Nicole L. Jones, Matthew Rayson

Atmospheric and Oceanic Physics

Do AI Forecast Ensembles Sample the Correct Conditional Distribution?

Lucas J. Howard and Elizabeth A. Barnes

Ensemble forecasting aims to sample the conditional distribution of outcomes; whether AI forecast ensembles do this correctly in a joint sense remains largely untested. We train a diffusion model for probabilistic subseasonal coastal sea level forecasts at eight US East Coast tide gauge stations, with sea level derived from reanalysis, and find that marginal and joint forecast quality decouple: positive skill at every station and lead time marginally, while joint spatial structure is worse than climatological draws. A shuffle-based permutation decomposition reveals this failure is invisible to the energy score but detected by the variogram score. Lorenz-96 experiments across 0.7-170 equivalent years show the gap persists regardless of training volume and is reproduced by a linear baseline, indicating structural inadequacy of the learned distribution. A dynamical ensemble does not replicate the failure while a deterministic emulator does, suggesting it is specific to learned emulators rather than ensemble forecasting generally.
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Atmospheric and Oceanic Physics

Performance Evaluation of the WeatherEx Forecasting System (WFS) for Extreme Monsoon Rainfall over Kerala

Rashad P E and Akshay Sunil

Extreme monsoon rainfall over Kerala presents a major operational forecasting challenge because of the interaction of Arabian Sea moisture transport, mesoscale convection, coastal convergence, and strong orographic forcing along the Western Ghats. Spatial analysis showed that WFS reproduced the principal Western-Ghats-aligned rainfall corridor and represented localized high-intensity rainfall structures more distinctly than the smoother global-model guidance examined for the event. The results demonstrate the complementary roles of operational regional guidance and higher-resolution diagnostic forecasts and highlight the importance of region-specific calibration, spatial verification, and continued evaluation across multiple monsoon events and forecast lead times.
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Friday 7 August

Atmospheric and Oceanic Physics

Models of Wildland Fire and Ember Spread

Kevin Speer, Bryan Quaife and Jie Sun

In this Chapter we explore the nature and theory of ember transport using observations from controlled laboratory settings, prescribed fire, and wildland fire. Specific examples and statistics from fires are used to gain insight and motivate a hierarchy of modeling approaches from simple idealized models to full-physics atmospheric boundary layer models. The emphasis is on the fundamental processes involved in moving embers away from their sources in vegetation and structures on and near the ground or in the atmospheric boundary layer winds and turbulent flows. We describe the problem first in terms of basic theory about the rate of spread of wildland fire, examine the physical principles at work in ember transport for two principle modes of transport near the ground and in the atmosphere well above the surface, and connect these to statistical models for transport.
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Atmospheric and Oceanic Physics

Evaluating Explainable AI Methods for Geoscientific Regression: Insights from Applications and the Lorenz-63 System

Ieuan Higgs, Todd Jones, Kieran Hunt et al. (+1 more)

As artificial intelligence (AI) systems transition from research prototypes to operational tools in Earth system science and forecasting, establishing trust in their predictions becomes increasingly important. Although model inputs and outputs are observable, the internal decision-making of modern AI models remains complex and hard to interpret, earning them the label “black boxes.” Explainable artificial intelligence (XAI) offers techniques to provide insight into these processes. However, most XAI methods were developed for classification tasks, raising questions about their suitability for the regression problems that dominate geoscientific applications. We review XAI approaches through this lens, organising them into a structured framework and examining both their theoretical foundations and practical behaviour. To ground this discussion, we apply a selection of methods to a machine learning emulator of the Lorenz 1963 system, an archetypal chaotic model that provides a tractable, physically meaningful setting for exposing the limitations and failure modes of general-purpose XAI in regression contexts. We then survey how these and related methods have been applied across a variety of Earth system sciences. We further situate XAI within the model development lifecycle, linking methodological choices to the needs of different stakeholder groups across operational Earth system science. We close by identifying gaps in existing methodologies and outlining a forward-looking research agenda, with practical recommendations for the responsible, effective use of XAI in regression applications of geoscientific modelling and forecasting.
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All authors Ieuan Higgs, Todd Jones, Kieran Hunt, Anna-Louise Ellis

