An Early Performance Evaluation of the NEXRAD Dual-Polarization Radar Rainfall Estimates for Urban Flood Applications

Cunha, L.; Smith, James A.; Baeck, Mary Lynn; Krajewski, Witold F.

doi:10.1175/waf-d-13-00046.1

Cited by 32 publications

(24 citation statements)

References 51 publications

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“…The decision to use the operational rainfall product rather than reprocessing KVNX datasets was motivated by the fact that level-III products were readily accessible and are familiar to the wider weather and climate community. Because the accuracy of operational dualpolarization radar products was relatively unproven in the literature, there was additional benefit to testing these products separate from research-level counterparts (e.g., Cunha et al 2013). We note that the KVNX differential-phase-processing methods were not as rigorously inspected as those implemented on the ARM datasets.…”

Section: B Kvnx Wsr-88d Datasetmentioning

confidence: 99%

Precipitation Estimation from the ARM Distributed Radar Network during the MC3E Campaign

Giangrande

Collis

Theisen

et al. 2014

Journal of Applied Meteorology and Climatology

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This study presents radar-based precipitation estimates collected during the 2-month U.S. Department of Energy Atmospheric Radiation Measurement Program (ARM)-NASA Midlatitude Continental Convective Clouds Experiment (MC3E). Emphasis is on the usefulness of radar observations from the C-band and X-band scanning ARM precipitation radars (CSAPR and XSAPR, respectively) for rainfall estimation products to distances within 100 km of the Lamont, Oklahoma, ARM facility. The study utilizes a dense collection of collocated ARM, NASA Global Precipitation Measurement, and nearby surface Oklahoma Mesonet gauge records to evaluate radar-based hourly rainfall products and campaign-optimized methods over individual gauges and for areal rainfall characterizations. Rainfall products are also evaluated against the performance of a regional NWS Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band dual-polarization radar product. Results indicate that the CSAPR system may achieve similar point-and areal-gauge bias and rootmean-square (RMS) error performance to a WSR-88D reference for the variety of MC3E deep convective events sampled. The best campaign rainfall performance was achieved when using radar relations capitalizing on estimates of the specific attenuation from the CSAPR system. The XSAPRs demonstrate limited capabilities, having modest success in comparison with the WSR-88D reference for hourly rainfall accumulations that are under 10 mm. All rainfall estimation methods exhibit a reduction by a factor of 1.5-2.5 in RMS errors for areal accumulations over a 15-km 2 NASA dense gauge network, with the smallest errors typically associated with dual-polarization radar methods.

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Section: B Kvnx Wsr-88d Datasetmentioning

confidence: 99%

Precipitation Estimation from the ARM Distributed Radar Network during the MC3E Campaign

Giangrande

Collis

Theisen

et al. 2014

Journal of Applied Meteorology and Climatology

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“…The range of NSE values were largest at KEAX, while the spread was relatively small for KLSX and KSGF. In spite of this, the overall spread of the performance of the 12 KDP algorithms varied greatly (average of 2 NSE units), exhibiting the sensitivity of KDP estimates on QPE (Ryzhkov et al, 2005;Cunha et al, 2013). In general, the NSSL-derived R(KDP) equations (i.e., Eqs.…”

Section: Overall Algorithm Performancementioning

confidence: 99%

“…The potential benefits of this upgrade were investigated by the National Severe Storms Laboratory (NSSL) and the Cooperative Institute for Mesoscale Meteorological Studies. These advantages include, but are not limited to, (1) significant improvement in radar rainfall estimation (Ryzhkov et al, 2005;Gourley et al, 2010) through better representation of precipitation shape (Brandes et al, 2002;Gorgucci et al, 2000Gorgucci et al, , 2006Berne and Uijlenhoet, 2005); (2) discrimination between solid and liquid precipitation (Zrnic and Ryzhkov, 1996), allowing for better distinction between areas of heavy rain and hail (Park et al, 2009;Giangrande and Ryzhkov, 2008;Cunha et al, 2013); (3) identifying the melting layer position in the radar field (Straka et al, 2000;Park et al, 2009); (4) hardware calibration (Holleman et al, 2010;Hubbert et al, 2017); and (5) calculating drop-size distributions retrieved from measurements of reflectivity (Z), differential reflectivity (ZDR), and specific differential phase shift (KDP) as opposed to using ground-based point-located disdrometers (Zhang et al, 2001;Brandes et al, 2004;Anagnostou et al, 2008). Rain-rate retrieval by weather radars is an estimation based upon the dielectric properties of the hydrometeors encountered in the atmosphere.…”

