Interpretable deep learning method integrating spatial self-attention for generating bias-corrected high-resolution GFS precipitation forecasts

Key Points

• Bias-corrected high-resolution precipitation forecasts demonstrate improved accuracy, offering clearer insights into weather patterns.
• Key metric shows significant enhancements in forecast precision when using spatial self-attention techniques.
• Implementation involved a deep learning model integrating spatial self-attention for effective bias correction of forecasts.
• High-resolution predictions could support better weather-related decision-making, particularly in agriculture and disaster management.