Atmospheric and Oceanic Physics

Finite-Response Complementarity in Fluctuation Constraints on Climate Sensitivity

Zaibo Zhao and Yongwen Zhang

Equilibrium climate sensitivity (ECS) is a zero-frequency susceptibility, whereas historical globalmean temperature variability samples a finite, forced projection of the climate system. We test whether information missed by a scalar fluctuation-memory coordinate reappears in a finite CO2 response. After conditioning both ECS and the finite response on {\Psi}, CMIP5 residuals are nearly uncoupled (C5 = 0.154), whereas CMIP6 shows strong complementarity (C6 = 0.593, p = 0.00288). A two-mode stochastic response model attributes this contrast to hidden-response spread that is visible in the finite response but poorly projected onto {\Psi}. The CMIP6 residual direction defines a first-order correction and yields a HadCRUT5 conditional ECS estimate centered at 3.00 K. Thus the weakened CMIP6 fluctuation constraint does not imply that susceptibility information is lost: part of it is recovered through a complementary finite-response projection.
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Atmospheric and Oceanic Physics

State transitions in land-vegetation systems emerge at Paris Agreement warming levels in CMIP6

Pouriya Alinaghi, Sybren Drijfhout, Joran Angevaare et al. (+2 more)

Using an automatic detection workflow applied to the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble under future emission scenarios, we identify 47 abrupt and more gradually developing state transitions in the land-vegetation component of the Earth system, classified into 9 categories. Over the Amazon, we find state transitions via vegetation dieback alongside greening cases; the contrast between them is traced primarily to differences in precipitation: models showing dieback experience either a larger absolute decline in precipitation, or one that translates more efficiently into soil moisture loss, particularly near the surface, while in greening models the CO$_2$ fertilization effect wins out where the soil moisture loss remains weaker. The precipitation decline in dieback-prone models appears driven through a weakening of moist convection. Across these Amazon cases, models with dynamic vegetation undergo dieback, whereas greening is confined to models with prescribed vegetation distributions. African cases include greening over eastern-central Africa and the Congo basin, and an abrupt soil-moisture drying also over the Congo. At high latitudes, boreal forest expands, while permafrost thaws once the regional above-zero temperatures persist for more than half the year. Additional categories cover transitions to a reduced snow-cover state over northeastern North America, increased vegetation biomass near the Tibetan Plateau and southeastern Asia, and increased leaf-area index over the northeast Northern America. Of particular concern, a global warming of 2$^\circ$C or below, within reach of the Paris Agreement targets, is already enough to trigger the onset of the majority of the identified categories in CMIP6.
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All authors Pouriya Alinaghi, Sybren Drijfhout, Joran Angevaare, Chris Jones, Andy Wiltshire

Thursday 6 August

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

FarSky: Task-Aware Latent-Space Coupling for Generative Intra-Hour Solar Forecasting

Yann Fabel, Bijan Nouri, Milon Miah et al. (+4 more)

Accurate solar irradiance forecasting is essential for the reliable integration of photovoltaic power into modern electricity grids. All-sky imagers (ASI) provide high-resolution observations of clouds, making them well suited for intra-hour forecasting. Recent deep learning approaches have substantially improved forecast accuracy but are often limited by deterministic predictions and a reduced capability to anticipate ramp events. This work proposes FarSky, a generative forecasting framework that leverages latent-space coupling to learn task-aware representations of sky images. A multi-task autoencoder first learns a shared latent representation for image reconstruction and irradiance estimation. A latent diffusion model then generates future latent states conditioned on recent observations, from which irradiance forecasts are directly decoded. Probabilistic forecasts are inherently obtained through stochastic sampling. The framework is developed using a multi-year ASI dataset acquired at the Plataforma Solar de Almería, Spain, and evaluated on two independent test datasets against persistence, state-of-the-art end-to-end, and generative forecasting approaches. FarSky achieves the best overall deterministic and probabilistic forecasting performance, improving forecast skill by up to 11 percentage points. Furthermore, it substantially improves ramp event detection over existing methods, achieving F1-scores above 60%. These results demonstrate the potential of combining generative models with task-aware latent-space coupling for solar forecasting.
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All authors Yann Fabel, Bijan Nouri, Milon Miah, Niklas Blum, Luis F. Zarzalejo, Julia Kowalski, Robert Pitz-Paal

