This work presents the design and characterization of a novel ground-penetrating magnetic ranging system tailored for underground exploration and positioning. The system leverages AC magnetic fields generated by surface coils to enable precise ranging of buried objects. Since the magnetic field can easily penetrate most materials, its use is suitable for the challenging undergorund scenario, which typically includes heterogeneous materials such as soil, water, and rock. Key design aspects include the optimization of transmitting (TX) and receiving (RX) coil parameters through meaningful simulations. Use of resonant coils was also investigated, since this method can significantly enhances signal-to-noise ratio and measurement accuracy. The system was subsequently realized and tested, demonstrating its capability to maintain robust signal detection while adhering to electromagnetic emission regulations, and the potential to perform reliable ranging and positioning of buried objects at distances up to 2 m. This work paves the way for efficient and accurate subsurface exploration, with many potential applications, including positioning of drilling drones and pollution detection in soil.

Weather forecasts are fundamentally uncertain, so predicting the range of probable weather scenarios is crucial for important decisions, from warning the public about hazardous weather to planning renewable energy use. Traditionally, weather forecasts have been based on numerical weather prediction (NWP)1, which relies on physics-based simulations of the atmosphere. Recent advances in machine learning (ML)-based weather prediction (MLWP) have produced ML-based models with less forecast error than single NWP simulations2,3. However, these advances have focused primarily on single, deterministic forecasts that fail to represent uncertainty and estimate risk. Overall, MLWP has remained less accurate and reliable than state-of-the-art NWP ensemble forecasts. Here we introduce GenCast, a probabilistic weather model with greater skill and speed than the top operational medium-range weather forecast in the world, ENS, the ensemble forecast of the European Centre for Medium-Range Weather Forecasts4. GenCast is an ML weather prediction method, trained on decades of reanalysis data. GenCast generates an ensemble of stochastic 15-day global forecasts, at 12-h steps and 0.25° latitude–longitude resolution, for more than 80 surface and atmospheric variables, in 8 min. It has greater skill than ENS on 97.2% of 1,320 targets we evaluated and better predicts extreme weather, tropical cyclone tracks and wind power production. This work helps open the next chapter in operational weather forecasting, in which crucial weather-dependent decisions are made more accurately and efficiently.