• Journal of Korea Water Resources Association
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• v.52 no.1
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• pp.21-33
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• 2019

This study performed to simulate the watershed storm runoff using data of S-band dual-polarization radar rain, GPM (Global Precipitation Mission) satellite rain, and observed rainfall at 21 ground stations operated by KMA (Korea Meteorological Administration) respectively. For the 3 water level gauge stations (Sancheong, Changchon, and Namgang) of NamgangDam watershed ($2,293km^2$), the KIMSTORM2 (KIneMatic wave STOrm Runoff Model2) was applied and calibrated with parameters of initial soil moisture contents, Manning's roughness of overland and stream to the event of typhoon CHABA (82 mm in watershed aveprage) in $5^{th}$ October 2016. The radar and GPM data was corrected with CM (Conditional Merging) method such as CM-corrected Radar and CM-corrected GPM. The CM has been used for accurate rainfall estimation in water resources and meteorological field and the method combined measured ground rainfall and spatial data such as radar and satellite images by the kriging interpolation technique. For the CM-corrected Radar and CM-corrected GPM data application, the determination coefficient ($R^2$) was 0.96 respectively. The Nash-Sutcliffe efficiency (NSE) was 0.96 and the Volume Conservation Index (VCI) was 1.03 respectively. The CM-corrected data of Radar and GPM showed good results for the CHABA peak runoff and runoff volume simulation and improved all of $R^2$, NSE, and VCI comparing with the original data application. Thus, we need to use and apply the radar and satellite data to monitor the flood within the watershed.

• Atmosphere
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• v.22 no.1
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• pp.29-45
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• 2012

An unusual autumn storm developed rapidly in the western part of the East sea on the early morning of 23 October 2006. This storm produced a record-breaking heavy rain and strong wind in the northern and middle part of the Yeong-dong region; 24-h rainfall of 304 mm over Gangneung and wind speed exceeding 63.7 m $s^{-1}$ over Sokcho. In this study, MTSAT-1R (Multi-fuctional Transport Satellite) water vapor and infrared channel imagery are examined to find out some features which are dynamically associated with the development of the storm. These features may be the precursor signals of the rapidly developing storm and can be employed for very short range forecast and nowcasting of severe storm. The satellite features are summarized: 1) MTSAT-1R Water Vapor imagery exhibited that distinct dark region develops over the Yellow sea at about 12 hours before the occurrence of maximum rainfall about 1100 KST on 23 October 2006. After then, it changes gradually into dry intrusion. This dark region in the water vapor image is closely related with the positive anomaly in 500 hPa Potential Vorticity field. 2) In the Infrared imagery, low stratus (brightness temperature: $0{\sim}5^{\circ}C$) develops from near Bo-Hai bay and Shanfung peninsula and then dissipates partially on the western coast of Korean peninsula. These features are found at 10~12 hours before the maximum rainfall occurrence, which are associated with the cold and warm advection in the lower troposphere. 3) The IR imagery reveals that two convective cloud cells (brightness temperature below $-50^{\circ}C$) merge each other and after merging it grows up rapidly over the western part of East sea at about 5 hours before the maximum rainfall occurrence. These features remind that there must be the upward flow in the upper troposphere and the low-layer convergence over the same region of East sea. The time of maximum growth of the convective cloud agrees well with the time of the maximum rainfall.

• Journal of Korea Water Resources Association
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• v.45 no.7
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• pp.631-641
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• 2012

In this study, satellite data (MTSAT-1R), a numerical weather prediction model, RDAPS (Regional Data Assimilation and Prediction System) output, ground weather station data, and artificial neural networks were used to improve the accuracy of summer rainfall forecasts. The developed model was applied to the Seoul station to forecast the rainfall at 3, 6, 9, and 12-hour lead times. Also to reflect the different weather conditions during the summer season which is related to the frontal precipitation and the cyclonic precipitation such as Jangma and Typhoon, the neural network models were formed for two different periods of June-July and August-September respectively. The rainfall forecast model was trained during the summer season of 2006 and 2008 and was verified for that of 2009 based on the data availability. The results demonstrated that the model allows us to get the improved rainfall forecasts until lead time of 6 hour, but there is still a large room to improve the rainfall forecast skill.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.34D no.6
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• pp.75-82
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• 1997

Today's wireless communication needs the super-high speed for picture transmission as well as voice. The optical communication with the very wide bandwidth is suitable for this demand. To fulfill the optical wireless communication, however, the atmospheric attenuation in rainy weather condition must be overcome. In the optical satellite up-link communication between geo-satellite and earth station, the factors of attenuation are turbulence, pointing error, scattering, and so on. The most serious factor for these is the scattering by rain. Under the weather conditiion of rain and cloud, in this paper, the atmospheic attenuation which affects the optical satellite up-link communication was considered, and the optimum idameter of the optical satellite transmitting antenna in the earth station versus elevation angles, data rates and rainfall rates was presented.

