Ground clutter caused by anomalous propagation (anaprop) can affect seriously radar rain rate estimates, particularly in fully automatic radar processing systems, and, if not filtered, can produce frequent false alarms. A statistical study of anomalous propagation detected from two operational C-band radars in the northern Italian region of Emilia Romagna is discussed, paying particular attention to its diurnal and seasonal variability. The analysis shows a high incidence of anaprop in summer, mainly in the morning and evening, due to the humid and hot summer climate of the Po Valley, particularly in the coastal zone. Thereafter, a comparison between different techniques and datasets to retrieve the vertical profile of the refractive index gradient in the boundary layer is also presented. In particular, their capability to detect anomalous propagation conditions is compared. Furthermore, beam path trajectories are simulated using a multilayer ray-tracing model and the influence of the propagation conditions on the beam trajectory and shape is examined. High resolution radiosounding data are identified as the best available dataset to reproduce accurately the local propagation conditions, while lower resolution standard TEMP data suffers from interpolation degradation and Numerical Weather Prediction model data (Lokal Model) are able to retrieve a tendency to superrefraction but not to detect ducting conditions. Observing the ray tracing of the centre, lower and upper limits of the radar antenna 3-dB half-power main beam lobe it is concluded that ducting layers produce a change in the measured volume and in the power distribution that can lead to an additional error in the reflectivity estimate and, subsequently, in the estimated rainfall rate.

Abstract. A large part of the research in the radar meteorology is devoted to the evaluation of the radar data quality and to the radar data processing. Even when, a set of absolute quality indexes can be produced (like as ground clutter presence, beam blockage rate, distance from radar, etc.), the final product quality has to be determined as a function of the task and of all the processing steps. In this paper the emphasis lies on the estimate of the rainfall at the ground level taking extra care for the correction for ground clutter and beam blockage, that are two main problems affecting radar reflectivity data in complex orography. In this work a combined algorithm is presented that avoids and/or corrects for these two effects. To achieve this existing methods are modified and integrated with the analysis of radar signal propagation in different atmospheric conditions. The atmospheric refractivity profile is retrieved from the nearest in space and time radiosounding. This measured profile is then used to define the `dynamic map' used as a declutter base-field. Then beam blockage correction is applied to the data at the scan elevations computed from this map. Two case studies are used to illustrate the proposed algorithm. One is a summer event with anomalous propagation conditions and the other one is a winter event. The new algorithm is compared to a previous method of clutter removal based only on static maps of clear air and vertical reflectivity continuity test. The improvement in rain estimate is evaluated applying statistical analysis and using rain gauges data. The better scores are related mostly to the ``optimum" choice of the elevation maps, introduced by the more accurate description of the signal propagation. Finally, a data quality indicator is introduced as an output of this scheme. This indicator has been obtained from the general scheme, which takes into account all radar data processing steps.

Abstract. Weather radar observations are currently the most reliable method for remote sensing of precipitation. However, a number of factors affect the quality of radar observations and may limit seriously automated quantitative applications of radar precipitation estimates such as those required in Numerical Weather Prediction (NWP) data assimilation or in hydrological models. In this paper, a technique to correct two different problems typically present in radar data is presented and evaluated. The aspects dealt with are non-precipitating echoes -caused either by permanent ground clutter or by anomalous propagation of the radar beam (anaprop echoes) -and also topographical beam blockage. The correction technique is based in the computation of realistic beam propagation trajectories based upon recent radiosonde observations instead of assuming standard radio propagation conditions. The correction consists of three different steps: 1) calculation of a Dynamic Elevation Map which provides the minimum clutter-free antenna elevation for each pixel within the radar coverage; 2) correction for residual anaprop, checking the vertical reflectivity gradients within the radar volume; and 3) topographical beam blockage estimation and correction using a geometric optics approach. The technique is evaluated with four case studies in the region of the Po Valley (N Italy) using a C-band Doppler radar and a network of raingauges providing hourly precipitation measurements. The case studies cover different seasons, different radio propagation conditions and also stratiform and convective precipitation type events. After applying the proposed correction, a comparison of the radar precipitation estimates with raingauges indicates a general reduction in both the root mean squared error and the fractional error variance indicating the efficiency and robustness of the proceCorrespondence to: P. P. Alberoni (palberoni@arpa.emr.it) dure. Moreover, the technique presented is not computationally expensive so it seems well suited to be implemented in an operational environment.

Abstract. There is a growing interest in emerging opportunistic sensors for precipitation, motivated by the need to improve its quantitative estimates at the ground. The scope of this work is to present a preliminary assessment of the accuracy of commercial microwave link (CML) retrieved rainfall rates in Northern Italy. The CML product, obtained by the open-source RAINLINK software package, is evaluated on different scales (single link, 5 km×5 km grid, river basin) against the precipitation products operationally used at Arpae-SIMC, the regional weather service of Emilia-Romagna, in Northern Italy. The results of the 15 min single-link validation with nearby rain gauges show high variability, which can be caused by the complex physiography and precipitation patterns. Known sources of errors (e.g. the attenuation caused by the wetting of the antennas or random fluctuations in the baseline) are particularly hard to mitigate in these conditions without a specific calibration, which has not been implemented. However, hourly cumulated spatially interpolated CML rainfall maps, validated with respect to the established regional gauge-based reference, show similar performance (R2 of 0.46 and coefficient of variation, CV, of 0.78) to adjusted radar-based precipitation gridded products and better performance than satellite-based ones. Performance improves when basin-scale total precipitation amounts are considered (R2 of 0.83 and CV of 0.48). Avoiding regional-specific calibration therefore does not preclude the algorithm from working but has some limitations in probability of detection (POD) and accuracy. A widespread underestimation is evident at both the grid box scale (mean error of −0.26) and the basin scale (multiplicative bias of 0.7), while the number of false alarms is generally low and becomes even lower as link coverage increases. Also taking into account delays in the availability of the data (latency of 0.33 h for CML against 1 h for the adjusted radar and 24 h for the quality-controlled rain gauges), CML appears as a valuable data source in particular from a local operational framework perspective. Finally, results show complementary strengths for CMLs and radars, encouraging joint exploitation.

Abstract.A spatial characterization of mid-latitude mesoscale rain fields from C-band radar measurements is performed by means of a systematic analysis and modelling of convective raincell shapes. To this aim a large rainfall dataset, derived from an operational C-band dualpolarized radar, has been continuously collected from 1996 to 1999. The radar-derived rain fields consist of 1558 grids of 256×256 km 2 with a spatial resolution of 1 km. A new accurate and adaptive algorithm for raincell identification is introduced and thoroughly discussed. From this analysis, a quality-controlled set of 2601 raincells, together with the radial rain intensities (or raincell horizontal profiles), is extracted. Three one-dimensional analytical models of rainfall horizontal profile are reviewed and tested by best fitting their parameters against estimated raincell data. The statistical results of this intercomparison are quantitatively analyzed and discussed in terms of mean rainfall horizontal profiles and root mean square errors.