Name
Title | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TGRS.2013.2266277 | Geoscience and Remote Sensing, IEEE Transactions |
Keywords | Field | DocType |
atmospheric humidity,atmospheric measuring apparatus,atmospheric techniques,meteorological radar,remote sensing by radar,advanced regional prediction system,compressive sensing application,compressive sensing emerging technology,constrained least squares minimization,convection initiation forecast,magnetic resonance imaging,near-surface moisture field,networked weather radars,radar imaging,radar-derived refractivity,refractivity field,refractivity retrieval,root-mean-squared error,stationary ground targets,$l_{1}$ minimization,compressive sensing (cs),constrained least squares (cls),networked radars,root mean squared error | Radar,Radar engineering details,Radar imaging,Underdetermined system,Remote sensing,Minification,Inverse problem,Observational error,Compressed sensing,Mathematics | Journal |
Volume | Issue | ISSN |
52 | 5 | 0196-2892 |
Citations | PageRank | References |
2 | 0.55 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Serkan Özturk | 1 | 2 | 0.55 |
| Tian-You Yu | 2 | 26 | 7.41 |
| Lei Ding | 3 | 37 | 8.03 |
| Robert D. Palmer | 4 | 42 | 13.12 |
| Nicholas Antonio Gasperoni | 5 | 2 | 0.55 |