• Atmosphere
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• v.32 no.4
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• pp.263-276
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• 2022

The observation error of satellite radiation data that assimilated into the Korean Integrated Model (KIM) was diagnosed by applying the Hollingsworth and Lönnberg and Desrozier techniques commonly used. The magnitude and correlation of the observation error, and the degree of contribution for the satellite radiance data were calculated. The observation errors of the similar device, such as Advanced Technology Microwave Sounder (ATMS) and Advanced Microwave Sounding Unit-A shows different characteristics. The model resolution accounts for only 1% of the observation error, and seasonal variation is not significant factor, either. The observation error used in the KIM is amplified by 3-8 times compared to the diagnosed value or standard deviation of first-guess departures. The new inflation value was calculated based on the correlation between channels and the ratio of background error and observation error. As a result of performing the model sensitivity evaluation by applying the newly inflated observation error of ATMS, the error of temperature and water vapor analysis field were decreased. And temperature and water vapor forecast field have been significantly improved, so the accuracy of precipitation prediction has also been increased by 1.7% on average in Asia especially.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.667-676
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• 1999

We investigate the effects of omission of initial observations in each individuals in the panel data regression model when the disturbances follow a serially correlated one way error components. We show that the first transformed observation can have a relative large hat matrix diagonal component and a large influence on parameter estimates when the correlation coefficient is large in absolute value.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.4
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• pp.269-276
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• 2012

The objective of this short communication is to introduce several evaluation methods to crop model users because the evaluation of crop model performance is an important step to develop or select crop model. In this paper, mean error, mean absolute error, index of agreement, root mean square error, efficiency of model, accuracy factor and bias factor were explained and compared in terms of dimension and observed number. Efficiency of model and index of agreement are dimensionless and independent of number of observation. Relative root mean square, accuracy factor and bias factor are dimensionless and not independent of number of observation. Mean error and mean absolute error are affected by dimension and number of observation.

• Atmosphere
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• v.33 no.1
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• pp.33-47
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• 2023

In this study, for the application of observation errors to the Korean Integrated Model (KIM) to utilize the Constellation Observing System for Meteorology, Ionosphere & Climate-2 (COSMIC-2) new satellites, the observation errors were diagnosed based on the Desroziers method using the cost function in the process of variational data assimilation. We calculated observation errors for all observational species being utilized for KIM and compared with their relative values. The observation error of the calculated the Global Navigation Satellite System Radio Occultation (GNSS RO) was about six times smaller than that of other satellites. In order to balance with other satellites, we conducted two experiments in which the GNSS RO data expanded by about twice the observation error. The performance of the analysis field was significantly improved in the tropics, where the COSMIC-2 data are more available, and in the Southern Hemisphere, where the influence of GNSS RO data is significantly greater. In particular, the prediction performance of the Southern Hemisphere was improved by doubling the observation error in global region, rather than doubling the COSMIC-2 data only in areas with high density, which seems to have been balanced with other observations.

• Journal of the Korea Society of Computer and Information
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• v.22 no.1
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• pp.1-7
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• 2017

In this paper, we propose an efficient method that is tracking an object in real time using particle filter and adaptive observation model. When tracking object, it happens object shape variation by camera or object movement in variety environments. The traditional method has an error of tracking from these variation, because it has fixed observation model about the selected object by the user in the initial frame. In order to overcome these problems, we propose a method that updates the observation model by calculating the similarity between the used observation model and the eight-way of edge model from the current position. If the similarity is higher than the threshold value, tracking the object using updated observation model to reset observation model. On the contrary to this, the algorithm which consists of a process is to maintain the used observation model. Finally, this paper demonstrates the performance of the stable tracking through comparison with the traditional method by using a number of experimental data.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.55 no.3
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• pp.206-216
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• 2019

This study is aimed to compare stock assessment models depending on how the models fit to observed data. Process-error model, Observation-error model, and Bayesian state-space model for the Korean Western coast fisheries were applied for comparison. Analytical results show that there is the least error between the estimated CPUE and the observed CPUE with the Bayesian state-space model; consequently, results of the Bayesian state-space model are the most reliable. According to the Bayesian State-space model, potential yield of fishery resources in the West Sea of Korea is estimated to be 231,949 tons per year. However, the results show that the fishery resources of West Sea have been decreasing since 1967. In addition, the amounts of stock in 2013 are assessed to be only 36% of the stock biomass at MSY level. Therefore, policy efforts are needed to recover the fishery resources of West Sea of Korea.

