• Journal of Wetlands Research
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• v.22 no.1
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• pp.15-23
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• 2020

The purpose of this study is to investigate how the degree of distribution influences the calibration of snow and runoff in distributed hydrological models using a multi-criteria calibration method. The Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) developed by NOAA-National Weather Service (NWS) is employed to estimate optimized parameter sets. We have 3 scenarios depended on the model complexity for estimating best parameter sets: Lumped, Semi-Distributed, and Fully-Distributed. For the case study, the Durango River Basin, Colorado is selected as a study basin to consider both snow and water balance components. This study basin is in the mountainous western U.S. area and consists of 108 Hydrologic Rainfall Analysis Project (HRAP) grid cells. 5 and 13 parameters of snow and water balance models are calibrated with the Multi-Objective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm. Model calibration and validation are conducted on 4km HRAP grids with 5 years (2001-2005) meteorological data and observations. Through case study, we show that snow and streamflow simulations are improved with multiple criteria calibrations without considering model complexity. In particular, we confirm that semi- and fully distributed models are better performances than those of lumped model. In case of lumped model, the Root Mean Square Error (RMSE) values improve by 35% on snow average and 42% on runoff from a priori parameter set through multi-criteria calibrations. On the other hand, the RMSE values are improved by 40% and 43% for snow and runoff on semi- and fully-distributed models.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.509-520
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• 2023

Various drought indices are widely used for assessing drought conditions which are affected by many factors such as precipitation, soil moisture, and runoff. The values of drought indices varies depending on hydro-meteorological data and calculation formulas, and the judgment of the drought condition may also vary. This study selected four calculation components such as precipitation data length, accumulation period, probability distribution function, and parameter estimation method as the sources of uncertainty in the calculation of standardized precipitation index (SPI), and evaluated their contributions to the uncertainty using root mean square error (RMSE) and linear mixed model (LMM). The RMSE estimated the overall errors in the SPI calculation, and the LMM was used to quantify the uncertainty contribution of each factor. The results showed that as the accumulation period increased and the data period extended, the RMSEs decreased. The comparison of relative uncertainty using LMM indicated that the sample size had the greatest impact on the SPI calculation. In addition, as sample size increased, the relative uncertainty related to the sample size used for SPI calculation decreased and the relative uncertainty associated with accumulation period and parameter estimation increased. In conclusion, to reduce the uncertainty in the SPI calculation, it is essential to collect long-term data first, followed by the appropriate selection of probability distribution models and parameter estimation methods that represent well the data characteristics.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.4
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• pp.317-324
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• 2015

GNSS was firstly proposed for application in weather forecasting in the mid-1980s. It has continued to demonstrate the practical uses in GNSS meteorology, and other relevant researches are currently being conducted. Precipitable Water Vapor (PWV), calculated based on the GNSS signal delays due to the troposphere of the Earth, represents the amount of the water vapor in the atmosphere, and it is therefore widely used in the analysis of various weather phenomena such as monitoring of weather conditions and climate change detection. In this study we calculated the PWV through the meteorological information from an Automatic Weather Station (AWS) as well as GNSS data processing of a Continuously Operating Reference Station (CORS) in order to analyze the heavy snowfall of the Ulsan area in early 2014. Song’s model was adopted for the weighted mean temperature model (T_m), which is the most important parameter in the calculation of PWV. The study period is a total of 56 days (February 2013 and 2014). The average PWV of February 2014 was determined to be 11.29 mm, which is 11.34% lower than that of the heavy snowfall period. The average PWV of February 2013 was determined to be 10.34 mm, which is 8.41% lower than that of not the heavy snowfall period. In addition, certain meteorological factors obtained from AWS were compared as well, resulting in a very low correlation of 0.29 with the saturated vapor pressure calculated using the empirical formula of Magnus. The behavioral pattern of PWV has a tendency to change depending on the precipitation type, specifically, snow or rain. It was identified that the PWV showed a sudden increase and a subsequent rapid drop about 6.5 hours before precipitation. It can be concluded that the pattern analysis of GNSS PWV is an effective method to analyze the precursor phenomenon of precipitation.

