Connected Radar: A New Paradigm for Atmospheric Observation and Experimentation

Abstract

Advancing atmospheric science increasingly depends on how effectively we design, execute, and adapt observational experiments. While major progress has been made in numerical modeling and artificial intelligence (AI), observational experimentation has not kept pace. Most observing systems—including radars—still operate as largely stand-alone instruments, using fixed or heuristically-defined strategies that limit our ability to capture fast-evolving phenomena and fully exploit emerging analytical tools. Here, we argue for a shift from static, hardware-centric observations toward connected, outcome-driven experimentation. Drawing on lessons from the Multisensor Agile Adaptive Sampling (MAAS) project, we introduce the concept of Connected Radar, in which radars operate as part of an integrated, cloud-based experimental infrastructure that incorporates satellite data, lightning observations, cameras, drones, and AI-driven data fusion. In this paradigm, radar operation is treated as part of a controlled experiment, where sensing decisions are guided by scientific intent and information gain rather than predefined scan strategies. Artificial intelligence plays a central role by linking sensing actions to experimental outcomes through self-supervised learning, uncertainty estimation, and active sampling. This reframes radar agility as information (or cognitive) agility, not just physical beam steering. We show how this framework provides a natural pathway for fully exploiting phased-array radar capabilities and for rethinking future radar facilities as autonomous laboratories for atmospheric research—where scientific value is defined by insight gained, not hardware alone.