On May 18, Miami's maximum temperature will settle at some point along a spectrum, and a set of linked prediction markets are actively tracking real-time expectations across several key bands. The four markets partition temperature outcomes into distinct ranges: 77°F or lower, 78–79°F, 94–95°F, and 96°F or higher. This strategic binning captures the most likely or meteorologically significant scenarios while leaving middle-range temperatures (80–93°F) unmapped by design. As you examine the probability estimates displayed for each market, note that they reflect something distinct from a traditional weather forecast: the collective expectations of participants who have capital deployed on their predictions. The prices tell a story through their movements. If odds on "96°F or higher" are rising, expect the cooler-outcome probabilities to shift downward in response—the markets are self-correcting toward coherence. The spread of probabilities across the four markets also reveals useful information about confidence. A scenario where one market is trading at 60 percent while others hover near 10–15 percent signals participants believe that outcome is significantly more likely than alternatives. Conversely, tightly bunched probabilities suggest genuine uncertainty about where the day will land. For those interested in how prediction markets estimate environmental variables, how distributed price discovery works in practice, or simply tracking where sophisticated participants expect Miami's weather to go, this collection of markets offers a transparent, continuously updated window into real-time probability assessment.