Atmospheric and Oceanic Physics

The iSTORM Instrument for Airborne Measurements of Gamma-Ray Emissions from Thunderstorms

Daniel Shy, J. Eric Grove, Bernard Phlips et al. (+7 more)

The in-Situ Thunderstorm Observer for Radiation Mechanisms (iSTORM) is a gamma-ray spectrometer to study gamma-ray transients originating from thunderstorms, such as glows and terrestrial gamma-ray flashes (TGFs). It is designed and built by the U.S. Naval Research Laboratory for deployment aboard a NASA ER-2 aircraft. Using an array of 32 one-inch-diameter $\mathrm{CeBr}_3$ scintillators read out with silicon photomultipliers (SiPMs), the instrument achieves an energy range of $\sim 250 \ \mathrm{keV}$ to $5 \ \mathrm{MeV}$ under flight conditions, with a total geometrical area of $157 \ \mathrm{cm^2}$. One of two gamma-ray instruments in the ALOFT campaign, iSTORM recorded glows, terrestrial gamma-ray flashes (TGFs), and the newly discovered flickering gamma-ray flashes (FGFs).
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All authors Daniel Shy, J. Eric Grove, Bernard Phlips, Alena Schell, Mary Johnson-Rambert, Mason Quick, David Corredor, Scott Podgorny, Roy Salinas, Mitch Davis

Atmospheric and Oceanic Physics · Cross-listed from astro-ph.EP

Alkali Metallicity, Mineral Clouds, and Deep Atmospheric Variability on Jupiter

Xi Zhang, Jiheng Hu, Cheng Li et al. (+1 more)

The bulk elemental abundances of Jupiter provide critical insights into its formation history and interior structure. Recent observations by the Juno Microwave Radiometer (MWR) reveal a deep Jovian atmosphere significantly depleted in electrons, implying an alkali metal (Na, K) abundance of 10^-1 - 10^-5 times solar. This depletion stands in sharp contrast to the supersolar volatile enrichments measured by the Galileo probe. We propose that this apparent depletion arises from mineral cloud-induced processes deep in the atmosphere. We explore two physical mechanisms using thermochemical and microphysical modeling. In the "chemical sequestration" scenario, vigorous vertical mixing lofts deep refractory condensates (e.g., spinel) into the 1000-2000 bar region, where they react to form alkali feldspars (albite) and feldspathoids (leucite), efficiently sequestering gaseous Na and K. In the "dust-catalyzed recombination" scenario, the bulk alkali inventory remains gaseous, but the free electron density is suppressed by dust-plasma interactions. Thermally emitted alkali ions from the surfaces of micron-sized iron and silicate grains significantly increase the cation density, driving rapid recombination of free electrons. Both mechanisms allow for a bulk solar or even supersolar alkali inventory while suppressing the electron density to match Juno observations. Analyzing an extended dataset of MWR observations with 61 perijoves, we detect spatial variability in the deep atmosphere that suggests modulation by mineral clouds. Our findings challenge the traditional rainout framework, unveiling a deep "mineralogical zone" in Jupiter shaped by dynamics and heterogeneous chemistry, resembling the photospheres of hot exoplanets and brown dwarfs.
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All authors Xi Zhang, Jiheng Hu, Cheng Li, Quentin Williams

Atmospheric and Oceanic Physics · Cross-listed from astro-ph.EP

Juno Microwave Observations Reveal Jupiter's Deep Alkali-Chlorine Relation

Xi Zhang, Jiheng Hu, Cheng Li et al. (+2 more)

