Storm Readiness: Using the Thunderstorm Probability Feature Team Team
  • Updated

The Global Thunderstorm Probability feature improves the capability to assess and forecast thunderstorms for a two-week period. This tool offers worldwide coverage, encompassing all probabilities associated with thunderstorms.

Key Features

  • Temporal and Spatial Analysis: Utilize both the timeline and map viewer to observe the evolution of thunderstorm threats over time and across different regions. This feature allows you to visualize and track the progression of potential thunderstorms effectively.
  • Actionable Insights: The tool enables you to generate actionable notifications based on conditions that indicate a significant threat, helping you to initiate timely response actions.
  • Data Source and Accuracy: The new Thunderstorm Probability Data Processing Layer (DPL) is sourced from the ECMWF-EPS (European Centre for Medium-Range Weather Forecasts Ensemble Prediction System) lightning flash density forecasts. It specifically measures the probability that lightning flash density will exceed 0.1 flashes per 100 square kilometers per hour.


This feature belongs to the Core Data Processing Layer group, and is available to all customers.

  • Supported Time Range: The feature provides forecasts to 340 hours into the future.
  • Supported Timesteps: Data is updated hourly, providing detailed and timely updates on thunderstorm probabilities.
  • Units of Measurement: Thunderstorm probabilities are presented as percentages, offering a straightforward interpretation of the data.
  • Display: The probability data can be viewed on a timeline, enhancing your ability to make informed decisions based on the progression of thunderstorm probabilities.
  • Supported Apps: In addition to its availability on the platform, the Global Thunderstorm Probability feature is also accessible via our API. For more details and integration guidelines, please visit this link

For any additional information or assistance please contact our support team at

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