Neural Network Based on Dynamic Collaboration of Flows for Temporal Downscaling

Time downscaling is one of the most challenging topics in remote sensing and meteorological data processing. Traditional methods often face the problems of high computing cost and poor generalization ability. The framework interpolation method based on deep learning provides a new idea for the time downscaling of meteorological data. A deep neural network for the time downscaling of multivariate meteorological data is designed in this paper. It estimates the kernel weight and offset vector of each target pixel independently among different meteorological variables and generates output frames guided by the feature space. Compared with other methods, this model can deal with a large range of complex meteorological movements. The 2 h interval downscaling experiments for 2 m temperature, surface pressure, and 1000 hPa specific humidity show that the MAE of the proposed method is reduced by about 14%, 25%, and 18%, respectively, compared with advanced methods such as AdaCof and Zooming Slow-Mo. Performance fluctuates very little over time, with an average performance fluctuation of only about 1% across all metrics. Even in the downscaling experiment with a 6 h interval, the proposed model still maintains a leading performance advantage, which indicates that the proposed model has not only good performance and robustness but also excellent scalability and transferability in the downscaling task in the multivariate meteorological field.