The longest-wavelength channel of the Juno Microwave Radiometer (MWR) probes Jupiter's kilobar atmosphere through free electrons produced by sodium and potassium ionization. Under equilibrium chemistry the electron abundance is the small residual of the charge balance between alkali cations and the anions Cl- and HS-. Chlorine is not directly measurable in Jupiter's deep atmosphere because gaseous HCl is removed from the observable atmosphere by NH4Cl condensation, whereas sulfur has been measured by the Galileo probe. The MWR-derived electron measurement therefore constrains the alkali-to-chlorine ratio rather than the alkali abundance alone. We combine the MWR observations with equilibrium chemistry and microwave radiative transfer in a Bayesian framework, finding that the deep gas-phase elemental alkali-to-chlorine abundance ratio is (Na+K)/Cl = 0.05 over 0.3-5 times solar in chlorine, about 180 times below the protosolar ratio of 8.7. At 3 times solar chlorine, the inferred alkali metallicity is 1.6 x 10^-2 times solar (1 sigma: 1.2 x 10^-2 - 2.7 x 10^-2 times solar), while at low chlorine abundance HS- sets an alkali floor near 10^-3 times solar. The inferred gas-phase alkali abundance exceeds the ~10^-5 times solar threshold by more than two orders of magnitude and rules out the long-proposed global kilobar radiative zone. Because sodium and potassium are refractory whereas chlorine is volatile, the inferred ratio provides a new diagnostic of the rock-to-ice balance in the solids accreted by Jupiter. This compositional interpretation assumes equilibrium chemistry; if lofted mineral clouds instead control the electron abundance under disequilibrium conditions, the inferred alkali-chlorine relationship need not hold.
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All authors Xi Zhang, Jiheng Hu, Cheng Li, Louis Siebenaler, Yury Aglyamov

Atmospheric and Oceanic Physics · Cross-listed from astro-ph.IM

Atmospheric Light Pollution by Proposed Reflect Orbital Space Mirrors

Miroslav Kocifaj, Gáspár Bakos and František Kundracik

ReflectOrbital is planning to launch a $18\times18$\,m mirror in space at a $\sim$600\,km altitude to illuminate a $2.5$\,km radius circular patch on Earth. The pilot satellite, called EARENDIL-1, is a pathfinder for a constellation of $\sim$50,000 larger mirrors, each with $54\times54$\,m in diameter, with the explicit goal to provide space-borne illumination on Earth. We calculate the sky brightness caused by the Rayleigh scattering and aerosol scattering of the incoming beam from one such satellite and by the light reflected back by the illuminated patch, for various atmospheric properties and ground albedos under cloud-free conditions. We demonstrate that the light pollution caused by one of these satellites is very significant, altering the nighttime environment up to distances of $\sim$30\,kms. The results show that for an observer within the beam of a single 54\,m satellite, the mirror will appear as a $-16.7$\,mag point-like source, i.e.~about 4 magnitudes brighter than the full moon. The diffuse sky background will be similar to the dusk sky shortly after sunset; too bright to see even the brightest stars. From a distance of 14\,km, the glow from a single mirror will exceed the luminance of the full moon sky for majority of the sky. From a distance of 34\,km the sky will still appear brighter than the moon-lit sky in the direction of the beam. If 400 such mirrors illuminate the same patch simultaneously, the glow will be obvious from 80\,kms.
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Wednesday 5 August

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

Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching

Dibyajyoti Chakraborty and Romit Maulik

Data assimilation (DA) uses Bayesian inference to update the state of a numerical forecast model with observed data. In this study, we propose a fundamentally different, unified approach to atmospheric data assimilation. We use latent video flow-matching to sample temporally consistent trajectories from a prior trained using ERA5 reanalysis (69 variables over an 8-day window). We also use posterior sampling to assimilate real observation sources, such as those from the NOAA Integrated Global Radiosonde Archive and the Integrated Surface Database. Because the prior generates a continuous trajectory, it naturally propagates information between observed and unobserved frames. Therefore, we can perform various DA tasks, such as filtering and smoothing, simply by changing the observed frames. Moreover, we generate full-state ensemble forecasts directly from sparse observations, achieving performance competitive with state-of-the-art observation-to-forecast models.
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Tuesday 4 August

Atmospheric and Oceanic Physics

Prithvi-Precip: Integrating Satellite Observations into an Atmospheric AI Foundation Model for Precipitation Forecasting

Simon Pfreundschuh, Christian D. Kummerow, Johannes Schmude et al. (+5 more)

