Search

Hail is generated within strong convective cells. In particular, hail from strong convective cells that develop rapidly can occur within 30 minutes. In that case, hail is difficult to be predicted and can cause a lot of damage in many part such as agriculture, aviation fields, and human life, etc. Pre-signal detection of developing hail will contribute to predicting hail and reducing damage. Therefore, Weather Radar Center of KMA (Korea Meteorological Administration) analyzed the characteristics of hail and developed an algorithm to detect hail pre-signal using the results of analysis.In this study, 3-dimensional gridded radar reflectivity and temperature data were used to detect hail pre-signals. The results of pre-signal detection have 2 types of areas: “hail possible” and “hail warning”. The “hail possible” area represents the area where hail can develop before hail exists in the upper atmosphere. The “hail warning” area represents the area with a possibility of hail size of 2cm or larger. And the “hail warning” area is included in the “hail possible” area. For signal classification, 3-dimensional data based VIL (Vertically Integrated Liquid), UVIL (Upper-level VIL, -10~-40 degrees Celsius), 35 dBZ Echo top, freezing level height, and hail ground observation information such as hail size, observed time and location were used. Hail ground observation cases for 5 years were analyzed to determine thresholds for signal classification variables and verify this algorithm.As a result of analyzing hail cases observed on 78 days, at 231 sites through this algorithm, all cases but 4 days were detected (ACC=0.81, POFD=0.22), and the hail signals were detected in advance of an average of about 40 minutes. And all 18 cases in which hail with a size of 2 cm or larger was observed were detected as “hail warning” signals. Comparing with the severe hail indices (SHI, POSH, MEHS) of previous studies, the “hail warning” areas in most of the cases were similar. ACKNOWLEDGEMENTSThis research was supported by “Development of integrated radar analysis and customized radar technology (KMA2021-03021)” of “Development of integrated application technology for Korea weather radar” project funded by the Weather Radar Center, Korea Meteorological Administration.

There are many potential sources of the biases in the radar rainfall estimation process. This study classified the biases from the rainfall estimation process into the reflectivity measurement bias and the rainfall estimation bias by the Quantitative Precipitation Estimation (QPE) model and also conducted the bias correction methods to improve the accuracy of the Radar-AWS Rainrate (RAR) calculation system operated by the Korea Meteorological Administration (KMA). In the Z bias correction for the reflectivity biases occurred by measuring the rainfalls, this study utilized the bias correction algorithm. The concept of this algorithm is that the reflectivity of the target single-pol radars is corrected based on the reference dual-pol radar corrected in the hardware and software bias. This study, and then, dealt with two post-process methods, the Mean Field Bias Correction (MFBC) method and the Local Gauge Correction method (LGC), to correct the rainfall estimation bias by the QPE model. The Z bias and rainfall estimation bias correction methods were applied to the RAR system. The accuracy of the RAR system was improved after correcting Z bias. For the rainfall types, although the accuracy of the Changma front and the local torrential cases was slightly improved without the Z bias correction the accuracy of the typhoon cases got worse than the existing results in particular. As a result of the rainfall estimation bias correction, the Z bias_LGC was especially superior to the MFBC method because the different rainfall biases were applied to each grid rainfall amount in the LGC method. For the rainfall types, the results of the Z bias_LGC showed that the rainfall estimates for all types was more accurate than only the Z bias and, especially, the outcomes in the typhoon cases was vastly superior to the others.

This study presents an empirical method for optimizing polarimetric variables in order to improve the accuracy of 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 – ZDR and Z – KDP 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 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 ZDR, and the third on Z, ZDR, and KDP. 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, ZDR, and KDP was used. However, stratiform precipitation events were better estimated by the algorithm using Z and ZDR. 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 %.

