Dallas's weather forecast for April 28 has attracted interest across five related markets, each covering a specific temperature range: 73°F or below, 74–75°F, 78–79°F, 80–81°F, and 82–83°F. Grouped together, these markets create a complete probability spectrum for Dallas's high temperature that day, offering a detailed view of market expectations across the full range of likely outcomes. These five markets are fundamentally linked—they all measure the same event: Dallas's maximum daily temperature on April 28. Presenting them together allows participants to see how the market prices different temperature scenarios relative to one another. When one range shows higher odds than adjacent ranges, it signals where collective market opinion clusters. Comparing prices across all five provides a fuller picture than examining any single range in isolation. Several factors typically influence daily temperature market pricing. National weather forecasts from services like the National Weather Service and NOAA serve as reference points many participants consult. Historical climate patterns for Dallas in late April provide useful baseline expectations—spring in North Texas generally brings warming toward the low 80s Fahrenheit. Current atmospheric conditions carry significant weight: high-pressure systems, approaching weather fronts, and moisture levels all shape temperature outcomes. As April 28 approaches, forecasts typically become more precise, and market prices often shift in response to updated meteorological data, sometimes substantially as the event date nears. When reviewing these temperature ranges, observe the gap between published weather forecasts and market-implied probabilities. Larger divergences may signal where participants perceive additional risk or where they expect forecast adjustments. Notice how prices evolve over time; early-period uncertainty often resolves into clearer probability patterns as the forecast window tightens. This granular approach to temperature outcomes—segmenting it into five discrete ranges—provides greater precision for understanding collective expectations than any single forecast summary.