• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.1
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• pp.1-10
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• 2021

In this study, Information for the case of seawater flooding and observation data over a period of 10 years (2009~2018) was collected. Using machine learning algorithms, the characteristics of the types of seawater flooding and observations by type were classified. Information for the case of seawater flooding was collected from the reports of the Korea Hydrographic and Oceanographic Agency (KHOA) and the Korea Land and Geospatial Informatics Corporation. Observation data for ocean and meteorological were collected from the KHOA and the Korea Meteorological Agency (KMA). The classification of seawater flooding incidence types is largely categorized into four types, and into 5 development types through combination of 4 types. These types were able to distinguish the types of seawater flooding according to the marine weather environment. The main characteristics of each was classified into the following groups: tidal movement, low pressure system, strong wind, and typhoon. Besides, in consideration of the geographical characteristics of the ocean, the thresholds of ocean factors for seawater flooding by region and type were derived.

• Atmosphere
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• v.32 no.1
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• pp.27-37
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• 2022

The Korean Integrated Model (KIM) forecast system was extended to assimilate Horizontal Line-Of-Sight (HLOS) wind observations from the Atmospheric Laser Doppler Instrument (ALADIN) on board the Atmospheric Dynamic Mission (ADM)-Aeolus satellite. Quality control procedures were developed to assess the HLOS wind data quality, and observation operators added to the KIM three-dimensional variational data assimilation system to support the new observed variables. In a global cycling experiment, assimilation of ALADIN observations led to reductions in average root-mean-square error of 2.1% and 1.3% for the zonal and meridional wind analyses when compared against European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) analyses. Even though the observable variable is wind, the assimilation of ALADIN observation had an overall positive impact on the analyses of other variables, such as temperature and specific humidity. As a result, the KIM 72-hour wind forecast fields were improved in the Southern Hemisphere poleward of 30 degrees.

• Atmosphere
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• v.34 no.2
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• pp.177-185
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• 2024

As increasing global interest in renewable energy due to the ongoing climate crisis, there is a growing need for efficient technologies to manage such resources. This study focuses on the predictive skill of daily solar power generation using weather observation and forecast data. Meteorological data from the Korea Meteorological Administration and solar power generation data from the Korea Power Exchange were utilized for the period from January 2017 to May 2023, considering both inland (Daejeon) and coastal (Busan) regions. Temperature, wind speed, relative humidity, and precipitation were selected as relevant meteorological variables for solar power prediction. All data was preprocessed by removing their systematic components to use only their residuals and the residual of solar data were further processed with weighted adjustments for homoscedasticity. Four models, MLR (Multiple Linear Regression), RF (Random Forest), DNN (Deep Neural Network), and RNN (Recurrent Neural Network), were employed for solar power prediction and their performances were evaluated based on predicted values utilizing observed meteorological data (used as a reference), 1-day-ahead forecast data (referred to as fore1), and 2-day-ahead forecast data (fore2). DNN-based prediction model exhibits superior performance in both regions, with RNN performing the least effectively. However, MLR and RF demonstrate competitive performance comparable to DNN. The disparities in the performance of the four different models are less pronounced than anticipated, underscoring the pivotal role of fitting models using residuals. This emphasizes that the utilized preprocessing approach, specifically leveraging residuals, is poised to play a crucial role in the future of solar power generation forecasting.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.1
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• pp.1-10
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• 2010

A regional climate model (RCM) can be a powerful tool to enhance spatial resolution of climate and weather information (IPCC, 2001). In this study we conducted dynamical downscaling using Weather Research and Forecasting Model (WRF) as a RCM in order to obtain high resolution regional agroclimate indices over the Korean Peninsula. For the purpose of obtaining detailed high resolution agroclimate indices, we first reproduced regional weather for the period of March to June, 2002-2008 with dynamic downscaling method under given lateral boundary conditions from NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis data. Normally, numerical model results have shown biases against observational results due to the uncertainties in the modelis initial conditions, physical parameterizations and our physical understanding on nature. Hence in this study, by employing a statistical method, the systematic bias in the modelis results was estimated and corrected for better reproduction of climate on high resolution. As a result of the correction, the systematic bias of the model was properly corrected and the overall spatial patterns in the simulation were well reproduced, resulting in more fine-resolution climatic structures. Based on these results, the fine-resolution agro-climate indices were estimated and presented. Compared with the indices derived from observation, the simulated indices reproduced the major and detailed spatial distributions. Our research shows a possibility to simulate regional climate on high resolution and agro-climate indices by using a proper downscaling method with a dynamical weather forecast model and a statistical correction method to minimize the model bias.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.208-220
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• 2016

