Every season. One map.
A year-round US weather platform, built in public.
Most weather sites pick a lane — radar apps for storms, model viewers for forecast nerds, seasonal pages for foliage or ski reports. seasonmap's premise is that the atmosphere doesn't work that way: the product should rotate with the seasons, the way weather actually does. Severe tools when spring convection fires, hurricane tracking through the warm season, foliage projections in fall, snowfall and ice products all winter — all riding the same always-on core of model maps, radar, and verification.
What's on the map
- Forecast models — GFS, ECMWF, HRRR, NBM, the AI models (AI-GFS, AIFS), and the GEFS ensemble with mean and spread, rendered as smooth GPU-animated fields or WPC-style filled contour bands.
- Single-site NEXRAD radar — Level II reflectivity, velocity, and correlation coefficient with split and quad panels and scan-by-scan scrubbing.
- Click-anywhere soundings — a full skew-T/log-P with hodograph, CAPE/CIN shading, the dendritic growth zone, and hover readouts at any pressure level, for multiple models.
- Global drivers — the MJO phase wheel, a Rossby wave-train Hovmöller, and an East Asian mountain-torque index computed in-house: the week-2/3 context behind the map.
- Season products — severe parameters (STP, SRH, shear), fire weather, snowfall/ice/p-type suites, ski-resort conditions, allergy & pollen, fall foliage projections, river gauges, drought.
Where the data comes from
| Source | What we take from it |
|---|---|
| NOAA Open Data (NODD) | GFS, HRRR, NBM, AI-GFS, GEFS, RTMA — raw GRIB2, byte-range subset and decoded by our own pipeline |
| ECMWF Open Data | IFS and AIFS forecasts under ECMWF's open-data license |
| NWS / SPC / NHC | Alerts, convective outlooks, tropical products (live feeds) |
| NEXRAD Level II | Single-site radar volumes |
| Bureau of Meteorology (AU) | The Wheeler–Hendon RMM index behind the MJO wheel |
No middleman APIs: seasonmap reads agency data directly, so nothing here depends on a third-party weather service staying friendly.
CAESAR — our in-house model, scored in public
seasonmap runs its own forecast system, CAESAR (Consensus And Ensemble Synthesis with Adaptive Recalibration), in four tiers:
- Consensus — a lead-weighted blend of every model we carry, the public baseline;
- CAESAR-OP — real AI-model rollouts (Pangu-Weather) run from fresh analysis states on our own schedule, with adaptive bias recalibration trained on our own verification history;
- CAESAR-ENS — the ensemble arm: the same rollouts run from an array of perturbed initial states, published as mean and spread;
- a verification store — every forecast on this site, ours included, is scored against the RTMA 2.5 km analysis every pass. The Scores panel shows the rankings, sortable by metric and time window, with clickable error maps. If our model loses, you'll see it lose.
That last part is the point. Forecast products usually ask for trust; this one publishes its receipts.
Learn the tools
The field guide covers the mechanics behind the map in short, practical explainers: reading a skew-T sounding, how verification scoring works, what each forecast model actually is, and the MJO.
Who builds this
seasonmap is an independent project, built lean on open agency data and free-tier infrastructure, improving continuously. Bugs and ideas: use the in-app feedback button — it goes straight into the triage queue.