Weather & Climate Market Update: Atlantic Hurricane Forecast and Temperature Outlook (August 6, 2026)

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

• 1.31°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
• 5°F, Largest error, Washington DC high. settled 89°F against 84°F from the Aviation Forecast
• 13.3×, Best return on daily temperatures yesterday. No on the Minneapolis low below 61° at 7¢, priced 32 hours before it resolved and 15 hours before the low itself happened, settled 61°, net of fees
• 4°F, Widest disagreement for Friday, Atlanta. 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) Atlantic hurricane forecast contracts, still no hurricanes

The hurricane research team at Colorado State University, whose seasonal Atlantic hurricane outlook is the most closely followed of its kind, held its numbers steady in its August 5th update and attributes the quiet season to El Niño, writing that it anticipates a powerful El Niño being the dominant factor for the season and driving very high levels of tropical Atlantic vertical wind shear, with Caribbean and tropical Atlantic sea surface temperatures only slightly above their long-term averages.

Their own landfall probabilities for the remainder of the season run well below the long-term averages, at 16% for the continental United States against a 43% long term average and 9% for the Gulf Coast against a 27% long term average.

The CPC Global Tropical Hazards Outlook from the NOAA Climate Prediction Center issued on Tuesdays and covering the period of August 12th to August 25th, echoes that strong El Niño related shearing combined with unfavorable phasing of the Madden-Julian Oscillation later in August precludes much confidence in hurricane formation over the basin.

Despite this, Google DeepMind’s cyclone-focused ensemble weather model, FNV3, as displayed by Weathernerds gives some indications of possible formation starting Sunday, August 9.

2) Southwest extreme heat, and what it means for daily temperature markets

The National Weather Service office in Las Vegas extended its Extreme Heat Warning and Heat Advisories through Sunday, August 9th, with afternoon highs running 8 to 10 degrees above seasonal normals, and noted that if 110-degree temperatures continue in Las Vegas through Sunday it would tie the longest running stretch of 110-degree days at 11 in a row, which the National Blend of Models puts at a 75 percent chance. The office also expects returning monsoon moisture to raise humidity over the next few days, which lifts overnight lows. ForecastEx has the Las Vegas high for Friday at 110°F against 113°F from the National Weather Service, and the Las Vegas low at 91°F against 89°F, so the two sides disagree in the direction the added moisture would imply, though it is unclear whether the cloud and humidity are being fully priced into the high.

3) How ForecastEx temperature markets compared with the weather models on Wednesday

Below is a scorecard for the forecasts as they stood at 5:00 PM ET on Tuesday, August 4th for the target day of Wednesday, August 5th. 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, ignoring whether it was too warm or too cold, from largest to smallest.

The margins of each grid below report that error, and both of them average all five systems rather than any single one. 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 grids below pull out four canonical tools for illustration, the American model, the European model, the National Weather Service, and the Aviation Forecast.

The largest errors on Wednesday were in Washington DC, where the five systems averaged 3.5°F on the high. Three cities averaged more than 3°F on the highs, while no city averaged more than 3.2°F on the lows.

Postmortem on the Washington DC miss. The high reached 89°F in Washington DC, against 84°F from the Aviation Forecast. At the Tuesday lead the National Weather Service office serving Washington DC was focused on convection, writing that a warm front lifting north brought a risk of localized urban flooding overnight into Wednesday morning. Rain fell at Washington National Airport from 4:52 AM to 8:35 AM and the wind swung from east to southeast at 8:35 AM, after which the temperature rose all afternoon to a late high of 89°F at 5:52 PM, about five degrees above where the Aviation Forecast had it peaking (black observed line vs. purple dashed line below).

Why more accurate forecasts matter

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.

On average, no public tool beats ForecastEx consistently, but the trade board 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 Wednesday 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 Minneapolis low below 61 degrees, 7 cents 32 hours before it resolved and 15 hours before the low itself arrived at 6:53 AM, for a 13.3 times return net of fees. Every one of the six most undervalued contracts on the lows was a No position, and the Aviation Forecast was the closest public tool on four of them.

4) ForecastEx temperature market forecasts and prices for Friday

Fore tomorrow the widest gap between ForecastEx and the National Weather Service is in Atlanta (87°F vs. 91°F). The pattern splits by metric, because ForecastEx is below the National Weather Service forecast on the high in every city with a gap, and above it on the low in all but Atlanta and Houston.

• 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 Wednesday proves exactly right tomorrow, the most undervalued contract on the high side is No on the Minneapolis high above 79 degrees at 9 cents, a 10.5 times return net of fees, with the Japanese model predicting 78. On the low side it is Yes on the Oklahoma City low below 73 degrees, also at 9 cents and also a 10.5 times return net of fees, with the American model predicting 72.

Temperature Market of the Day, Los Angeles

The highlighted market for Friday is again Los Angeles (KLAX)‘s daily high temperature contract. The figure above runs through the end of Friday, 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 19.7°F on Friday’s high, from the European model at 92.5 down to the American model at 72.8, and the middle half of them sit 3.6°F apart. The European model is the outlier that creates most of that range, because it sits 8.1°F above the next-highest model and the other twelve span 11.6°F. The reason for the large spread is again uncertainty in the location and timing of breakup of the marine layer boarder.

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.