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Abstract A plume model applied to radiosonde observations and the fifth generation ECMWF atmospheric reanalysis (ERA5) is used to assess the relative importance of lower-tropospheric moisture and temperature variability in the convective coupling of equatorial waves. Regression and wavenumber–frequency coherence analyses of satellite precipitation, outgoing longwave radiation (OLR), and plume model estimates of lower-tropospheric vertically integrated buoyancy (〈B〉) are used to identify the spatial and temporal scales where these variables are highly correlated. Precipitation and OLR show little coherence with 〈B〉 when zero entrainment is prescribed in the plume model. In contrast, precipitation and OLR vary coherently with 〈B〉 when “deep inflow” entrainment is prescribed, highlighting that entrainment occurring over a deep layer of the lower troposphere plays an important role in modifying the thermodynamic properties of convective plumes in the tropics. Consistent with previous studies, moisture variability is found to play a more dominant role than temperature variability in the convective coupling of the Madden–Julian oscillation (MJO) and equatorial Rossby (ER) waves, while temperature variability is found to play an important role in the convective coupling of Kelvin (KW) and inertio-gravity (IG) waves. Convective coupling is most strongly impacted by moisture variations in the 925–850- and 825–600-hPa layers for the MJO and ERs, and by 825–600-hPa temperature variations in KWs and IGs, with 1000–950-hPa moist static energy variations playing a relatively small role in convective coupling. Simulations of the Energy Exascale Earth System Model (E3SM), version 2, and a preoperational prototype of NOAA Global Forecast System (GFS) V17 are examined, the former showing unrealistically high coherence between precipitation and 1000-hPa moist static energy, the latter a more realistic relationship. 

Abstract Motivated by recent experimental observations of opposite Chern numbers in R-type twisted MoTe₂and WSe₂homobilayers, we perform large-scale density-functional-theory calculations with machine learning force fields to investigate moiré band topology across a range of twist angles in both materials. We find that the Chern numbers of the moiré frontier bands change sign as a function of twist angle, and this change is driven by the competition between moiré ferroelectricity and piezoelectricity. Our large-scale calculations, enabled by machine learning methods, reveal crucial insights into interactions across different scales in twisted bilayer systems. The interplay between atomic-level relaxation effects and moiré-scale electrostatic potential variation opens new avenues for the design of intertwined topological and correlated states, including the possibility of mimicking higher Landau level physics in the absence of magnetic field. 

We address the security of a network of Connected and Automated Vehicles (CAVs) cooperating to safely navigate through a conflict area (e.g., traffic intersections, merging roadways, roundabouts). Previous studies have shown that such a network can be targeted by adversarial attacks causing traffic jams or safety violations ending in collisions. We focus on attacks targeting the V2X communication network used to share vehicle data and consider as well uncertainties due to noise in sensor measurements and communication channels. To combat these, motivated by recent work on the safe control of CAVs, we propose a trust-aware robust event-triggered decentralized control and coordination framework that can provably guarantee safety. We maintain a trust metric for each vehicle in the network computed based on their behavior and used to balance the tradeoff between conservativeness (when deeming every vehicle as untrustworthy) and guaranteed safety and security. It is important to highlight that our framework is invariant to the specific choice of the trust framework. Based on this framework, we propose an attack detection and mitigation scheme which has twofold benefits: (i) the trust framework is immune to false positives, and (ii) it provably guarantees safety against false positive cases. We use extensive simulations (in SUMO and CARLA) to validate the theoretical guarantees and demonstrate the efficacy of our proposed scheme to detect and mitigate adversarial attacks.