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This paper is concerned with the temporal variation characteristics of two meteorological factors (temperature and humidity) in four metropolitan cities (Seoul, Busan, Daegu, Daejeon) in South Korea. Data are extracted from seven years (2008 to 2014) of hourly time series data in meteorological offices of the Korea Meteorological Administration. Using the detrended cross-correlation analysis (DCCA) method, the DCCA coefficient of temperature is compared to that of humidity from daily time series data during four seasons in the four metropolitan cities. In particular, as window size s increases, the DCCA cross-correlation coefficient approaches 0.034 at a time lag of 14 days in the case of spring in Seoul. We find the weak cross-correlation between temperature and humidity at different time lags of 14, 21 and 28 days in spring in Seoul, and the errors E T in the ANN are relatively larger values than that of any other season in both the ANN and the MRM. Particularly, in the ANN model, there exist relatively a large error value of temperature as the characteristics of the non-stationary, deterministically chaotic and noisy meteorological data extracted for the short-term prediction rather than the long-term prediction.

Typhoon Khanun caused over 226 mm of accumulated rainfall for 6 h (0700 to 1300 UTC), localized around the summit of Mt. Halla (height 1950 m), with a slanted rainfall pattern to the northeast. In this study, we investigated the enhancement mechanism for precipitation near the mountains as the typhoon passed over Jeju Island via dual-Doppler radar analysis and simple trajectory of passive tracers using a retrieved wind field. The analysis of vertical profiles of the mountain region show marked features matching the geophysical conditions. In the central mountain region, a strong wind (≥ 7 m s− 1) helps to lift low-level air up the mountain. The time taken for lifting is longer than the theoretical time required for raindrop growth via condensation. The falling particles (seeder) from the upper cloud were also one of the reasons for an increase in rainfall via the accretion process from uplifted cloud water (feeder). The lifted air and falling particles both contributed to the heavy rainfall in the central region. In contrast, on the leeward side, the seeder-feeder mechanism was important in the formation of strong radar reflectivity. The snow particles (above 5 km) were accelerated by strong downward winds (≤−6 m s− 1). Meanwhile, the nonlinear jumping flow (hydraulic jump) raised feeders (shifted from the windward side) to the upper level where particles fall. To support these development processes, a numerical simulation using cloud-resolving model theoretically carried out. The accreting of hydrometeors may be one of the key reasons why the lee side has strong radar reflectivity, and a lee side weighted rainfall pattern even though lee side includes no strong upward air motion.

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.

To investigate the impact of rainfall type on rainfall estimation using polarimetric variables, rainfall relations such as those between rain rate (R) and specific differential phase (KDP), between R and KDP/differential reflectivity (ZDR), and between R and reflectivity (Z)/ZDR, were examined with respect to the precipitation type classified using drop size distributions (DSDs) measured by a disdrometer. The classification of rainfall type was assessed using four different methods: temporal rainfall variation, and the relations between intercept parameter (N0) and R, normalized intercept parameter (Nw) and median diameter (D0), and slope parameter (&Lambda, ) and R. The logN0&ndash, R relation discriminated between convective and stratiform rain with less standard deviation than the other methods as shown by the Z&ndash, ZDR scatter with respect to the rainfall types. The transition type from convective to stratiform and vice versa occurred in the stratiform rain region for all methods. To apply the classified rainfall relations to radar rainfall estimation, logNw and D0 were retrieved from polarimetric variables to discriminate the rainfall types in the radar domain. The DSD classification was verified with the vertical profile of reflectivity extracted at two positions corresponding to gage sites. Statistical analysis of four different rainfall events showed that rainfall estimation using the relations with precipitation type were better than those obtained without classification. The R(KDP,ZDR) relation with classification performed best on rainfall estimation for all rainfall events. The greatest improvement in rainfall estimation was obtained from R(Z,ZDR) with classification. We conclude that the classification of rainfall type leads to more accurate rainfall estimation. The different relations R(KDP), R(KDP,ZDR), and R(Z,ZDR) with respect to the rain types using polarimetric radar show improvement compared to estimation without consideration of rainfall type, in Korea.

