A Robust Error-Based Rain Estimation Method for Polarimetric Radar. Part II: Case Study

Pepler, Acacia; May, Peter T.

doi:10.1175/jamc-d-11-0159.1

Cited by 8 publications

(5 citation statements)

References 20 publications

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“…The data accuracy of CP2 has been verified through extensive comparisons of radar-based rainfall estimates with rain gauge and video disdrometer measurements (Pepler and May 2012), as well as with a recent calibration ), and surface temperatures (also in 8C, red solid contours) at 0100 UTC 16 Nov 2008 across QLD. The observed 500-hPa winds at radiosonde locations are overlaid using larger red wind barbs (otherwise following the same notation as for the surface winds).…”

Section: Methodsmentioning

confidence: 87%

Analysis of a Destructive Wind Storm on 16 November 2008 in Brisbane, Australia

Richter

Peter

Collis

2014

Monthly Weather Review

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During the late afternoon on 16 November 2008 the Brisbane (Queensland, Australia) suburb of ''The Gap'' experienced extensive wind damage caused by an intense local thunderstorm. The CP2 research radar nearby detected near-surface radial velocities exceeding 43 m s 21 above The Gap while hail size reports did not exceed golf ball size, and no tornadoes were reported. The storm environment was characterized by a layer of very moist near-surface air and strong storm-relative low-level flow, whereas the storm-relative winds aloft were weak. While the thermodynamic storm environment contained a range of downdraft-promoting ingredients such as a ;4-km-high melting level above a ;2-km-deep layer with nearly dry-adiabatic lapse rates mostly collocated with dry ambient air, a ;1-km-deep stable layer near the ground would generally lower expectations of destructive surface winds based on the downburst mechanism. Once observed reflectivities exceed 70 dBZ, downdraft cooling due to hail melting and downdraft acceleration based on hail loading are found to likely become nonnegligible forcing mechanisms. The event featured the close proximity of a hydrostatically and dynamically driven mesohigh at the base of the downdraft to a dynamically driven mesolow associated with a low-level circulation. This proximity was instrumental in the anisotropic horizontal acceleration of the nearground outflow and the ultimate strength of the Gap storm surface winds. Weak storm-relative midlevel winds are speculated to have allowed the downdraft to descend close to the low-level circulation, which set up this strong horizontal perturbation pressure gradient.

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Section: Methodsmentioning

confidence: 87%

Analysis of a Destructive Wind Storm on 16 November 2008 in Brisbane, Australia

Richter

Peter

Collis

2014

Monthly Weather Review

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“…These dual-polarization phase measurements are immune to partial attenuation in rain, radar miscalibration, and partial beam blockages (e.g., Park et al 2005b;Pepler and May 2012;Vulpiani et al 2012;Thurai et al 2012). Previous studies using shorter-wavelength radar have demonstrated the practical advantages of these radars for rainfall estimates within light-to-moderate rain and in the Oklahoma environment (e.g., Matrosov et al 2006;Wang and Chandrasekar 2010;Borowska et al 2011).…”

Section: Introductionmentioning

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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“…The installed X-band dual-polarimetric radar generates dual-polarization variables (e.g., unfiltered reflectivity DZ, corrected reflectivity CZ, reflectivity at horizontal polarization transmit Z h , differential reflectivity Z DR , differential phase ψ dp , specific differential phase K DP , and correlation coefficient ρ hv ). This study used the Colorado State University (CSU) algorithm [19] for rainfall estimation among several radar rainfall estimation algorithms [20][21][22]. In the CSU-Hydrometeor Identification Rainfall Optimization (CSU-HIDRO) algorithm, precipitation echo is classified into snowfall, rainfall, and mixed particles through fuzzy logic in the hydrometeor classification process.…”

Section: Methodology 221 Precipitation Estimation Algorithm Using X-b...mentioning

confidence: 99%

“…To calculate the spatially distributed precipitation of each rainfall estimation technique, Barnes objective analysis was performed on the data from the automatic weather station of KMA and the Ministry of Environment's telemetry data. The Korea Meteorological Administration (KMA) have used the Barnes scheme previously to draw a spatial distribution map of ground information for real-time analysis [21,22]. The distributed rainfall from ground rain gauges cannot be considered the actual rainfall distribution field.…”

Section: Quantitative Precipitation Estimationmentioning

confidence: 99%

Analyzing the Application of X-Band Radar for Improving Rainfall Observation and Flood Forecasting in Yeongdong, South Korea

Yoon

Lim

2021

Remote Sensing

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The mountainous Yeongdong region of South Korea contains mountains over 1 km. Owing to this topographic blockage, the region has a low-density rain-gauge network, and there is a low-altitude (~1.5 km) observation gap with the nearest large S-band radar. The Korean government installed an X-band dual-polarization radar in 2019 to improve rainfall observations and to prevent hydrological disasters in the Yeongdong region. The present study analyzed rainfall estimates using the newly installed X-band radar to evaluate its hydrological applicability. The rainfall was estimated using a distributed specific differential phase-based technique for a high-resolution 75 m grid. Comparison of the rainfall estimates of the X-band radar and the existing rainfall information showed that the X-band radar was less likely to underestimate rainfall compared to the S-band radar. The accuracy was particularly high within a 10 km observation radius. To evaluate the hydrological applicability of X-band radar rainfall estimates, this study developed a rain-based flood forecasting method—the flow nomograph—for the Samcheok-osib stream, which is vulnerable to heavy rain and resultant floods. This graph represents the flood risk level determined by hydrological–hydraulic modeling with various rainfall scenarios. Rainfall information (X-band radar, S-band radar, ground rain gauge) was applied as input to the flow nomograph to predict the flood level of the stream. Only the X-band radar could accurately predict the actual high-risk increase in the water level for all studied rainfall events.

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A Robust Error-Based Rain Estimation Method for Polarimetric Radar. Part II: Case Study

Cited by 8 publications

References 20 publications

Analysis of a Destructive Wind Storm on 16 November 2008 in Brisbane, Australia

Analysis of a Destructive Wind Storm on 16 November 2008 in Brisbane, Australia

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

Analyzing the Application of X-Band Radar for Improving Rainfall Observation and Flood Forecasting in Yeongdong, South Korea

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