Fair Value Weather & Climate — Tuesday, August 4, 2026

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

• 1.29°F, ForecastEx ranks 1st of 12. Average error on daily highs, 7 days, 5:00 PM ET day before through 5:00 AM ET. Aviation Forecast next at 1.58°F.
• 7°F, Largest error, Philadelphia high. Settled 89°F against 82°F from the American model.
• 9.5×, Best return on daily temperatures yesterday, net of fees. Yes on the Austin low below 70° at 10¢, priced 32 hours before it resolved and 14 hours before the low itself happened, settled 69°.
• 6°F, Widest disagreement for Wednesday, Philadelphia. ForecastEx below the National Weather Service forecast on the high.
• 30%, At least one major Atlantic hurricane in August. A Yes contract at 30¢ pays 3.3× the money put in, net of fees.
• 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) Extreme heat in the Southwest, and what it means for daily temperature markets

Phoenix settled at 115°F on Monday, August 3rd, and all five systems in the grid below had it cooler, by two to four degrees. Extreme heat warnings remain in effect for southern Nevada and metropolitan Phoenix through Wednesday, with desert nights reflecting similar heat signals as the daytime highs. The National Weather Service predicts an overnight low of 86°F in Las Vegas, whereas ForecastEx estimates it at 90°F, which is the second-largest discrepancy for the low temperatures today..

2) How ForecastEx temperature markets compared with the weather models on Monday

Below is a scorecard for the forecasts as they stood at 5:00 PM ET on Sunday, August 2nd, for the target day of Monday, August 3rd. The highs and lows are shown as separate grids below. Each grid below orders its cities by mean absolute error (MAE) for that metric, which is the average size of the miss in degrees regardless of direction, from largest to smallest. ForecastEx is scored against eleven public forecast tools throughout this letter, all listed with links in the FAQ at the end. The grids below pull out four canonical tools for illustration, the American model, the European model, the National Weather Service, and the Aviation Forecast.

Philadelphia carried the largest error on highs, at 5.6°F averaged across the five systems, and every system in the grid above was on the cold side of a 89°F afternoon. The rest of the country was quieter, with no other city above 2.6°F.

The low errors were led by Miami and Austin at 5.2°F in the grid above.

Forecast skill in daily highs and lows corresponds directly to forecast skill in metrics called “cooling degree days” and “heating degree days” used extensively in the energy industry. These metrics measure deviations from daily average temperatures (average of the high and low for the day) of 65°F and reflect that people tend to activate their air conditioners when daily averages exceed 65°F (cooling degree days) and switch on their heat when daily averages fall below 65°F (heating degree days).

The range of plausible next-day temperature outcomes can translate into millions of dollars in differences in energy expenditure and many organizations make day-ahead decisions based on cooling degree and heating degree day forecasts.

Philadelphia led the degree-day errors at 4.3 cooling degree days, and ForecastEx’s implied degree days came second of the five systems at 0.81, just behind the Aviation Forecast at 0.79 and well ahead of the American model at 2.73.

Postmortem on the Philadelphia miss. The high reached 89°F in Philadelphia. The American model had carried 82°F from the Sunday afternoon lead, a 7°F miss on the cold side, and no tool in the canonical panel ran warmer than 84°F.

At the lead, the National Weather Service office serving Philadelphia opened its forecast discussion with showers and thunderstorms increasing in coverage overnight and into Monday, with the potential for heavy rain and a risk of flash flooding. However, the rain past and clouds began breaking up by early afternoon leading to the spike in temperature beyond expectations.

Over the window ending Monday, the ForecastEx prediction market records the lowest error of the twelve systems at 1.29°F on daily highs, with the Aviation Forecast second at 1.58°F. Each system is averaged across every hour it stood from 5:00 PM ET the day before through 5:00 AM ET on the target day, so this is a block of leads rather than a single snapshot.

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 tool 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 which encourages improvement).

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 for Monday were

• 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 Austin low below 70 degrees, 10 cents 32 hours before it resolved and 14 hours before the low itself arrived at 5:53 AM, for a 9.5 times return net of fees. The Canadian model was closest on that market at 68.5 against the settled 69, and it was closest again on the Denver low that returned the same multiple. Each of these contracts resolved at midnight at the end of Monday, local time in that city, so the West Coast markets were still live three hours after the East Coast ones had settled.

3) ForecastEx temperature market forecasts and prices for Wednesday

The widest disagreement on the map above is the Philadelphia high, where ForecastEx sits 6.5°F below the National Digital Forecast Database, the United States government’s official public forecast behind weather.gov, produced by National Weather Service forecasters. Every high-side gap on the map points the same way except Minneapolis, with ForecastEx cooler than the National Weather Service across the South and the Northeast, and Seattle next widest at 5.1°F. Prices on the map are as of 12:40 PM ET, which is 35 to 38 hours before these contracts resolve depending on the city.

• 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.

If the tool that was closest for each market on Monday proves exactly right, the most undervalued contract on the high side is No on the Denver high above 81 degrees at 15 cents, a 6.5 times return net of fees, with the UK model predicting 79. On the low side it is No on the Oklahoma City low below 80 degrees at 11 cents, an 8.7 times return, with the Canadian model predicting 80.

Temperature Market of the Day, Los Angeles

The highlighted market for Wednesday is Los Angeles (KLAX)‘s daily high temperature contract. The figure above runs through the end of Wednesday, showing two independent hourly forecasts, each model’s forecast high and low for the contract day, and the most recent high ladder at right.

