Fair Value Weather & Climate — Wednesday, August 5, 2026

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

• 1.27°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 1.59°F.
• 6°F, Largest error, Seattle high. Settled 80°F against 86°F from the National Weather Service.
• 15.4×, Best return on daily temperatures yesterday. No on the Dallas low below 80° at 6¢, priced 32 hours before it resolved and 15 hours before the low itself happened, settled 80°, net of fees.
• 6°F, Widest disagreement for Thursday, Philadelphia. ForecastEx above the National Weather Service forecast on the low.
• 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) Pacific Northwest wildfire smoke, and what it means for daily temperature markets

Smoke from fires in eastern Washington, Oregon and British Columbia has been affecting temperatures in the Pacific Northwest. The National Weather Service office in Seattle wrote that the smoke is thick enough to take three to five degrees off the maximum temperatures today and Thursday, while the office in Portland wrote that the smoke may be responsible for a one to three degree reduction. The smoke seems to be affecting the forecasts as Seattle-Tacoma settled at 80°F on Tuesday, August 4th against an 86°F forecast from the National Weather Service, the largest error in the ForecastEx markets on the day. For tomorrow, Thursday, August 6th, ForecastEx has the Seattle high at 86°F against 89°F from the National Weather Service though it’s unclear if the effect of the smoke is being fully priced in.

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

Below is a scorecard for the forecasts as they stood at 5:00 PM ET on Monday, August 3rd, for the target day of Tuesday, August 4th. The grid below orders its cities by mean absolute error (MAE), which is the average size of the miss in degrees, regardless of direction, from largest to smallest.

The margins of the grid below report that error, and both of them average all five systems. The right column is each city’s average error across the five systems, and the bottom row is each system’s average error across all the cities.

ForecastEx is scored against eleven public forecast tools throughout this letter, all listed with links in the FAQ at the end. The grid below pulls out four canonical tools for illustration, the American model, the European model, the National Weather Service, and the Aviation Forecast.

Seattle carried the largest error on highs, at 4.8°F averaged across the five systems, and every system in the grid above was warm by four to six degrees. Denver was next at 4.2°F, also warm at every system, while the American model ran warm almost everywhere and finished the day at 3.57°F.

Postmortem on the Seattle miss. The high reached 80°F in Seattle. The National Weather Service carried 86°F from the Monday 5:00 PM ET lead, a 6°F miss, and no tool in the canonical panel came in below 84°F.

At that lead, the National Weather Service office was discussing the temperature forecast mainly as hinging on how the large-scale winds would steer the smoke in the region. reasoning about the same smoke it is describing now.

Seattle-Tacoma weather station reported wildfire smoke from 3:53 PM to 4:53 PM right as the peak warming of the day was expected to occur and this appears to be the clear culprit in what held down temperatures relative to the forecast (black observed line vs. purple dashed line below).

Why more accurate forecasts matter

On another note, the range of plausible next-day temperature outcomes can translate into millions of dollars in differences in energy expenditure, which makes the accuracy of temperature forecast extremely valuable.

The standings below measure such accuracy indicating that ForecastEx has been the most accurate tool of the 12 publicly available tools tracked here over the past 7 days.

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.

Daily weather forecasting is one of the most mature, established, and scientifically principled fields of science and industry. It is not an exaggeration to describe conventional weather forecasting systems as the frontier of applied physics, statistics, and computer science, resting on decades, if not centuries, of scientific inquiry.

As such, conventional weather forecasts should hardly represent a target ripe for a novel forecasting system to improve upon. Despite the formidable challenge, ForecastEx prediction markets seem to be doing just that, not just echoing public forecasts but aggregating dispersed information in a way that improves upon sophisticated established systems.

This is possible because prediction markets are not substitutes for standard forecasts but rather sit downstream of them. They incorporate conventional forecast information as one input and convert it into refined probabilities through direct financial rewards for being accurate and direct financial penalties for being inaccurate. This creates a dual effect of attracting accurate individuals and systems into the market while deterring those who are inaccurate. People or systems that consistently make poor forecasts are heavily motivated to either improve or leave the market.

