Infrared Precipitation Retrieval Method Based on Residual Deep Forest

Abstract

Precipitation inversion techniques are crucial in meteorological research, aiming to accurately detect the location and intensity of precipitation events, which is vital for extreme weather response strategies. This study introduces a new infrared precipitation inversion method, residual deep forest, which is based on a deep forest model with two key enhancements. First, a feature optimization filter module is used to ensure high correlation among features in the cascade forest, a component of the deep forest model. This optimization reduces computational burden while maintaining efficiency. Second, embedding a residual structure within the cascade forest creates the residual cascade forest, improving feature expression, information processing, and computational efficiency on high-dimensional, large-scale precipitation data. Experimental results show that residual deep forest surpasses traditional deep forest models and other classical machine learning techniques in precipitation inversion accuracy, especially in identifying high-intensity rainfall. The model achieves an average recall (AR) of 85.97%, a probability of detection of 75.46%, and a false alarm ratio of 49.13%. Ablation experiments demonstrate that the feature optimization filter module reduces training time by 3.56%, testing time by 1.24%, and memory usage by 3.97%, while the residual cascade forest module reduces these metrics by 1.30%, 0.90%, and 2.23%, respectively. The improvements in AR, probability of detection, and false alarm ratio, along with the reduction in computational resources, highlight the model's enhanced efficiency and performance. This method leverages infrared remote sensing to enhance the scope and accuracy of precipitation monitoring, reducing reliance on radar and other ground-based instruments, and significantly improving precipitation inversion accuracy while minimizing spatial and temporal complexity.