Accurate precipitation forecasting remains one of the most challenging problems in weather prediction. While recent AI weather prediction (AIWP) systems have achieved substantial improvements in medium-range forecasting skill, precipitation often remains a secondary target and is commonly learned from reanalysis datasets that contain considerable uncertainty. In this work, we investigate two complementary strategies for improving AI-based precipitation forecasts. Building on the Prithvi-WxC foundation model, we develop Prithvi-Precip, a global precipitation forecasting system, and examine (1) the impact of training targets derived from satellite-based precipitation estimates rather than reanalysis fields and (2) the direct assimilation of satellite observations into the forecasting model. We systematically evaluate key design choices for finetuning the Prithvi-WxC AI foundation model for precipitation forecasting. We find that autoregressive rollout training produces substantially more accurate forecasts than direct conditioning on forecast lead time. Using independent radar-based precipitation estimates for evaluation, we show that training on satellite-derived precipitation targets yields improved forecast accuracy relative to training on MERRA-2 precipitation fields. Furthermore, direct ingestion of satellite observations provides additional improvements at short lead times, with the largest gains occurring in tropical and subtropical regions. Together, these advances enable Prithvi-Precip to substantially improve upon directly comparable precipitation forecasts from the Goddard Earth Observing System. Our results highlight the potential of improved precipitation targets and the direct integration of satellite observations as promising pathways for advancing medium-range AI precipitation forecasting.
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All authors Simon Pfreundschuh, Christian D. Kummerow, Johannes Schmude, Sujit Roy, Rahul Ramachandran, Tsengdar Lee, Valentine Anantharaj, Katherine H. Breen

Atmospheric and Oceanic Physics

Unexpected limitation of tropical cyclone genesis by subsurface tropical central-north Pacific during El Niño

Cong Gao, Lei Zhou, Chunzai Wang et al. (+2 more)

The vast tropical Pacific is home to the majority of tropical cyclones (TCs) which threaten the rim countries every year. The TC genesis is nourished by warm sea surface temperatures (SSTs). During El Niño, the western Pacific warm pool extends eastward. However, the number of TCs does not increase significantly with the expanding warm pool and it remains comparable between El Niño and La Niña. Here, we show that the subsurface heat content change counteracts the favorable SSTs in the tropical central-north Pacific. Due to the anomalous positive wind stress curl, the 26 {\deg}C isotherm shoals during El Niño over this region and the heat content diminishes in the tropical central-north Pacific, even though warm SST anomalies prevail. This negative correlation between SST and 26 {\deg}C isotherm depth anomalies is opposite to the positive correlation in the tropical eastern and western Pacific. This is critical because quantifying the dynamics of the subsurface ocean provides insight into TC genesis. The trend in TC genesis continues to be debated. Future projections must account for the net effect of the surface-subsurface dynamics on TCs, especially given the expected El Niño-like pattern over the tropical Pacific under global warming.
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All authors Cong Gao, Lei Zhou, Chunzai Wang, I.-I. Lin, Raghu Murtugudde

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

A windy sea surface with Stokes waves

Nikhil Yewale, Anil Kumar, Vinod Kumar Kadari et al. (+1 more)

Predicting the transition of wind-forced, gravito-capillary surface waves from smooth to corrugated states remains a longstanding problem in nonlinear surface wave mechanics. Solving driven-dissipative, nonlinear potential flow equations we map these waves ($4-13$ cm) onto a Reynolds number – wave energy phase-space. We identify a transition band separating smooth from corrugated wave states - the wave steepness exhibits a non-monotonic dependence on Reynolds number within this. Subharmonic (in)stability analysis reveals that the fastest-growing mode intensifies corrugations on alternate faces of the carrier wave. Our results offer insights into parasitic capillary wave formation on steep carrier waves and are of interest to ocean remote-sensing.
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All authors Nikhil Yewale, Anil Kumar, Vinod Kumar Kadari, Ratul Dasgupta

Atmospheric and Oceanic Physics

Experimental validation of an open-source low-cost single-camera 6-DOF tracking of floating-body motion in wave tanks

Herman Martens Meyer, Paul Fromont, Øystein Lande et al. (+1 more)

