A New Dual-Polarization Radar Rainfall Algorithm: Application in Colorado Precipitation Events

Cifelli, Robert; Chandrasekar, V.; Lim, Sanghun; Kennedy, Patrick C.; Wang, Y.; Rutledge, Steven A.

doi:10.1175/2010jtecha1488.1

Cited by 181 publications

(119 citation statements)

References 36 publications

(38 reference statements)

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“…The lowest NSE values were, typically, closest to each of the radars (between 0.4 and 0.8), with the notable exception of the closest gauge to KSGF. In general, the RREC performed worst at the largest of ranges, potentially due to the algorithm's ability to incorrectly assess the hydrometeors present (Cifelli et al, 2010;Yang et al, 2016). Additionally, the poor performance by the R(DSMZ)-tropical equation is due to the lack of tropical precipitation within central Missouri.…”

Section: Overall Algorithm Performancementioning

confidence: 99%

Dual-polarized quantitative precipitation estimation as a function of range

Simpson

Fox

2018

Hydrol. Earth Syst. Sci.

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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.

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Section: Overall Algorithm Performancementioning

confidence: 99%

Dual-polarized quantitative precipitation estimation as a function of range

Simpson

Fox

2018

Hydrol. Earth Syst. Sci.

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“…15 Algorithm R2 was developed over 22 summer storm events by WRC (2014) as mentioned in Step 1. Algorithm R3 is the CSU-ICE algorithm (Cifelli et al, 2011). Algorithm R3 itself consists of four different algorithms, R(Z), R(Z, Z DR ), R(K DP , Z DR ), and R(K DP ) for the given ranges of the three polarimetric variables.…”

Section: Steps 4 and 5: 10mentioning

confidence: 99%

“…Ryzhkov and Zrnić (1995a) found that algorithms using Z DR and K DP were better than other algorithms. The Colorado State University (CSU) ICE algorithm and Joint POLarization Experiment (JPOLE) algorithm are synthesized methods that selectively use Z, Z DR , and K DP with respect to the range of polarimetric variables or rain rate (Ryzhkov et al, 2003;Cifelli et al, 2011). 15…”

Section: Introduction 20mentioning

confidence: 99%

An empirical QPE method based on polarimetric variable adjustments

Yoon

Park

Kim

et al. 2017

Preprint

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Abstract. This study presents an empirical method for optimizing polarimetric variables in order to improve the accuracy of 5 dual-polarization radar rainfall estimation using data derived from radars operated by different agencies. The empirical method was developed using the Yong-In Testbed (YIT) radar operated by the Korea Meteorological Administration (KMA).The method is based on the determination of relations between polarimetric variables. Relations for Z − Z DR and Z − K DP are derived from the measurements of a two-dimensional video disdrometer installed about 30 km away from the YIT radar.These relations were used to adjust the polarimetric variables of the dual-polarization constant altitude plan position 10 indicator (CAPPI) at a height of 1.5 km. The CAPPI data with the adjusted polarimetric variables were used to estimate rainfalls using three different radar rainfall estimation algorithms. The first algorithm is based on Z, the second on Z and Z DR , and the third on Z, Z DR , and K DP . The accuracy of the radar-estimated rainfall was then assessed using raingauge observations. Three rainfall events with more than 40 mm of maximum hourly rainfall were shown to have the best estimation when the method using Z, Z DR , and K DP was used. However, stratiform precipitation events were better estimated by the algorithm 15 using Z and Z DR . The method was also applied to the data of three radars that belong to KMA and the Ministry of Land, Infrastructure, and Transport. The evaluation was done for six months (May-October) in 2015. The results show an improvement in radar rainfall estimation accuracy for stratiform, frontal, and convective precipitation from approximately 50 % to 70 %.

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“…In particular, dual-polarization radar that provides information on the reflectivity (Z H ), differential reflectivity (Z DR ), differential phase ( DP ), specific differential phase (K DP ), and cross-correlation coefficient (ρ hv ) can distinguish precipitation types by means of backscatter and the differential propagation phase of hydrometeors. Dual-polarization radar can also be used to obtain more information about the raindrop size distribution (DSD), and this in turn can help to reduce the impact of DSD variability on rainfall estimations (Cifelli et al, 2011). The DSD can be characterized by parameters such as the diameter, concentration, orientation, and shape.…”

Section: Introductionmentioning

confidence: 99%

Dual-polarization radar rainfall estimation in Korea according to raindrop shapes obtained by using a 2-D video disdrometer

et al. 2016

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Abstract. Polarimetric measurements are sensitive to the sizes, concentrations, orientations, and shapes of raindrops. Thus, rainfall rates calculated from polarimetric radar are influenced by the raindrop shapes and canting. The mean raindrop shape can be obtained from long-term raindrop size distribution (DSD) observations, and the shapes of raindrops can play an important role in polarimetric rainfall algorithms based on differential reflectivity (Z DR ) and specific differential phase (K DP ). However, the mean raindrop shape is associated with the variation of the DSD, which can change depending on precipitation types and climatic regimes. Furthermore, these relationships have not been studied extensively on the Korean Peninsula. In this study, we present a method to find optimal polarimetric rainfall algorithms for the Korean Peninsula by using data provided by both a twodimensional video disdrometer (2DVD) and the Bislsan Sband dual-polarization radar. First, a new axis-ratio relation was developed to improve radar rainfall estimations. Second, polarimetric rainfall algorithms were derived by using different axis-ratio relations. The rain gauge data were used to represent the ground truth situation, and the estimated radar-point hourly mean rain rates obtained from the different polarimetric rainfall algorithms were compared with the hourly rain rates measured by a rain gauge. The daily calibration biases of horizontal reflectivity (Z H ) and differential reflectivity (Z DR ) were calculated by comparing Z H and Z DR radar measurements with the same parameters simulated by the 2DVD. Overall, the derived new axis ratio was similar to the existing axis ratio except for both small particles (≤ 2 mm) and large particles (≥ 5.5 mm). The shapes of raindrops obtained by the new axis-ratio relation carried out with the 2DVD were more oblate than the shapes obtained by the existing relations. The combined polarimetric rainfall relations using Z DR and K DP were more efficient than the single-parameter rainfall relation for estimated 2DVD rainfall; however, the R(Z H , Z DR ) algorithm showed the best performance for radar rainfall estimations, because the rainfall events used in the analysis consisted mainly of weak precipitation and K DP is relatively noisy at lower rain rates (≤ 10 mm h −1 ). Some of the polarimetric rainfall algorithms can be further improved by new axis-ratio relations.

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A New Dual-Polarization Radar Rainfall Algorithm: Application in Colorado Precipitation Events

Cited by 181 publications

References 36 publications

Dual-polarized quantitative precipitation estimation as a function of range

Dual-polarized quantitative precipitation estimation as a function of range

An empirical QPE method based on polarimetric variable adjustments

Dual-polarization radar rainfall estimation in Korea according to raindrop shapes obtained by using a 2-D video disdrometer

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