• Dynamically blended forecast - Best-combined forecast from multiple model sources • Model weight maps - Indication of which model is more reliable for each region/lead time • Improved forecast skill - Better...
• Problem Statement Different forecasting systems perform differently depending on region, season, lead time and weather situation Physical NWP models, ensemble forecasts and AI/ML weather models may each have strengths under different conditions Therefore, there is a need for an intelligent blending system that can dynamically combine multiple forecasts
• Dynamically blended forecast - Best-combined forecast from multiple model sources • Model weight maps - Indication of which model is more reliable for each region/lead time • Improved forecast skill - Better performance than individual models • Extreme weather guidance - Improved signals for heavy rainfall, heat wave and high-wind events • Operational workflow - Automated script/dashboard for routine forecast blending