Short-term risk forecasts of heavy rainfall

Area of Science:

• Meteorology and Atmospheric Science
• Hydrology and Water Resource Management
• Geosciences and Environmental Science

Background:

• Current severe weather risk forecasting methodologies are limited for nowcasting (0-3 hours).
• Accurate short-term prediction of heavy precipitation from local thunderstorms is crucial for disaster preparedness.

Purpose of the Study:

• To develop and present a methodology for short-term risk forecasts of heavy precipitation.
• To provide a probabilistic approach to heavy rainfall prediction within a 0-3 hour nowcasting window.

Main Methods:

• Utilized the COTREC/RainCast procedure for extrapolating radar images into the near future.
• Developed an error density function to account for uncertainties in extrapolated radar patterns.
• Integrated radar intensity probability distributions with a precipitation intensity conversion algorithm to generate risk maps.

Main Results:

• The developed method produces a probability distribution of radar intensities, accounting for location errors.
• The algorithm successfully converts radar intensities into precipitation intensity values, enabling risk assessment.
• Demonstrated the methodology's application using a case study of a flood event from summer 2000.

Conclusions:

• The COTREC/RainCast procedure, combined with an error density function, provides a robust framework for nowcasting heavy precipitation risk.
• This approach enhances the accuracy and reliability of short-term severe weather risk forecasts.
• The methodology offers valuable insights for flood prediction and mitigation strategies.