Evaluation of the gap filler radar as an implementation of the 1.5 km <scp>CAPPI</scp> data in Korea

Yoo, Chulsang; Yoon, Jin Gu; Kim, Jungho; Ro, Yonghun

doi:10.1002/met.1531

Cited by 18 publications

(3 citation statements)

References 14 publications

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“…Secondly, quantitative precipitation estimation (QPE) is generated by converting the radar reflectivity (Z) to rainfall (R) based on the Marshall-Palmer (M-P) equation, Z = 200R 1.6 (Yoo et al, 2016). From the QPE, the rainfall field QPF is separated using image filtering, the movement of small-scale rainfall fields is forecasted by a storm tracker nowcasting model (STNM), the advection vector of large-scale rainfall fields is estimated by a translation model (TM), and then each forecasted field is composited (Yoon, 2019).…”

Section: B Radar-based Qpfmentioning

confidence: 99%

A novel hybrid model for hourly streamflow and water level prediction from radar reflectivity using deep learning approaches

Dinh,

Phung,

Kwon

et al. 2023

Preprint

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Urban flooding has become an important challenge for metropolitan areas; thus, reliable water level and streamflow predictive models are crucial to flood control and planning. In this study, we develop a hybrid model, namely SGGP, for hourly water level and streamflow predictions in the Jungrang urban basin, located on the Han River, South Korea. This model includes two sub-models in which the first model is established for producing three-hour mean areal precipitation (MAP) from quantitative precipitation forecasts (QPFs) based on the Spatial-scale Decomposition method (SCDM) using Gate Recurrent Units (GRU), and the second model is utilized to predict hourly-ahead water level and streamflow by integrating a GRU with a particle swarm optimization (PSO) algorithm. The radar data, rainfall, water level, and streamflow data were collected from 2008 to 2022, and are used to establish and evaluate the performance of the model. The SGGP model is evaluated using root mean square error (RMSE), correlation coefficient (CC), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and mean absolute percentage error (MAPE) in comparison with four other deep learning models. The results show that the proposed SGGP model achieves accurate results in multistep-ahead water level and streamflow predictions.

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Section: B Radar-based Qpfmentioning

confidence: 99%

A novel hybrid model for hourly streamflow and water level prediction from radar reflectivity using deep learning approaches

Dinh,

Phung,

Kwon

et al. 2023

Preprint

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show abstract

“…In order to validate the empirical method based on the WRC's relations, three radar rainfall estimation algorithms are employed in this study, as summarized in the Table 1. Algorithm R1 is considered to be the method most commonly used by hydrologists and KMA in Korea (Yoo et al, 2016). It is a conventional estimation algorithm (Marshall and Palmer, 1948).…”

Section: Steps 4 and 5: 10mentioning

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 order to validate the empirical method based on the WRC"s relations, six radar rainfall estimation algorithms were hired for this study, as summarized in the Table 4. The algorithm, R1, is considered as most popularly used method by hydrologist and KMA in Korea (Yoo et al, 2016). It is a conventional estimation algorithm (Marshall and Palmer, 1948).…”

Section: Radar Rainfall Estimation and Assessment Of Accuracymentioning

confidence: 99%

An empirical method to improve rainfall estimation of dual polarization radar using ground measurements

Yoon

Suk

Nam

et al. 2016

Preprint

Self Cite

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Abstract. This study presents an easy and convenient empirical method to optimize polarimetric variables and produce more accurate dual polarization radar rainfall estimation. Weather Radar Center (WRC) in Korea Meteorological Administration (KMA) suggested relations between polarimetric variables (Z -Z DR and Z -K DP ) based on a 2-D Video Distrometer (2DVD) measurements in 2014. Observed polarimetric variables from CAPPI (Constant Altitude Plan Position Indicator) images 10 composed at 1.5 km of height were adjusted using the WRC"s relations. Then dual polarization radar rainfalls were estimated by six different radar rainfall estimation algorithms, which are using either Z, Z and Z DR , or Z, Z DR and K DP . Accuracy of radar rainfall estimations derived by the six algorithms using the adjusted variables was assessed through comparison with raingauge observations. As a result, the accuracy of the radar rainfall estimation using adjusted polarimetric variables has improved from 50 % to 70 % approximately. Three high rainfall events with more than 40 mm of maximum hourly rainfall 15 were shown the best accuracy on the rainfall estimation derived by using Z, Z DR and K DP . Meanwhile stratiform event was gained better radar rainfalls estimated by algorithms using Z and Z DR .

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Evaluation of the gap filler radar as an implementation of the 1.5 km CAPPI data in Korea

Cited by 18 publications

References 14 publications

A novel hybrid model for hourly streamflow and water level prediction from radar reflectivity using deep learning approaches

A novel hybrid model for hourly streamflow and water level prediction from radar reflectivity using deep learning approaches

An empirical QPE method based on polarimetric variable adjustments

An empirical method to improve rainfall estimation of dual polarization radar using ground measurements

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