Kalshi Trader Rejects Google's WeatherNext 2 After Finding 5.2°F Cold Bias

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A data engineer building a weather forecasting system for Kalshi temperature contracts tested Google's WeatherNext 2 model against 16 settlement stations over two weeks. Despite the model beating global forecasts 97% of the time and offering a free, easy-to-integrate API, it showed a consistent 5.2°F cold bias on daily maximum temperatures — the exact metric Kalshi contracts settle on. Since Kalshi temperature contracts span only 2–3 degree ranges, such a systematic error would point to the wrong contract on nearly every trade. WeatherNext 2 is optimized for large-scale atmospheric metrics like wind fields and mean temperatures, not the localized daily highs recorded at specific airport weather stations. The engineer chose to retain existing models while keeping WeatherNext 2's uncertainty-calibration features on the roadmap for future use.

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