{"id":241431,"date":"2026-04-15T10:00:00","date_gmt":"2026-04-15T14:00:00","guid":{"rendered":"https:\/\/ibkrcampus.com\/campus\/?p=241431"},"modified":"2026-04-15T10:37:57","modified_gmt":"2026-04-15T14:37:57","slug":"april-13-2026-update-prediction-markets-might-already-be-the-best-source-for-todays-weather-forecast","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/traders-insight\/april-13-2026-update-prediction-markets-might-already-be-the-best-source-for-todays-weather-forecast\/","title":{"rendered":"April 15, 2026 Update: Prediction markets might already be the best source for today\u2019s weather forecast"},"content":{"rendered":"\n

<\/p>\n\n\n\n

In my March 19, 2026, article, \u201cPrediction markets might already be the best source for today\u2019s weather forecast<\/a>,\u201d I presented preliminary evidence from the first week of formal tracking (March 11\u201317) that ForecastEx prediction markets<\/a> appear to meaningfully improve daily high-temperature accuracy compared with National-Weather-Service-LAMP, the most directly comparable conventional forecast system.<\/p>\n\n\n\n

<\/p>\n\n\n\n

I noted at the time that these were preliminary results and that I would provide regular updates as the sample grew. This article is the first update, now based on data through April 14, 2026.<\/p>\n\n\n\n

<\/p>\n\n\n\n

Over the full period from March 11 through April 14, the dataset now includes 24 cities and 501 city-days. The lead-time summary figure (average absolute error vs. lead time) continues to show the same core structure as in the preliminary week, but with a much larger sample and thus greater confidence.<\/p>\n\n\n\n

<\/p>\n\n\n\n

Using bins with at least N=30 city-days, ForecastEx\u2019s weighted average absolute error is about 1.95\u00b0F, compared with 2.61\u00b0F for LAMP, a 25.0% lower error overall.<\/p>\n\n\n\n

<\/p>\n\n\n\n

At long lead times, the two systems are closer (for example, around 24 hours out, they are near parity), but as the target day progresses, ForecastEx increasingly pulls ahead: roughly 19% lower error by 18 hours, ~25% by 12 hours, and about 55% lower error in the final 1\u20136 hours before end-of-day.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Figure produced with Python Matplotlib<\/p>\n\n\n\n

<\/p>\n\n\n\n

The city-level map view (pooled over local 5:00 am\u201312:00 pm hours) remains broadly consistent with the first week’s data. ForecastEx now outperforms LAMP in 20 of 23 cities with valid morning-window samples, with a median improvement of about +17.2%. The strongest gains appear in cities such as Denver, Nashville, Seattle, San Francisco, and Houston, while only a small minority of cities are currently negative. As before, geographic variation remains an open question, but the dominant signal remains positive and widespread.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Figure produced with Python Matplotlib<\/p>\n\n\n\n

<\/p>\n\n\n\n

So, what was initially a promising one-week result has held up over a larger out-of-sample period. We continue to see that ForecastEx is not merely echoing conventional forecasts but is able to aggregate and distill the best available information into more accurate same-day high-temperature forecasts than conventional methods. With nearly a month of data now in hand, the evidence that prediction markets can improve practical weather forecasting seems to be only getting stronger.<\/p>\n","protected":false},"excerpt":{"rendered":"

In my March 19, 2026, article, \u201cPrediction markets might already be the best source for today\u2019s weather forecast,\u201d I presented preliminary evidence from the first week of formal tracking (March 11\u201317) that ForecastEx prediction markets appear to meaningfully improve daily high-temperature accuracy compared with National-Weather-Service-LAMP, the most directly comparable conventional forecast system. I noted at […]<\/p>\n","protected":false},"author":1601,"featured_media":241444,"comment_status":"open","ping_status":"closed","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[18931,3],"tags":[],"contributors-categories":[13576],"class_list":{"0":"post-241431","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-ibkr-climate-energy","8":"category-traders-insight","9":"contributors-categories-interactive-brokers"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"\nApril 15, 2026 Update: Prediction markets might already be the best source for today\u2019s weather forecast<\/title>\n<meta name=\"description\" content=\"The evidence that prediction markets can improve practical weather forecasting seems to be only getting stronger.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, 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