ATMOS delivers the longest-range, highest-fidelity weather intelligence on Earth. Powered by multi-model ensemble fusion and a 7B-parameter atmospheric foundation model — forecasting weeks further than any legacy provider with per-hour confidence scoring.
ATMOS maintains actionable confidence far beyond the point where legacy systems become noise. Here's the data.
Point-by-point comparison against every major weather platform on Earth.
Interactive 45-day forecast with per-point confidence intervals. Select a location to explore.
ATMOS doesn't run better models. It fundamentally reimagines how weather data is ingested, fused, and predicted.
We ingest from 14,000+ WMO ground stations, 3,200 Argo ocean floats, 47 satellite constellations (including GOES-18, Himawari-9, MetOp), and 250,000+ IoT atmospheric sensors globally.
Our proprietary fusion engine runs GFS, ECMWF IFS, ICON, GEM, UKMO, NAVGEM, and 6 proprietary NWP models in parallel — dynamically weighting each by regional skill score, season, and lead time.
Our 7-billion parameter atmospheric foundation model, trained on 80 years of ERA5 reanalysis, identifies teleconnection patterns — ENSO signatures, MJO propagation, QBO phase coupling, and NAO regime shifts — that deterministic NWP cannot resolve.
Every forecast point carries a machine-learned confidence score, calibrated via isotonic regression against 40 years of verification data. When we say 85% confidence, the observation falls within bounds exactly 85% of the time.
Our methodology is grounded in peer-reviewed atmospheric science. Verified against WMO standards.
We present a 7B-parameter transformer architecture trained on ERA5 reanalysis (1940–2024) that achieves state-of-the-art skill scores at 15–45 day lead times, outperforming ECMWF S2S by 42% CRPS.
Analysis of dynamic vs. static ensemble weighting across 12 NWP models, demonstrating 3.2x accuracy improvement at D14 and 2.1x at D30 over best-member selection.
Our confidence calibration methodology achieves 0.98 reliability diagrams across all lead times, ensuring statistical consistency between predicted probabilities and observed frequencies.
ATMOS predicted the formation and track of Hurricane Maria 18 days before NHC advisory, with 89% confidence on landfall timing. Post-season verification shows 91% categorical accuracy at D+14 for TC genesis.
RESTful API with sub-50ms response times, streaming WebSocket feeds, and SDKs for Python, Node, Go, and Rust. From maritime routing systems to agriculture platforms — integrate the world's best forecasts in minutes.
import atmos client = atmos.Client("your-api-key") # 45-day forecast with confidence scores forecast = client.forecast( lat=37.7749, lon=-122.4194, days=45, resolution="hourly", include=["confidence", "ensemble_spread"] ) for day in forecast.days: print(f"D+{day.lead}: {day.temp_high}F | {day.confidence}%") # Route-based maritime forecast route = client.route_forecast( waypoints=[ (51.5, -0.12), # London (40.71, -74.0), # New York ], vessel_speed_kts=18, parameters=["wind", "wave", "current"] )
ATMOS adapts to your domain. Select your industry and get forecasts tuned to exactly what matters.
Start free. Upgrade when the edge pays for itself.
Enterprise clients report 8–12% fuel savings on shipping routes, 40% reduction in crop loss, and 3x faster strategic decisions. ROI typically within the first month.
What beta participants are reporting from the field.
"We rerouted a container ship 3 days before a storm system that NOAA didn't flag until 36 hours out. That decision saved $2.4M in cargo delays. ATMOS isn't a weather app — it's a competitive weapon."
"The 30-day frost forecast saved our entire Pinot crop. We had frost protection deployed 2 weeks before the event. Neighbors lost 60% of yield. This is generational for agriculture."
"We used ATMOS for Sydney–Hobart race prep. Wind shift predictions at D+12 were dead accurate. We finished 4 positions above rating. The data was worth more than any sail we own."
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