Application of Compressive Sensing to Refractivity Retrieval Using Networked Weather Radars

Abstract

Radar-derived refractivity from stationary ground targets can be used as a proxy of near-surface moisture field and has the potential to improve the forecast of convection initiation. Refractivity retrieval was originally developed for a single radar and was recently extended for a network of radars by solving a constrained least squares (CLS) minimization. In practice, the number of high-quality ground returns can be often limited, and consequently, the retrieval problem becomes ill-conditioned. In this paper, an emerging technology of compressive sensing (CS) is proposed to estimate the refractivity field using a network of radars. It has been shown that CS can provide an optimal solution for the underdetermined inverse problem under certain conditions and has been applied to different fields such as magnetic resonance imaging, radar imaging, etc. In this paper, a CS framework is developed to solve the inversion. The feasibility of CS for refractivity retrieval using single and multiple radars is demonstrated using simulations, where the model refractivity fields were obtained from the Advanced Regional Prediction System. The root-mean-squared error was introduced to quantify the performance of the retrieval. The performance of CS was assessed statistically and compared to the CLS estimates for various amounts of measurement errors, numbers of radars, and model refractivity fields. Our preliminary results have shown that CS can consistently provide relatively robust and high-quality estimates of the refractivity field.

Compressive Sampling of Polarimetric Doppler Weather Radar Processing Via Inverse Fast Fourier Transform

“…In this paper, we follow the CS-based approach to reduce the large amount of data measured in FMCW polarimetric weather radar while preserving important information of the targets. Unlike existing literatures which utilized DCT, DWT, and SVD in creating the sparse signal [15]- [19], our previous results give indication that the sparsity of beat signal is best represented using FFT [20], [21]. Specifically, the PSNR using FFT sparsity representation is almost three times higher than that of the DCT and DWT in low CR.…”

“…Different from the others, Mishra et al [19] represented the sparsity data by modeling the matrix of weather data into the low-rank matrix using singular value decomposition (SVD). Although DCT, DWT and SVD have been used to exploit the sparsity of radar signal [15]- [19], we have demonstrated that fast fourier transform (FFT) exploits the sparsity of the beat signal better as quantified by a higher peak signal-to-noise ratio (PSNR) [20]. We have also showed that by increasing the compression rate (CR), the PSNR is reduced [21].…”

“…Utilizing sparsity representation in CS has consistently provided high-quality estimates of precipitation on weather radar. Specifically, Oztruct et al [15] employed discrete cosine transform (DCT) for sparsity technique to reconstruct the refractivity field of weather radar network. Shimamura et al [16] exploited the discrete wavelet transform (DWT) and DCT to represent a one-dimensional range in sparsity domain and then reduced the data using Gaussian random selection.…”

Passive Remote Study of the Aerosol in the Upper Atmosphere of the Earth

“…The combined effect of these factors can lead to threatening phenomena for mankind -a decrease in the power of the ozone layer, the formation of "ozone holes" and global warming on the planet and other disasters. A special place in the atmosphere is occupied by the stratosphere and its ozone layer which protects the Earth from hard ultraviolet (UV) radiation [3][4][5][6]. Stratospheric aerosol at altitudes of more than 30 km can be in the atmosphere for years and play an important role in the formation of the thermal regime and the thickness of the ozone layer.…”

“…It is more efficient to carry out with spacecraft in the ultraviolet wavelength range. This approach allows you to study the stratospheric aerosol, projects of polarimetric experiments in the ultraviolet spectral region were proposed [5,6]. Another method for obtaining data on the physical properties of an aerosol in the upper atmosphere of the planets [11][12][13][14][15] was considered using the results of polarimetric measurements of a cloudless sky [16,17].…”