Tokyo's weather on April 27 is the subject of this five-market ensemble, where each market represents a distinct outcome for the day's highest temperature. Rather than asking whether it will be hot or cold, these markets slice the forecast into granular degree increments—12°C, 13°C, 14°C, 15°C, and 16°C—allowing predictors to express precise beliefs about where the daily high will land. Grouped together, these markets form a complete probability distribution: the prices across all five outcomes should reflect the collective expectation of where Tokyo's peak temperature is most likely to occur. When viewed as a set, higher prices on adjacent outcomes (say, 14°C and 15°C) suggest the crowd expects the actual result to cluster in that range, while lower prices on outlier temperatures indicate lower conviction in extreme results. This structure reveals not just a single prediction, but the full shape of market expectations—where the consensus peaks, how much uncertainty exists, and which temperature ranges the crowd deems most likely. Reading across these five markets tells a richer story than any single yes-or-no question: it shows whether forecasters are confident in a narrow band or spread across a wider range, whether there's consensus or divergent views, and how the probabilities shift over time as new information arrives. For those interested in weather prediction markets or how collective intelligence prices specific meteorological outcomes, this ensemble offers a real-time window into how multiple competing forecasts distill into market prices.