• Korean Journal of Remote Sensing
• /
• v.16 no.1
• /
• pp.55-64
• /
• 2000

The Tropical Rainfall Measuring Mission(TRMM) Satellite was launched in November 1997, carving into orbit the first space-borne Precipitation Radar(PR). The main objective of the TRMM is to obtain and study multi-year science data sets of tropical and subtropical rainfall measurements. In the present investigation, the characteristics of heavy rainfall cases over Korea in 1998 and 1999 are analyzed using the TRMM/PR dat3. We compare the rainrate measured from TRMM/PR with the accumulated rainfall data for 10 minutes tv Automatic Weather System(AWS). Especially, horizontal cross-section of rainrate with height and longitude in the precipitating clouds are investigated. As a result of the comparison with GMS-5 IR1, the TRMM/PR data delineate well the rain type( i.e. convective, stratiform cloud and others), height of storm top and instantaneous rainrate in the precipitating clouds. The vertical structure with height and horizontal cross-section of rainrate along the longitude show the orographic effect on the rainfall. TRMM/PR instrument measures the rainrate below 6 ㎜/hr more than AWS rainguages and inclined to underestimate the rainrate than rainguages for the whole area.

• Proceedings of the KSRS Conference
• /
• 1999.11a
• /
• pp.103-106
• /
• 1999

The Tropical Rainfall Measuring Mission(TRMM) is a United States-Japan project for rain measurement from space. The first spaceborne Precipitation Radar(PR) has been installed aboard the TRMM satellite. The ground based validation of the TRMM satellite observations was conducted by TRMM science team through a Global Validation Program(GVP) consisted of 10 or more ground validation sites throughout the tropics. However, TRMM radar should always be validated and assessed against reference data to be used in Korean Peninsula because the rainrates measured with satellite varies by time and space. We have analyzed errors in the comparison of rainrates measured with the TRMM/PR and the ground-based instrument i.e. Automatic Weather System(AWS) by means of statistical methods. Preliminary results show that the near surface rainrate of TRMM/PR are highly correlated with ground measurements especially for the very deep convective rain clouds, though the correlation is changed according to the type and amount of precipitating clouds. Results also show that TRMM/PR instrument is inclined to underestimate the rainrate on the whole over Korea than the AWS measurement for the cases of heavy rainfall.

• Journal of Environmental Science International
• /
• v.25 no.3
• /
• pp.345-359
• /
• 2016

This study analyze the synoptic meteorological cause of rainfall, rainfall intensity, drop size distribution(DSD), fall velocity and oblateness measured by the 2D-Video distrometer(2DVD) by comparing two cases which are heavy rainfall event case and a case that is not classified as heavy rainfall but having more than $30mm\;h^{-1}$ rainrate in July, 2014 at Gimhae region. As a results; Over the high pressure edge area where strong upward motion exists, the convective rain type occurred and near the changma front, convective and frontal rainfall combined rain type occurred. Therefore, rainrate varies based on the synoptic meteorological condition. The most rain drop distribution appeared in the raindrops with diameters between 0.4 mm and 0.6 mm and large particles appeared for the convective rain type since strong upward motion provide favorable conditions for the drops to grow by colliding and merging so the drop size distribution varies based on the location or rainfall types. The rainfall phases is mainly rain and as the diameter of the raindrop increase the fall velocity increase and oblateness decrease. The equation proposed based on the 2DVD tends to underestimated both fall velocity and oblateness compared with observation. Since these varies based on the rainfall characteristics of the observation location, standard equation for fall velocity and oblateness fit for Gimhae area can be developed by continuous observation and data collection hereafter.

• Water Engineering Research
• /
• v.1 no.2
• /
• pp.147-158
• /
• 2000

In this study, we characterized the seasonal variation of rainrate fields in the Han river basin using the WGR multi-dimensional precipitation model (Waymire, Gupta, and Rodriguez-Iturbe, 1984) by estimating and comparing the parameters derived for each month and for the plain area, the mountain area and overall basin, respectively. The first-and second-order statistics derived from observed point gauge data were used to estimate the model parameters based on the Davidon-Fletcher-Powell algorithm of optimization. As a result of the study, we can find that the higher rainfall amount during summer is mainly due to the arrival rate of rain bands, mean number of cells per cluster potential center, and raincell intensity. However, other parameters controlling the mean number of rain cells per cluster, the cellular birth rate, and the mean cell age are found invariant to the rainfall amounts. In the application to the downstream plain area and upstream mountain area of the Han river basin, we found that the number of storms in the mountain area was estimated a little higher than that in the plain area, but the cell intensity in the mountain area a little lower than that in the plain area. Thus, in the mountain area more frequent but less intense storms can be expected due to the orographic effect, but the total amount of rainfall in a given period seems to remain the same.

