Dual-polarized quantitative precipitation estimation as a function of range

Abstract

Abstract. Since the advent of dual-polarization radar technology, many studies have been conducted to determine the extent to which the differential reflectivity (ZDR) and specific differential phase shift (KDP) add benefits to estimating rain rates (R) compared to reflectivity (Z) alone. It has been previously noted that this new technology provides significant improvement to rain-rate estimation, primarily for ranges within 125 km of the radar. Beyond this range, it is unclear as to whether the National Weather Service (NWS) conventional R(Z)-convective algorithm is superior, as little research has investigated radar precipitation estimate performance at larger ranges. The current study investigates the performance of three radars -St. Louis (KLSX), Kansas City (KEAX), and Springfield (KSGF), MO -with 15 tipping bucket gauges serving as ground truth to the radars. With over 300 h of precipitation data being analyzed for the current study, it was found that, in general, performance degraded with range beyond, approximately, 150 km from each of the radars. Probability of detection (PoD) in addition to bias values decreased, while the false alarm rates increased as range increased. Bright-band contamination was observed to play a potential role as large increases in the absolute bias and overall error values near 120 km for the cool season and 150 km in the warm season. Furthermore, upwards of 60 % of the total error was due to precipitation being falsely estimated, while 20 % of the total error was due to missed precipitation. Correlation coefficient values increased by as much as 0.4 when these instances were removed from the analyses (i.e., hits only). Overall, due to the lowest normalized standard error (NSE) of less than 1.0, a National Severe Storms Laboratory (NSSL) R(Z,ZDR) equation was determined to be the most robust, while a R(ZDR,KDP) algorithm recorded NSE values as high as 5. The addition of dual-polarized technology was shown to estimate quantitative precipitation estimates (QPEs) better than the conventional equation. The analyses further our understanding of the strengths and limitations of the Next Generation Radar (NEXRAD) system overall and from a seasonal perspective.

Assessing the Applicability of Random Forest, Stochastic Gradient Boosted Model, and Extreme Learning Machine Methods to the Quantitative Precipitation Estimation of the Radar Data: A Case Study to Gwangdeoksan Radar, South Korea, in 2018

“…In particular, a combination of Z and DR for input variables of the ML-based models leads to a good QPE performance. Studies have reported that this combination leads to the best performance among combinations of Z, DR, and KD for the ZR relationship-based model [46,47]. In many cases, application of DR, except for combinations of DR and KD, lead to a large improvement in the QPE using the ML-based model, unlike the ZR relationship-based model.…”

The Role of Weather Radar in Rainfall Estimation and Its Application in Meteorological and Hydrological Modelling—A Review

“…Various precipitation estimators, most often in the form of R(Z H ), R(Z H , Z DR ), R(K DP ), and R(K DP , Z DR ), are proposed for dual-pol C-band and S-band weather radars [54][55][56]. Algorithms used for radar rainfall estimation are based on different combinations of the above-listed products.…”

Sensors for rain measurements

“…Whereas traditional Z-R conversion is well documented in numerous applications of radar, there are recent advances in the application of dual-polarized radars which enable more accurate QPE assessment using polarimetric parameters (e.g. Anagnostou & Anagnostou, 2008;Anagnostou et al, 2004;Bringi & Chandrasekar, 2001;Bringi et al, 2011;Li, 2020;Mishra et al, 2016;Ochoa-Rodriguez et al, 2019;Scarchilli et al, 1993;Simpson & Fox, 2018). Polarization of a radar signal characterizes the orientation of the electric field (both transmitted and received).…”