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Radar data with high spatiotemporal resolution and automatic weather station (AWS) data are used in the data assimilation experiment to improve the precipitation forecast of a numerical model. The numerical model considered in this study is the Weather Research and Forecasting (WRF) model with double-moment 6-class microphysics scheme (WDM6). We calculated the radar equivalent reflectivity factor using high resolution WRF and compared it with radar observations in South Korea. To compare the precipitation forecast characteristics of the three-dimensional variational (3D-Var) assimilation of radar data, four experiments were performed based on the scales of precipitation systems. Comparison of the 24 h accumulated rainfall with surface observation data, contoured frequency by altitude diagram (CFAD), time–height cross sections (THCS), and vertical hydrometeor profiles was used to evaluate the accuracy of the simulation of precipitation. The model simulations were performed with and without 3D-VAR radar reflectivity, radial velocity and AWS assimilation for two mesoscale convective cases and two synoptic scale cases. The combined effect of the radar and AWS data assimilation experiment improved the location of the precipitation area and rainfall intensity compared to the control run. There is a noticeable scale dependence in the improvement of precipitation systems. Improvements in simulating mesoscale convective systems were larger compared to synoptically driven precipitation systems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The extended Kalman filter is an extended version of the Kalman filter for a non-linear problem. This study applies this extended Kalman filter to the real-time estimation of the parameters of the dual-pol radar rain rate estimator. The estimated parameters are also compared with those based on the least squares method. As an application example, this study considers four storm events observed by the Beaslesan radar in Korea. The findings derived include, first, that the parameters of the radar rain rate estimator obtained by the extended Kalman filter are totally different from those by the least squares method. In fact, the parameters obtained by the extended Kalman filter are found to be more reasonable, and are similar to those reported in previous studies. Second, the estimated rain rates based on the parameters obtained by the extended Kalman filter are found to be similar to those observed on the ground. Even though the parameters estimated by applying the least squares method are quite different from previous studies as well as those based on the extended Kalman filter, the resulting radar rain rate is found to be quite similar to that based on the extended Kalman filter. In conclusion, the extended Kalman filter can be a reliable method for real-time estimation of the parameters of the dual-pol radar rain rate estimator. The resulting rain rate is also found to be of sufficiently high quality to be applicable for other purposes, such as various flood warning systems. [ABSTRACT FROM AUTHOR]

A commercial aircraft, departing from Seoul to Jeju Island in South Korea, encountered a convectively induced turbulence (CIT) at about z = 2.2 km near Seoul on 28 October 2018. At this time, the observed radar reflectivity showed that the convective band with cloud tops of z = 6–7 km passed the CIT region with high values of spectral width (SW; larger than 4 m s–1). Using the 1 Hz wind data recorded by the aircraft, we estimated an objective intensity of the CIT as a cube root of eddy dissipation rate (EDR) based on the inertial range technique, which was about 0.33–0.37 m2/3 s−1. Radar-based EDR was also derived by lognormal mapping technique (LMT), showing that the EDR was about 0.3–0.35 m2/3 s−1 near the CIT location, which is consistent with in situ EDR. In addition, a feasibility of the CIT forecast was tested using the weather and research forecast (WRF) model with a 3 km horizontal grid spacing. The model accurately reproduced the convective band passing the CIT event with an hour delay, which allows the use of two methods to calculate EDR: The first is using both the sub-grid and resolved turbulent kinetic energy to infer the EDR; the second is using the LMT for converting absolute vertical velocity (and its combination with the Richardson number) to EDR-scale. As a result, we found that the model-based EDRs were about 0.3–0.4 m2/3 s−1 near the CIT event, which is consistent with the estimated EDRs from both aircraft and radar observations. [ABSTRACT FROM AUTHOR]

