An Algorithm for Real-Time Rainfall Rate Estimation by Using Polarimetric Radar: RIME

Abstract

Polarimetric radars provide measurements that describe the shape and dimensions of hydrometeors and are unaffected by calibration, attenuation, and the presence of ice. These measurements can potentially lead to a more detailed description of hydrometeors and to an improvement in quantitative rainfall rate estimation. The authors present an algorithm that exploits polarimetric measures for rain-rate estimation and investigate its application in a real-time framework by using measurements from the C-band polarimetric radar at Monte Settepani in Savona, Italy. It is based on a flowchart decision tree that allows the use of the best rain-rate retrieval algorithm, depending on the value of polarimetric variables. The methodology was applied to a realtime framework for more than a year, and the results were presented for all the significant events observed during the test period. To evaluate the performance of the algorithm, a comparison was made with rain gauge observation from a dense regional network. The performances of the algorithm were compared with those obtained by standard operational Z-R formulations to evaluate the benefit of this approach for operational applications.

ICT approaches to integrating institutional and non-institutional data services for better understanding of hydro-meteorological phenomena

“…Further work will be devoted in the future to combine the raingauges data and radar data (Silvestro et al, 2009) with and without the PWS network and see if the resulting fused data show less bias compared to an independent set of raingauges.…”

Operational verification of a framework for the probabilistic nowcasting of river discharge in small and medium size basins

“…Since we use data from a polarimetric radar, we employed an algorithm named RIME, which uses the polarimetric variables as input; this is described in Silvestro et al (2009). It is a flow chart algorithm that employs different sets of radar variables that depend on the values of the variables themselves; the aim is to use the optimal measured variables for calculating the precipitation.…”

“…We checked that the radar system was correctly functioning and that the rainfall estimation was unaffected by bias or evident errors. To reach this goal, we applied the methodology of radar-gauge comparison illustrated in Silvestro et al (2009). It is true that the radar rainfall fields in this work are corrected with rain gauge data, but we wanted to be sure to start from a good quantitative rainfall estimation.…”

A Robust Error-Based Rain Estimation Method for Polarimetric Radar. Part II: Case Study

“…This study uses data collected by the 10-cm CP2 radar during the Queensland Cloud Seeding Research Project, which ran between 2007and 2009in Brisbane (Tessendorf et al 2012. During the study period, the CP2 radar was primarily used for research operations, with data collected under a variety of scanning strategies during the project.…”

“…This is most commonly achieved through composite algorithms, with various polarimetric estimators applied depending on some explicit criteria. The criteria used can be thresholds of reflectivity alone (e.g., Ryzhkov et al 2005) or multileveled criteria that are based on both reflectivity thresholds and the values of the polarimetric variables (e.g., Bringi et al 2004;Silvestro et al 2009). Morecomplex criteria can also include the initial separation of convective from stratiform rain (e.g., Le Bouar et al 2001) or the inclusion of hydrometeor classification regimes (e.g., Giangrande and Ryzhkov 2008;Cifelli et al 2011).…”