Correct your forecast before it becomes “history.” Stop waiting for observation data to learn you were wrong. ForecastWatch provides the industry’s first real-time risk detection engine, allowing you to identify accuracy drift and model outliers before the forecast day even arrives.
Eliminate the “verification lag.” Traditional verification is a post-mortem; by the time you have the data, the damage is done. Forecast Risks flips the script. Use our real-time benchmarks to operationalize automatic forecast improvements. By identifying discrepancies against market standards in advance, you can pivot your models while they are still active.
Fix performance issues where they start: at the station. Broad regional corrections often miss the nuance of local model failure. We provide station-specific data across thousands of sites, giving your meteorologists the surgical precision needed to adjust specific locations. Turn a potential miss into a “bold and right” win by identifying station-level risks in real-time.
Instantly interpret live performance. Don’t get buried in spreadsheets while a weather event is unfolding. Our active 2×2 Matrix categorizes your live forecasts into Consensus Risk, Unusual Weather, or Normal Weather. Instantly see if you are “Going it Alone” or if your model is drifting from the market, allowing for immediate, data-backed decision making.
We use a “consensus-based” verification. By comparing your live forecast against the aggregate of the world’s leading producers, we can identify when your model is a statistical outlier—flagging a “Consensus Risk” before the observation data is even recorded.
The matrix highlights four key areas: Normal Weather (low risk), Consensus Risk (you differ from the market), Unusual Weather (market-wide event), and Missing Event (you missed a signal others caught).
Yes. Our API allows you to pull these risk metrics directly into your own modeling environment, enabling your team to build automated flags or “guardrails” that trigger whenever a forecast exceeds a certain risk threshold.