Thirteen models span 16.5°F on Wednesday’s high, from the European model at 87.6 down to the American model at 71.1, and the middle half of them sit 4.1°F apart. The European model is the outlier, six degrees above the next tool, and the range without it is 10.5°F. The reason for the large spread is uncertainty in how long the coastal low clouds hold on. The National Weather Service office serving Los Angeles describes the setup in their forecast discussion. They note seasonably warm weather away from the immediate coast, far interior areas continuing to bake under triple-digit temperatures all week, and low clouds with locally dense fog developing along the coast that could linger into the afternoon in some areas. Just exactly how long it lingers will decide the high.

Note: uncertainty in day-ahead temperatures corresponds to significantly different energy use and expenditure across metro areas, which can inform operating and financial considerations for various entities involved in energy. Prediction markets on daily temperature, such as those at ForecastEx, allow for probability calibration across the distribution and can be used as financial instruments to hedge against the worst scenarios.

4) Atlantic hurricane forecast contracts, still no hurricanes

The Bahamas lead the major hurricane landfall map above at 10.6%, equivalent to a Yes contract at 11 cents, with Florida just behind at 10.2%. The probability of a Category 4 United States landfall (by November 30th) stands at 7.8%, which represents the seasonal forecasts and is not influenced by any current storm. The season’s two named storms, Arthur at 46 mph and Bertha at 58 mph, both peaked as tropical storms.

The probability of a major hurricane making landfall within 50 miles of Miami-Dade, Florida currently stands at around 4.6%. Thus, a purchase of a “Yes” at 5 cents can be thought of as an analog to parametric insurance, which pays out when a measured weather parameter crosses a threshold, with no claims process and no need to prove a loss. In this case it would pay 18X of the purchase price.

The NOAA Climate Prediction Center’s Global Tropical Hazards Outlook covering August 5th to August 18th has El Niño conditions suppressing Atlantic tropical cyclone activity, with formation chances lower than usual over the Caribbean and the central Atlantic through that window.

In Google DeepMind’s cyclone-focused ensemble weather model, its Tuesday morning run develops a train of tropical waves across the deep Atlantic, the first of them entering the map on Tuesday, August 11th. That leading system is carried west past the Lesser Antilles and then north near the Bahamas over the following weekend. Two more follow on Thursday, August 13th and Friday, August 14th along the same latitude.

Two named storms have formed which is exactly the expectation of what the season should have produced so far if it finishes at the total forecast by Colorado State.

Frequently asked questions

What is a weather prediction market?

A market in which each contract pays one dollar if a stated weather outcome occurs and nothing if it does not, so the contract’s price is the market’s probability of that outcome. ForecastEx lists these on daily high and low temperatures at individual weather stations, on Atlantic named storm and hurricane counts, and on major hurricane landfall by location, among many others.

What are the four canonical forecast systems compared here?

All four 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 (they are not explicitly trained on historical data but rather adhere to the laws of physics), but forecasts apply to a relatively large discrete grid box that contains the weather station rather than the single point where the station sits, causing them to carry systemic biases relative to the stations.

The Aviation Forecast is a model output statistics system. It compares historical forecasts from physical models with 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 in 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 in dynamic weather situations but introduces subjective judgment (more on all of this here).

What are all the forecast tools used here?

• National Weather Service — the official public forecast, human judgment layered on model guidance; that judgment is subjective and can lag a fast-changing day.
• Aviation Forecast — statistically corrected to each station and strong inside a day; reaches only about 25 hours and is anchored in past situations.
• National Blend of Models — averaging many models cancels much of their individual error; a blend can be slow to commit when the models genuinely split.
• European model — consistently among the most accurate global physical models; raw grid-box output still carries station-level bias.
• American model — a global physical model updated four times a day; prone to systematic warm or cold stretches in conditions it resolves poorly.
• German model — a modern global physical model with strong mid-latitude performance; the same grid-box limitations as its peers.
• German statistical model — station-calibrated output built on multiple physical models; anchored in past relationships between forecast and outcome.
• Canadian model — an independent global physical model whose errors differ usefully from the American and European models; raw station-level accuracy is middling.
• UK model — a long-developed global physical model; coarser at United States stations than the domestic systems.
• French model — an independent global physical model that adds diversity to the panel; raw station-level accuracy trails the leaders.
• Japanese model — an independent global physical model; often the weakest of the panel on the daily-high standings, which is itself informative about model diversity.

How does a ForecastEx ladder become a single forecast temperature?

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.

Further Reading

Prediction market basics

• The Value of Climate Prediction Markets — The case for why prediction markets can represent the best collective knowledge about our energy and climate future.
• Daily High Temperature Markets at ForecastEx — how the daily temperature contracts are built and settled.

Temperature markets and energy

• Energy expenditure implications of next-day temperature forecasts — what day-ahead temperature uncertainty costs utilities, energy traders and risk managers.

Hurricane and climate contracts

• Hurricane Forecast Contracts — how the count and landfall contracts work.
• Disaster Insurance Applications of Forecast Contracts — using landfall contracts as parametric cover.
• Hurricane Forecast Contracts as a Diversifying Asset in an Investment Portfolio — correlation properties against conventional assets.
• Bundling “NO” Hurricane Landfall Forecast Contracts to Reduce Downside Risk — constructing a bundle across locations.

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).

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.