• Atmosphere
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• v.28 no.2
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• pp.201-209
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• 2018

Benefits of the next generation geostationary meteorological satellite observation (e.g., GEO-KOMPSAT-2A) are qualitatively and comprehensively described and discussed. Main beneficial phenomena for application can be listed as tropical cyclones (typhoon), high impact weather (heavy rainfall, lightning, and hail), ocean, air pollution (particulate matter), forest fire, fog, aircraft icing, volcanic eruption, and space weather. The next generation satellites with highly enhanced spatial and temporal resolution images, expanding channels, and basic and additional products are expected to create the new valuable benefits, including the contribution to the reduction of socioeconomic losses due to weather-related disasters. In particular, the new satellite observations are readily applicable to early warning and very-short time forecast application of hazardous weather phenomena, global climate change monitoring and adaptation, improvement of numerical weather forecast skill, and technical improvement of space weather monitoring and forecast. Several policy plans for expanding the application of the next generation satellite data are suggested.

• Journal of the Korean earth science society
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• v.39 no.2
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• pp.131-138
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• 2018

The purpose of this study is to improve the quantitative precipitation estimation method based on satellite brightness temperature. The non-linear equation for rainfall estimation is improved by analysing precipitation cases around the Korean peninsula in summer. Radar reflectivity is adopted the CAPPI 1.5 and CMAX composite fields that provided by the Korea Meteorological Agency (KMA). In addition, the satellite data are used infrared, water vapor and visible channel measured from meteorological imager sensor mounted on the Chollian satellite. The improved algorithm is compared with the results of the A-E method and CRR analytic function. POD, FAR and CSI are 0.67, 0.76 and 0.21, respectively. The MAE and RMSE are 2.49 and 6.18 mm/h. As the quantitative error was reduced in comparison to A-E and qualitative accuracy increased in compare with CRR, the disadvantage of both algorithms are complemented. The method of estimating precipitation through a relational expression can be used for short-term forecasting because of allowing precipitation estimation in a short time without going through complicated algorithms.

• International Journal of Aeronautical and Space Sciences
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• v.16 no.3
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• pp.445-450
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• 2015

Generally, Low Earth Orbit (LEO) satellites are used to collect image or video from earth's surface. The collected data are stored on-board and/or transmitted to the main ground station directly or via polar ground station using terrestrial line. Today, an intersatellite link between a LEO and a GEO satellite allows transmission of the collected data to the main ground station through the GEO satellite. In this study, an approach for a continuous communication starting from LEO through GEO to ground station is proposed by determining the optimum ground station locations. In doing so, diverse ground stations help to determine the GEO orbit as well. Cross-correlation of the long term daily rainfall averages are multiplied with the logarithmic correlation of the sites to calculate the joint correlation of the diverse ground station locations. The minimum values of this joint correlation yield the optimum locations of the ground stations for Q/V-band communication and satellite control operations. Results for several case studies are listed.

• Water Engineering Research
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• v.3 no.2
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• pp.113-122
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict flash floods. In this study, a Quantitative Flood Forecasting (QFF) model was developed by incorporating the evolving structure and frequency of intense weather systems and by using neural network approach. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as lifetime, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. All these processes stretched leadtime up to 18 hours. The QFF model will be applied to the mid-Atlantic region of United States in a forthcoming paper.

• Journal of IKEEE
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• v.15 no.1
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• pp.86-95
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• 2011

In this paper, we propose a testbed system for the crisis management system of the power grid(CMS-PG) using satellite communication network. For the verification of CMS-PG, the proposed system composed of the simulator of satellite communication network and the simulator of phase measurement unit. Proposed satellite communication simulator can evaluate the delay and the robustness of the communication according to the rainfall and the humidity of local site. And the proposed simulator can calculates the voltage stability by hardware implementation using FPGA. Using the proposed testbed system, we adapted its function of crisis management system for the conventional power grid.

• Journal of Korea Water Resources Association
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• v.56 no.2
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• pp.75-89
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• 2023

As the Mekong River basin is a nationally shared river, it is difficult to collect precipitation data, and the quantitative and qualitative quality of the data sets differs from country to country, which may increase the uncertainty of hydrological analysis results. Recently, with the development of remote sensing technology, it has become easier to obtain grid-based precipitation products(GPPs), and various hydrological analysis studies have been conducted in unmeasured or large watersheds using GPPs. In this study, rainfall-runoff simulation in the Mekong River basin was conducted using the SWAT model, which is a quasi-distribution model with three satellite GPPs (TRMM, GSMaP, PERSIANN-CDR) and two GPPs (APHRODITE, GPCC). Four water level stations, Luang Prabang, Pakse, Stung Treng, and Kratie, which are major outlets of the main Mekong River, were selected, and the parameters of the SWAT model were calibrated using APHRODITE as an observation value for the period from 2001 to 2011 and runoff simulations were verified for the period form 2012 to 2013. In addition, using the ConvAE, a convolutional neural network model, spatio-temporal correction of original satellite precipitation products was performed, and rainfall-runoff performances were compared before and after correction of satellite precipitation products. The original satellite precipitation products and GPCC showed a quantitatively under- or over-estimated or spatially very different pattern compared to APHPRODITE, whereas, in the case of satellite precipitation prodcuts corrected using ConvAE, spatial correlation was dramatically improved. In the case of runoff simulation, the runoff simulation results using the satellite precipitation products corrected by ConvAE for all the outlets have significantly improved accuracy than the runoff results using original satellite precipitation products. Therefore, the bias correction technique using the ConvAE technique presented in this study can be applied in various hydrological analysis for large watersheds where rain guage network is not dense.