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Abstract Sprites have been recorded at ∼100,000 frames per second. One hundred and sixty five essentially vertically propagating streamers, 110 downward and 55 upward, have been selected for analysis. The initial velocity increase is exponential as predicted by theory. Growth rates could be determined for 76 downward and 46 upward propagating streamers, and, in individual streamers, they are independent of altitude. The average growth rate increases from 1.6 10³in C‐sprites, to 2.6 10³in carrots, to 8.4 10³/s in jellyfish sprites. With a streamer model the driving electric field can be derived. Evaluating the field at 70 km altitude, we find fields of 98 (0.45 E_k), 121 (0.56 E_k), and 188 (0.87 E_k) V/m for the 3 sprite types, indicating that jellyfish sprites are the most energetic. High‐speed imaging can provide streamer growth rates and combined with a streamer model, the electric fields associated with various sprite features can be investigated. 

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore's Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator's macro-scale physical state.The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society. 

Abstract This report is on the synthesis by electrospinning of multiferroic core-shell nanofibers of strontium hexaferrite and lead zirconate titanate or barium titanate and studies on magneto-electric (ME) coupling. Fibers with well-defined core–shell structures showed the order parameters in agreement with values for nanostructures. The strength of ME coupling measured by the magnetic field-induced polarization showed the fractional change in the remnant polarization as high as 21%. The ME voltage coefficient in H-assembled films showed the strong ME response for the zero magnetic bias field. Follow-up studies and potential avenues for enhancing the strength of ME coupling in the core–shell nanofibers are discussed. 

Local thermal magnetization fluctuations in Li-doped MnTe are found to increase its thermopower α strongly at temperatures up to 900 K. Below the Néel temperature ( T N ~ 307 K), MnTe is antiferromagnetic, and magnon drag contributes α md to the thermopower, which scales as ~ T 3 . Magnon drag persists into the paramagnetic state up to >3 × T N because of long-lived, short-range antiferromagnet-like fluctuations (paramagnons) shown by neutron spectroscopy to exist in the paramagnetic state. The paramagnon lifetime is longer than the charge carrier–magnon interaction time; its spin-spin spatial correlation length is larger than the free-carrier effective Bohr radius and de Broglie wavelength. Thus, to itinerant carriers, paramagnons look like magnons and give a paramagnon-drag thermopower. This contribution results in an optimally doped material having a thermoelectric figure of merit ZT > 1 at T > ~900 K, the first material with a technologically meaningful thermoelectric energy conversion efficiency from a spin-caloritronic effect.