Data of vertically pointing microrain radars (MRRs), located at various sites around the Baltic Sea, were analyzed for a period of several years. From the Doppler spectra profiles of drop size distributions (DSDs) are obtained. A significant height dependence of the shape of the DSDs—and thus of the Z–R relations—is observed at high rain rates. This implies, for the considered sites, that ground-based Z–R relations lead to underestimation of high rain rates by weather radars.

The classical rain attenuation correction scheme of Hitschfeld and Bordan (HIBO) and the newer iterative approach by Hildebrand (HL) are reconsidered. Although the motivation for the HL algorithm was an extension into ranges, where HIBO tends to be unstable, it is shown here that the contrary is the case. The finiterange resolution causes an intrinsic instability of HL already at moderate attenuation, where HIBO would still deliver stable results. Therefore, the authors concentrate the further analysis on HIBO, and confirm that the usual implementation of HIBO does not account correctly for finite-range resolution. They suggest a modified scheme that produces exact retrievals in the ideal case of perfect measurements.For vertically pointing Doppler radars a new element is explored in the attenuation correction-namely, calculating rain attenuation k and rainfall R from Doppler spectra via the raindrop size distributions (RSDs). Although this spectral scheme (SIBO) avoids the uncertainty of Z-R and Z-k relations, the superiority of this approach is not a priori obvious because of its sensitivity to vertical wind. Therefore, radar rain rates, based on a Z-R relation and on RSDs, respectively, are compared with in situ measurements. The results indicate better agreement for RSD-based retrievals. Because k is closely correlated with R, the authors assert the advantage of RSD-based retrievals of k.The application of HIBO and SIBO to real data shows that the uncertainty of standard Z-R relations is the main source of deviation between the two versions. In addition, the comparison of profiles suggests that the parameters of Z-R relations aloft can deviate considerably from near-surface values. Although artifacts cannot be excluded with certainty, there is some evidence that this observation actually reflects microphysical processes.

Precipitation is one of the main components in the water balance, and probably the component determined with the greatest uncertainties. In the present paper we focus on precipitation (mainly rain) over the Baltic Sea as a part of the BALTEX project to examine the present state of the art concerning different precipitation estimates over that area. Several methods are used, with the focus on 1) interpolation of available synoptic stations; 2) a mesoscale analysis system including synoptic, automatic, and climate stations, as well as weather radar and an atmospheric model; and 3) measurements performed on ships. The investigated time scales are monthly and yearly and also some long-term considerations are discussed. The comparison shows that the differences between most of the estimates, when averaged over an extended period and a larger area, are in the order of 10-20%, which is in the same range as the correction of the synoptic gauge measurements due to wind and evaporation losses. In all data sets using gauge data it is important to include corrections for high winds. To improve the structure of precipitation over sea more focus is to be put on the use of radar data and combinations of radar data and other data. Interpolation methods that do not consider orographic effects must treat areas with large horizontal precipitation gradients with care. Due to the large variability in precipitation in time and space, it is important to use long time periods for climate estimates of precipitation Ship measurements are a valuable contribution to precipitation information over sea, especially for seasonal and annual time scales.

Abstract. This publication intends to prove that a network of low-cost local area weather radars (LAWR) is a reliable and scientifically valuable complement to nationwide radar networks. A network of four LAWRs has been installed in northern Germany within the framework of the Precipitation and Attenuation Estimates from a High-Resolution Weather Radar Network (PATTERN) project observing precipitation with a temporal resolution of 30 s, a range resolution of 60 m and a sampling resolution of 1 • in the azimuthal direction. The network covers an area of 60 km × 80 km. In this paper, algorithms used to obtain undisturbed precipitation fields from raw reflectivity data are described, and their performance is analysed. In order to correct operationally for background noise in reflectivity measurements, noise level estimates from the measured reflectivity field are combined with noise levels from the last 10 time steps. For detection of non-meteorological echoes, two different kinds of clutter algorithms are applied: single-radar algorithms and networkbased algorithms. Besides well-established algorithms based on the texture of the logarithmic reflectivity field (TDBZ) or sign changes in the reflectivity gradient (SPIN), the advantage of the unique features of the high temporal and spatial resolution of the network is used for clutter detection. Overall, the network-based clutter algorithm works best with a detection rate of up to 70 %, followed by the classic TDBZ filter using the texture of the logarithmic reflectivity field.A comparison of a reflectivity field from the PATTERN network with the product from a C-band radar operated by the German Meteorological Service indicates high spatial accordance of both systems in the geographical position of the rain event as well as reflectivity maxima. Long-term statistics from May to September 2013 prove very good accordance of the X-band radar of the network with C-band radar, but, especially at the border of precipitation events, higher-resolved X-band radar measurements provide more detailed information on precipitation structure because the 1 km range gate of C-band radars is only partially covered with rain. The standard deviation within a range gate of the C-band radar with a range resolution of 1 km is up to 3 dBZ at the borders of rain events. The probability of detection is at least 90 %, the false alarm ratio less than 10 % for both systems. Therefore, a network of high-resolution low-cost LAWRs can give valuable information on the small-scale structure of rain events in areas of special interest, e.g. urban regions, in addition to the nationwide radar networks.

The variability of the raindrop size distribution (DSD) contributes to large parts of the uncertainty in radar-based quantitative rainfall estimates. The variety of microphysical processes acting on the formation of rainfall generally leads to significantly different relationships between radar reflectivity Z and rain rate R for stratiform and convective rainfall. High-resolution observation data from three Micro Rain Radars in northern Germany are analyzed to quantify the potential of dual Z–R relationships to improve radar rainfall estimates under idealized rainfall type identification and separation. Stratiform and convective rainfall are separated with two methods, establishing thresholds for the rain rate-dependent mean drop size and the α coefficient of the power-law Z–R relationship. The two types of dual Z–R relationships are tested against a standard Marshall–Palmer relationship and a globally adjusted single relationship. The comparison of DSD-based and reflectivity-derived rain rates shows that the use of stratiform and convective Z–R relationships reduces the estimation error of the 6-month accumulated rainfall between 30% and 50% relative to a single Z–R relationship. Consistent results for neighboring locations are obtained at different rainfall intensity classes. The range of estimation errors narrows by between 20% and 40% for 10-s-integrated rain rates, dependent on rainfall intensity and separation method. The presented technique also considerably reduces the occurrence of extreme underestimations of the true rain rate for heavy rainfall, which is particularly relevant for operational applications and flooding predictions.