Abstract Accurate snowfall measurements are vital for disaster mitigation, climate studies, and many other applications. Widely deployed surveillance cameras offer novel possibilities for fine‐scale snowfall observation. This paper proposes HySnowNet, a hybrid framework that first extracts snowflakes using physical frequency domain techniques, then applies deep learning network to estimate snowfall intensity. Extensive experiments demonstrate that the snowflakes extraction module significantly enhances performance across varying snowfall intensities and remains stable under winds up to 5 m/s, enabling HySnowNet to achieve Root Mean Square Deviation values of 2.42 and 1.23 mm/hr on the self‐constructed data set and real‐world observations, respectively. Moreover, cumulative snowfall estimation accuracy improved by 20.3% over S‐band radar, demonstrating its value in supporting radar networks. However, its performance declines under extreme conditions (>5 mm/hr). The findings can build on existing surveillance resources to open a new avenue for high spatiotemporal‐resolution ground snowfall measurements, supporting remote sensing observation networks and enabling effective blowing snow monitoring.
Wang et al. (Thu,) studied this question.