Route-Aware Adaptive Variable-Resolution Storage of Gridded Meteorological Data: A Case Study Using Weather Radar Data

The increasing availability of high-resolution gridded meteorological data poses significant challenges for efficient storage and rapid data access. This study proposes an adaptive variable-resolution storage (AVRS) strategy for gridded meteorological datasets, in which the spatial resolution of data blocks is dynamically adjusted according to local feature characteristics. Composite radar reflectivity (CREF) data are employed as a representative case to evaluate the performance of the proposed method. The AVRS approach partitions the data into fixed-size spatial blocks and assigns multiple resolution levels based on block-level statistical properties, enabling high-resolution preservation in feature-intensive regions while applying coarser resolution in spatially homogeneous areas. Experimental results indicate that the proposed strategy achieves effective storage reduction, with compression ratios ranging from 11.60% to 14.44% of the original data volume. Despite the substantial reduction in storage size, high reconstruction accuracy is maintained. The MSE ranges from 0.74 to 1.54, with RMSE values between 0.86 and 1.24, while the MAE remains low (0.10–0.22). The PSNR consistently exceeds 34.90 dB, with an average value above 37 dB, demonstrating limited information loss and good structural preservation. In addition, the AVRS strategy significantly improves query efficiency, reducing the average query time from 0.5 s for fixed-resolution storage to 0.2 s. Overall, the proposed method provides a practical and efficient solution for managing large-scale gridded meteorological data in atmospheric research and operational applications.