Assessment of an adjustment factor to model radar range dependent error

Quantitative radar precipitation estimates are affected by errors determined by many causes such as radar miscalibration, range degradation, attenuation, ground clutter, variability of Z-R relation, variability of drop size distribution, vertical air motion, anomalous propagation and beam-blocking. Range degradation ( including beam broadening and sampling of precipitation at an increasing altitude) and signal attenuation, determine a range dependent behavior of error. The aim of this work is to model the range-dependent error through an adjustment factor derived from the G/R ratio trend against the range, where G and R are the corresponding rain gauge and radar rainfall amounts computed at each rain gauge location. Since range degradation and signal attenuation effects are negligible close to the radar, resultsshowthatwithin 40 km from radar the overall range error is independent of the distance from Polar 55C and no range-correction is needed. Nevertheless, up to this distance, the G/R ratiocan showa concave trend with the range, which is due to the melting layer interception by the radar beam during stratiform events

Data selection to assess bias in rainfall radar estimates. An entropy-based method

Miscalibration of radar determines a systematic error (i.e., bias) that is observed in radar estimates of rainfall. Although a rain gauge can provide a pointwise rainfall measurement, weather radar can cover an extended area. To compare the two measurements, it is necessary to individuate the weather radar measurements at the same location as the rain gauge. Bias is measured as the ratio between cumulative rain gauge measurements and the corresponding radar estimates. The rainfall is usually cumulated, taking into account all rainfall events registered in the target area. The contribution of this work is the determination of the optimal number of rainfall events that are necessary to calibrate rainfall radar. The proposed methodology is based on the entropy concept. In particular, the optimal number of events must fulfil two conditions, namely, maximisation of information content and minimisation of redundant information. To verify the methodology, the bias values are estimated with 1) a reduced number of events and 2) all available data. The proposed approach is tested on the Polar 55C weather radar located in the borough area of Rome (IT). The radar is calibrated against rainfall measurements of a couple of rain gauges placed in the Roman city centre. Analysing the information content of all data, it is found that it is possible to reduce the number of rainfall events without losing information in evaluating the bias