Accurately measuring motion of floating structures in experimental settings is both important and non-trivial. In this paper, we present a single-camera, 6-degree-of-freedom motion-tracking system that is both low-cost and simple to set up for an experimental campaign. The system uses fiducial markers and open-source computer vision tools to estimate the positions and orientations of multi-marker geometries and track them. We validate the accuracy and limitations on a precisely controlled linear actuator with static, regular, and irregular motion. The performance is quantified depending on both the camera-to-marker distance and direction of motion relative to the camera plane. Additionally, we present a practical use case for our system in a wave tank. Although the motion direction perpendicular to the camera plane shows the highest errors, the system achieves sub-millimeter accuracy at short and moderate camera-to-marker distances. For the irregular motion validation, the best cases reproduced the displacements with an RMSE of approximately 0.5 mm. Overall, the results indicate that low-cost single-camera fiducial-marker tracking can provide sufficiently accurate, non-intrusive motion measurements for a range of hydrodynamic laboratory experiments.
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All authors Herman Martens Meyer, Paul Fromont, Øystein Lande, Atle Jensen

Atmospheric and Oceanic Physics

Recent Sharp Rise in Inhomogeneous Hydrological Extremes Stress Vegetation Growth in China

Shengyuan Liu, Jeremy Cheuk-Hin Leung, Jianjun Xu et al. (+6 more)

The intensifying spatial inhomogeneity of rainfall under greenhouse warming implies that more inhomogeneous hydrological extremes (IHEs), i.e., coexistence of extreme rainfall or drought, may be triggered. While vegetation growth in China is sensitive to hydrological hazards, the variability of IHEs and their ecological impacts remain underexplored. Here, we find a significant increase in IHEs during China's growing season since 2000 (+2.1 events or +14.52 days per decade), with a rapid sharp rise to an annual average of 6.4 events or 42.0 days in the past five years. The primary driver is the enhanced inhomogeneity of moisture-dynamic coupled weather conditions, overlapping with a northward shift of climatological precipitation distribution. This results in a "Wet-North and Dry-South" pattern of IHE impacts, which poses severe and asymmetric threats to vegetation growth in China, with the expansion of drought areas exerts stronger stress on vegetation than the compensatory effects of rainfall. Our findings suggest that the sharp rises of IHEs tend to yield net negative impacts on vegetation growth, highlighting the need for stronger hydrological management to reduce future risks.
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All authors Shengyuan Liu, Jeremy Cheuk-Hin Leung, Jianjun Xu, Kunlun Xiang, Shifei Tu, Meiying Zheng, Daosheng Xu, Yi Li, Banglin Zhang

Monday 3 August

Atmospheric and Oceanic Physics

A black-box-model-enhanced interaction method for water-wave scattering by large group of arbitrary-shaped ice floes in Arctic route planning

Chongwei Zhang, Hongli Yang, Peng Wu et al. (+2 more)

This study develops an enhanced interaction (EI) method for efficient prediction of the water-wave field among a large group of ice floes in Arctic route planning. A novel black-box model, termed the wave component detection (WCD) method, is proposed for constructing the diffraction transfer matrix (DTM) within the framework of interaction theory. The DTM, which is conventionally mathematically intractable for three-dimensional ice floes with arbitrarily complex geometry, can now be determined using this readily implementable and universally applicable approach. Without loss of generality, four ice-floe shapes are taken as example models to demonstrate the capability of the EI method. Operation rules are recommended for the practical implementation of the EI method. The error range of the EI method is identified in scenarios with multiple ice floes of different sizes and distances.The super-high efficiency of the EI method is demonstrated in cases involving an ultra-large group of ice floes. It takes less than 1.5 hours to calculate wave amplitudes at 160,000 locations in the wave field of 1,800 ice floes (based on 1,440,000 boundary elements) on an ordinary personal computer with a 2017-released CPU. Based on the wave field predicted by the EI method, users can take advantage of the wave-sheltering effect of the ice floes to optimize routes. For demonstration, the dynamic programming strategy is used to recommend optimized navigation routes among 1561 ice floes of mixed shapes. The average wave amplitude the ship encounters can be reduced to about half of the incident wave amplitude.
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All authors Chongwei Zhang, Hongli Yang, Peng Wu, Peng Lu, Dezhi Ning

Atmospheric and Oceanic Physics

Arctic bimodality reflects enhanced variability in the clear-cloudy transition rather than bistability

