Polling Frequency vs. Data Freshness: Why Hitting the API More Often Isn’t Getting You Fresher Data
More API requests don’t mean fresher weather data. Here’s why model run cadence caps your real freshness ceiling, and how to poll smarter.
More API requests don’t mean fresher weather data. Here’s why model run cadence caps your real freshness ceiling, and how to poll smarter.
Condition codes tell you what’s happening, not how bad it is. Here’s how to build a real severity layer from WeatherAPI’s raw fields.
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.
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.
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.
Wind chill and heat index are not interchangeable. Here’s how to build logic that picks the right feels-like formula from WeatherAPI data — and when both break down.