Dew point tells you more about outdoor discomfort than humidity does. Here’s how to detect the exact forecast hour it crosses into oppressive territory.
Forecast accuracy degrades predictably with time. Here’s how to model skill decay and weight WeatherAPI data differently depending on how far out you’re looking.
Coordinate precision in weather API requests isn’t just a formatting choice — it directly affects which station gets selected and the data you get back.
Build a nuanced thermal comfort score for outdoor apps using temp, humidity, wind, and solar radiation from WeatherAPI’s hourly forecast endpoint.
When a weather API returns a number, do you know where it came from? METAR, NWP model, or blended? Here’s how to figure that out and why it matters.
GFS, NAM, and HRRR don’t publish instantly after their run cutoff. Here’s how model run latency affects what your weather API actually returns.
chance_of_rain is one number, but building a useful rain alert from it isn’t trivial. Here’s how to filter false positives with duration, intensity, and timing logic.
GFS and HRRR don’t update on the hour. Here’s how model run cycles affect which forecast data you’re actually getting back — and how to build around it.
Generic cache TTLs waste quota or serve stale forecasts. Here’s how to align expiry times with HRRR, GFS, and NAM model run schedules instead.
What actually happens when your weather API request lands in a data desert — and how to build around sparse station coverage without serving garbage to your users.
