Next-Generation Radar (NEXRAD) is an important decision-support tool for air traffic control (ATC). It is currently being introduced into aircraft cockpits, and will be equally important for remote piloting of unmanned aerial systems (UAS). Unfortunately, numerous studies strongly suggest that manually estimation of closest point of approach (CPA) to heavy weather is a difficult task for pilots, and that current-generation NEXRAD may be intrinsically inadequate for that task. In this study, we begin with an ecological information analysis of the looping NEXRAD format itself, and show that inherent task difficulty is predicted mathematically and by the neural structure of the visual system. Then, to confirm empirically, we develop a mathematical model of an extremely simple “ideal storm” to generate various types of simulated weather cells. A looping NEXRAD-type part-task simulation is created, and 21 general aviation (GA) pilots are tested to measure effects of weather system depth (19 vs. 40 nm), and the opening and closing of gaps at various closure rates between weather cells (−14, −7, 0, 7, 14 kt). For the values tested, weather system depth has no significant effect on eventual CPA from heavy weather (>40 dBZ reflectivity). However, redesign and retesting may be warranted for this variable. In contrast, weather movement clearly and greatly degrades safety. It does not seem to matter if weather movement is as slow as 7 scale kt, nor whether gaps are opening or closing. Any weather movement makes an already hard task harder. We can now say with certainty that current-generation looping NEXRAD is inadequate to manually keep aircraft safely separated from weather. Ecological analysis indicates necessity of adding 30–45 minutes of future-predicted (forecast) weather and a 20 nm range ring to the looping NEXRAD display to enable safe tactical weather avoidance.