| Outcome | Probability | Yes Bid | Yes Ask | 24h Change | Volume | |
|---|---|---|---|---|---|---|
| 26° or below | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| 27° to 28° | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| 29° to 30° | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| 31° to 32° | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| 33° to 34° | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| 35° or above | 0% | 0¢ | 0¢ | — | $0 | Trade → |
This market asks which temperature bracket will be the lowest observed in New York City on March 29, 2026; it matters for traders, weather-sensitive businesses, and anyone tracking short-term temperature risk.
Late March is a transitional period in the northeastern U.S., so day-to-day temperature outcomes can swing widely depending on synoptic patterns. Local factors—coastal proximity, urban heat island effects, cloud cover, and recent snow cover—regularly shift expected lows, making markets like this responsive to short-range forecasts and observations.
Market odds aggregate participants' expectations about the lowest-observed temperature outcome for that calendar date; prices will move as new forecast guidance, observations, and model runs arrive, reflecting changing consensus rather than immutable truth.
The event’s official rules on the Kalshi page specify the exact observing station or dataset used for resolution and any tie‑breaking procedure; consult the event rules for the named data source (e.g., a particular NWS station or archived dataset).
Most weather markets use the local calendar date (00:00 to 23:59 local time) for the named location, but you should confirm the precise measurement window and timestamp standards in the event’s resolution rules on Kalshi.
The event rules will identify the primary observing site or dataset; common options include official National Weather Service stations, airport automated stations, or a designated Central Park sensor—station choice matters because readings vary across the city.
Late March is climatologically transitional with substantial variability—some years remain wintry while others trend milder—so historical data mainly provides a range of typical variability and highlights that outlier cold nights are possible but relatively uncommon; use historical context alongside short‑range forecasts.
Resolution follows the event’s stated primary data source and tie rules; if multiple stations are mentioned, the rules should describe which station or dataset prevails or how discrepancies are adjudicated—refer to the event resolution section for that procedure.