• Journal of the Optical Society of Korea
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• v.18 no.5
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• pp.605-610
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• 2014

Clouds play an important role in climate change, in the prediction of local weather, and also in aviation safety when instrument assisted flying is unavailable. Presently, various ground-based instruments used for the measurements of the cloud base height or velocity. Lidar techniques are powerful and have many applications in climate studies, including the clouds' temperature measurement, the aerosol particle properties, etc. Otherwise, it is very circumscribed in cloud velocity measurements because there is no Doppler effect if the clouds move in the perpendicular direction to the laser beam path of Doppler lidar. In this paper, we present a method for the measurement of cloud velocity using lidar's range detection and DIC (Digital Image Correlation) system to overcome the disadvantage of Doppler lidar. The lidar system acquires the distance to the cloud, and the cloud images are tracked using the developed fast correlation algorithm of DIC. We acquired the velocities of clouds using the calculated distance and DIC algorithm. The measurement values had a linear distribution.

• Atmosphere
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• v.29 no.5
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• pp.659-669
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• 2019

This study assesses the quality of the total column ozone (TCO) data from five reanalysis datasets against nine independent observation in East Asia. The assessed datasets are the ECMWF Interim reanalysis (ERAI), Monitoring Atmosphere Composition and Climate reanalysis (MACC), Copernicus Atmosphere Monitoring Service reanalysis (CAMS), the NASA Modern-Era Retrospective analysis for Research and Applications, Version2 (MERRA2), and NCEP Climate Forecast System Reanalysis (CFSR). All datasets reasonably well capture the spatial distribution, annual cycle and interannual variability of TCO in East Asia. In particular, characteristics of TCO according to the latitude difference were similar at all points with a maximum bias of less than about 4%. Among them, CAMS and CFSR show the smallest mean bias and root-mean square error across all nine ground-based observations. This result indicates that while TCO data in modern reanalyses are reasonably good, CAMS and CFSR TCO data are the best for analysing the spatio-temporal variability and change of TCO in East Asia.

• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.709-719
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• 2022

Recently, as the importance of environmental protection has emerged, interest in new and renewable energy is also increasing worldwide. In particular, the solar energy sector accounts for the highest production rate among new and renewable energy in Korea due to its infinite resources, easy installation and maintenance, and eco-friendly characteristics such as low noise emission levels and less pollutants during power generation. However, although climate prediction is essential since solar power is affected by weather and climate change, solar radiation, which is closely related to solar power, is not currently forecasted by the Korea Meteorological Administration. Solar radiation prediction can be the basis for establishing a reasonable new and renewable energy operation plan, and it is very important because it can be used not only in solar power but also in other fields such as power consumption prediction. Therefore, this study was conducted for the purpose of improving the accuracy of solar radiation. Solar radiation was predicted by a total of three weather variables, temperature, humidity, and cloudiness, and solar radiation outside the atmosphere, and the results were compared using various models. The CatBoost model was best obtained by fitting and comparing the Boosting series (XGB, CatBoost) and RNN series (Simple RNN, LSTM, GRU) models. In addition, the results were further improved through Time series cross-validation.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.126-126
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• 2021

Stormwater reduction plays an important role in the safety and resilience to flooding in urban areas. Due to rapid climate change, the world is experiencing abnormal climate phenomena, and sudden floods and concentrated torrential rains are frequently occurring in urban basins and the amount of outflow due to stormwater increases. In addition, the damage caused by urban flooding and inundation due to extreme rainfall exceeding the events that occurred in the past increases. To solve this problem, water supply, drainage, and water supply for sustainable urban development, the water management paradigm is shifting from sewage maintenance to water circulation and water-sensitive cities. So, in this study, The purpose of this study is to examine measures to increase the resilience of urban ecosystem systems for urban excellence reduction by analyzing the effects of green infra structures and LID techniques and evaluating changes in resilience. In this study, for simulating and analysis of runoff for various stormwater patterns and LID applications, Storm Water Management Model (SWMM) was used.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.2
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• pp.39-52
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• 2020