Automatic Mountain Meteorology Observation System (AMOS) is an important ingredient for several climatological and forest disaster prediction studies. In this study, we select the optimal sites for AMOS in the mountain areas of Honam and Jeju in order to prevent forest disasters such as forest fires and landslides. So, this study used spatial dataset such as national forest map, forest roads, hiking trails and 30m DEM(Digital Elevation Model) as well as forest risk map(forest fire and landslide), national AWS information to extract optimal site selection of AMOS. Technical methods for optimal site selection of the AMOS was the firstly used multifractal model, IDW interpolation, spatial redundancy for 2.5km AWS buffering analysis, and 200m buffering analysis by using ArcGIS. Secondly, optimal sites selected by spatial analysis were estimated site accessibility, observatory environment of solar power and wireless communication through field survey. The threshold score for the final selection of the sites have to be higher than 70 points in the field assessment. In the result, a total of 159 polygons in national forest map were extracted by the spatial analysis and a total of 64 secondary candidate sites were selected for the ridge and the top of the area using Google Earth. Finally, a total of 26 optimal sites were selected by quantitative assessment based on field survey. Our selection criteria will serve for the establishment of the AMOS network for the best observations of weather conditions in the national forests. The effective observation network may enhance the mountain weather observations, which leads to accurate prediction of forest disasters.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.793-806
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• 2020

In this study,for YSU (Yonsei University), MYJ(Mellor-Yamada-Janjic), ACM2 (Asymmetric Convective Model), and BouLac (Bougeault-Lacarrere) PBL schemes, numerical experiments were performed for the case period (June 26-30, 2014). The PBLH calculated by using the backscatter signal produced by the mobile vehicle-mounted lidar system (LIVE) and the PBLH calculated by the prediction of each PBL schemes of WRF were compared and analyzed. In general, the experiments using the non-local schemes showed a higher correlation than the local schemes for lidar observation. The standard deviation of the PBLH difference for daylight hours was small in the order of YSU (≈0.39 km), BouLac (≈0.45 km), ACM2 (≈0.47 km), MYJ (≈0.53 km) PBL schemes. In the RMSE comparison for the case period, the YSU PBL scheme was found to have the highest precision. The meteorological lider mounted on the vehicle is expected to provide guidance for the analysis of the planetary boundary layer in a numerical model under various weather conditions.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.4
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• pp.281-289
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• 2015

Information on sunshine duration and solar radiation are indispensable to the understanding of crop growth and development. Yet, relevant variables are not available in the Korea Meteorological Administration's (KMA) digital forecast. We proposed the methods of estimating sunshine duration and solar radiation based on the 'sky condition' of digital forecast products and validated using the observed data. The sky condition values (1 for clear, 2 for partly cloudy, 3 for cloudy, and 4 for overcast) were collected from 22 weather stations at 3-hourly intervals from August 2013 to July 2015. According to the observed relationship, these data were converted to the corresponding amount of clouds on the 0 to 10 scale (0 for clear, 4 for partly cloudy, 7 for cloudy, and 10 for overcast). An equation for the cloud amount-sunshine duration conversion was derived from the 3-year observation data at three weather stations with the highest clear day sunshine ratio (i.e., Daegwallyeong, Bukgangneung, and Busan). Then, the estimated sunshine hour data were used to run the Angstrom-Prescott model which was parameterized with the long-term KMA observations, resulting in daily solar radiation for the three weather stations. Comparison of the estimated sunshine duration and solar radiation with the observed at those three stations showed that the root mean square error ranged from 1.5 to 1.7 hours for sunshine duration and from 2.5 to $3.0MJ\;m^{-2}\;day^{-1}$ for solar radiation, respectively.

