Forecast verification, historical pattern analysis, impact modeling, and alert optimization—processed before the next system moves in. Turn atmospheric data into operational intelligence.
High-volume data, time-critical decisions, operational consequences. We understand.
You issue forecasts daily but verification analysis runs weeks behind. You can't improve what you can't measure in real-time.
Decades of data but no capacity to extract actionable trends. The signal is there—you just can't see it.
Too many false alarms and people tune out. Too few and you miss real events. Finding the balance is trial and error.
You know weather affects operations, but proving the dollar impact for budget decisions? That's where the data falls short.
Station data, satellite feeds, model outputs, radar archives—different formats, different resolutions, different quality.
Your meteorologists are issuing forecasts, not running statistical analyses. Deep dives only happen when there's a crisis.
Comprehensive skill metrics by parameter, lead time, and location. Identify systematic biases and improvement opportunities.
Statistically rigorous trend analysis with significance testing. Identify meaningful changes vs. natural variability.
The storm system doesn't wait for your analysis. Neither do the operational decisions that depend on your data—flight schedules, crop management, energy trading, emergency response.
Post-event analysis that arrives weeks later helps next time. Analysis that arrives hours later helps right now—when it still matters.
Systematic evaluation of forecast performance. Identify skill trends, biases, and improvement opportunities.
Multi-decade pattern detection with statistical rigor. Separate signal from noise in historical records.
Data-driven warning criteria based on impact correlation. Balance detection vs. false alarm rates.
Post-event deep dives with return period calculation. Historical context and attribution analysis.
Weather-to-business correlation modeling. Quantify operational and financial impacts for planning.
Multi-model performance evaluation. Identify which models excel for specific parameters and regions.
Submit your atmospheric data. Get decision-ready insights before the next forecast cycle.