I Tracked Weather Forecasts for 3 Weeks to See How Wrong They Really Are

说明与用途

构建了一个收集天气预报并与实际数据比较的管道，揭示了按时间和城市的准确性模式，并显示简单线性模型优于朴素基线。

问题

Weather apps present a 7‑day forecast as fact, but the accuracy of day‑7 predictions is unclear.

结果

Accuracy drops with lead time, varies by city, and rain predictions degrade; Ridge regression outperforms a simple baseline in error prediction.

工作流

• Pull 8‑day forecast for Islamabad, London, New York via Open‑Meteo API
• Store forecast in Neon Postgres
• Check historical archive for actual weather using Open‑Meteo historical API
• Store actual values in database
• Create SQL view joining forecast and actual
• Run Python analysis to compute errors and build models
• Display results on Streamlit dashboard

证据

Open‑Meteo API --> GitHub Actions (daily) --> Neon Postgres --> Python analysis --> Streamlit dashboard

来源

Same‑day forecasts were off by 0.58°C on average. By 7 days out, that grew to roughly 1.5–2.0°C.

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New York's forecasts were consistently the least accurate — close to double Islamabad's error at most lead times (3.22°C vs 1.35°C at day 6).

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I tried to predict how wrong a forecast would be, using lead time and the forecast's own values as features. I compared a plain average-by-lead-time baseline against Ridge regression, Random Forest, and Gradient Boosting... Ridge won by about 28%.

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