The cloud measurements for two years from the vertical pointing Ka-band cloud radar at Boseong in Korea are used to analyze detailed cloud properties. The reflectivity of the cloud radar is calibrated with other vertical pointing radars compared with the two disdrometers. A simple threshold-based quality control method is applied to eliminate non-meteorological echoes (insects and noise) in conjunction with despeckling along the radial direction. Clouds are classified into five types: high (HC), middle (MC), low (LC) for non-precipitating clouds, and deep (RainDP) and shallow (RainSH) for precipitating clouds. The average cloud frequency was about 35.9% with the maximum frequency of 50% in June for the total two-year sampling period. The RainDP occurred most frequently (11.8%), followed by HC (9.3%), MC (7.4%), RainSH (4.4%), and LC (2.9%) out of the average occurrence of the total 35.9%. HC and RainDP were frequently observed in summer and autumn, while RainSH, LC, and MC were dominant in the winter due to the dominant cloud development by the air-sea interaction during the cold air outbreak. The HC showed a significant seasonal variation of the maximum height and the rapid growth in the layer above 7 km (about −15 °C) in summer and autumn. This rapid growth appears in HC, MC, LC, and RainDP and is linked with rapid increases in Doppler velocity and mass flux. Thus, this growth is originated from the dominant riming processes in addition to depositional growth and is supported by an updraft in the layer between 6 and 8 km. MC showed a single frequency peak around 6 km with rapid growth above and strong evaporation below. The Doppler velocity of MC rapidly increases above 8 km and is nearly constant below this height due to strong evaporation except in the summer. LC had a similar trend of reflectivity (rapid growth in the HC region and strong evaporation in the lower region) lacking high frequency in the MC region. Unlike LC, the RainDP had continuous growth toward the ground in the entire layer with rapid growth in the HC and MC regions. In addition, two modes (cloud and precipitation) appear on the ground in spring and fall with the vertical continuity of the high frequency in the precipitation mode. The precipitation growth was most efficient in RainSH in summer with a reflectivity gradient of about 20 dBZ km−1 and frequent updrafts larger than 1 m s−1 and was smaller in the MC and HC regions. [ABSTRACT FROM AUTHOR]

The microphysical characteristics of a mesoscale convective system (MCS) during a summer monsoon of South Korea are investigated using the generalized drop size distributions (DSD) that are derived from S-band dual-polarization radar data. The characteristics parameters of generalized DSDs (generalized number concentration, N0′ and generalized mean diameter, Dm) are directly calculated from DSD's two moments without any assumption on the DSD model. Relationships between ZDR and generalized DSD parameters normalized by ZH are derived in the form of the polynomial equation. Verification of the retrieved DSD parameters is conducted with the 2-D video disdrometer (2DVD) located about 23 km from the radar. The standard deviations (SD) of retrieved DSD parameters are about 0.26 for log N0′, and about 0.11 for Dm because of the variability of DSDs. The SD of the retrieved log N0′ from the dual-polarimetric measurement reaches to about 0.46 (almost double) for 11 rain events while the accuracy of retrieved Dm is quite higher (~0.19). This higher error in retrieved log N0′ is likely attributed to the larger discrepancy in radar-observed and DSD-calculated ZDR when ZH is low. This retrieval technique is applied to a mesoscale convective system (MCS) case to investigate the Lagrangian characteristics of the microphysical process. The MCS is classified into the leading edge and trailing stratiform region by using the storm classification algorithm. The leading edge dominated by strong updraft showed the broad DSD spectra with a steady temporal increase of Dm throughout the event, likely because of the dominant drop growth by the collision-coalescence process. On the other hand, the drop growth is less significant in the trailing stratiform region as shown by the nearly constant Dm for the entire period. The DSD variation is also controlled by the new generation of drops in the leading edge and less extent in the trailing stratiform during the early period when precipitation systems grow. When the system weakens, the characteristic number concentration decreases with time, indicating the new generation of drops becomes less significant in both regions. [ABSTRACT FROM AUTHOR]

Tools to identify and classify stratiform and convective rains at various times of the 12 days from June 2015 to March 2016 in Jincheon, Korea, were developed by using a Parsivel disdrometer and S-band polarimetric (S-POL) radar data. Stratiform and convective rains were identified using three different methods (vertical profile of reflectivity (VPR), the method proposed by Bringi et al. (BR03), and a combination of the two (BR03-VPR)) by using a Parsivel disdrometer for its applications to radar as a reference. BR03-VPR exhibits a better classification scheme than the VPR and BR03 methods. The rain types were compared using the drop size distribution (DSD) retrieved from polarimetric variables and reflectivity only. By using the DSD variables, a new convective/stratiform classification line of the log-normalized droplet number concentration ( log 10 N w) − median volume diameter ( D 0 ) was derived for this area to classify the rainfall types using DSD variables retrieved from the polarimetric radar. For the radar variables, the method by Steiner et al. (SHY95) was found to be the best method, with 0.00% misclassification of the stratiform rains. For the convective rains, the DSD retrieval method performed better. However, for both stratiform and convective rains, the fuzzy method performed better than the SHY95 and DSD retrieval methods. [ABSTRACT FROM AUTHOR]