Atlanta's high temperature on May 17 will unfold according to atmospheric conditions none of us can perfectly predict—but prediction markets offer a way to see how thousands of participants collectively assess that uncertainty. The three markets here all answer the same core question: how hot will it get in Atlanta that day? Rather than forcing that question into a simple yes-or-no format, these markets let you specify your forecast more precisely by breaking the temperature range into distinct bands. The coolest scenario, 79°F or below, would represent a notably mild day for mid-May. The 80–81°F range captures what many would consider typical spring weather for Atlanta. The 96–97°F band reflects what would be an unusually warm, early-summer feel—a heat event rather than routine spring conditions. What makes these three markets valuable taken together is that they reconstruct the entire probability distribution prediction market participants hold about May 17's weather. A market with a higher price implies that participants collectively think that outcome is more likely; a lower price suggests they see it as less probable. By comparing prices across all three scenarios, you can reverse-engineer what the market expects at different temperature thresholds. This aggregated view—three different predictions on the same day, same location, same underlying event—gives you a complete picture of where market consensus leans across the range of plausible outcomes. For anyone curious about weather prediction, probability assessment, or how markets translate uncertain information into prices, these three markets offer a practical case study in how decentralized forecasting works.