To classify precipitation types as either convective or stratiform, drop size distributions (DSDs) measured by the Parsivel (PARticle size VELocity) instrument, and DSD parameters including median volume diameter (D 0 ) and normalized number concentration (Nw) retrieved by S-band polarimetric radar (BSL), were analyzed for a heavy rainfall event that occurred in southern Korea on 25 August 2014. The rainfall system was clearly identified as stratiform or convective rain at various times of day, at measurement sites at Changwon and Busan. New rainfall classification lines were derived from the Parsivel and BSL data, and were compared with existing classification methods based on climatological rainfall data. The classification methods using logNw-D 0 , logN 0 -rainrate, and slope-rainrate domain proposed in previous two studies performed well when applied to the new data if the slope and/or intercept values were changed. Another method using logN 0 -slope domain was not possible to classify the precipitation types well in the study area, as the best-fit line could not be obtained. The average measured D 0 and Nw values obtained from polarimetric radar were compared with climatological precipitation data, measured in both the tropics and mid-latitudes. And new separation line was obtained for the rainfall at the southern part of Korea.

Three methods for determining the reflectivity bias of single polarization radar using dual polarization radar reflectivity and disdrometer data (i.e., the equidistance line, overlapping area, and disdrometer methods) are proposed and evaluated for two low-pressure rainfall events that occurred over the Korean Peninsula on 25 August 2014 and 8 September 2012. Single polarization radar reflectivity was underestimated by more than 12 dB and 7 dB in the two rain events, respectively. All methods improved the accuracy of rainfall estimation, except for one case where DSDs were not observed, as the precipitation system did not pass through the disdrometer location. The use of these bias correction methods reduced the RMSE by as much as 50%. Overall, the most accurate rainfall estimates were obtained using the overlapping area method to correct radar reflectivity. A combination of all three methods would produce more accurate rainfall estimates, provided optimal values are determined for the domain size for the overlapping area method, the sample number threshold for the equidistance line method, and the reflectivity threshold for the disdrometer method.

The empirical relationships used to estimate rainfall amounts from polarimetric radar observations were developed and tested by using long-period drop size distribution (DSD) data and monitoring of rainfall events prior to operational use in Korea. Rainfall associated with Typhoon Meari in 2011 was selected to assess the performance of these relationships for point and areal rainfall amounts. Data quality was checked in regions of light rain to enable the quantitative use of polarimetric variables. The distributions of the cross-correlation co-efficient and standard deviation of the differential phase shift agreed with those established in a previous study, but the absolute average deviation of differential reflectivity (ZDR) was a little distorted. Biases in reflectivity (Z) and differential reflectivity were calculated following established methods and found to be –0.18 and 0.47 dB, respectively. The accuracy of rainfall amounts calculated from R(KDP) and R(KDP, ZDR) was poor. The best estimates of rainfall were obtained using R(Z, ZDR) based on DSDs from Oklahoma (OKC) in the USA and Busan (BSC) for both the point and areal mean cases. Correlation co-efficients of R(Z, ZDR) using the BSC DSDs were better than those using the OKC DSDs for areal mean rainfall amounts. Rainfall amounts in this particular case in Korea were estimated more accurately using the Brandes drop shape for R(Z, ZDR) than the equilibrium drop shape.

In countries with frequent aerial military exercises, chaff particles that are routinely spread by military aircraft represent significant noise sources for ground-based weather radar observation. In this study, a cost-effective procedure is proposed for identifying and removing chaff echoes from single-polarization Doppler radar readings in order to enhance the reliability of observed meteorological data. The proposed quality control procedure is based on three steps: 1) spatial and temporal clustering of decomposed radar image elements, 2) extraction of the clusters’ static and time-evolution characteristics, and 3) real-time identification and removal (or censoring) of target echoes from radar data. Simulation experiments based on this procedure were conducted on site-specific ground-echo-removed weather radar data provided by the Korea Meteorological Administration (KMA), from which three-dimensional (3D) reflectivity echoes covering hundreds of thousands of square kilometers of South Korean territory within an altitude range of 0.25–10 km were retrieved. The algorithm identified and removed chaff clutter from the South Korean data with a novel decision support system at an 81% accuracy level under typical cases in which chaff and weather clusters were isolated from one another with no overlapping areas.

In order to improve the accuracy of quantitative precipitation estimates, we correct radar reflectivity measurements by a “sorting and moving average” (SMA) method and with a POSS (precipitation occurrence sensing system) disdrometer. The correction procedure, optimized by a polynomial least-square fit of the data, greatly reduces errors in rainfall estimates. The Zc–R relationships for different cloud types are calculated using a classification algorithm. A VPR threshold algorithm is used to investigate the accuracy of rainfall estimates depending on the cloud type. For stratiform and convective cloud types, rainfall estimates were more accurate when the correction was taken into account, with statistical errors substantially reduced. The developed algorithm successfully reduced errors in the rainfall estimates and improved their accuracy. This new quantitative precipitation estimate (QPE) algorithm will improve the reliability of radar-based quantitative rainfall measurements and the accuracy of weather forecasts.

