Market Explainer

How Weather Data Feeds Influence Prediction Market Prices

Why updates from satellites, radar, and station networks move market probabilities for weather events.

By Top Prediction Markets EditorialReviewed September 14, 20265 min read

Answer first

Weather data feeds are the observations and model outputs traders and platforms use to update the probability of weather events. When a feed adds or corrects information—like a new radar sweep or a revised temperature from a station—markets quickly translate that into a price change that reflects the revised implied probability.

A concrete scenario: City X July 15 temperature market

Market question: “Will the official station in City X record a maximum temperature above 30°C on July 15?” A Yes contract pays $1 if the event happens.

The sequence of public facts that follows is fixed:

At 14:00 the official station reports 29.7°C and the market price for Yes is $0.62 (implied probability 62%).
Nearby professional station (watched by traders but not the official settlement feed) reports 31.0°C one hour later.
After that report the market price moves to $0.78.

Keep these figures in mind — they are the spine of every calculation and every interpretation below.

Running the numbers: who wins or loses at each price

The market mechanics are simple to convert into cash outcomes.

Buy one Yes contract at $0.62:

Cost: $0.62
If official station records >30°C: payoff $1 → profit $0.38
If not: payoff $0 → loss $0.62

Buy one Yes contract at $0.78:

Cost: $0.78
If official station records >30°C: payoff $1 → profit $0.22
If not: payoff $0 → loss $0.78

Small table of those outcomes:

Purchase price	Outcome if event happens	Outcome if event does not happen
$0.62	+$0.38	-$0.62
$0.78	+$0.22	-$0.78

The price change from $0.62 to $0.78 reflects a higher crowd-implied probability after traders saw the nearby station's 31.0°C report. Buyers who purchased at $0.62 have their exposure locked at that entry price; later price moves change the market consensus but not their realized cost.

How different weather feeds actually move prices in this case

Tie the abstract feed types to the City X sequence so you can see cause and effect.

Radar: high-frequency reflectivity and velocity scans are ideal for short-term storm or precipitation events. In a temperature market radar is unlikely to be decisive, but for a two-hour storm arrival market a radar update that shows an intensifying cell will often move prices faster than a slower feed.
Satellite: broad-area imagery and derived products (cloud-top temperature, moisture channels) give context on regional heating or cloud cover. Geostationary satellites provide frequent updates; polar-orbiting ones give higher spectral detail but less cadence. For City X on a summer day, a rapid clearing of cloud evident in satellite loops could bolster a higher temperature outlook, but traders typically combine that with other feeds.
Station networks: surface observations from official stations and crowdsourced sensors give high-accuracy point readings. In our scenario the official station’s 29.7°C at 14:00 is the settlement reference point, but the professional nearby station reporting 31.0°C an hour later is the signal that pushed the market to $0.78. A single station report can flip market odds only if traders believe it is representative of the settlement location or signals an imminent change there.
Numerical models and reanalysis: gridded forecasts are used for broader expectation and uncertainty. A high-resolution model output and nearby station temperatures suggesting warming was already present before the 31.0°C report; the model set market priors, the station feed provided a real-time observation that updated that prior.

Timing matters: a radar or station update that arrives first will often cause the fastest price reaction. Spatial resolution matters: a single point station report can move a local contract but not a statewide one unless traders deem it representative.

What happens at settlement and why the chosen feed matters

Markets may price using many inputs, but payouts are determined by the platform’s settlement feed. That choice can produce surprises.

In City X the official station is the settlement feed. That means:

If the official station later records 30.1°C, contracts that paid $0.62 or $0.78 both resolve to $1.
If the official station records 29.9°C, both contracts resolve to $0.

Traders often watch additional feeds (nearby stations, automated aggregators, model ensembles) to form their trades. Aggregation of inputs smooths noise but can hide a strong localized signal; a single high-quality station report can move prices even when it is not the settlement source. If the settlement feed differs from the watched feed, final payouts can surprise participants who assumed settlement would follow the signal that moved prices.

How the maths shifts if the price moves and common pitfalls to avoid

Price moves change risk/reward for new entrants and reveal which information the market privileged.

Entry exposure is fixed by the price at purchase. Buying at $0.62 locks in the $0.62 downside even if price later rises to $0.78; buying after the rise exposes you to a smaller upside (+$0.22 instead of +$0.38) and a larger downside (-$0.78 instead of -$0.62).
Small ticks can be noise. A tiny move might reflect low liquidity, a hedge trade, or a single trader’s position rather than a genuine consensus shift. Look for sustained movement across successive updates before treating a change as a durable re-evaluation.
Feed frequency ≠ accuracy. Fast feeds like radar produce many updates and can contain transient noise. High cadence doesn’t guarantee correctness; traders need context from other feeds to judge whether an immediate signal is reliable.
Treat stations according to their representativeness. Stations differ in siting and instrument quality. A nearby professional station reporting 31.0°C dominated market reaction here because traders judged it a credible proxy; if that station were poorly sited, the same report might not have moved prices.
Revisions matter. Some feeds perform post-processing or quality control. A corrected feed that reverses an earlier value can force the market to reverse a prior price move if traders trust the revised record.

Being explicit about which feed moves you and how risk shifts once prices move prevents misreading market action as irrationality. Often price moves reflect differences in the information content of feeds, not gambler behavior.

Further reading on prediction market prices

Frequently asked questions

Which weather feed moves prices fastest?

Radar and geostationary satellite products tend to move short-term event prices fastest because they update frequently and directly show evolving storms.

Do platforms always use the same feed to settle an event?

No. Platforms usually specify the official settlement feed in the contract rules up front. Traders should check that choice since settlement can differ from the feeds they watch.

How should I interpret a sudden price jump after a feed update?

A sudden jump typically means traders saw new information that increases the implied probability of the outcome. Consider feed type, resolution, and whether the move is supported by subsequent updates.

Can data-feed errors reverse market moves?

Yes. Revised or corrected feeds can undo earlier moves if the market trusts the correction more than the initial report.

Are all station reports equally reliable?

No. Official, professionally maintained stations are generally more reliable than crowdsourced sensors. Traders weight station quality when reacting to reports.