You check one app: 20% chance of rain this afternoon, high of 79°F (26°C). You check another on the same phone, for the same address, and it says 70% and 75°F (24°C). One of them has to be wrong — except that usually neither of them is. They're just answering slightly different questions using slightly different information, and nobody tells you that on the forecast screen.
Here's what's actually happening underneath, from someone who runs a forecast front-end and has to reconcile these differences daily.
Almost no weather app produces its own forecast. What they do is take output from one or more numerical weather prediction models — enormous physics simulations run on supercomputers by national weather agencies — and put a friendly interface on top. There are several of these models, run by different countries, and they don't produce identical answers.
The three you're most likely to be looking at without knowing it:
| Model | Run by | Grid spacing | How far ahead |
|---|---|---|---|
| GFS | NOAA (United States) | ~13 km | 16 days |
| IFS (HRES) | ECMWF (Europe) | ~9 km | 10 days |
| ICON | DWD (Germany) | ~13 km global | Varies by configuration |
NOAA's Global Forecast System runs four times a day and reaches 16 days out, though its resolution coarsens to roughly 34 km beyond day 10 — which is part of why two-week forecasts are so shaky. The ECMWF high-resolution forecast runs on a finer 9 km grid; its 00 and 12 UTC runs extend to 10 days, while the 06 and 18 UTC runs cover roughly the first four. Germany's DWD runs ICON globally at about 13 km and a regional version, ICON-D2, at 2.2 km over central Europe, refreshed every three hours.
Two apps pulling from two of those models will disagree, and both are faithfully reporting what their source said.
A 13 km grid means the model divides the atmosphere into boxes roughly 13 km (8 miles) across and calculates one set of values per box. Your house isn't a point in that system — it's somewhere inside a box, averaged together with everything else in it.
If your box contains a lake, a ridge, a coastline, or a city center, the model's single answer for that box is a compromise across all of them. This is why a sea-breeze front can soak one side of town while the other stays dry, and why apps disagree far more in complex terrain than they do over flat, uniform ground. Two apps interpolating from slightly different grids to your exact coordinates will land on different numbers from the same underlying physics.
Models don't update continuously. They run on a fixed schedule — GFS at 00, 06, 12 and 18 UTC, for example — and each run takes time to compute and distribute before an app can display it. So at any given moment, one app might be showing the run from three hours ago and another the run from nine hours ago.
During calm, stable weather this barely matters. During a fast-developing situation — a line of storms forming, a front arriving earlier than expected — a six-hour-old forecast can be visibly out of date while the newer one has already corrected. If two apps disagree sharply and one has clearly "seen" something the other hasn't, freshness is often the reason.
Probability of precipitation is the single most misread number in any forecast. In the U.S. National Weather Service definition, it's the chance that at least 0.01 inch of measurable precipitation falls at any given point in the forecast area during the period — not the percentage of the area that gets wet, and not how heavy the rain will be.
Different providers compute and round that figure differently, and some blend model output with their own statistical corrections. Two apps can hold genuinely similar expectations about the day and still print 40% and 60%. We covered the full explanation in what a 30% chance of rain actually means, and it resolves a surprising number of apparent contradictions.
Raw model output is rarely shown to the public as-is. Agencies correct it for known local biases — cold air pooling in valleys, temperature inversions, urban heat islands, coastlines the grid is too coarse to resolve. MET Norway, for instance, documents that its Locationforecast service uses its own MEPS model over Scandinavia with observation-based post-processing, and ECMWF data elsewhere.
That correction layer is proprietary and different at every provider. It's a real reason two apps drawing on the same underlying model can still show you different temperatures.
Since this article is about apps disagreeing, it's only fair to say exactly what this one does. ClearCast pulls its forecasts from Open-Meteo, which aggregates output from a range of national weather services, with MET Norway's Locationforecast as a fallback if the primary source is unavailable.
Open-Meteo's default selection isn't an average of every model. It picks the highest-resolution model available for your specific location — and the model chosen can differ from one variable to another. In parts of Europe and North America that means a 1–2 km local model for the first days, handing off to a global model at roughly 9–11 km further out.
The practical consequence: if your phone's built-in app leans on a single global model everywhere, ClearCast may well show you a different number for the same afternoon. Neither is lying. They asked different simulations.
Weather apps disagree because they're built on different physics models, run on different schedules, resolved at different grid sizes, corrected by different post-processing, and reporting a rain probability that's widely misunderstood. Very little of that is visible on the forecast screen, which is why the disagreement feels like a contradiction rather than what it actually is: honest uncertainty, shown twice.
The useful habit isn't picking one app and trusting it absolutely. It's noticing whether your sources agree. When they converge, plan confidently. When they scatter, build in a margin and check again closer to the time.
→ See the current forecast for your location on ClearCast