Compressed Sensing Applied to Weather Radar

We propose an innovative meteorological radar, which uses reduced number of spatiotemporal samples without compromising the accuracy of target information. Our approach extends recent research on compressed sensing (CS) for radar remote sensing of hard point scatterers to volumetric targets. The previously published CS-based radar techniques are not applicable for sampling weather since the precipitation echoes lack sparsity in both range-time and Doppler domains. We propose an alternative approach by adopting the latest advances in matrix completion algorithms to demonstrate the sparse sensing of weather echoes. We use Iowa X-band Polarimetric (XPOL) radar data to test and illustrate our algorithms.Comment: 4 pages, 5 figrue

Super-resolution Line Spectrum Estimation with Block Priors

We address the problem of super-resolution line spectrum estimation of an undersampled signal with block prior information. The component frequencies of the signal are assumed to take arbitrary continuous values in known frequency blocks. We formulate a general semidefinite program to recover these continuous-valued frequencies using theories of positive trigonometric polynomials. The proposed semidefinite program achieves super-resolution frequency recovery by taking advantage of known structures of frequency blocks. Numerical experiments show great performance enhancements using our method.Comment: 7 pages, double colum

Off-The-Grid Spectral Compressed Sensing With Prior Information

Recent research in off-the-grid compressed sensing (CS) has demonstrated that, under certain conditions, one can successfully recover a spectrally sparse signal from a few time-domain samples even though the dictionary is continuous. In this paper, we extend off-the-grid CS to applications where some prior information about spectrally sparse signal is known. We specifically consider cases where a few contributing frequencies or poles, but not their amplitudes or phases, are known a priori. Our results show that equipping off-the-grid CS with the known-poles algorithm can increase the probability of recovering all the frequency components.Comment: 5 pages, 4 figure

Precise Semidefinite Programming Formulation of Atomic Norm Minimization for Recovering d-Dimensional (d≥2d\geq 2) Off-the-Grid Frequencies

Recent research in off-the-grid compressed sensing (CS) has demonstrated that, under certain conditions, one can successfully recover a spectrally sparse signal from a few time-domain samples even though the dictionary is continuous. In particular, atomic norm minimization was proposed in \cite{tang2012csotg} to recover $1$-dimensional spectrally sparse signal. However, in spite of existing research efforts \cite{chi2013compressive}, it was still an open problem how to formulate an equivalent positive semidefinite program for atomic norm minimization in recovering signals with $d$-dimensional ($d\geq 2$) off-the-grid frequencies. In this paper, we settle this problem by proposing equivalent semidefinite programming formulations of atomic norm minimization to recover signals with $d$-dimensional ($d\geq 2$) off-the-grid frequencies.Comment: 4 pages, double-column,1 Figur

Evaluating NEXRAD Multisensor Precipitation Estimates for Operational Hydrologic Forecasting

Copyright 2000 American Meteorological SocietyNext-Generation Weather Radar (NEXRAD) multisensor precipitation estimates will be used for a host of applications that include operational streamflow forecasting at the National Weather Service River Forecast Centers (RFCs) and nonoperational purposes such as studies of weather, climate, and hydrology. Given these expanding applications, it is important to understand the quality and error characteristics of NEXRAD multisensor products. In this paper, the issues involved in evaluating these products are examined through an assessment of a 5.5-yr record of multisensor estimates from the Arkansas–Red Basin RFC. The objectives were to examine how known radar biases manifest themselves in the multisensor product and to quantify precipitation estimation errors. Analyses included comparisons of multisensor estimates based on different processing algorithms, comparisons with gauge observations from the Oklahoma Mesonet and the Agricultural Research Service Micronet, and the application of a validation framework to quantify error characteristics. This study reveals several complications to such an analysis, including a paucity of independent gauge data. These obstacles are discussed and recommendations are made to help to facilitate routine verification of NEXRAD products

Hydro-NEXRAD-2: Real-time Access To Customized Radar-rainfall For Hydrologic Applications

