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Developing an optimized parameterization scheme for deriving a lightning threat product from a global model for all
Greeshma M Mohan1, M Sandhya2, A Jayakumar2
1National Centre for Medium Range Weather Forecasting (NCMRWF), Ministry of Earth Sciences (MoES), Noida, UP, 201309, India. greeshma.m90@nic.in.
Scientific Reports
|October 1, 2025
Summary
This study introduces a new method for forecasting cloud-to-ground (CG) lightning threats in India. The Revised PR92-Lopez Blended (RPLB) scheme improves accuracy and reduces false alarms for early warnings.
Area of Science:
- Atmospheric Science
- Meteorology
- Lightning Physics
Background:
- Accurate forecasting of cloud-to-ground (CG) lightning is crucial for disaster management.
- Existing lightning parameterization schemes often overestimate lightning counts and spatial extent.
Purpose of the Study:
- To develop and evaluate an improved scheme for medium-range CG lightning threat forecasting over India.
- To enhance the accuracy and reliability of lightning prediction for early warning systems.
Main Methods:
- Utilized medium-range forecasts from a global model for all seasons.
- Evaluated Price and Rind (PR92) and Lopez lightning parameterization schemes against Earth Network data.
- Developed a Revised PR92-Lopez Blended (RPLB) scheme by redefining storm detection and applying regression-based weights for land and ocean.
Main Results:
- Initial schemes (PR92, Lopez) overestimated lightning counts and spatial extent.
- The RPLB scheme demonstrated improved spatial and frequency distribution of CG flashes.
- RPLB significantly reduced false alarms up to a five-day lead time compared to individual schemes.
Conclusions:
- The RPLB scheme offers enhanced skill in predicting CG lightning threats over India.
- Categorizing CG flashes into threat levels provides valuable data for decision support systems.
- This improved forecasting capability aids in effective early warning and disaster preparedness.
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