Weather radars give quantitative precipitation estimates (QPE) over large areas with high spatial and temporal resolutions not achieved by conventional rain gauge networks. Therefore, the derivation and analysis of a radar-based precipitation climatology are highly relevant. For that purpose, radar reflectivity data were obtained from two C-band Doppler weather radars covering the land surface of the Netherlands (approximately 35500 km2). From these reflectivities 10 years of radar rainfall depths were constructed for durations D of 1, 2, 4, 8, 12 and 24 hour with a spatial resolution of 2.4 km and a data availability of approximately 80%. Different methods are compared for adjusting the bias in the radar precipitation depths. Using a dense manual gauge network, a vertical profile of reflectivity (VPR) and a spatial adjustment are applied separately to 24-hour (0800-0800 UTC) unadjusted radar-based precipitation depths. Further, an automatic rain gauge network is employed to perform a mean-field bias adjustment to unadjusted 1-hour rainfall depths. A new adjustment method is developed (MFBS) which combines the hourly mean-field bias and the daily spatial adjustment methods. The record of VPR gradients, obtained from the VPR adjustment, reveals a seasonal cycle which can be related to the type of precipitation. A verification with automatic (D =< 24 hour) and manual (D = 24 hour) rain gauge networks demonstrates that the adjustments remove the systematic underestimation of precipitation by radar. The MFBS adjustment gives the best verification results and reduces the residual (radar minus rain gauge depth) standard deviation considerably. Finally, the adjusted radar data set is used to obtain exceedance probabilities, maximum rainfall depths, mean annual rainfall frequencies and spatial correlations. Such a radar rainfall climatology is potentially valuable for the improvement of rainfall parameterization in weather and climate models and the design of hydraulic structures.
A Overeem, I Holleman, TA Buishand. Derivation of a 10-year radar-based climatology of rainfall
published, J. Appl. Meteor., 2009, 48