• Journal of the Korean association of regional geographers
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• v.20 no.4
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• pp.393-408
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• 2014

In this study, the spatio-temporal characteristics of summer extreme precipitation events in the Republic of Korea are examined based on the daily precipitation data observed at approximately 360 sites of both Automatic Weather Station (AWS) and Automated Synoptic Observation System (ASOS) networks by the Korea Meteorological Administration for the recent decade(2002~2011). During the summer Changma period(late June~mid July), both the frequency of extreme precipitation events exceeding 80mm of daily precipitation and their decadal maximum values are greatest at most of weather stations. In contrast, during the Changma pause period (late July~early August), these patterns are observed only in the northern regions of Geyeonggi province and western Kangwon province as such patterns are detected around Mt. Sobaek and Mt. Halla as well as in the southern regions of Geyeonggi province and western Kangwon province during the late Changma period (mid August~early September) due to north-south oscillation of the Changma front. Investigation of their regional patterns confirms that not only migration of the Changma front but also topological components in response to the advection of moistures such as elevation and aspect of major mountain ridges are detrimental to spatio-temporal patterns of extreme precipitation events. These results indicate that each local administration needs differentiated strategies to mitigate the potential damages by extreme precipitation events due to the spatiotemporal heterogeneity of their frequency and intensity during each Changma period.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.315-329
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• 2014

In order to analyze the Land Surface Temperature (LST) in metropolitan area including Seoul, Landsat and MODIS land surface temperature, Automatic Weather Station (AWS) temperature, digital elevation model and landuse are used. Analysis method among the Landsat and MODIS LST and AWS temperature is basic statistics using by correlation coefficient, root-mean-square error and linear regression etc. Statistics of Landsat and MODIS LST are a correlation coefficient of 0.32 and Root Mean Squared Error (RMSE) of 4.61 K, respectively. And statistics of Landsat and MODIS LST and AWS temperature have the correlations of 0.83 and 0.96 and the RMSE of 3.28 K and 2.25 K, respectively. Landsat and MODIS LST have relatively high correlation with AWS temperature, and the slope of the linear regression function have 0.45 (Landsat) and 1.02 (MODIS), respectively. Especially, Landsat 5 has lower correlation about 0.5 or less in entire station, but Landsat 8 have a higher correlation of 0.5 or more despite of lower match point than other satellites. Landsat 7 have highly correlation of more than 0.8 in the center of Seoul. Correlation between satellite LSTs and AWS temperature with landuse (urban and rural) have 0.8 or higher. Landsat LST have correlation of 0.84 and RMSE of more than 3.1 K, while MODIS LST have correlation of more than 0.96 and RMSE of 2.6 K. Consequently, the difference between the LSTs by two satellites have due to the difference in the optical observation and detection the radiation generated by the difference in the area resolution.

• Journal of the Korean association of regional geographers
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• v.19 no.3
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• pp.384-400
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• 2013

In this study, spatio-temporal patterns of extreme precipitation events caused by typhoons are examined based on observational daily precipitation data at approximately 340 weather stations of Korea Meterological Administration's ASOS (Automated Synoptic Observation System) and AWS (Automatic Weather System) networks for the recent 10 year period (2002~2011). Generally, extreme precipitation events by typhoons exceeding 80mm of daily precipitation commonly appear in Jeju Island, Gyeongsangnam-do, and the eastern coastal regions of the Korean Peninsula. However, the frequency, intensity and spatial extent of typhoon-driven extreme precipitation events can be modified depending on the topography of major mountain ridges as well as the pathway of and proximity to typhoons accompanying the anti-clockwise circulation of low-level moisture with hundreds of kilometers of radius. Yellow Sea-passing type of typhoons in July cause more frequent extreme precipitation events in the northern region of Gyeonggi-do, while East Sea-passing type or southern-region-landfall type of typhoons in August-early September do in the interior regions of Gyeongsangnam-do. These results suggest that when local governments develop optimal mitigation strategies against potential damages by typhoons, the pathway of and proximity to typhoons are key factors.