A high quality, long-term, high-resolution precipitation dataset is an essential in climate analyses and global water cycles. Rainfall data from station observations are inadequate over many parts of the world, especially North Korea, due to non-existent observation networks, or limited reporting of gauge observations. As a result, satellite-based rainfall estimates have been used as an alternative as a supplement to station observations. The Climate Hazards Group Infrared Precipitation (CHIRP) and CHIRP combined with station observations (CHIRPS) are recently produced satellite-based rainfall products with relatively high spatial and temporal resolutions and global coverage. CHIRPS is a global precipitation product and is made available at daily to seasonal time scales with a spatial resolution of 0.05° and a 1981 to near real-time period of record. In this study, we analyze the applicability of CHIRPS data on the Korean Peninsula by supplementing the lack of precipitation data of North Korea. We compared the daily precipitation estimates from CHIRPS with 81 rain gauges across Korea using several statistical metrics in the long-term period of 1981-2017. To summarize the results, the CHIRPS product for the Korean Peninsula was shown an acceptable performance when it is used for hydrological applications based on monthly rainfall amounts. Overall, this study concludes that CHIRPS can be a valuable complement to gauge precipitation data for estimating precipitation and climate, hydrological application, for example, drought monitoring in this region.

• Proceedings of the KSRS Conference
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• v.1
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• pp.150-152
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• 2006

The DISCOVER Project (${\underline{D}}istributed$ ${\underline{I}}nformation$ ${\underline{S}}ervices$ for ${\underline{C}}limate$ and ${\underline{O}}cean$ products and ${\underline{V}}isualizations$ for ${\underline{E}}arth$ ${\underline{R}}esearch$) is a NASA funded Earth Science REASoN project that strives to provide highly accurate, carefully calibrated, long-term climate data records and near-real-time ocean products suitable for the most demanding Earth research applications via easy-to-use display and data access tools. A key element of DISCOVER is the merging of data from the multiple sensors on multiple platforms into geophysical data sets consistent in both time and space. The project is a follow-on to the SSM/I Pathfinder and Passive Microwave ESIP projects which pioneered the simultaneous retrieval of sea surface temperature, surface wind speed, columnar water vapor, cloud liquid water content, and rain rate from SSM/I and TMI observations. The ocean products available through DISCOVER are derived from multi-sensor observations combined into daily products and a consistent multi-decadal climate time series. The DISCOVER team has a strong track record in identifying and removing unexpected sources of systematic error in radiometric measurements, including misspecification of SSM/I pointing geometry, the slightly emissive TMI antenna, and problems with the hot calibration source on AMSR-E. This in-depth experience with inter-calibration is absolutely essential for achieving our objective of merging multi-sensor observations into consistent data sets. Extreme care in satellite inter-calibration and commonality of geophysical algorithms is applied to all sensors. This presentation will introduce the DISCOVER products currently available from the web site, http://www.discover-earth.org and provide examples of the scientific application of both the diurnally corrected optimally interpolated global sea surface temperature product and the 4x-daily global microwave water vapor product.

• Journal of Forest and Environmental Science
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• v.34 no.4
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• pp.338-351
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• 2018

This study was conducted in a long-term experimental forest in the central hardwoods region of southwestern Michigan to retrospectively examine the role of past forest management practices and climate on red oak (Quercus rubra L.) productivity. Initially, in 1971, plots within the experimental forest were treated separately with a clearcut and shelterwood regeneration harvest in an attempt to increase red oak regeneration. From 1987-1989, a new study was initiated within a portion of the clearcut and shelterwood plots to evaluate the effectiveness of additional oak crop tree release using mechanical and chemical applications. Cumulative diameter and mortality rates of 719 red oaks were monitored across the four silvicultural treatments: Clearcut-A (clearcut without additional release treatment), Clearcut-B (clearcut with additional release treatment), Shelterwood-A (shelterwood without additional release), and Shelterwood-B (shelterwood with additional release) plus an untreated control. Increment cores were obtained from red oak trees and neighboring competitor species. Tree-ring analyses (dendrochronology) were applied to examine the effect of these silvicultural treatments and climatic factors (temperature and precipitation) on red oak productivity. The results indicated that crop tree release following a clearcut or shelterwood harvest reduced mortality rates and thus increased survival of red oak. Red oak in control plots or plots only receiving the initial regeneration harvesting treatment and no additional competition release were negatively affected by climatic stress, which included summer moisture stress. In contrast, red oak in plots that received the competition release treatment from shade tolerant tree species not only had higher tree level productivity (i.e., tree basal area) and lower mortality rates, but were also relatively more resilient to climatic stress by showing limited or no associations between climate and growth.

