San Francisco's weather on May 18 has drawn significant attention from weather forecasters and market participants alike. This event aggregates five prediction markets focused on the city's highest temperature that day, each covering a specific range: 58–59°F, 60–61°F, 62–63°F, 64–65°F, and 66–67°F. Together, these markets span the realistic spectrum of possible outcomes and allow participants to express exactly where they expect the high temperature to land. By breaking the forecast into discrete bands, the markets enable a more nuanced picture of probability than a simple point estimate would allow—some participants might be confident the temperature falls between 62–63°F, while others might hedge across multiple ranges. The real value of this market structure lies in what the pricing reveals about collective expectations. When you look across all five markets, the distribution of probabilities tells a story. If most probability weight concentrates in just one or two adjacent ranges, the market is signaling high confidence in a narrow forecast window. If probabilities spread across multiple ranges, that indicates broader uncertainty about May 18's conditions. The relative price differences between adjacent temperature bands can also signal where the market expects the boundary of most likely outcomes to be—sudden shifts between consecutive ranges might reflect new weather model updates or changes in meteorological thinking. As May 18 approaches, these markets will continue to update in response to incoming weather forecasts. The instant settlement mechanism means prices shift quickly as new information emerges. Whether you're tracking these predictions as a weather enthusiast, a researcher interested in forecast accuracy, or simply curious about how markets aggregate distributed knowledge, these five linked markets offer a transparent window into real-time probability assessments for one specific meteorological question.