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Raindrop size distribution (DSD) observed using a disdrometer can be represented by a constrained-gamma (C-G) DSD model based on the empirical relationship between shape (µ) and slope (Λ). The C-G DSD model can be used to retrieve DSDs and rain microphysical parameters from dual-polarization radar measurements of reflectivity (ZH) and differential reflectivity (ZDR). This study presents a new µ-Λ relationship to characterize rain microphysics in South Korea using a two-dimensional video disdrometer (2DVD) and Yong-in S-band dual-polarization radar. To minimize sampling errors from the 2DVD and radar measurements, measured size distributions are truncated by particle size and velocity-based filtering and compared with rain gauge measurement. The calibration biases of radar ZH and ZDR were calculated using the self-consistency constraint and vertical pointing measurements. The derived µ-Λ relationship was verified using the mass-weighted mean diameter (Dm) and standard deviation of the size distribution (σm), calculated from the 2DVD, for comparison with existing µ-Λ relationships for Florida and Oklahoma. The Dm-σm relationship derived from the 2DVD corresponded well with the µ-Λ relationship. The µ-Λ relationship derived for the Korean Peninsula was similar to Florida, and both generally had larger µ values than Oklahoma for the same Λ. The derived µ-Λ relationship was applied to retrieve DSD parameters from polarimetric radar data, and the retrieved DSDs and derived physical parameters were evaluated and compared with the 2DVD measurements. The polarization radar-based C-G DSD model characterized rain microphysics more accurately than the exponential DSD model. The C-G DSD model based on the newly derived µ-Λ relationship performed the best at retrieving rain microphysical parameters. [ABSTRACT FROM AUTHOR]

A millimetre‐wave cloud radar has limitations for observing heavy rainfall because the short wavelength leads to strong attenuation from raindrops. However, recent studies have attempted to estimate the rain rate using attenuation, which becomes greater as the rain intensity increases. The rain‐rate‐retrieval algorithm is developed in the present paper using the Ka‐band cloud radar (KaCR) installed at the Boseong Global Standard Observatory (BGSO) in the Republic of Korea. First, rain profiles were identified using the threshold of reflectivity (Z) and Doppler velocity (DV) averaged for the analysis of layer that minimized the effects of the receiver saturation and classified as low or high rain‐rate cases using the averaged DV. The reflectivity and rain‐rate (Z‐R) relationship was then derived for low rain‐rate cases that showed insignificant effects for signal attenuation. On the other hand, the attenuation and rain‐rate (A‐R) relationship was derived using high rain‐rate cases that showed the dominant effects of attenuation. The attenuation was calculated using a reflectivity gradient between the upper and lower boundaries of the attenuated layer. Finally, the rain‐rate‐retrieval algorithm was designed using the derived Z‐R and A‐R relationship and then applied back to the KaCR. The estimated rain rate in the KaCR was a similar trend to the observed rain rate in the optical rain gauge (ORG), but slightly underestimated. [ABSTRACT FROM AUTHOR]

Hallasan Mountain is located at the center of Jeju Island, Korea. Even though Hallasan Mountain has a height of just 1,950 m, the temperature during the winter decreases below −20 degrees Celsius. On the contrary, the temperature on the coastal areas remains just above freezing. Therefore, large snowfalls in the mountain and rainfall in the coastal areas are very common in Jeju Island. Most of the rain gauges are available around highly populated coastal areas, and snow measurements are available at just four locations on the coastal areas. Therefore, it is practically impossible to distinguish the rainfall and snowfall in Jeju Island. Fortunately, two radars (Seongsan and Gosan radars) operate on Jeju Island, which fully covers Hallasan Mountain. This study proposes a method of using both the radar and rain gauge information to map the snowy region in Jeju Island, including Hallasan Mountain. As a first step, this study analyzed the Z-R and Z-S relationships to derive a fixed threshold of radar reflectivity to separate snowfall from rainfall, and, in the second step, this study additionally considered the observed rain rate information to implement the problem of using the fixed threshold. This proposed method was applied to radar reflectivity data collected during November 1, 2014, to April 30, 2015, and the results indicate that the method considering both the radar and rain gauge information was satisfactory. This method also showed good performance, especially when the rain rate was very low. [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]

• Distinct differences in the bias-correction parameters over stations are identified. • An inverse relationship between the parameters is clearly identified. • A spatially structured pattern in the parameters exists. • Corrected Z-R relationships fit the radar-gauge pairs reasonably well. A fixed Z-R relationship approach, such as the Marshall-Palmer relationship, for an entire year and for different seasons can be problematic in cases where the relationship varies spatially and temporally throughout a region. From this perspective, this study explores the use of a long-term radar reflectivity factor for South Korea to obtain a nationwide calibrated power law relationship, Z = α ∙ R β , where R is the rainfall intensity and Z is the reflectivity factor, in the context of bias-correction and the associated uncertainties within a Bayesian regression framework. This study also investigates seasonal differences in the bias-correction parameters (i.e., additive and multiplicative bias-correction factors) and their roles in reducing systematic error. Distinct differences in the calibrated parameters over stations are identified, and more importantly, an inverse relationship between the two reflectivity parameters α and β (i.e., decreasing trend in β with increasing α) is clearly identified with different parameter space based on the seasons. A spatially structured pattern in the parameters exists, particularly parameter α, for the wet season and parameter β for the dry season. A pronounced region of high values in the power law parameters (i.e., α and β) during the wet and dry seasons may be partially due to the overestimation of the radar reflectivity factor associated with enhancement of moisture transport directly over the coastal region. Finally, the radar rainfall estimates through the calibrated Z-R relationship are compared with the currently used Z-R relationships for estimating stratiform rainfall and convective rainfall. Overall, the radar rainfall fields based on the proposed modeling procedure are similar to the observed rainfall fields, whereas the radar rainfall fields obtained from the currently used Marshall-Palmer Z-R relationship show a systematic underestimation. The calibrated Z-R relationships are further validated by testing the predictions of unseen radar-gauge pairs in the year 2018, in the context of cross-validation. The cross-validation results are largely similar to those in the calibration process, suggesting that the derived Z-R relationships fit the radar-gauge pairs reasonably well. [ABSTRACT FROM AUTHOR]

Recently, a small rain radar in the 24GHz band is under development in Korea. This instrument is intended for the measurement of quantitative rainfall within a radius of 1 Km. Quantitative rainfall measurements for small watersheds are predicated on flood forecasting. The purpose of this device development was focused on the amount of rainfall reaching the ground rather than the amount of rainfall in the atmosphere. The device is currently being developed as a prototype and is undergoing various performance verification with the aim of commercialization.In this presentation, we show the comparison result of performance verification of small rain radar and show the performance superiority. In order to verify the performance of this device, it took a long time from the selection of the comparative verification site to the design and installation of a pit gauge. The comparative site was equipped with 2 parsivels, one pit gauge(composed of one weighing type of rain gauge and 12 tipping bucket type rain gauges) and 17 tipping bucket type gauges. The comparative analysis method of the measurement result converts the scanning result of this device into the reflectance and compares it with the verification device. [ABSTRACT FROM AUTHOR]