https://github.com/flowm/meteocompare
https://github.com/flowm/meteocompare
open-meteo weather
Last synced: 10 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/flowm/meteocompare
- Owner: Flowm
- License: mit
- Created: 2026-05-20T03:00:13.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2026-06-14T19:28:28.000Z (11 days ago)
- Last Synced: 2026-06-14T21:21:34.482Z (11 days ago)
- Topics: open-meteo, weather
- Language: TypeScript
- Homepage: https://meteocompare.frcy.workers.dev/
- Size: 831 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# MeteoCompare
Multi-model weather forecast comparison with a weighted aggregate and a per-timestep confidence score.
Frontend-only (Vue 3 + Vite). Forecasts come straight from [open-meteo.com](https://open-meteo.com) — no MeteoCompare backend, no API key.
## Features
- **21 forecast models/products**, automatically dropped in/out based on geographic coverage and forecast horizon.
- **Aggregate-first UI**: temperature + ±1σ confidence band, precipitation bars, daily strip with weather icon / high / low / precip prob / wind.
- **Confidence score** per timestep — derived from inter-model spread normalised against typical seasonal spread, a model-count penalty, and lead-time decay encoded in the model weights.
- **Per-model overlay** (opt-in) — one line per contributing model drawn over the aggregate, with per-model toggles, switchable between temperature, precipitation, precipitation probability, wind speed, and cloud cover.
- **Window toggle** — 24 h / 3 d / 7 d on both charts.
- **Locations** — open-meteo geocoding search, browser geolocation, URL-shareable state, favourites and recent-search in localStorage.
- **Units** — °C ⇄ °F, mm ⇄ in, km/h ⇄ mph; persisted.
## How the aggregation works
Per timestep and per variable:
1. **Pick the contributing models.** Each model has a home region (rough bbox) and a max useful lead time. Models that don't cover the location, or whose horizon has been exceeded, are filtered out.
2. **Weight them.**
- Base weight = 1.
- Region bonus of +0.2 (mid-resolution) or +0.3 (convection-allowing) when the location is inside the model's home region.
- Lead-time decay per model class: convection-allowing models fade out by 60 h, mid-resolution regionals by 120 h, globals decay gently from 72 h → 0.4× by 240 h, and AI plus ensemble-mean products follow global decay with a smaller vote.
- Variable boost: CAMs get ×1.3 for precipitation and precipitation probability, since they explicitly resolve convection.
3. **Aggregate**:
- **Temperature / precip / cloud cover / wind speed** → weighted mean + weighted standard deviation.
- **Wind direction** → weighted circular mean via unit-vector sum (so 350° + 10° averages to 0°, not 180°). Angular standard deviation via Mardia's formula on the mean resultant length.
- **Weather code** → severity-weighted modal class: bin WMO codes into severity groups (clear / mostly_clear / cloudy / fog / drizzle / rain / snow / storm), pick the group with the highest summed weight, then within that group pick the most-weighted code.
## How the confidence score works
For each numeric variable:
```
spreadScore = clamp(1 − stdDev / typicalSpread, 0, 1)
typicalSpread ramps with lead time; daily accumulated variables
(precipitation_sum) use a day-scale typical spread (mm/day) rather
than the hourly rate scale (mm/h).
modelFactor = min(1, n / 3) where n = number of contributing models
1 model → ⅓, 2 models → ⅔, 3+ models → 1
confidence = clamp(spreadScore × modelFactor, 0, 1)
```
Wind direction uses the same formula with circular standard deviation in degrees.
Weather codes have no meaningful stdDev, so they use severity-group agreement instead:
`confidence = clamp(weightShare(same severity group) × modelFactor, 0, 1)`.
Lead-time decay is handled entirely in the model weighting layer (not as a separate
multiplier here): CAMs fade out by 60 h, regionals by 120 h, globals decay past 72 h,
and AI plus ensemble-mean products follow global decay with a smaller vote.
The badge maps the result to one of three tiers — high (≥70 %, emerald), mid (≥40 %, amber), low (rose).
