Weather Radar Data Compression Based on Spatial and Temporal Prediction
AbstractThe 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. View Full-Text
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Zeng, Q.; He, J.; Shi, Z.; Li, X. Weather Radar Data Compression Based on Spatial and Temporal Prediction. Atmosphere 2018, 9, 96.
Zeng Q, He J, Shi Z, Li X. Weather Radar Data Compression Based on Spatial and Temporal Prediction. Atmosphere. 2018; 9(3):96.Chicago/Turabian Style
Zeng, Qiangyu; He, Jianxin; Shi, Zhao; Li, Xuehua. 2018. "Weather Radar Data Compression Based on Spatial and Temporal Prediction." Atmosphere 9, no. 3: 96.
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