Extreme precipitation events are projected to increase both in frequency and intensity due to climate change. High-resolution climate projections are essential to effectively model the convective phenomena responsible for severe precipitation and to plan any adaptation and mitigation action. Existing numerical methods struggle with either insufficient accuracy in capturing the evolution of convective dynamical systems, due to the low resolution, or are limited by the excessive computational demands required to achieve kilometre-scale resolution. To fill this gap, we propose a novel deep learning regional climate model (RCM) emulator called graph neural networks for climate downscaling (GNN4CD) to estimate high-resolution precipitation. The emulator is innovative in architecture and training strategy, using graph neural networks (GNNs) to learn the downscaling function through a novel hybrid imperfect framework. GNN4CD is initially trained to perform reanalysis to observation downscaling and then used for RCM emulation during the inference phase. The emulator is able to estimate precipitation at very high resolution both in space ($ 3 $km) and time ($ 1 $h), starting from lower-resolution atmospheric data ($ \sim 25 $km). Leveraging the flexibility of GNNs, we tested its spatial transferability in regions unseen during training. The model trained on northern Italy effectively reproduces the precipitation distribution, seasonal diurnal cycles, and spatial patterns of extreme percentiles across all of Italy. When used as an RCM emulator for the historical, mid-century, and end-of-century time slices, GNN4CD shows the remarkable ability to capture the shifts in precipitation distribution, especially in the tail, where changes are most pronounced.

Full-coverage pedestrian survey to record cultural features on unexplored archaeological landscapes is costly in terms of time, money, and personnel. Over the past two decades, researchers have implemented remote sensing and landscape data collection techniques using unmanned aerial vehicles (UAVs) to combat some of these burdens, but the initial cost of equipment, software, and processing power has hindered the ubiquitous implementation of UAV technology as an accessible companion tool to traditional archaeological survey. This article presents a free and open-source, technology-independent analytical framework for the collection and processing of UAV images to produce high-resolution digital terrain models limited only by the equipment available to the researcher. Results from the free and open-source protocol are directly compared to those produced using proprietary software to illustrate the capabilities of freely available data processing tools for UAV-collected images. By replicating the methods outlined here, researchers should be able to identify and target areas of interest to increase fieldwork efficiency, decrease costs of implementing this technology, and produce high-resolution digital terrain models to conduct spatial analyses that pursue a deeper understanding of cultural landscapes.

The Sun's magnetic field is produced throughout the solar interior; it emerges and is dispersed by surface and subsurface flows, and then expands above the surface to dominate the structure of the corona. To resolve the effects of the magnetic field it is necessary to image the interior and measure its rotation and flow systems; track the responses of the magnetic fields to flows in the surface; and to follow the evolution of structures in the corona. Because the Sun is dynamic both high spatial and temporal resolution are essential. Because the Sun's magnetic field effects encompass the entire spherical exterior, the entire surface and outer atmosphere must be mapped. And because the magnetic field is cyclic high-resolution observations must be maintained over multiple cycles.

The article presents the multigroup kinetic approximation of radiation transport in hohlraum systems. The view factor-based method of numerical solution is proposed. Its advantages are a detailed account of the problem geometry and accurate handling of the solution angular anisotropy and discontinuities. The method is computationally efficient and easily parallelizable. Coupled to the kinetic + hydro description of the wall dynamics, it represents a powerful tool for integrated investigation of the systems with strongly inhomogeneous optical properties.