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Abstract. This study delves into the small-scale temperature structure inside the turbulent convection Π Chamber under three temperature differences (10, 15, and 20 K) at Rayleigh number Ra∼109 and Prandtl number Pr≈0.7. We performed high-frequency measurements (2 kHz) with the UltraFast Thermometer (UFT) at selected points along the vertical axis. The miniaturized design of the sensor with a resistive platinum-coated tungsten wire, 2.5 µm thick and 3 mm long, mounted on a miniature wire probe, allowed for vertically undisturbed temperature profiling through the chamber's depth spanning from 8 cm above the bottom to 5 cm below the top. The collected data, consisting of 19 and 3 min time series, were used to investigate the variability of the temperature field within the chamber, aiming to better address scientific questions related to its primary objective: understanding small-scale aerosol–cloud interactions. The analyses reveal substantial variability in both variance and skewness of temperature distributions near the top and bottom plates and in the bulk (central) region, which were linked to local thermal plume dynamics. We also identified three spectral regimes termed “inertial range” (slopes of ∼-7/5), “transition range” (slopes of ∼-3), and “dissipative range”, characterized by slopes of ∼-7. Furthermore, the analysis showed a power law relationship between the periodicity of large-scale circulation (LSC) and the temperature difference. Notably, the experimental results are in good agreement with direct numerical simulation (DNS) conducted under similar thermodynamic conditions, illustrating a comparative analysis of this nature. 

Model immunization is an emerging direction that aims to mitigate the potential risk of misuse associated with open-sourced models and advancing adaptation methods. The idea is to make the released models' weights difficult to fine-tune on certain harmful applications, hence the name immunized. Recent work on model immunization focuses on the single-concept setting. However, in real-world situations, models need to be immunized against multiple concepts. To address this gap, we propose an immunization algorithm that, simultaneously, learns a single difficult initialization for adaptation methods over a set of concepts. We achieve this by incorporating a differentiable merging layer that combines a set of model weights adapted over multiple concepts.In our experiments, we demonstrate the effectiveness of multi-concept immunization by generalizing prior work's experiment setup of re-learning and personalization adaptation to multiple concepts. 

Abstract The task of crafting procedural programs capable of generating structurally valid 3D shapes easily and intuitively remains an elusive goal in computer vision and graphics. Within the graphics community, generating procedural 3D models has shifted to using node graph systems. They allow the artist to create complex shapes and animations through visual programming. Being a high‐level design tool, they made procedural 3D modelling more accessible. However, crafting those node graphs demands expertise and training. We present GeoCode, a novel framework designed to extend an existing node graph system and significantly lower the bar for the creation of new procedural 3D shape programs. Our approach meticulously balances expressiveness and generalization for part‐based shapes. We propose a curated set of new geometric building blocks that are expressive and reusable across domains. We showcase three innovative and expressive programs developed through our technique and geometric building blocks. Our programs enforce intricate rules, empowering users to execute intuitive high‐level parameter edits that seamlessly propagate throughout the entire shape at a lower level while maintaining its validity. To evaluate the user‐friendliness of our geometric building blocks among non‐experts, we conduct a user study that demonstrates their ease of use and highlights their applicability across diverse domains. Empirical evidence shows the superior accuracy of GeoCode in inferring and recovering 3D shapes compared to an existing competitor. Furthermore, our method demonstrates superior expressiveness compared to alternatives that utilize coarse primitives. Notably, we illustrate the ability to execute controllable local and global shape manipulations. Our code, programs, datasets and Blender add‐on are available athttps://github.com/threedle/GeoCode. 

Impacts of aerosol particles on clouds, precipitation, and climate remain one of the significant uncertainties in climate change. Aerosol particles entrained at cloud top and edge can affect cloud microphysical and macrophysical properties, but the process is still poorly understood. Here we investigate the cloud microphysical responses to the entrainment of aerosol-laden air in the Pi convection-cloud chamber. Results show that cloud droplet number concentration increases and mean radius of droplets decreases, which leads to narrower droplet size distribution and smaller relative dispersion. These behaviors are generally consistent with the scenario expected from the first aerosol-cloud indirect effect for a constant liquid water content (L). However, L increases significantly in these experiments. Such enhancement of L can be understood as suppression of droplet sedimentation removal due to small droplets. Further, an increase in aerosol concentration from entrainment reduces the effective radius and ultimately increases cloud optical thickness and cloud albedo, making the clouds brighter. These findings are of relevance to the entrainment interface at stratocumulus cloud top, where modeling studies have suggested sedimentation plays a strong role in regulating L. Therefore, the results provide insights into the impacts of entrainment of aerosol-laden air on cloud, precipitation, and climate. 

Abstract. It is known that aqueous haze particles can be activated into cloud droplets in a supersaturated environment. However, haze–cloud interactions have not been fully explored, partly because haze particles are not represented in most cloud-resolving models. Here, we conduct a series of large-eddy simulations (LESs) of a cloud in a convection chamber using a haze-capable Eulerian-based bin microphysics scheme to explore haze–cloud interactions over a wide range of aerosol injection rates. Results show that the cloud is in a slow microphysics regime at low aerosol injection rates, where the cloud responds slowly to an environmental change and droplet deactivation is negligible. The cloud is in a fast microphysics regime at moderate aerosol injection rates, where the cloud responds quickly to an environmental change and haze–cloud interactions are important. More interestingly, two more microphysics regimes are observed at high aerosol injection rates due to haze–cloud interactions. Cloud oscillation is driven by the oscillation of the mean supersaturation around the critical supersaturation of aerosol due to haze–cloud interactions. Cloud collapse happens under weaker forcing of supersaturation where the chamber transfers cloud droplets to haze particles efficiently, leading to a significant decrease (collapse) in cloud droplet number concentration. One special case of cloud collapse is the haze-only regime. It occurs at extremely high aerosol injection rates, where droplet activation is inhibited, and the sedimentation of haze particles is balanced by the aerosol injection rate. Our results suggest that haze particles and their interactions with cloud droplets should be considered, especially in polluted conditions.