Enhanced radar precipitation estimates using a combined clutter and beam blockage correction technique

Abstract

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.

Radar rainfall estimation of stratiform winter precipitation in the Belgian Ardennes

“…In literature, multiple GC identification techniques have been proposed, using either pulse to pulse reflectivity fluctuations [ Wessels and Beekhuis , 1994], radial Doppler velocity information [ Joss and Lee , 1995], spatial reflectivity information [ Alberoni et al , 2001], or dual‐polarization data [ Giuli et al , 1991]. Other sources of data have also been used to identify GC, such as digital elevation models, temperature, or satellite information [e.g., Delrieu et al , 1995; Michelson and Sunhede , 2004; Fornasiero et al , 2006]. Currently, most GC identification algorithms make use of a classification scheme using multiple information criteria [e.g., Joss and Pittini , 1991; Joss and Lee , 1995; Steiner and Smith , 2002; Grecu and Krajewski , 2000; Berenguer et al , 2006; Cho et al , 2006].…”

Comparison of spectral characteristics of precipitation from radar estimates and COSMO-model predicted fields

“…Then, a 2D dynamic map is constructed for each pixel, taking into account the beam trajectory simulated using the vertical profiles from radio soundings. By adopting these information, the mean clutter should be avoided and the beam blockage reduced to values lower than 50 % (Bech et al 2003;Fornasiero 2006;Fornasiero et al 2006).…”

Geospatial blending to improve spatial mapping of precipitation with high spatial resolution by merging satellite-based and ground-based data

“…However, various studies have revealed that estimating precipitation from radar also poses significant problems; electronic miscalibration (Goudenhoofdt and Delobbe, 2009), signal contamination by non-meteorological echoes or range effect (Fries et al, 2014), beam blocking by natural impediments (e.g. mountain region, surrounding hills with similar or higher altitudes) and the variance in the reflectivity and rainfall rate relationships (Jameson and Kostinski, 2002;Fornasiero et al, 2006;Vulpiani et al, 2012;Yoon and Bae, 2013;Lim et al, 2014;Qin et al, 2014).…”