Although the Radar-AWS Rainrate (RAR) calculation system operated by Korea Meteorological Administration estimated precipitation using 2-dimensional composite components of single polarization radars, this system has several limitations in estimating the precipitation accurately. To to overcome limitations of the RAR system, the Korea Meteorological Administration developed and operated the RMQ (Radar-based Multi-sensor Quantitative Precipitation Estimation) system, the improved version of NMQ (National Mosaic and Multi-sensor Quantitative Precipitation Estimation) system of NSSL (National Severe Storms Laboratory) for the Korean Peninsula. This study introduced the RMQ system domestically for the first time and verified the precipitation estimation performance of the RMQ system. The RMQ system consists of 4 main parts as the process of handling the single radar data, merging 3D reflectivity, QPE, and displaying result images. The first process (handling of the single radar data) has the pre-process of a radar data (transformation of data format and quality control), the production of a vertical profile of reflectivity and the correction of bright-band, and the conduction of hydrid scan reflectivity. The next process (merger of 3D reflectivity) produces the 3D composite reflectivity field after correcting the quality controlled single radar reflectivity. The QPE process classifies the precipitation types using multi-sensor information and estimates quantitative precipitation using several Z-R relationships which are proper for precipitation types. This process also corrects the precipitation using the AWS position with local gauge correction technique. The last process displays the final results transformed into images in the web-site. This study also estimated the accuracy of the RMQ system with five events in 2012 summer season and compared the results of the RAR (Radar-AWS Rainrate) and RMQ systems. The RMQ system ( in RMSE on average) is superior to the RAR system ( in RMSE) and improved by 73.25% in RMSE and 25.56% in correlation coefficient on average. The precipitation composite field images produced by the RMQ system are almost identical to the AWS (Automatic Weather Statioin) images. Therefore, the RMQ system has contributed to improve the accuracy of precipitation estimation using weather radars and operation of the RMQ system in the work field in future enables to cope with the extreme weather conditions actively.

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–ZDR and Z–KDP) based on a 2-D Video Distrometer (2DVD) measurements in 2014. Observed polarimetric variables from CAPPI (Constant Altitude Plan Position Indicator) images composed at 1 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 ZDR, or Z, ZDR and KDP. 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 were shown the best accuracy on the rainfall estimation derived by using Z, ZDR and KDP. Meanwhile stratiform event was gained better radar rainfalls estimated by algorithms using Z and ZDR.

The shapes of raindrops play an important role in inducing polarimetric rainfall algorithms with differential reflectivity (ZDR) and specific differential phase (KDP). The shapes of raindrops have a direct impact on rainfall estimation. However, the characteristics of raindrop size distribution (DSD) are different depending on precipitation type, storm stage of development, and regional and climatological conditions. Therefore, it is necessary to provide assumptions based on raindrop shapes that reflect the rainfall characteristics of the Korean peninsula. In this study, we presented a method to find optimal polarimetric rainfall algorithms on the Korean peninsula using the 2-Dimensional Video Disdrometer (2DVD) and Bislsan S-Band dual-polarization radar. First, a new axis ratio of raindrop relations was developed for the improvement of rainfall estimation. Second, polarimetric rainfall algorithms were derived using different axis ratio relations, and estimated radar-point one-hour rain rate for the differences in polarimetric rainfall algorithms were compared with the hourly rain rate measured by gauge. In addition, radar rainfall estimation was investigated in relation to calibration bias of reflectivity and differential reflectivity. The derived raindrop axis ratio relation from the 2DVD was more oblate than existing relations in the D < 1.5 mm and D > 5.5 mm range. The R(KDP, ZDR) algorithm based on a new axis ratio relation showed the best result on DSD statistics; however, the R(Zh, ZDR) algorithm showed the best performance for radar rainfall estimation, because the rainfall events used in the analysis were mainly weak precipitation and KDP is noisy at lower rain rates ( ≤ 5 mm hr−1). Thus, the R(KDP, ZDR) algorithm is suitable for heavy rainfall and R(Zh, ZDR) algorithm is suited for light rainfall. The calibration bias of reflectivity (ZH) and differential reflectivity (ZDR) were calculated from the comparison of measured with simulated ZH and ZDR from the 2DVD. The calculated ZH and ZDR bias was used to reduce radar bias, and to produce more accurate rainfall estimation.

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 (ZDR) and specific differential phase (KDP). 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 two-dimensional video disdrometer (2DVD) and the Bislsan S-band 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 (ZH) and differential reflectivity (ZDR) were calculated by comparing ZH and ZDR 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 ZDR and KDP were more efficient than the single-parameter rainfall relation for estimated 2DVD rainfall; however, the R(ZH, ZDR) algorithm showed the best performance for radar rainfall estimations, because the rainfall events used in the analysis consisted mainly of weak precipitation and KDP 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.

Measurement of the background radio noise is very important process being used in survey of radio noise environment, calculating the threshold level for the frequency occupancy measurement and so forth. First step of background radio noise measurement is to select the sample sub-band which is mostly dominated by the background white Gaussian noise (WGN) within the target band. The second step is to carry out the main measurement of radio noise on this selected sample sub-band for the representative value of the noise power. In this paper, an algorithm for selection of sample sub-band for the effective background radio noise measurement using SVD is proposed under the assumption that background radio noise is WGN. The performance of the proposed algorithm is compared with that of the APD method which is widely used for the same purpose. Simulation results are shown to demonstrate the high performance of the proposed algorithm in comparison with the existing APD method.