Section: Introductionmentioning

confidence: 99%

“…However, many of these studies have looked at a small sample of rain events (on the order of 10-50 h) (Kitchen and Jackson, 1993;Smith et al, 1996;Ryzhkov et al, 2003;Gourley et al, 2010;Cunha et al, 2013). Long-term performances of weather radar are becoming more common in recent years as the availability of data becomes more abundant (e.g., Haylock et al, 2008;Goudenhoofdt andDelobbe, 2012, 2016;Fairman et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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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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“…Additionally, rainfall estimates contingent on hydrometeor classification, for example, Giangrande and Ryzhkov (2008) and Park et al (2009), are used for QPE in the WSR-88D system. Recent assessment of the NEXRAD DP-QPE algorithms has been studied by Cocks et al (2012) and Cunha et al (2013). Recent assessment of the NEXRAD DP-QPE algorithms has been studied by Cocks et al (2012) and Cunha et al (2013).…”

Section: B S-band Radarmentioning

confidence: 99%

Quantitative Precipitation Estimation from a C-Band Dual-Polarized Radar for the 8 July 2013 Flood in Toronto, Canada

Boodoo

Hudak

Ryzhkov

et al. 2015

Journal of Hydrometeorology

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A heavy rainfall event over a 2-h period on 8 July 2013 caused significant flash flooding in the city of Toronto and produced 126 mm of rain accumulation at a gauge located near the Toronto Pearson International Airport. This paper evaluates the quantitative precipitation estimates from the nearby King City C-band dualpolarized radar (WKR). Horizontal reflectivity Z and differential reflectivity Z DR were corrected for attenuation using a modified ZPHI rain profiling algorithm, and rain rates R were calculated from R(Z) and R(Z, Z DR ) algorithms. Specific differential phase K DP was used to compute rain rates from three R(K DP ) algorithms, one modified to use positive and negative K DP , and an R(K DP , Z DR ) algorithm. Additionally, specific attenuation at horizontal polarization A was used to calculate rates from the R(A) algorithm. Hightemporal-resolution rain gauge data at 44 locations measured the surface rainfall every 5 min and produced total rainfall accumulations over the affected area. The nearby NEXRAD S-band dual-polarized radar at Buffalo, New York, provided rain-rate and storm accumulation estimates from R(Z) and S-band dualpolarimetric algorithm. These two datasets were used as references to evaluate the C-band estimates. Significant radome attenuation at WKR overshadowed the attenuation correction techniques and resulted in poor rainfall estimates from the R(Z) and R(Z, Z DR ) algorithms. Rainfall estimation from the Brandes et al. R(K DP ) and R(A) algorithms were superior to the other methods, and the derived storm total accumulation gave biases of 2.1 and 26.1 mm, respectively, with correlations of 0.94. The C-band estimates from the Brandes et al. R(K DP ) and R(A) algorithms were comparable to the NEXRAD S-band estimates.

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An Early Performance Evaluation of the NEXRAD Dual-Polarization Radar Rainfall Estimates for Urban Flood Applications

Cited by 32 publications

References 51 publications

Precipitation Estimation from the ARM Distributed Radar Network during the MC3E Campaign

Precipitation Estimation from the ARM Distributed Radar Network during the MC3E Campaign

Dual-polarized quantitative precipitation estimation as a function of range

Quantitative Precipitation Estimation from a C-Band Dual-Polarized Radar for the 8 July 2013 Flood in Toronto, Canada

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