This improvement in accuracy is valuable for a variety of reasons. For example, wholesale electricity is bought and sold a day ahead in regional markets run by grid operators, with prices settling at benchmark trading hubs within each region, and the primary cause of daily variations in electricity demand and prices is variations in temperature.

The figure below pairs each city’s daily mean temperature with the daily electricity demand of the region serving it, defined by the Energy Information Administration, and with the day-ahead price at the region’s trading hub, over the past 30 days.

Across those four panels the temperature and demand correlation runs from 0.70 in San Francisco to 0.92 in Chicago, all of it positive because in the cooling/AC season a hotter day means more electricity demand.

The dots past the divider are Thursday’s implied forecast from the ForecastEx market and the demand and price that temperature implies through the fitted relationships over the past 30 days. Thursday indicates 15 percent below the 30-day average demand in Chicago at $32 per megawatt hour, and 12 percent above it in Washington DC at $87 per megawatt hour.

Improving forecast accuracy is critical, but just as critically, if not more so, the structure of these contracts allows for direct hedges for industries exposed to high prices. Buying Yes contracts on lower-probability temperature extremes entails disproportionately large return multiples that would compensate for the associated disproportionately large electricity price spikes.

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 Tuesday 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 No on the Dallas low below 80 degrees, 6 cents 32 hours before it resolved and 15 hours before the low itself arrived at 6:53 AM, for a 15.4 times return net of fees, with the German model closest at 80. Thirteen of the twenty contracts above were No, so the day rewarded taking the side that the market’s own strike placement treated as less likely. Each of these contracts resolved at midnight at the end of Tuesday, local time in that city.

3) ForecastEx temperature market forecasts and prices for Thursday

The widest disagreement on the map above is the Philadelphia low, where ForecastEx sits 6°F above the National Digital Forecast Database, the United States government’s official public forecast behind weather.gov, produced by National Weather Service forecasters. The gaps line up by metric rather than by region, with ForecastEx cooler than the National Weather Service on almost every labeled high and warmer on almost every labeled low, so ForecastEx implies a narrower spread between Thursday’s high and low in most cities. San Francisco is the exception, 4°F above on the high. Prices on the map are as of 12:30 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 Tuesday proves exactly right, the most undervalued contract on the high side is No on the Minneapolis high above 82 degrees at 9 cents, a 10.5 times return net of fees, with the Japanese model predicting 82. On the low side it is Yes on the Denver low below 56 degrees at 10 cents, a 9.5 times return, with the UK model predicting 55.

Temperature Market of the Day, Los Angeles

The highlighted market for Thursday is Los Angeles (KLAX)‘s daily high temperature contract. The figure above runs through the end of Thursday, 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 19.1°F on Thursday’s high, from the European model at 90.1 down to the American model at 71.0, and the middle half of them sit 3.5°F apart. The European model is the outlier, almost six degrees above the next tool, and the range without it is 13.2°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 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. Like yesterday, the high will depend on how far towards the coast the extremely hot temperatures extend.

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 7.7%, which represents the seasonal forecasts and is not influenced by any current storm. The same question trades at Polymarket at around 24 cents. 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 5%. Thus, a purchase of a “Yes” at 5 cents is 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, issued this week and covering August 12th to August 25th, has strong El Niño related shear combining with an unfavorable phasing of the Madden-Julian Oscillation later in the month, which leaves it with little confidence in Atlantic formation over that window.

Google DeepMind’s cyclone-focused ensemble weather model, FNV3, as displayed by Weathernerds gives some indications of possible formation starting Saturday August 8th.

Two named storms have formed against the 2.1 the season would normally have produced by now if it finishes at the total forecast by the hurricane research team at Colorado State University.

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 were built and settled when they were first listed (not all information is up to date and an update is forthcoming).

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.