ABSTRACT:A set of quality control algorithms developed in the framework of the RADVOL-QC system is described. The algorithms are dedicated to 3-D radar reflectivity data (volumes) provided by a weather radar with single-polarization of the beam. The system consists of two paths of data processing: quality correction and quality characterization. Processing by means of data correction algorithms allows the reduction of uncertainty in the data, whereas quality characterization algorithms generate a map of quality index (QI) that can be attached to the data. The following correction algorithms are included in the proposed scheme: (1) removal of geometrically shaped non-meteorological echoes (from the Sun and other emitters); (2) removal of measurement noise (specks); (3) beam blockage correction, and, (4) attenuation in the rain correction. The list of quality characterization algorithms is longer, as it also includes evaluation of data uncertainty due to technical radar parameters, distance to radar related effects, and ground clutter. All the algorithms have been developed paying special attention to possibility of operational application. The system is planned to be extended successively by other quality factors.
One of the quantitative metrics of quality of radar measurements of precipitation is the quality index (QI): a field of numbers whose values depend on the quality. Such an approach is operationally used in some national meteorological services. Difficulties in using this approach can be observed due to hardware and software differences and continuous quality control algorithm improvement. An overall review of commonly used approaches and connected difficulties is made. The challenges in hydrological applications using the QI are listed, as the technique is used to generate precipitation field ensembles. Recommendations for future common considerations are suggested.
Abstract. Precipitation radar-based data constitute essential input to Numerical Weather Prediction (NWP) and rainfallrunoff models, however the data introduce a number of errors. Thus their uncertainty should be determined to provide end-users with more reliable information about forecasts. The common idea is to use Quality Index (QI) scheme for some number of quality parameters on the assumption that: (1) relationship between the parameters and relevant quality indexes is linear; (2) averaged QI is a weighted average of all particular indexes. The uncertainty parameters can be topography-dependent, resulting from spatial and temporal distribution of data, etc. Uncertainty in radar-based data is described by gamma PDF of precipitation, and it is proposed to determine the probability density function (PDF) parameters basing on QI values. Practically, precipitation is presented as ensemble of quantiles of the PDF and such an ensemble can constitute input to rainfall-runoff modelling. Since the ensemble is a precipitation input, the hydrological model needs to be activated according to a number of input members.
A quality index scheme is proposed which is designed to evaluate the quality of different radar-derived rainfall products including processed radar data and precipitation accumulations. The idea of the quality index scheme is based on selection of quality factors, determination of their quality indices and computation of one final quality index. The factors were selected depending on the particular kind of precipitation field. In the proposed scheme the quality index for each quality factor is determined using regression relationships between quality factors and data errors calculated from rain gauge -radar observation differences. Finally, all the individual quality indices are summarized to a final quality index applying appropriate weights. The quality index is computed for each pixel of radar-derived precipitation field independently. The quality information field obtained in this way is attached to the radar-based precipitation product and can be used to generate the precipitation field as percentiles of probability density functions.
BALTRAD software exchanges weather-radar data internationally, operationally, and in real-time, and it processes the data using a common toolbox of algorithms available to every node in the decentralized radar network. This approach enables each node to access and process its own and international data to meet its local needs. The software system is developed collaboratively by the BALTRAD partnership, mostly comprising the national Meteorological and Hydrological institutes in the European Union's Baltic Sea Region. The most important subsystems are for data exchange, data management, scheduling and event handling, and data processing. C, Java, and Python languages are used depending on the subsystem , and subsystems communicate using well-defined interfaces. Software is available from a dedicated Git server. BALTRAD software has been deployed throughout Europe and more recently in Canada.
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