Radar calibration by gage, disdrometer, and polarimetry: Theoretical limit caused by the variability of drop size distribution and application to fast scanning operational radar data

Lee, GyuWon; Zawadzki, Isztar

doi:10.1016/j.jhydrol.2005.11.046

Cited by 56 publications

(50 citation statements)

References 32 publications

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“…The calibration bias is calculated from the comparison of measured Φ DP_obs with calculated Φ DP_cal derived from Z H using the Z H -K DP self-consistency relationship as following procedure (Lee and Zawadzki 2006).…”

Section: Calibration Of Radar Reflectivitymentioning

confidence: 99%

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Rainfall Estimation from an Operational S-Band Dual-Polarization Radar: Effect of Radar Calibration

Kwon

Lee

Kim

2015

Journal of the Meteorological Society of Japan

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The calibration biases of reflectivity (Z H ) and differential reflectivity (Z DR ) from an operational Mt. Bisl S-band dual polarization radar are derived to improve the accuracy of rainfall estimation. The effect of radar calibration in rain estimation is examined by using data from the dense rain gauge network.The calibration biases of Z H are calculated by using the self-consistency constraint between Z H and specific differential phase shift (K DP ). This procedure is performed every 2.5 min. The biases are varied from −3.3 dB to 0.8 dB during the period between July 2010 to October 2011. The Z DR calibration biases are obtained by two methods: 1) vertically pointing measurements, and 2) comparison of observed data with the average Z H -Z DR relationship derived from disdrometric data. The Z DR biases are varied from 0.25 dB to 0.7 dB and both methods show similar results. This Z H -Z DR technique can be applied for a volume scan and does not require a special scan.

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Section: Calibration Of Radar Reflectivitymentioning

confidence: 99%

“…A more thorough procedure is shown in Lee and Zawadzki (2006). The rain region is selected by using the threshold of ρ HV and of the standard deviation of Z H , Z DR , Φ DP , and ρ HV at each PPI.…”

Section: Calibration Of Radar Reflectivitymentioning

confidence: 99%

Rainfall Estimation from an Operational S-Band Dual-Polarization Radar: Effect of Radar Calibration

Kwon

Lee

Kim

2015

Journal of the Meteorological Society of Japan

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“…In comparisons between radar measurements and Lidar, simple regression also shows a good fit [32]. For calibration of polarimetric radar by rain gauge and Disdrometer based on daily time, power regression showed the best fit according to Lee and Zawadzki (2006). Moreover, other types of regression models to compare other rainfall measurement instruments such as variety of Disderometer showed acceptable results [34].…”

Section: A Applications Of the Regression Approachmentioning

confidence: 91%

Advanced of Mathematics-Statistics Methods to Radar Calibration for Rainfall Estimation; A Review

Nikahd¹

2015

IJRITCC

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Abstract-Ground-based radar is known as one of the most important systems for precipitation measurement at high spatial and temporal resolutions. Radar data are recorded in digital manner and readily ingested to any statistical analyses. These measurements are subjected to specific calibration to eliminate systematic errors as well as minimizing the random errors, respectively. Since statistical methods are based on mathematics, they offer more precise results and easy interpretation with lower data detail. Although they have challenge to interpret due to their mathematical structure, but the accuracy of the conclusions and the interpretation of the output are appropriate. This article reviews the advanced methods in using the calibration of ground-based radar for forecasting meteorological events include two aspects: statistical techniques and data mining. Statistical techniques refer to empirical analyses such as regression, while data mining includes the Artificial Neural Network (ANN), data Kriging, Nearest Neighbour (NN), Decision Tree (DT) and fuzzy logic. The results show that Kriging is more applicable for interpolation. Regression methods are simple to use and data mining based on Artificial Intelligence is very precise. Thus, this review explores the characteristics of the statistical parameters in the field of radar applications and shows which parameters give the best results for undefined cases.

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“…The distance between two POSSs are about 16 km and these disdrometers are about 31 km and 25 km apart from the St-Remi. The POSS1 is used to calculate the proper R-Z relationship and POSS2 to calibrate the McGill S-band radar by the calibration method of Lee and Zawadzki (2006). The calibration bias of 2.5 dB is obtained and applied.…”

Section: Datamentioning

confidence: 99%

Effect of R-Z Relationships Derived from Disdrometer Data on Radar Rainfall Estimation during the Heavy Rain Event on 5 July 2005

Lee¹,

Kwon²

2012

Journal of the Korean earth science society

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The R-Z relationship is one of important error factors to determine the accuracy of radar rainfall estimation. In this study, we have explored the effect of the R-Z relationships derived from disdrometer data in estimating the radar rainfall. The heavy rain event that produced flooding in St-Remi, Quebec, Canada has been occurred. We have tried to investigate the severity of rain for this event using high temporal (2.5 min) and spatial resolution (1 o by 250 m) radar data obtained from the McGill S-band radar. Radar data revealed that the heavy rain cells pass directly over St-Remi while the coarse raingauge network was not sufficient to detect this rain event. The maximum 30 min (1 h) accumulation reaches about 39 (42) mm in St-Remi. During the rain event, the two disdrometers (POSS; Precipitation Occurrence Sensor System) were available: One used for the reflectivity calibration by comparing disdrometer Z and radar Z and the other for deriving disdrometric R-Z relationships. The result shows the significant improvement with the disdrometric reflectivity-dependent R-Z relationships against the climatological R-Z relationship. The bias in radar rain estimation is reduced from +12% to −2% and the root-mean squared error from 16 to 10% for daily accumulation. Using the estimated radar rainfall rate with disdrometric R-Z relationships, the flood event was well captured with proper timing and amount.

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Radar calibration by gage, disdrometer, and polarimetry: Theoretical limit caused by the variability of drop size distribution and application to fast scanning operational radar data

Cited by 56 publications

References 32 publications

Rainfall Estimation from an Operational S-Band Dual-Polarization Radar: Effect of Radar Calibration

Rainfall Estimation from an Operational S-Band Dual-Polarization Radar: Effect of Radar Calibration

Advanced of Mathematics-Statistics Methods to Radar Calibration for Rainfall Estimation; A Review

Effect of R-Z Relationships Derived from Disdrometer Data on Radar Rainfall Estimation during the Heavy Rain Event on 5 July 2005

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