## Models
| Open-meteo id | Provider | Resolution / scope | Class | Max lead |
| ----------------------------- | ------------------ | ---------------------------------- | ------------- | -------- |
| `ecmwf_ifs` | ECMWF | 9 km HRES global | global | 240 h |
| `gfs_seamless` | NOAA | seamless NOAA global/U.S. coverage | global | 384 h |
| `gem_seamless` | Environment Canada | 2.5–15 km, NA focus | regional-mid | 240 h |
| `ukmo_seamless` | UK Met Office | 2 km UKV / 10 km global | regional-mid | 168 h |
| `meteofrance_seamless` | Météo-France | 1.3 km AROME / 25 km ARPEGE | regional-cam | 102 h |
| `cma_grapes_global` | CMA | 15 km global, East Asia focus | global | 240 h |
| `bom_access_global` | BOM | 15 km global, Aus. focus | global | 240 h |
| `jma_seamless` | JMA | 5 km Japan / 55 km global | regional-mid | 264 h |
| `kma_seamless` | KMA | 1.5–13 km, Korea focus | regional-mid | 288 h |
| `icon_global` | DWD | 11 km global | global | 180 h |
| `icon_eu` | DWD | 7 km Europe | regional-mid | 120 h |
| `icon_d2` | DWD | 2 km central Europe CAM | regional-cam | 48 h |
| `knmi_harmonie_arome_europe` | KNMI | 2 km Harmonie AROME Europe | regional-cam | 60 h |
| `dmi_harmonie_arome_europe` | DMI | 2 km Harmonie AROME Europe | regional-cam | 60 h |
| `metno_nordic` | MET Norway | 2.5 km Nordics | regional-cam | 60 h |
| `meteoswiss_icon_seamless` | MeteoSwiss | 1–2 km ICON Switzerland seamless | regional-cam | 120 h |
| `geosphere_arome_austria` | GeoSphere Austria | AROME Austria | regional-cam | 60 h |
| `ecmwf_aifs025_single` | ECMWF | 0.25° AI forecast | ai | 360 h |
| `gfs_graphcast025` | NOAA | 0.25° GraphCast forecast | ai | 384 h |
| `ncep_aigfs025` | NOAA | 0.25° AI-enhanced GFS | ai | 384 h |
| `ncep_hgefs025_ensemble_mean` | NOAA | 0.25° ensemble mean | ensemble-mean | 384 h |
## Tech
- **Vue 3** (``, Composition API) + **Vite** + **TypeScript** (strict)
- **Tailwind CSS v4** via `@tailwindcss/vite`
- **vue-echarts** (ECharts 6) for the charts
- **vue-router** for URL state, **@vueuse/core** for localStorage / debounce
- **Erik Flowers' [weather-icons](https://github.com/erikflowers/weather-icons)** for the icon set
- **Vitest** for unit tests
- **oxlint** (Rust-based linter) + **oxfmt** for formatting
- **wrangler** for deploys to Cloudflare Workers static assets
## Architecture
```
UI (Vue components) Composables Domain layer (pure TS)
┌──────────────────────────────────┐ ┌──────────────────┐ ┌────────────────────────────────┐
│ LocationBar │ │ useLocation │ │ models.ts │
│ AggregateSummary │ │ ─ URL sync │ │ ─ registry + bboxes │
│ HourlySeriesChart (shared) │ │ ─ favourites │ │ weighting.ts │
│ DailyStrip / DayCard │ │ useForecast │ │ ─ region bonus + decay │
│ VerificationDayCard ►│◄─┤ ─ fetch+aggreg.│◄─┤ aggregate.ts │
│ HitMissStrip │ │ useVerification │ │ aggregateVariables.ts (triad) │
│ WeatherIcon / ConfidenceBadge │ │ ─ fetch+score │ │ confidence.ts │
│ │ │ useUnits │ │ verification.ts (bias/MAE …) │
│ ForecastView · VerificationView │ │ ─ formatters │ │ weatherCodes.ts │
└──────────────────────────────────┘ └──────────────────┘ └────────────────────────────────┘
▲
│
┌───────────────────────────────┴──────────────────┐
│ api/omForecast.ts ─ live forecast client │
│ api/omSingleRuns.ts ─ historical model runs │
│ api/omHistoricalWeather.ts ─ ERA5-Seamless truth │
│ api/geocoding.ts ─ location search │
│ (HTTP caching via the service worker, SWR) │
└────────────────────────────────────────────────────┘
```
The **domain layer** is pure TS, unit-tested with Vitest. The UI sits on top of it via the
composables. There is no global store — the URL is the source of truth for the location, and
localStorage holds units, favourites, and recent searches.
## Develop
```bash
npm install
npm run dev # http://localhost:5173
```
### Scripts
```bash
npm run dev # Vite dev server
npm run build # type-check + production build to ./dist
npm run preview # serve ./dist locally
npm test # Vitest unit tests
npm run test:watch # interactive
npm run lint # oxlint + oxfmt --check + vue-tsc (CI gate)
npm run lint:fix # oxlint --fix + oxfmt --write (local autofix)
npm run deploy # build + wrangler deploy (Cloudflare Workers)
```
The lint script is the single quality gate — it runs the linter, asserts formatting, and type-checks in one command.
## Deploy
The app is shipped as static assets via Cloudflare Workers. Configuration lives in `wrangler.jsonc`:
- `assets.directory: ./dist` — the Vite build output
- `assets.not_found_handling: "single-page-application"` — Cloudflare serves `index.html` for any unmatched path, which is exactly what `vue-router`'s history mode needs
```bash
npx wrangler login # one-time
npm run deploy
```
## Limitations
- **No bias correction.** Weights are static — no weight calibration against ERA5 reanalysis. Some models systematically run cold/warm or under/over-predict precipitation in some regions; that bias passes through to the aggregate.
- **No ensemble members.** We pull deterministic runs only, not full ensemble distributions. Confidence is derived from inter-model spread, not from individual ensemble forecasts.
- **Verification covers temperature and precipitation only.** ERA5-Seamless also provides wind and cloud-cover truth, but the verification page does not yet score them.
## Acknowledgements
- **[open-meteo.com](https://open-meteo.com)** — free, generous, CORS-friendly weather API that makes the whole frontend-only design possible. Forecasts are CC BY 4.0.
- **Erik Flowers' [weather-icons](https://github.com/erikflowers/weather-icons)** — SIL OFL 1.1 font + MIT CSS.
- The numeric weather prediction community at ECMWF, NOAA, DWD, Météo-France, UK Met Office, KNMI, MET Norway, JMA, KMA, BOM, Environment Canada, and others — open-meteo aggregates their public model outputs.
The multi-model aggregate is informational and not a forecast of record. For severe weather decisions, consult your local meteorological service.