Weather forecasts have become more accurate

Overall perception of forecast accuracy

• Many commenters agree long-range forecasts (3–14 days) have improved versus decades ago, citing better storm tracking and temperature trends.
• Others report worsening or unreliable forecasts in specific locations (e.g., Boston, Bay Area, Southern California), especially for rain intensity, wind direction, or coastal/mountain microclimates.
• Several note that 1–2 day forecasts are usually solid, but hour-by-hour or minute-level predictions feel poor.

Hyperlocal rain & “nowcasting”

• Former Dark Sky users widely praise its minute-precise rain alerts; many feel Apple’s integration degraded accuracy.
• Complaints about apps saying “no rain” while it’s raining (or vice versa) are frequent; explanations offered include coarse spatial grids, stale model outputs, radar gaps, and microbursts.
• Some regions (e.g., Netherlands, parts of Europe) rely heavily on live rain radar rather than textual forecasts for short-term decisions like cycling.

Models, AI, and technical aspects

• Discussion of physics-based Numerical Weather Prediction vs. newer AI models (GraphCast, Pangu, ECMWF’s AIFS).
• Consensus in-thread: AI models are roughly on par or slightly better than state-of-the-art traditional models, not a dramatic leap for end users.
• Short-range “rain in X minutes” products often use radar + simple optical flow, not deep physics.
• Grid resolution and terrain complexity (mountains, urban heat islands) limit local accuracy.

Climate change and model reliability

• Some suspect climate change makes forecasts worse via more volatile weather or invalidated historical data.
• Others counter that core physics-based models remain valid; climate shifts mostly affect statistical post-processing and bias corrections, not basic forecast skill.

Human perception, probability, and media

• Commenters highlight confirmation bias: people remember the misses, not the many quiet hits.
• Misunderstanding of “chance of rain” leads to judging good probabilistic forecasts as “wrong.”
• Consumer-facing providers may inflate rain probabilities or dramatize storms due to clickbait and user expectations.

Tools, strategies, and equity

• Many recommend raw or government sources (e.g., hourly charts, radar, forecast discussions) over simplified apps.
• Local expert meteorologists are valued for interpreting models in context.
• The article’s point that poorer countries pay more (as GDP share) for worse forecasts is echoed as a serious equity issue.