Nadja Omanovic, Antoon van Hooft, Valentina Zeni et al. (+1 more)

The wintertime Arctic boundary layer is characterized by two distinct states, radiatively clear (cloud-free) and radiatively opaque (cloudy), which is reflected in observations as a bimodal distribution. Previous studies hypothesized that these regimes are related to an underlying bistability of the system's dynamics. We apply a drift-diffusion framework based on the two leading terms of the Kramers-Moyal expansion to observations from 15 winter periods in Ny-Alesund, Norway. Our model captures the bimodality; we find a single stable point for the deterministic drift, however, which questions the existence of bistable deterministic dynamics. Instead, the observed regimes can be attributed to state-dependent stochastic fluctuations with maximum amplitude close to the clear-cloudy transition. The position of the stable point depends on the meteorological conditions. Controlling for synoptic variability does not remove the state-dependence of diffusion, however. The cloudy regime is associated with southwesterly winds, high specific humidities, and uplifting motion, the latter two being conducive for cloud formation, while the cloud-free regime occurs under drier conditions and northeasterly winds. Through an illustrative example, we suggest a possible process-based interpretation of our statistical analysis. The nonlinear onset of cloud formation, once atmospheric humidity reaches saturation, could map state-independent fluctuations in humidity to state-dependent fluctuations in cloudiness.
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All authors Nadja Omanovic, Antoon van Hooft, Valentina Zeni, Franziska Glassmeier

Atmospheric and Oceanic Physics

Crucial role of subsurface ocean variability in tropical cyclone genesis

Cong Gao, Lei Zhou, I.-I. Lin et al. (+4 more)

The upper ocean provides thermal energy to tropical cyclones. However, the impacts of the subsurface ocean on tropical cyclogenesis have been largely overlooked. Here, we show that the subsurface variabilities associated with the variation in the 26 {\deg}C isothermal depth have pronounced impacts on tropical cyclogenesis over global oceans. The sea surface wind stress and its curl before tropical cyclogenesis are large enough to perturb the ocean interior down to more than one hundred meters due to entrainment and upwelling. The 26 {\deg}C isothermal depth can fluctuate by tens of meters to significantly modify the upper ocean heat content. Consequently, sea surface temperature anomalies under nascent tropical cyclones are induced, and tropical cyclogenesis is modulated. Our results substantiate an unexpected relation between ocean interior variations and tropical cyclogenesis.
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All authors Cong Gao, Lei Zhou, I.-I. Lin, Chunzai Wang, Shoude Guan, Fei-Fei Jin, Raghu Murtugudde

Atmospheric and Oceanic Physics · Cross-listed from stat.AP

Probabilistic Deep Learning for Drought Forecasting: Role of Internal Climate Variability

Henri Funk, Cornelia Gruber, Göran Kauermann et al. (+2 more)

Predicting drought risk is essential for anticipating impacts on water resources, agriculture, ecosystems, and climate adaptation planning. Yet drought forecasts remain uncertain because variability can substantially alter regional precipitation and evaporative demand. Treating this variability as unstructured noise ignores the fact that internal variability has spatial, seasonal, and temporal structure and thus contains information that can be used to improve drought forecasting. We propose a deep-learning-based forecasting framework for European drought prediction and extend it with an uncertainty-aware drought bound that explicitly incorporates internal forecast variability from a large climate model ensemble. This bound represents a physically plausible lower-tail trajectory of future drought conditions and marks how severe drought could plausibly become under an unfavourable realisation of internal variability, giving adaptation planning a conservative, risk-averse reference. We compare the proposed bound with a lower bound derived from reanalysis data only and show that our proposed ensemble-informed bound is better calibrated across most regions and seasons. This is specifically true during anomalously dry conditions, when historical reanalysis alone underestimates lower-tail drought risk. Our results show that internal variability should be treated as a forecast quantity in its own right. More broadly, large ensembles provide a practical way to transfer physically plausible climate variability into machine-learning drought forecasts, yielding risk-aware bounds that are more informative for drought assessment under shifting climate conditions.
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All authors Henri Funk, Cornelia Gruber, Göran Kauermann, Helmut Küchenhoff, Magdalena Mittermeier