Bayesian statistical analysis of ground-clutter for the relative calibration of dual polarization weather radars

A new data processing methodology, based on the statistical analysis of ground-clutter echoes and aimed at investigating the stability of the weather radar relative calibration, is presented. A Bayesian classification scheme has been used to identify meteorological and/or ground-clutter echoes. The outcome is evaluated on a training dataset using statistical score indexes through the comparison with a deterministic clutter map. After discriminating the ground clutter areas, we have focused on the spatial analysis of robust and stable returns by using an automated region-merging algorithm. The temporal series of the ground-clutter statistical parameters, extracted from the spatial analysis and expressed in terms of percentile and mean values, have been used to estimate the relative clutter calibration and its uncertainty for both co-polar and differential reflectivity. The proposed methodology has been applied to a dataset collected by a C-band weather radar in southern Italy

On the Implementation of a regional X-bandweather radar network

In the last few years, the number of worldwide operational X-band weather radars has rapidly been growing, thanks to an established technology that offers reliability, high performance, and reduced efforts and costs for installation and maintenance, with respect to the more widespread C- and S-band systems. X-band radars are particularly suitable for nowcasting activities, as those operated by the LaMMA (Laboratory of Monitoring and Environmental Modelling for the sustainable development) Consortium in the framework of its institutional duties of operational meteorological surveillance. In fact, they have the capability to monitor precipitation, resolving very local scales, with good spatial and temporal details, although with a reduced scanning range. The Consortium has recently installed a small network of X-band weather radars that partially overlaps and completes the existing national radar network over the north Tyrrhenian area. This paper describes the implementation of this regional network, detailing the aspects related with the radar signal processing chain that provides the final reflectivity composite, starting from the acquisition of the signal power data. The network performances are then qualitatively assessed for three case studies characterised by different precipitation regimes and different seasons. Results are satisfactory especially during intense precipitations, particularly regarding what concerns their spatial and temporal characterisation

Radar multi-sensor (RAMS) quantitative precipitation estimation (QPE)

Includes bibliographical references.2015 Summer.Quantitative precipitation estimation (QPE) continues to be one of the principal objectives for weather researchers and forecasters. The ability of radar to measure over broad spatial areas in short temporal successions encourages its application in the pursuit of accurate rainfall estimation, where radar reflectivity-rainfall (Z-R) relations have been traditionally used to derive quantitative precipitation estimation. The purpose of this research is to present the development of a regional dual polarization QPE process known as the RAdar Multi-Sensor QPE (RAMS QPE). This scheme applies the dual polarization radar rain rate estimation algorithms developed at Colorado State University into an adaptable QPE system. The methodologies used to combine individual radar scans, and then merge them into a mosaic are described. The implementation and evaluation is performed over a domain that occurs over a complex terrain environment, such that local radar coverage is compromised by blockage. This area of interest is concentrated around the Pigeon River Basin near Asheville, NC. In this mountainous locale, beam blockage, beam overshooting, orographic enhancement, and the unique climactic conditions complicate the development of reliable QPE's from radar. The QPE precipitation fields evaluated in this analysis will stem from the dual polarization radar data obtained from the local NWS WSR-88DP radars as well as the NASA NPOL research radar

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

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