Hydro-NEXRAD-2 (HNX2) is a prototype system that allows hydrologic users real-time access to NEXRAD radar data in support of a wide range of research. The system processes basic radar data (Level II) and delivers radar-rainfall products based on the user\u27s custom selection of features such as spatial domain, rainfall product space and time resolution, and rainfall estimation algorithms. HNX2 collects real-time, unprocessed data from multiple NEXRAD radars as they become available, processes them through a user-configurable pipeline of data-processing modules, and publishes the processed data-products at regular intervals. Modules in the data-processing pipeline encapsulate algorithms such as non-meteorological echo detection, radar range correction, radar-reflectivity-rain rate (Z-R) conversion, echo advection correction, mosaicking of products from multiple radars, and grid projections and transformations. This paper describes the challenges involved in HNX2\u27s development and implementation, which include real-time error-handling, time-synchronization of data from multiple asynchronous sources, generation of multiple-radar metadata products, and distribution of products to a user base with diverse needs and constraints. HNX2 publishes products through automation and allows multiple users access to published products. Currently, HNX2 is serving near real-time rain-rate maps for Iowa in the USA using data from seven radars covering the state. Hydrologic models operated by The University of Iowa\u27s Iowa Flood Center use these products. © IWA Publishing 2013

Hydro-NEXRAD Radar-rainfall Estimation Algorithm Development, Testing And Evaluation

The Hydro-NEXRAD radar-rainfall estimation algorithms involve three main components: 1) preprocessing, 2) rain rate, and 3) rainfall accumulation. The preprocessing algorithm performs the quality control of reflectivity volume data and generates a hybrid scan. That is, reflectivity values for each azimuth and range bin are assigned from the several lowest elevation angles. It optionally estimates an azimuth-dependent vertical reflectivity profile and performs a correction for range effects. The rain rate algorithm converts the corrected reflectivity to rainfall intensity. The user can specify any power-law type empirical relationship between reflectivity and rainfall intensity. The last step of rainfall estimation is to integrate consecutive rate scans for specific time duration ranging from 15 minutes to daily. The algorithm mimics real-time calculations and involves advection correction. © 2007 ASCE

Multiple Radar Data Merging In Hydro-NEXRAD

The Hydro-NEXRAD merging algorithms include two options: (1) data-based merging; and (2) product-based merging. Data-based merging algorithm takes volume scan reflectivity data from all radars involved through preprocessing algorithm that performs volume data quality control, interpolates data to synchronize temporal scale between individual radars, and finally combines data onto a common geographic grid. Reflectivity values for a given location are assigned by a weighting function with respect to the distance from the radar. This single reflectivity field is then converted to rainfall amounts using a user-requested standard approach. In product-based merging algorithm reflectivity data from multiple radars are all converted to rainfall using the same, user-specified algorithm. These products are then combined into the final one using a weighting function that expresses the uncertainty of estimated rainfall amounts. © 2008 ASCE

Radar-rainfall Estimation Algorithms Of Hydro-NEXRAD

Hydro-NEXRAD is a prototype software system that provides hydrology and water resource communities with ready access to the vast data archives of the U.S. weather radar network known as NEXRAD (Next Generation Weather Radar). This paper describes radar-rainfall estimation algorithms and their modular components used in the Hydro-NEXRAD system to generate rainfall products to be delivered to users. A variety of customized modules implemented in Hydro-NEXRAD perform radar-reflectivity data processing, produce radar-rainfall maps with user-requested space and time resolution, and combine multiple radar data for basins covered by multiple radars. System users can select rainfall estimation algorithms that range from simple (\u27Quick Look\u27) to complex and computing-intensive (\u27Hi-Fi\u27). The \u27Pseudo NWS PPS\u27 option allows close comparison with the algorithm used operationally by the US National Weather Service. The \u27Custom\u27 algorithm enables expert users to specify values for many of the parameters in the algorithm modules according to their experience and expectations. The Hydro-NEXRAD system, with its rainfall-estimation algorithms, can be used by both novice and expert users who need rainfall estimates as references or as input to their hydrologic modelling and forecasting applications. © IWA Publishing 2011