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.1
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• pp.1-14
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• 2006

Tropospheric aerosols are highly variant in time and space due to non-uniform source distribution and strong influence of meteorological conditions. Backscatter lidar measurement is useful to understand vertical distribution of aerosol. However, the backscatter lidar equation is undetermined due to its dependence on the two unknowns, extinction and backscattering coefficient. This dependence necessitates the exact value of the ratio between two parameters, that is, the lidar ratio. Also, Iidar ratio itself is useful optical parameter to understand properties of aerosols. Tropospheric aerosols were observed to understand variance of lidar ratio at Anmyeon island ($36.32^{/circ}N$, $126.19^{/circ}E$), Korea using a multi-wavelength raman lidar system developed by the Advanced Environmental Monitoring Research Center (ADEMRC), Gwangju Institute Science and Technology (GIST), Korea during measurement periods; March 15$\sim$April $16^{th}$, 2004 and May 24$\sim$ $8^{th}$ 2005. Extinction coefficient, backscattering coefficient, and lidar ratio were measured at 355 and 532 nm by the Raman method. Different types of aerosol layers were distinguished by the differences in the optical properties such as Angstrom exponent, and lidar ratio. The average value of lidar ratio during two observation periods was found to be $50.85\pm4.88$ sr at 355 nm and $52.43\pm15.15$ sr at 532 nm at 2004 and $57.94\pm10.29$ sr at 355 nm and $82.24\pm15.90$ sr at 532 nm at 2005. We conduct hysplit back-trajectory to know the pathway of airmass during the observation periods. We also calculate lidar ratio of different type of aerosol, urban, maritime, dust, continental aerosols using OPAC (Optical Properties of Aerosols and Clouds), Remote sensing of atmospheric aerosol using a multi-wavelengh lidar system with Raman channels is quite and powerful tool to characterize the optical propertises of troposheric aerosols.

• Atmosphere
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• v.23 no.1
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• pp.93-102
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• 2013

Atmospheric transport pathway of an air mass is an important constraint controlling the chemical properties of the air mass observed at a designated location. Such information could be utilized for understanding observed temporal variabilities in atmospheric concentrations of long-lived chemical compounds, of which sinks and/or sources are related particularly with natural and/or anthropogenic processes in the surface, and as well as for performing inversions to constrain the fluxes of such compounds. The Lagrangian particle dispersion model FLEXPART provides a useful tool for estimating detailed particle dispersion during atmospheric transport, a significant improvement over traditional "single-line" trajectory models that have been widely used. However, those without a modeling background seeking to create simple back-trajectory maps may find it challenging to optimize FLEXPART for their needs. In this study, we explain how to set up, operate, and optimize FLEXPART for back-trajectory analysis, and also provide automatization programs based on the open-source R language. Discussions include setting up an "AVAILABLE" file (directory of input meteorological fields stored on the computer), creating C-shell scripts for initiating FLEXPART runs and storing the output in directories designated by date, as wells as processing the FLEXPART output to create figures for a back-trajectory "footprint" (potential emission sensitivity within the boundary layer). Step by step instructions are explained for an example case of calculating back trajectories derived for Anmyeon-do, Korea for January 2011. One application is also demonstrated in interpreting observed variabilities in atmospheric $CO_2$ concentration at Anmyeon-do during this period. Back-trajectory modeling information introduced in this study should facilitate the creation and automation of most common back-trajectory calculation needs in atmospheric research.

• Korean Journal of Remote Sensing
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• v.29 no.4
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• pp.415-421
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• 2013

A new approach to retrieve the single-scattering albedo (SSA) of Asian dust plume, mixed with pollution particles, using multi-wavelength Raman lidar system was suggested in this study. Asian dust plume was separated as dust and non-dust particle (i.e. spherical particle) by the particle depolarization ratio at 532 nm. The vertical profiles of optical properties (the particle extinction coefficient at 355 and 532 nm and backscatter coefficient at 355, 532 and 1064 nm) for non-dust particle were used as input parameter for the inversion algorithm. The inversion algorithm provides the vertical distribution of microphysical properties of non-dust particle only so that the estimation of the SSA for the Asian dust in mixing state was suggested in this study. In order to estimate the SSA for the mixed Asian dust, we combined the SSA of non-dust particles retrieved by the inversion algorithms with assumed the SSA of 0.96 at 532 nm for dust. The retrieved SSA of Asian dust plume by lidar data was compared with the Aerosol Robotics Network (AERONET) retrieved values and showed good agreement.

