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Grzegorz J. Ciach


This study presents empirical analysis of the errors in tipping-bucket rain gauges that manifest themselves as random differences between closely collocated instruments. It is based on a substantial data sample from 15 collocated rain gauges. The errors are shown to be considerable and highly dependent on rainfall intensity and timescale. These dependencies are estimated using nonparametric regression. Strong dependence of the errors on the data collecting and processing strategy is also demonstrated. An analytical model and estimates of its coefficients are provided to concisely quantify the results in different scenarios. Finally, possible improvements of the accuracy and reliability of the surface rainfall measurements are discussed.

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Stanislaw Moszkowicz, Grzegorz J. Ciach, and Witold F. Krajewski


The problem of anomalous propagation (AP) echoes in weather radar observations has become especially important now that rainfall data from fully automatic radar systems are sometimes applied in operational hydrology. Reliable automatic detection and suppression of AP echoes is one of the crucial problems in this area.

This study presents characteristics of AP patterns and the initial results of applying a statistical pattern classification method for recognition and rejection of such echoes. A classical radar (MRL-5) station operates in central Poland performing volume scanning every 10 min. Two months of hourly data (June and September of 1991) were chosen to create learning and verification samples for the AP detection algorithm. Each observation was thoroughly analyzed by an experienced radar meteorologist. The features taken into account were visually estimated local texture and overall morphology of echo pattern, vertical echo structure, time evolution (using animation), and the general synoptic information. For each 4 km × 4 km pixel of 933 observations the human classification was recorded resulting in a sample of 631 166 points with recognized echo type, 14.6% of them being AP echoes. The unsuppressed AP echo impact on monthly accumulated precipitation was 59% of the actual sum for the month of June and as much as 97% for September.

Three Bayesian discrimination functions were investigated. They differ in selection of the feature vector. This vector consisted of various local radar echo parameters: for example, maximum reflectivity, echo top, and horizontal gradients. The coefficients of the functions were calibrated using the June sample. The AP echo recognition error was about 6% for the best-performing function, when applied to an independent (September) sample, which would make the method acceptable for operational use.

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Paweł Licznar, Janusz Łomotowski, Sławomir Błoński, and Grzegorz J. Ciach


This study presents the construction and calibration of a low-cost piezoelectric microprocessor impactometer designed for the field measurements of the rainfall kinetic energy (KE) flux. Its precise calibration was performed in laboratory conditions using waterdrops of different sizes and fall velocities. High-speed photography was applied to measure the velocity of each waterdrop. Although the impactometer constructed for this study is not able to measure the momentum of waterdrops, its accuracy for measuring their KE is excellent. It was found that the processing of the piezoelectric signal might determine which physical quantity is measured by different impactometers. It was also found that the distance between the waterdrop impact position and the impactometer center has a significant effect on the sensor output. A scheme to account for this effect is developed in this study, and the calibration curve for field applications of the impactometer is derived. In addition, an example comparison of the concurrent field measurements of KE flux using the impactometer and rainfall rates using a weighing rain gauge is given.

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