• Atmosphere
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• v.25 no.4
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• pp.669-683
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• 2015

In this study, we investigated the prediction skills of four multiple linear regression methods for monthly air temperature over South Korea. We used simulation results from four regional climate models (RegCM4, SNURCM, WRF, and YSURSM) driven by two boundary conditions (NCEP/DOE Reanalysis 2 and ERA-Interim). We selected 15 years (1989~2003) as the training period and the last 5 years (2004~2008) as validation period. The four regression methods used in this study are as follows: 1) Homogeneous Multiple linear Regression (HMR), 2) Homogeneous Multiple linear Regression constraining the regression coefficients to be nonnegative (HMR+), 3) non-homogeneous multiple linear regression (EMOS; Ensemble Model Output Statistics), 4) EMOS with positive coefficients (EMOS+). It is same method as the third method except for constraining the coefficients to be nonnegative. The four regression methods showed similar prediction skills for the monthly air temperature over South Korea. However, the prediction skills of regression methods which don't constrain regression coefficients to be nonnegative are clearly impacted by the existence of outliers. Among the four multiple linear regression methods, HMR+ and EMOS+ methods showed the best skill during the validation period. HMR+ and EMOS+ methods showed a very similar performance in terms of the MAE and RMSE. Therefore, we recommend the HMR+ as the best method because of ease of development and applications.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.904-913
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• 2020

As the data volumes and complexity rapidly increase, data-intensive science handling large-scale scientific data needs to investigate new techniques for intelligent storage and data distribution over networks. Recently, Named Data Networking (NDN) and data-intensive science communities have inspired innovative changes in distribution and management for large-scale experimental data. In this article, analysis on NDN testbeds for large-scale scientific data such as climate science data and High Energy Physics (HEP) data is presented. This article is the first attempt to analyze existing NDN testbeds for large-scale scientific data. NDN testbeds for large-scale scientific data are described and discussed in terms of status, NDN-based application, and features, which are NDN testbed instance for climate science, NDN testbed instance for both climate science and HEP, and the NDN testbed in SANDIE project. Finally various issues to prevent pitfalls in NDN testbed establishment for large-scale scientific data are analyzed and discussed, which are drawn from the descriptions of NDN testbeds and features on them.

• Journal of Korea Water Resources Association
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• v.51 no.8
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• pp.629-642
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• 2018

Future variability of the spatial patterns of rainfall events is the point of water-related risks and impacts of climate change. Recent related researches are mostly conducted based on the outcomes from General Circulation Models (GCMs), especially Coupled Model Intercomparison Project, phase 5 (CMIP5) GCMs which are the most advanced version of climate modeling system. GCM data have been widely used for various studies as the data utility keep getting improved. Meanwhile the model performances especially for raw GCM outputs are rarely evaluated prior to the applications although the process would essential for reasonable use of model forecasts. This study attempt to quantitatively evaluate the skills of 29 CMIP5 GCMs in reproducing spatial climatologies of precipitation in East Asia. We used 3 different gridded observational data as the references available over the study area and calculated correlation and errors of spatial patterns simulated by GCMs. As a result, the study presented diversity of the GCM evaluation in the performance, rank, or accuracy by different configurations, such as target area, evaluation method, and observation data. Yet, we found that Hadley-centre affiliated models comparatively performs better for the meso-scale area in East Asia and MPI_ESM_MR and CMCC family showed better performance specifically for the korean peninsula. We expect that the results and thoughts of this study would be considered in screening suitable GCMs for specific area, and finally contribute to extensive utilization of the results from climate change related researches.