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Quantitative precipitation estimation (QPE) using ground-based weather radaris affected by many sources of error. The most important of these are(1) radar calibration, (2) ground clutter, (3) wet-radome attenuation,(4) rain-induced attenuation, (5) vertical variability in rain drop sizedistribution (DSD), (6) non-uniform beam filling and (7) variations in DSD. This study presents an attempt to separate and quantifythese sources of error in flat terrain very close to the radar (1–2 km),where (4), (5) and (6) only play a minor role. Other important errors exist,like beam blockage, WLAN interferences and hail contamination and arebriefly mentioned, but not considered in the analysis. A 3-day rainfall event(25–27 August 2010) that produced more than 50 mm of precipitation in DeBilt, the Netherlands, is analyzed using radar, rain gauge and disdrometerdata. Without any correction, it is found that the radar severely underestimates thetotal rain amount (by more than 50 %). The calibration of the radarreceiver is operationally monitored by analyzing the received power from thesun. This turns out to cause a 1 dB underestimation. The operationalclutter filter applied by KNMI is found to incorrectly identify precipitationas clutter, especially at near-zero Doppler velocities. An alternative simpleclutter removal scheme using a clear sky clutter map improves the rainfallestimation slightly. To investigate the effect of wet-radome attenuation,stable returns from buildings close to the radar are analyzed. It is shownthat this may have caused an underestimation of up to 4 dB. Finally, adisdrometer is used to derive event and intra-event specific Z–Rrelations due to variations in the observed DSDs. Such variations may resultin errors when applying the operational Marshall–Palmer Z–R relation. Correcting for all of these effects has a large positive impact on theradar-derived precipitation estimates and yields a good match between radar QPE andgauge measurements, with a difference of 5–8 %. This shows thepotential of radar as a tool for rainfall estimation, especially at closeranges, but also underlines the importance of applying radar correctionmethods as individual errors can have a large detrimental impact on the QPEperformance of the radar

Close-range radar rainfall estimation and error analysis