| Outcome | Probability | Yes Bid | Yes Ask | 24h Change | Volume | |
|---|---|---|---|---|---|---|
| 86° or below | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| 91° to 92° | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| 95° or above | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| 89° to 90° | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| 93° to 94° | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| 87° to 88° | 0% | 0¢ | 0¢ | — | $0 | Trade → |
This market asks what the single highest air temperature recorded in Dallas on March 19, 2026 will be. Outcomes matter for weather-sensitive industries and individuals making plans or hedging weather exposure.
Dallas in mid-March sits in a seasonal transition where large swings between cool and warm conditions are common, driven by frontal passages and Gulf moisture. Year-to-year variability is high, and longer-term warming trends and the state of large-scale patterns (e.g., ENSO) can bias expectations for early-spring temperatures.
Market odds reflect the collective expectations of traders and update as new meteorological data, model runs, and official observations become available; they are a dynamic signal, not a guarantee.
The close time is listed on the Kalshi event page as TBD; Kalshi sets the official trading close and resolution timing. Resolution will follow the contract rules and use the specified official observation source and time window indicated on the market page.
The contract specifies the exact station or dataset used for resolution—check the event details. Kalshi markets typically reference an official NWS/NOAA observing station for the named location; the event page will identify which station or dataset governs resolution.
Most contracts use the local calendar day for the specified observation station (00:00–23:59 local time) unless the event rules state otherwise. Confirm on the market page whether local time or UTC and the specific observation interval are used.
Deterministic model runs (e.g., ECMWF, GFS), ensemble spread and consensus, NWS forecast updates, and short-range mesoscale guidance typically have the largest impact—especially sudden changes in front timing, cloud/precipitation forecasts, or surface observations near the day in question.
Consider Dallas climatology for mid-March (a season with frequent swings between cool and warm), recent seasonal anomalies, and any ongoing large-scale patterns like ENSO that affect regional temperatures. Historical extremes and their drivers (late-season cold snaps or early warm spells) are also useful context when assessing risk.