Fair Value Weather & Climate — Thursday, July 30, 2026

A detailed daily look at some of the most interesting weather and climate prediction markets at ForecastEx and elsewhere.

• Access to live probabilities and the ability to trade is available via IBKR Prediction Markets.
• General primers on how these prediction markets work are available in the IBKR prediction markets FAQ (scroll down) and the ForecastEx FAQ.

1) In the news

The Pacific Northwest fire outbreak is still the dominant feature on the national map, with smoke drifting east around the heat dome over the central United States and turning skies hazy as far as the Upper Midwest. Relatedly, of the eight largest single-forecast-system misses on Wednesday’s temperature contracts, six belonged to the American model and every one of those ran too warm, by as much as 11°F at Minneapolis. Smoke aloft filters sunlight and is not well-represented in the model.

2) Looking back, how the forecasts from Tuesday did against what actually happened on Wednesday

Below is a scorecard for the forecasts as they stood at 5:00 PM ET on Tuesday, July 28th, for the target day of Wednesday, July 29th. The highs and lows are shown as separate grids below. Each grid below orders its cities by mean absolute error (MAE) for that metric, from largest to smallest. The columns present some commonly referenced forecast systems compared to ForecastEx (see the appendix for more details).

Minneapolis carried the largest error on highs, at 5.1°F averaged across the five systems, and the American model was the weakest system on that metric by a wide margin at 4.07°F against ForecastEx at 1.13°F. This was not a scattered day. Six of the eight largest single-system misses were the American model, all of them too warm.

Postmortem on the Minneapolis miss. The high reached 90°F. The American model had carried 101°F from the Tuesday afternoon lead, an 11°F overshoot, and the European model was not much closer at 96°F. The National Weather Service and the Aviation Forecast were both at 93°F, so every system on the panel finished too warm, but only one of them was double digits wrong.

At the lead, the office in the Twin Cities had the region on the northeastern edge of a strong upper level ridge, calling highs in the lower 90s with middle or upper 90s possible only in portions of western Minnesota, and lower dew points keeping conditions less oppressive than earlier in the week. By the target day they were describing milky skies from elevated smoke with temperatures in the 80s through late morning. A model reading the ridge without the smoke had every reason to price a day in the high 90s, and the 11°F gap is the cost of that omission.

The lows ranked differently, led by Atlanta at 4.78°F in the grid above.

Over the window ending Wednesday, the ForecastEx prediction market records the lowest error of the twelve systems at 1.35°F on daily highs, with the Aviation Forecast second at 1.46°F.

This constitutes ongoing evidence that weather prediction markets like ForecastEx may be the most accurate short-range weather forecasts available, and the difference becomes greater as lead time decreases.

This is because a ForecastEx price is a confidence-weighted consensus of participants who can condition on every model shown here, plus whatever additional knowledge they bring to the table at live timescales. One way to think about it is that ForecastEx is a constantly offered rewards program for anyone who can push prices and probabilities toward their true, most-calibrated values (and a penalty for those who try but fail).

On average, no public tool beats ForecastEx consistently, but the section below asks the narrower question of where a single tool could potentially be used to identify edge in particular circumstances.

The most undervalued contracts

• Access to live probabilities and the ability to trade is available via IBKR Prediction Markets.
• General primers on how these prediction markets work are available in the IBKR prediction markets FAQ (scroll down) and the ForecastEx FAQ.

The most undervalued contract above was Yes on the Charlotte low below 71 degrees, 9 cents 31 hours before it resolved and 12 hours before the low itself arrived at 4:52 AM, for a 10.5 times payout net of fees. The American model was exactly on that outcome at 70 degrees, which is worth noting on a day when the same model produced six of the eight worst misses on the highs.

Unlike Tuesday, no public tool was more accurate than the ForecastEx market on Wednesday.

3) The forecasts and prices for Friday

The widest disagreement on the map above is the Los Angeles high, where ForecastEx sits 5.8°F below the National Weather Service forecast.

• Access to live probabilities and the ability to trade is available via IBKR Prediction Markets.
• General primers on how these prediction markets work are available in the IBKR prediction markets FAQ (scroll down) and the ForecastEx FAQ.

Hypothetically, if the National Blend of Models, which had the lowest error on Wednesday among the eleven public tools (with currently available forecasts for Friday), were to prove exactly right, the strike paying the most on the high side would be No on the New York City high above 82 degrees at 19 cents, a 5.1 times payout net of fees. On the low side it would be No on the Denver low below 66 degrees at 13 cents, a 7.4 times payout.

Temperature Market of the Day, San Francisco

The highlighted market for Friday is San Francisco (KSFO). The figure above runs from today’s noon listing through the end of Friday, showing observations so far, two independent hourly forecasts.