• Atmosphere
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• v.30 no.1
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• pp.17-30
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• 2020

KMA performed the special observation program to provide information about severe weather and to monitor typhoon PRAPIROON using the ship which called the Gisang 1 from 29 June 2018 to 4 July 2018 (UTC). For this period, upper-air was observed 21 times with 6 hour intervals using rawinsonde in the Gisang 1. We investigated the impact of upper-air observation data from the Gisang 1 on the performance of the operational convective scale model (we called LDAPS). We conducted two experiments that used all observation data including upper-air observation data from the Gisang 1 (OPER) and without it (EXPR). For a typhoon PRAPIROON case, track forecast error of OPER was lower than EXPR until forecast 24 hours. The intensity forecast error of OPER for minimum sea level pressure was lower than EXPR until forecast 12 hours. The intensity forecast error of OPER for maximum wind speed was mostly lower than EXPR until forecast 30 hours. OPER showed good performance for typhoon forecast compared with EXPR at the early lead time. Two precipitation cases occurred in the south of the Korean peninsula due to the impact of Changma on 1 July and typhoon on 3 July. The location of main precipitation band predicted from OPER was closer to observations. As assimilating upper-air data observed in the Gisang 1 to model, it showed positive results in typhoon and precipitation cases.

• Ocean and Polar Research
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• v.41 no.3
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• pp.183-191
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• 2019

This study sought to find which model is most appropriate for estimating potential yield in the East Sea, Republic of Korea. For comparison purposes, the Process-error model, ASPIC model, Maximum entropy model, Observation-error model, and Bayesian state-space model were applied using data from catch amounts and total efforts of the whole catchable fishes in the East Sea. Results showed that the Bayesian state-space model was estimated to be the most reliable among the models. Potential yield of catchable species was estimated to be 227,858 tons per year. In addition, it was analyzed that the amount of fishery resources in 2016 was about 63% of the biomass that enables a fish stock to deliver the maximum sustainable yield.

• Journal of the Korean earth science society
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• v.24 no.1
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• pp.7-21
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• 2003

The state-of-art third generation wave prediction model WAM was applied to the Korean seas for a winter monsoon period of January 1997. The wind field used in the present study is the global NSCAT-ERS/NCEP blended winds, which was further interpolated using a bi-cubic spline interpolator to fine grid limited area shallow water regime surrounding the Korean seas. To evaluate and investigate the accuracy of WAM, the hindcasted wave heights are compared with observed data from two shallow water buoys off Chil-Bal and Duk-Juk. A detailed study has been carried with the various meteorological parameters in observed buoy data and its inter-dependency on model computed wave fields was also investigated. The RMS error between the observation and model computed wave heights results to 0.489 for Chil-Bal and 0.417 for Duk-Juk. A similar comparison between the observation and interpolated winds off Duk-Juk show RMS error of 2.28 which suggest a good estimate for wave modelling studies.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.53 no.6
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• pp.925-933
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• 2020

This study is aimed to compare stock assessment models which are effective in assessing red snow crab Chinonoecetes japonicus resources and to select and apply an effective stock assessment model in the future. In order to select an effective stock assessment model, a process-error model, observation-error model, and a Bayesian state-space model were estimated. Analytical results show that the least error is observed between the estimated CPUE (catch per unit effort) and the observed CPUE when using the Bayesian state-space model. For the Bayesian state-space model, the 95% credible interval(CI) ranges for the maximum sustainable yield (MSY), carrying capacity (K), catchability coefficient (q), and intrinsic growth (r) are estimated to be 10,420-47,200 tons, 185,200-444,800 tons, 3.81E-06-9.02E-06, and 0.14-0.66, respectively. The results show that the Bayesian state-space model was most reliable among models.