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Caroline Muller

Publications and source records attributed to Caroline Muller.

6 recordsLinked to original sources

Maximum updraft velocity beyond CAPE: the role of boundary layer dynamics and pressure perturbations

Deep convective updraft velocities play a key role in the Earth's climate system, influencing precipitation extremes, lightning, and the planetary energy budget. While Convective Available Potential Energy (CAPE) is widely used to explain maximum updraft velocity ($w_{\max}$), CAPE is an imperfect predictor as updrafts are also influenced by entrainment, boundary layer dynamics, pressure perturbations, and condensate loading. However, the relative importance of these processes and how they interact to set $w_{\max}$ in individual clouds remains unclear. Here, we use equation learning to identify compact, physically interpretable relationships linking environmental and in-cloud conditions to $w_{\max}$ in individual tracked clouds across idealized radiative-convective equilibrium regimes spanning a range of sea surface temperatures and radiative cooling rates. For pre-storm prediction, CAPE and local mean boundary layer vertical velocity ($\overline{w_{\mathrm{bl}}}$) together explain nearly half the variance in $w_{\max}$ across regimes ($R^2=0.47$). While CAPE captures regime-mean differences, it has little predictive value within a single simulation. $\overline{w_{\mathrm{bl}}}$ is essential for capturing cloud-to-cloud variability, including the suppression of $w_{\max}$ even at high CAPE values. At the time of peak intensity, a simple approximate Bernoulli-like invariant combining maximum pressure perturbation and maximum cloud condensate explains 89\% of the variance ($R^2=0.89$). The tight link between $w_{\max}$ and pressure perturbation supports the sticky thermals hypothesis and highlights the importance of dynamic pressure effects, often neglected in updraft theories. These results highlight $\overline{w_{\mathrm{bl}}}$ as an important regulator of convective intensity alongside CAPE, and demonstrate that dynamic pressure plays an important role within individual updrafts.

physics.ao-ph

The Perception-Physics Paradox: Probing Scientific Alignment with TC-Bench

While Vision Foundation Models (VFMs) excel at predictive tasks on satellite imagery, their performance can arise from visual correlations rather than underlying structural invariants, making even perception-based out-of-distribution accuracy a poor proxy for scientific utility. As a result, models may look correct without reasoning correctly, a discrepancy we term the Perception-Physics Paradox. To address this gap, we introduce scientific alignment as an implicit objective for representation learning in scientific domains. We study a principled, testable aspect of scientific alignment through structural isomorphism, which requires latent representations to uniquely identify physical systems up to a linear reparameterization. This perspective induces a hierarchy of necessary conditions and yields a systematic probing protocol for physical and causal interpretability. To operationalize this framework, we release TC-Bench, a global, reproducible benchmark dataset with an automated construction pipeline for tropical cyclone research, and show that current VFMs rely on visual shortcuts that collapse in intense regimes, indicating that scientific alignment does not arise as a natural byproduct of scaling alone.

cs.LG

Discharge at the Microscale: Using Optical Tweezers to Observe Muon-Induced Discharges of a Levitated Microparticle in Air

Electrical discharge at the smallest possible length and charge scales is not well understood. Using optical tweezers, we investigate spontaneous discharges of a single micron-scale particle levitated in air. These ``microdischarges'' have a typical size of $\sim$40 $|e|$, but can be as small as a few $|e|$ and as large as several hundred. The absence of a well-defined trigger charge and the weak dependence on particle size suggest events are not classical gaseous breakdown. Instead, we show that microdischarge events arise from the rapid capture of ions left in the tracks of nearby passing ionizing radiation. Our results highlight the role of natural ionizing radiation in initiating micron-scale discharges and provide a platform for studying discharge physics in electrode-free environments and at the smallest scales.

cond-mat.soft

Using optical tweezers to simultaneously trap, charge and measure the charge of a microparticle in air

Optical tweezers are widely used as a highly sensitive tool to measure forces on micron-scale particles. One such application is the measurement of the electric charge of a particle, which can be done with high precision in liquids, air, or vacuum. We experimentally investigate how the trapping laser itself can electrically charge such a particle, in our case a $\sim 1\,\mathrm{\mu m\;SiO_2}$ sphere in air. We model the charging mechanism as a two-photon process which reproduces the experimental data with high fidelity.

cond-mat.soft

Marrying Causal Representation Learning with Dynamical Systems for Science

Causal representation learning promises to extend causal models to hidden causal variables from raw entangled measurements. However, most progress has focused on proving identifiability results in different settings, and we are not aware of any successful real-world application. At the same time, the field of dynamical systems benefited from deep learning and scaled to countless applications but does not allow parameter identification. In this paper, we draw a clear connection between the two and their key assumptions, allowing us to apply identifiable methods developed in causal representation learning to dynamical systems. At the same time, we can leverage scalable differentiable solvers developed for differential equations to build models that are both identifiable and practical. Overall, we learn explicitly controllable models that isolate the trajectory-specific parameters for further downstream tasks such as out-of-distribution classification or treatment effect estimation. We experiment with a wind simulator with partially known factors of variation. We also apply the resulting model to real-world climate data and successfully answer downstream causal questions in line with existing literature on climate change.

cs.LG

Dynamics and Scaling of Internally Cooled Convection

Our goal is to investigate fundamental properties of the system of internally cooled convection. The system consists of an upward thermal flux at the lower boundary, a mean temperature lapse-rate and a constant cooling term in the bulk with the bulk cooling in thermal equilibrium with the input heat flux. This simple model represents idealised dry convection in the atmospheric boundary layer, where the cooling mimics the radiative cooling to space notably through longwave radiation. We perform linear stability analysis of the model for different values of the mean stratification to derive the critical forcing above which the fluid is convectively unstable to small perturbations. The dynamic behaviour of the fluid system is described and the scaling of various important measured quantities such as the total vertical convective heat flux and the upward mass flux is measured. We introduce a lapse-rate dependent dimensionless Rayleigh-number $Ra_\gamma$ that determines the behaviour of the system, finding that the convective heat-flux and mass-flux scale scale approximately as $Ra_\gamma^{0.5}$ and $Ra_\gamma^{0.7}$ respectively. The area-fraction of the domain that is occupied by upward and downward moving fluid and the skewness of the vertical velocity are studied to understand the asymmetry inherent in the system. We conclude with a short discussion on the relevance to atmospheric convection and the scope for further investigations of atmospheric convection using similar simplified approaches.

physics.flu-dyn