Thirteen models span 12.2°F on Friday’s high, from the European model at 77.2 down to the Japanese model at 65.0, and even the middle half of them sit 4.5°F apart. That is a very large range for a single station a day out, and the reason is the marine layer. The National Weather Service office serving San Francisco describes the marine layer clouds that had gone away earlier in the evening slowly reforming along portions of the coast, breezy onshore winds expected each afternoon and evening, and building high pressure continuing to warm the interior through Saturday. Whether the stratus clears the station or sits on it can affect the high ~ ten degrees at this airport.

4) Tropics, a basin that has still not produced a hurricane

The Bahamas lead the major hurricane landfall map at 10.9%, equivalent to a Yes contract at 11 cents, with Florida just behind at 10.4%. The probability of a Category 4 or stronger United States landfall stands at 7.9%, which is almost exactly climatology, with no live storm contributing to the probability.

The probability of a major hurricane making landfall within 50 miles of Miami-Dade, Florida currently stands at around 4.7%. Thus, a purchase of a “Yes” at 5 cents can be thought of as an analog to parametric insurance, where the occurrence of a disaster based on a simple weather parameter would pay out, in this case 18X of the purchase price.

The NOAA Climate Prediction Center’s Global Tropical Hazards Outlook covering August 5th to August 18th, expects El Niño conditions to continue to suppress Atlantic tropical cyclone activity, with formation unlikely over the Caribbean and the central Atlantic through that window.

Two named storms have formed against the 1.7 the season would normally have produced by now if it finishes at the total forecast by the hurricane research team at Colorado State University. On the hurricane count ladder below, Kalshi continues to price the season above ForecastEx across the strikes the two venues share, and the gap persists into the tail.

Further Reading

• Hurricane Forecast Contracts
• Daily High Temperature Markets at ForecastEx
• Energy expenditure implications of next-day temperature forecasts
• Disaster Insurance Applications of Forecast Contracts
• Hurricane Forecast Contracts as a Diversifying Asset in an Investment Portfolio
• Bundling “NO” Hurricane Landfall Forecast Contracts to Reduce Downside Risk

Climate Contracts

• Global Crop Yield Forecast Contracts (view here).
• How Quickly Will The Globe Warm? Paris Agreement Forecast Contracts (view here).
• Will the Atlantic Overturning Circulation (AMOC) Collapse? Prediction Markets Can Quantify Sentiment (view here).

Appendix

All four displayed commonly referenced forecast systems rely on physical numerical weather prediction models that solve the equations of the atmosphere forward in time. ECMWF (the European model) and GFS (the American model) are shown here in their raw form. Raw physical models are flexible and can handle weather situations they have never seen, but they carry systematic biases, and forecasts apply to a relatively large discrete grid box that encapsulates the weather station rather than the single point where a weather station sits.

The Aviation Forecast is a model output statistics system. It compares historical forecasts from physical models to what actually occurred and uses the errors to statistically correct systemic biases at the level of individual weather stations. The tradeoff is that a statistical fit is anchored to past situations and bends less readily to a genuinely unusual one. The National Weather Service’s National Digital Forecast Database is undergirded by the National Blend of Models but layers on human forecasters’ judgment, which is more flexible to dynamic weather situations but introduces subjective judgment (more on all of this here).

ForecastEx does not publish a single forecast temperature. It lists a ladder of contracts at different strikes, and each price is the ForecastEx probability that the day’s extreme passes that strike. Comparing ForecastEx with the other systems therefore requires converting that ladder into a central estimate. The 50 percent level is the ForecastEx implied median, so where two adjacent listed strikes bracket that crossing, the central value is interpolated between them. Where the ladder does not bracket 50 percent, because every listed strike sits far in or far out of the money, no central value is recorded and ForecastEx is left unscored rather than extrapolated beyond the quoted ladder. That reflects how the strikes happened to be listed rather than anything about forecast quality, so it should count neither for nor against ForecastEx.

About the author

Patrick T. Brown is the Head of Climate Analytics at Interactive Brokers, where his work focuses on the information discovery and risk-transfer applications of prediction markets in weather, climate, and natural disasters.

He holds a PhD in Earth and Climate Science from Duke University, a master’s degree in Meteorology and Climate Science from San Jose State University, and a bachelor’s degree in atmospheric and oceanic sciences from the University of Wisconsin, Madison. He is an adjunct faculty member (lecturer) in the Energy Policy and Climate Program at Johns Hopkins University and has conducted research at the Carnegie Institution at Stanford University, NASA JPL at Caltech, NASA Langley in Virginia, NASA Goddard in Washington, D.C., and NOAA’s GFDL at Princeton University. He has published scientific papers in Nature, PNAS, and Nature Climate Change, as well as many disciplinary journals, and his research and commentary have appeared in The New York Times, The Wall Street Journal, The Economist, CNBC, CNN, The BBC, The Washington Post, NPR, Newsweek, The Guardian, The Atlantic, Foreign Policy, and The Los Angeles Times, among other venues.