Browsing by Subject "attenuation"

Accuracy and general performance of weather radar measurements are of great importance to society due to their use in quantitative precipitation estimation and its role on flood hazard risks prevention, agriculture or urban planning, among others. However, radars normally suffer from systematic errors such as attenuation, misscalibration in Z field or bias in Zdr field, or random errors such as clutter, beam blockage, noise, non-meteorological echoes or non-uniform beam filling, which affect directly the rain rate estimates or any other relevant product to meteorologists. Impact of random errors is reduced by exploiding the polarimetric properties of polarimetric radars by identifying and classifying measurements according to their signature and a classification scheme based on the available polarimetric variables, but systematic errors are more difficult to address as they require a ’’true’’ or reference value in order to be corrected. The reference value can either be absolute or obtained from another radar variable. In reality, an absolute reference value is not feasible because we normally do not know what we are observing with the radar. Therefore, a way of assesing this issue is by elaborating theoretical relations between radar variables based on their consistency when measuring a volume with hydrometeors of known characteristics such as size and concentration. This procedure is known as self-consistency theory and it is a powerful tool for checking radar measurements quality and correcting offsets causing bias, misscalibration or attenuation. The theoretical radar variables themselves can be simulated using available T-Matrix scattering algorithms, that estimate the scattered phase and amplitude for a given distribution of drops of a given size. Information of distribution of drops of a given size, commonly referred as drop size distributions, can be obtained, for instance, from gauge or disdrometer measurements. Once the theoretical relations among radar variables are established, it is possible to check the consistency of, for instance, measured differential reflectivity with respect to differential reflectivity calculated as function of measured reflectivity, assuming the latter has been filtered properly, and any discrepancy between the observed and theoretical differential reflectivity can be thus attributed to offsets in the radar. This work thus presents a methodology for the revision of radar measurements filtering and quality for their improvement by correcting bias and calibration, using theoretical relations between radar variables through self-consistency theory. Furthermore, as the aforementioned issues are easier to track and resolve in the liquid rain regime of precipitation, this work presents a detailed description of methodologies to exclude ice-phased hydrometeors such as the melting layer detection algorithm and its operational implementation along with other complementary filters suggested in the literature. Examples of the melting layer detection and filtering as well as self-consistency curves for radar measurement performance evaluation are also provided.