• Journal of Satellite, Information and Communications
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• v.6 no.1
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• pp.37-44
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• 2011

COMS(Chollian) satellite which was launched on June 26, 2010 has three payloads for Ka-band communications, geostationary ocean color imaging and meteorological imaging. In order to make efficient use of the geostationary satellite, a concept of mission operations has been considered from the beginning of the satellite ground control system development. COMS satellite mission operations are classified by daily, weekly, monthly, and seasonal operations. Daily satellite operations include mission planning, command planning and transmission, telemetry processing and analysis, ranging and orbit determination, ephemeris and event prediction, and wheel off-loading set point parameter calculation. As a weekly operation, North-South station keeping maneuver and East-West station keeping maneuver should be performed on Tuesday and Thursday, respectively. Spacecraft oscillator updating parameter should be calculated and uploaded once a month. Eclipse operations should be performed during a vernal equinox and autumnal equinox season. In this paper, operational validations of the major functions in COMS SGCS are presented for the first three month of in-orbit test operations. All of the major functions have been successfully verified and the COMS SGCS will be used for the mission operations of the COMS satellite for 7 years of mission life time and even more.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.2
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• pp.15-22
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• 2012

The variation in the wind speed with height above ground is called the wind shear profile. In the field of wind resource assessment, analysts typically use one of two mathematical relations to characterize the measured wind shear profile: the logarithmic profile (log law) and the power law profile (power law). The logarithmic law uses the surface roughness as a parameter, and the power law uses the power law exponent as a parameter. The shape of the wind shear profile typically depends on several factors, most notably the roughness of the surrounding terrain and the stability of the atmosphere. Since the atmospheric stability changes with season, time of day, and meteorological conditions, the surface roughness and the power law exponent also tends to change in time. For this study, Using the observed data from Met-mast, located in Pyeongdae, Handong in Jeju. we used the matlab and windograper to calculate roughness length and the law exponents. These calculations are similar to reference the data, but they have different ranges. In the ocean case, each reference data and calculated data was the same, but the crop area is higher than the earlier studies. In addition, the agricultural village is lower than the earlier studies.

• Journal of Korean Society of Environmental Engineers
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• v.22 no.4
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• pp.785-796
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• 2000

In this study, the possibility of application of TUNVEN model was investigated through the validation and calibration processes. In order to validate and calibrate the TUNVEN model developed in USA to obtain prediction of the quasi-steady state longitudinal air velocities and the pollutants concentrations by solving the coupled one-dimensional steady state tunnel aerodynamic and advection equations. The major input parameters such as the concentration data for CO and $NO_x$, meteorological data and traffic volume in Hawngryung tunnel were measured. Prior to preparing the input parameters, the sensitivity analysis was conducted to identify the input parameters which need to be most accurately estimated in TUNVEN program. In order to establish the relationships between the model values and the measured values, the linear regression analysis was applied. In linear regression analysis, the model values were taken as independent parameter(X) and the measured values were taken as dependent parameter(Y) for four cases of data sef. From the results of linear regression analysis, the correlation coefficient(r) for four cases were calculated more than 0.91 and the values of slope and interception were analyzed as 0.5~2.2 and 0.01~2.3 respectively. From the above results, we concluded that the suitability of TUNVEN model was identified in prediction the longitudinal pollutant concentrations in tunnel.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.1
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• pp.27-35
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• 2017

This research was to elucidate the generation method of daily precipitation data from monthly data. We applied a combined method of Markov chain and gamma distribution function using 4 specific parameters of ${\alpha}$, ${\beta}$, p(W/W) and p(W/D) for generation of daily rainfall data using daily precipitation data for the past 30 years which were collected from the country's 23 meteorological offices. Four parameters, applied to use for the combination method, were calculated by maximum likelihood method in location of 23 sites. There are high correlations of 0.99, 0.98 and 0.98 in rainfall days, rainfall probability and mean amount of daily rainfall between measured and simulated data in case of those parameters. In case of using parameters estimated from monthly precipitation, correlation coefficients in rainfall days, rainfall probability and mean amount of daily rainfall are 0.84, 0.83 and 0.96, respectively. We concluded that a combination method with parameter estimation from monthly precipitation data can be applied, in practical purpose such as assessment of climate change in agriculture and water resources, to get daily precipitation data in Korea.