Three-Dimensional Data Compression and Fast High-Quality Reconstruction for Phased Array Weather Radar

Abstract

This paper proposes a fast high-quality three-dimensional (3D) compressed sensing for a phased array weather radar (PAWR), which is capable of spatially and temporally high-resolution observation of the atmosphere. Because of the high-resolution, the PAWR generates huge observation data of approximately 500 megabytes every thirty seconds. To transfer this huge data in a public internet line for real time weather forecast, an efficient data compression technology is required. The proposed method compresses the PAWR data by randomly transferring several measurements only in the troposphere, and then reconstructs the missing measurements for each small 3D tensor data by minimizing a cost function based on a prior knowledge on weather phenomena. The minimizer of the cost function can be quickly computed by using a convex optimization algorithm with Nesterov's acceleration technique. Numerical simulations using real PAWR data show the effectiveness of the proposed method compared to conventional two-dimensional methods.