Weather forecasting has traditionally been the domain of meteorological models and expert prediction, but prediction markets offer a complementary view into collective expectations about future atmospheric conditions. This event aggregates five related prediction markets focused on a specific forecast: Seoul's lowest temperature on April 27, 2026. Each market asks whether the day's minimum temperature will reach or fall below a particular Celsius point—13°C, 14°C, 15°C, 16°C, or 18°C. Together, these markets form a granular temperature distribution built not by meteorologists but by traders with real financial exposure to the outcome. As you review the probabilities across these five markets, you'll notice a natural ordering: the probability of the temperature reaching 18°C is higher than reaching 13°C, since warmer thresholds are naturally easier to achieve. The probability gaps between these markets—what traders call the implied distribution—reveal the market's expectations about where temperatures will most likely settle. A tight spread suggests high confidence in a narrow temperature range; a wide spread signals disagreement or genuine uncertainty about Seoul's weather on that day. These markets attract diverse participants: some rely on meteorological models and historical precedent, others monitor broader seasonal patterns and climate factors, and still others bring local Seoul weather expertise. The real-time prices displayed below represent the current market consensus probability for each threshold, continuously updated as new information arrives and participants adjust their views. Whether you're interested in Seoul's weather outlook, exploring how markets encode probabilistic forecasts, or examining the mathematical relationship between different temperature thresholds, these five markets offer a concrete illustration of how distributed decision-making produces numerical predictions about the physical world.