Abstract
The transmission and storage of weather radar products will be an important problem for future weather radar applications. The aim of this work is to provide a solution for real-time transmission of weather radar data and efficient data storage. By upgrading the capabilities of radar, the amount of data that can be processed continues to increase. Weather radar compression is necessary to reduce the amount of data for transmission and archiving. The characteristics of weather radar data are not considered in general-purpose compression programs. The sparsity and data redundancy of weather radar data are analyzed. A lossless compression of weather radar data based on prediction coding is presented, which is called spatial and temporal prediction compression (STPC). The spatial and temporal correlations in weather radar data are utilized to improve the compression ratio. A specific prediction scheme for weather radar data is given, while the residual data and motion vectors are used to replace the original values for entropy coding. After this, the Level-II product from CINRAD SA is used to evaluate STPC. Experimental results show that the STPC achieves a better performance than the general-purpose compression programs, with the STPC yield being approximately 26% better than the next best approach.
Highlights
Meteorological disasters have always been among the most devastating natural phenomena on Earth, which are capable of spreading destruction and result in loss of life across wide areas.Weather radars provide continuous, high-resolution, and multi-parameter observation abilities in large geographical areas in real time
The results showed that general-proposed lossless compression algorithms are usually based on arithmetic coding and good performance on general data, the weather radar data characteristics are not taken into account
The data structure of residual and motion vectors is easier to compress than raw data. These results prove the outstanding performance of spatial and temporal prediction compression (STPC) for Level-II weather radar data
Summary
Meteorological disasters have always been among the most devastating natural phenomena on Earth, which are capable of spreading destruction and result in loss of life across wide areas. Compression ratio that outperforms conventional two-dimensional methods These approaches use the compression sensing technique to reduce the amount of data based on the sparsity of weather radar signals. Ai et al [24] described the redundancy in PPI image and proposed a lossless compression approach using optical prediction in PPI images The premise of these works involves the exploration of using this data structure to reduce the amount of data. The characteristics of weather radar data are analyzed, a block-based prediction method is presented to reduce the correlation, and we proposed a weather radar lossless compression approach that is called STPC (spatial and temporal prediction compression). The STPC performance was compared with general-purpose compression programs and a weather radar-specific compression approach
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