Why every weather app is giving you a different forecast

Why every weather app is giving you a different forecast

Open three weather apps right now. The temperature will disagree. The rain chance will disagree. Tomorrow's high will disagree.

This isn't a bug. It's a business decision. Every mainstream weather app picks one data source, usually whichever global model their provider happens to resell, and hands you that answer with confidence. Apple ran on Dark Sky, then The Weather Channel, then their own blend. Google uses weather.com. AccuWeather uses AccuWeather. Each of them is looking at a different grid cell from a different model and rounding to a different decimal.

The honest answer is that no single source is right. Global models (GFS, ECMWF, ICON) are optimized for a 10 to 30 km grid cell, so the forecast for your zip code is an interpolation, not a measurement. Regional models do better locally but worse globally. Personal weather stations are precise but can be miscalibrated, in a shaded corner, or sitting on an asphalt roof. Airport stations are well-calibrated but miles away.

What we do instead

DewLogic blends 25+ sources at your exact coordinates and produces what we call a Virtual Station: a single set of values that represents the weighted truth from every sensor and model in range.

The blending is Inverse Distance Weighting with a twist: every source has a DataTier weight that reflects how authoritative it is for that measurement.

PWS 10.0 // backyard stations (nearby + high cadence)
CWOP 4.0 // ham radio weather network
Soil sensor 3.0 // agricultural/ground truth
Surface station 1.5 // airport METAR
Gov regional 1.2 // HRRR, NAM, national mesoscale
Global model 0.8 // GFS, ECMWF, ICON


Closer and higher-tier sources pull the blend toward themselves. A PWS 400 meters away will dominate the ECMWF grid cell it sits inside. An ECMWF cell 10 km away will dominate when there's no other ground truth.

Handling bad data

Individual stations lie. A sensor in direct sun reports 110°F on a 75°F day. We run a sanity check against a reference (nearest well-sited station plus nearest model). Anything more than 3°C off gets dropped as an anomaly. Sources that disagree catastrophically with everyone else don't get to vote.

We also damp model weight as local density increases: max(0.1, 1 / (1 + localCount)). One nearby PWS already tells us more than ECMWF. Ten nearby PWS make ECMWF almost irrelevant for current conditions (forecasts are a different story, since models still rule those).

The result

Your Virtual Station knows things no single-source app can know. It knows your backyard is 4°F warmer than the airport because two PWS next door both agree. It knows the rain gauge that just reported 2 inches in 5 minutes is broken because no other nearby source saw it. It knows the ECMWF grid cell has you in a valley even though the actual valley is two ridges over.

This is what weather data should look like when you stop asking one source to be right.

Curious about the blending math, anomaly detection, or how we weight specific networks? Ask below.