Fog detection via weather API is trickier than it looks. Here’s how to combine visibility, dew point spread, and condition codes reliably in your app.
WeatherAPI condition codes are not a direct METAR passthrough. Here’s what the mapping actually does, where it loses fidelity, and when raw data matters.
Relative humidity alone misleads. Here’s how to derive VPD and absolute humidity from WeatherAPI data, and when each one actually matters for your use case.
Build a real-time heat stress index from WeatherAPI hourly data using WBGT approximation, humidity, and solar load — with working Python code.
wind_dir and wind_degree look redundant until they disagree. Here’s what each field actually represents and how to use both without getting burned.
WeatherAPI’s feelslike_c blends wind chill and heat index — but the thresholds and edge cases matter. Here’s what’s actually being calculated and where it breaks.
precip_type looks simple until you’re near 0°C. Here’s why the field misleads, when to distrust it, and what to cross-check it against.
WeatherAPI returns solar radiation data — but what does it actually measure, where does it come from, and how should you use it in real applications?
Caching weather API responses saves money and reduces latency — but done wrong, it serves stale forecasts at exactly the wrong time. Here’s how to get it right.
Snow, sleet, rain — or all three? How WeatherAPI separates precip type from amount, where each breaks down, and which field to actually build logic on.