• Korean Journal of Remote Sensing
• /
• v.22 no.6
• /
• pp.485-493
• /
• 2006

This study is to combine precipitation data with different spatial-temporal characteristics using an optimal weighting method. This optimal weighting method is designed for combination of AWS rain gage data and S-band RADAR-estimated rain data with weighting function in inverse proportion to own mean square error for the previous time step. To decide the optimal weight coefficient for optimized precipitation according to different training time, the method has been performed on Changma case with a long spell of rainy hour for the training time from 1 hour to 10 hours. Horizontal field of optimized precipitation tends to be smoothed after 2 hours training time, and then optimized precipitation has a good agreement with synoptic station rainfall assumed as true value. This result suggests that this optimal weighting method can be used for production of high-resolution quantitative precipitation rate using various data sets.

• Water for future
• /
• v.26 no.4
• /
• pp.47-60
• /
• 1993

The evaluation of grounwater flow in the Ingulam valley catchment area in the vicinity of SamKwang mine was studied. In this study, field hydraulic tests, groundwater flow measurement, and MODFLOW model application were carried out. The results of analysis are described as follows. The rainrate infiltrated into the ground in the study area, hardly reached the gneiss region deeper than EL.(+)100m above the surface of seawater. The rainwater infiltrated into the ground near the water system boundary, flowed out into the vicinity of streams and the travel time was between 15 and 263 years. Also, the estimated total flow rate of the groundwater in the study area was 307㎥/day.

• Atmosphere
• /
• v.24 no.3
• /
• pp.433-444
• /
• 2014

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 ($2.36mm\;hr^{-1}$ in RMSE on average) is superior to the RAR system ($8.33mm\;hr^{-1}$ 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.

• Journal of the Korean earth science society
• /
• v.34 no.1
• /
• pp.13-27
• /
• 2013

Quantitative precipitation estimation (QPE) is one of the most important elements in meteorological and hydrological applications. In this study, we adjusted the QPE from an S-band weather radar based on co-kriging method using the geostatistical structure function of error distribution of radar rainrate. In order to estimate the accurate quantitative precipitation, the error of radar rainrate which is a primary variable of co-kriging was determined by the difference of rain rates from rain gauge and radar. Also, the gauge rainfield, a secondary variable of co-kriging is derived from the ordinary kriging based on raingauge network. The error distribution of radar rain rate was produced by co-kriging with the derived theoretical variogram determined by experimental variogram. The error of radar rain rate was then applied to the radar estimated precipitation field. Locally heavy rainfall case during 6-7 July 2009 is chosen to verify this study. Correlation between adjusted one-hour radar rainfall accumulation and rain gauge rainfall accumulation improved from 0.55 to 0.84 when compared to prior adjustment of radar error with the adjustment of root mean square error from 7.45 to 3.93 mm.

• Journal of Environmental Science International
• /
• v.24 no.4
• /
• pp.525-540
• /
• 2015

Spatial distribution of precipitation has been estimated based on the local gauge correction (LGC) with a fixed inverse distance weighting (IDW), which is not optimized in taking effective radius into account depending on the radar error. We developed an algorithm, improved local gauge correction (ILGC) which eliminates outlier in radar rainrate errors and optimize distance power for IDW. ILGC was statistically examined the hourly cumulated precipitation from weather for the heavy rain events. Adjusted radar rainfall from ILGC is improved to 50% compared with unadjusted radar rainfall. The accuracy of ILGC is higher to 7% than that of LGC, which resulted from a positive effect of the optimal algorithm on the adjustment of quantitative precipitation estimation from weather radar.

• Atmosphere
• /
• v.24 no.3
• /
• pp.365-378
• /
• 2014

The limits of S-band dual-polarization radars in Korea are not reflected on the recent weather forecasts of Korea Meteorological Administration and furthermore, they are only utilized for rainfall estimations and hydrometeor classification researches. Therefore, this study applied four merging methods [SA (Simple Average), WA (Weighted Average), SSE (Sum of Squared Error), TV (Time-varying mergence)] to the QPE (Quantitative Precipitation Estimation) model [called RAR (Radar-AWS Rainfall) calculation system] using single-polarization radars and S-band dual-polarization radar in order to improve the accuracy of the rainfall estimation of the RAR calculation system. As a result, the merging results of the WA and SSE methods, which are assigned different weights due to the accuracy of the individual model, performed better than the popular merging method, the SA (Simple Average) method. In particular, the results of TVWA (Time-Varying WA) and TVSSE (Time-Varying SSE), which were weighted differently due to the time-varying model error and standard deviation, were superior to the WA and SSE. Among of all the merging methods, the accuracy of the TVWA merging results showed the best performance. Therefore, merging the rainfalls from the RAR calculation system and S-band dual-polarization radar using the merging method proposed by this study enables to improve the accuracy of the quantitative rainfall estimation of the RAR calculation system. Moreover, this study is worthy of the fundamental research on the active utilization of dual-polarization radar for weather forecasts.