Received: 03 Dec 2017 – Discussion started: 07 Dec 2017
Abstract. Over the past decade, polarized weather radars have been at the forefront of the search for a replacement of estimating precipitation over the spatially, and temporally inferior tipping buckets. However, many radar-coverage gaps exist within the Continental US (CONUS), proposing a dilemma in that radar rainfall estimate quality degrades with range. One possible solution is that of X-band weather radars. However, the literature as to their long-term performance is lacking. Therefore, the overarching objective of the current study was to analyze two year’s worth of radar data from the X-band dualpolarimetric MZZU radar in central Missouri at four separate ranges from the radar, utilizing tippingbuckets as ground-truth precipitation data. The conventional R(Z)-Convective equation, in addition to several other polarized algorithms, consisting of some combinations of reflectivity (Z), differential reflectivity (ZDR), and the specific differential phase shift (KDP) were used to estimate rainfall. Results indicated that the performance of the algorithms containing ZDR were superior in terms of the normalized standard error (NSE), missed and false precipitation amounts, and the overall precipitation errors. Furthermore, the R(Z,ZDR) and R(ZDR,KDP) algorithms were the only ones which reported NSE values below 100 %, whereas R(Z) and R(KDP) equations resulted in false precipitation amounts equal to or greater than 65 % of the total gauge recorded rainfall amounts. The results show promise in the utilization of the smaller, more cost-effective X-band radars in terms of quantitative precipitation estimation at ranges from 30 to 80 km from the radar.
How to cite. Simpson, M. J. and Fox, N. I.: X-band dual-polarized radar quantitative precipitation estimate analyses in the Midwestern United States, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2017-439, 2017.
The current study analyzes two year's worth of X-band weather radar data while utilizing over 50 different rain rate algorithms in Central Missouri. Results indicate that algorithms containing the differential reflectivity (ZDR) were the most robust due to its low normalized standard error values (below 100 %). Quantitative analyses provided insight into the fact that the majority of errors were due to falsely detected precipitation in comparison to missed precipitation or mean absolute errors.
The current study analyzes two year's worth of X-band weather radar data while utilizing over 50...