To improve the accuracy of polarimetric rainfall relations for heavy rainfall, an extreme rainfall case was analysed and some methods were examined. The observed differential reflectivity (ZDR) quality check was theoretically investigated using the relation between the standard deviation of differential reflectivity and cross correlation, and the light rain method for ZDR bias was also applied to the rainfall estimation. The best performance for this heavy rainfall case was obtained when the moving average of ZDR over a window size of 9 gates was applied to the rainfall estimation using horizontal reflectivity (ZH) and ZDR and to the calculation of ZH bias. The differential reflectivity calculated by disdrometer data may be an alternative to the vertical pointing scan for calculating ZDR bias. The accuracy of the combined rainfall relation, R(Z,ZDR,KDP,AH) was relatively insensitive to ZDR and ZH biases in both observations and simulations.

In this study, to get an optimum rainfall estimation using polarimetric variables observed from Bislsan radar which is the first polarimetric radar in Korea, rainfall cases for 84 hours caused by different conditions, which are Changma front and typhoon, Changma front only, and typhoon only, occurred in 2011, were analyzed. And rainfall algorithms were developed by using long period drop size distributions with six different raindrop axis ratio relations. The combination of the relations betweenRandZ,ZDR,RandKDP,ZDR, andRandKDPwith different rainfall intensity would be an optimum rainfall algorithm if the reference of rainfall would be defined correctly. In the case the reference is not defined adequately, the relation betweenRandZ,ZDR,KDP,AHandRandZ,KDP,AHcan be used as a representative rainfall relation. Particularly if the qualifiedZDRis not available, the relation betweenRandZ,KDP,AHcan be used as an optimum rainfall relation in Korea.

In the summer rainy season, the Korean Peninsula is frequently influenced by severe weather phenomena such as floods and rain-induced landslides. A band-shaped precipitation system associated with unstable atmospheric conditions occurred over northwest Korea on 27 July 2011. This precipitation system produced heavy rainfall over the Seoul metropolitan area, which received over 80 mm h −1 of rainfall and suffered 70 weather-related fatalities. To investigate the precipitation system, we used diverse meteorological data of environmental condition and estimated three-dimensional wind field from dual-Doppler radar measurements of vertical air motion. Environmental conditions included high equivalent potential temperature ( θ e ) of over 355 K at low levels, and low θ e of under 330 K at middle levels, causing vertical instability. Furthermore, a pressure trough was located to the northwest of Korea, favouring the development of the band-shaped precipitation system. The tip of the band-shaped precipitation system was made up of line-shaped convective systems (LSCSs) that caused flooding and landslides, and the LSCSs were continuously enhanced by merging between new cells and the pre-existing cell. The position of merging moved from the coast to offshore areas and influenced the positioning of the regions of enhanced convection. In turn, this affected the roughness of the convective cell and the internal structure of the enhanced convective regions. Onshore, the convective area was higher than in offshore areas because of strong convergence (≤−4×10 −4 s −1 ) at low levels caused by friction over land. The strong convergence generated strong updraft (≥4 m s −1 ) that influenced the height of the convective area. The convective region offshore was wider than that onshore because of weak convergence (≥−2.2×10 −4 s −1 ) at low levels. Updraft in offshore areas was weak (≤3 m s −1 ) compared with onshore, resulting in a lower and wider convective area. Spatial variations in surface roughness result in different structural features and profiles of divergence within LSCSs, even if they originate in the same convective region. Keywords: line-shaped convective system, back-building, low-level convergence, nearshore (Published: 27 May 2013) Citation: Tellus A 2014, 66 , 23453, http://dx.doi.org/10.3402/tellusa.v66.23453

To assess the performance of rainfall estimation using specific differential phase observed by Bislsan radar, the first polarimetric radar in Korea, three rainfall cases occurring in 2011 were selected, each caused by different conditions: the first is the Changma front and typhoon, the second is only the Changma front, and the third is only a typhoon. For quantitative use of specific differential phase (KDP), a data quality algorithm was developed for differential phase shift (ΦDP), composed of two steps; the first involves removal of scattered noise and the second is unfolding ofΦDP. This order of the algorithm is necessary so as not to remove unfolded areas, which are the real meteorological target. All noise was removed and the foldedΦDPwere unfolded successfully for this study.RKDPrelations for S-band radar were calculated for 84,754 samples of observed drop size distribution (DSD) using different drop shape assumptions. The relation for the Bringi drop shape showed the best statistics: 0.28 for normalized error, and 6.7 mm for root mean square error for rainfall heavier than 10 mm h-1. Because the drop shape assumption affects the accuracy of rainfall estimation differently for different rainfall types, such characteristics should be taken into account to estimate rainfall more accurately using polarimetric variables.