• 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.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1215-1224
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• 2016

Climate information of the high resolution grid units is an important factor to explain the phenomenon in a variety of research field. Statistical linear interpolation models are computationally inexpensive and applicable to any climate data compared to the dynamic simulation method at regional scales. In this paper, we considered four different linear-based statistical interpolation models: general linear model, generalized additive model, spatial linear regression model, and Bayesian spatial linear regression model. The climate variable of interest was the daily mean temperature, where the spatial variability was explained using geographic terrain information: latitude, longitude, elevation. The data were collected by weather stations in January from 2003 and 2012. In the sense of RMSE and correlation coefficient, Bayesian spatial linear regression model showed better performance in reflecting the spatial pattern compared to the other models.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.1
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• pp.61-69
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• 2004

A long-term growth simulation was performed at 99 land units in Yeoncheon county to test the potential adaptability of each land unit for growing soybean cultivars. The land units for soybean cultivation(CZU), each represented by a geographically referenced land patch, were selected based on land use, soil characteristics, and minimum arable land area. Monthly climatic normals for daily maximum and minimum temperature, precipitation, number of rain days and solar radiation were extracted for each CZU from digital climate models(DCM). The DCM grid cells falling within a same CZU were aggregated to make spatially explicit climatic normals relevant to the CZU. A daily weather dataset for 30 years was randomly generated from the monthly climatic normals of each CZU. Growth and development parameters of CROPGRO-soybean model suitable for 2 domestic soybean cultivars were derived from long-term field observations. Three foreign cultivars with well established parameters were also added to this study, representing maturity groups 3, 4, and 5. Each treatment was simulated with the randomly generated 30 years' daily weather data(from planting to physiological maturity) for 99 land units in Yeoncheon to simulate the growth and yield responses to the inter-annual climate variation. The same model was run with input data from the Crop Experiment Station in Suwon to obtain a 30 year normal performance of each cultivar, which was used as a "reference" for evaluation. Results were analyzed with respect to spatial and temporal variation in yield and maturity, and used to evaluate the suitability of each land unit for growing a specific cultivar. A computer program(MAPSOY) was written to help utilize the results in a decision-making procedure for agrotechnology transfer. transfer.

• Journal of Forest and Environmental Science
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• v.37 no.4
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• pp.280-291
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• 2021

The study examined the influence of climate and soil dynamics on vegetation health across the ecological zones in Rivers State, Nigeria. MODIS imagery was used to assess the vegetation health through NDVI and point grid pattern of meteorological data for total precipitation (TP), air temperature (AT), soil moisture (SM) and soil temperature (ST) of 2000, 2003, 2006, 2009, 2012 and 2015 were used for the study. Descriptive and inferential statistics were used for data analysis. Findings showed that NDVI ranged between 0.420 and 0.612 in the freshwater swamp (FWS) while between 0.465 and 0.611 in the rainforest and the NDVI in the mangrove was generally low. The highest mean AT was experienced in the mangrove ecological zone and the least was experienced in the rainforest. The mean SM was generally highest in the rainforest with highest value in 2000 (774.44 m³/m³). The ST was highest in the mangrove and the least was experienced in the rainforest while the TP was highest in the mangrove. NDVI correlated significantly with SM (r=0.720; p<0.05) and ST (r= -0.493; p<0.05). NDVI, SM, TP and ST significantly varied among the ecological zones. Regression analysis showed that vegetation health was significantly related to the combination of soil temperature and soil moisture (R²=0.641; p=0.000). Thus, monitoring the factors that affect vegetation health in a changing climate and soil environments is highly required.

• Journal of Environmental Science International
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• v.26 no.2
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• pp.173-181
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• 2017

Catch data for the common squid (Todarodes pacificus), classified by squid-jigging fisheries per grid (size: $0.5^{\circ}latitude{\times}0.5^{\circ}longitude$), and the water temperature values KODC (Korea Ocean Data Center) were collected for the 1980-2009 period to study the changes in squid distribution and migration with climate regimes (1980s, 1990s, and 2000s). The primary fishing period in the 1990s and 2000s was approximately 2-3 months earlier than that in the 1980s. Especially in the East Sea, the fishing grounds in the 1980s stayed longer at high latitudes than those in the other decades. Moreover, in the 1980s, centers of the fishing ground were located near the Yamato bank (central East Sea), whereas in the 1990s and 2000s, they were situated near the southeastern coast of the Korean peninsula.

• Journal of Climate Change Research
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• v.6 no.4
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• pp.345-355
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• 2015

In this study, we researched a space allocation method of emissions from domestic coastal shipping (passenger ship, cargo ship, fishing boats). We were building a geographic information data that is used in GIS for emissions of space allocation, and presented a methodology capable of performing the space allocated for each grid by calculating the emissions. Passenger ships were assigned emissions of routes each the ship that operated at a fixed. Emissions of crusing mode of cargo ship, was equally assigned to the safe operation line, which is defined for domestic cargo ship. Also, Emissions of maneuvering (moving into or out of port) and hoteling (operations while stationary at dock), was assigned in consideration of anchorage in the major port. Fishing boat was evenly allocated monthly emissions in the country every month fishing grounds formation zone. Based on these results, it tries to provide the basic data that can be used when you want to create a local government measures to reduce scenario in the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.330-330
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• 2022

우리나라에서 발생하는 기상 재해 현상은 주로 태풍, 집중호우, 장마 등 인명 및 경제적인 피해가 크며, 단기간에 국지적으로 나타난다. 현재 재해 감시 및 예보는 주로 종관기상관측체계를 이용하고 있다. 하지만, 우리나라의 복잡한 지형, 인구 밀집 지형, 관측 시기가 일정하지 않은 지형과 같은 조건에서 미계측 자료 및 지역이 다수 존재 때문에 강수의 공간 분포와 강도에 대한 정밀한 정보를 제공하지 못하는 실정이다. 최근 광범위한 관측영역과 공간 분해능의 개선, 자료추출 알고리즘의 개발로 전세계적으로 위성영상 기반 기상관측 자료의 활용성이 증대되고 있다. 본 연구에서는 한반도 지역의 지상 관측데이터와 전지구 격자형 위성 강우자료를 비교하여 한반도의 적용성을 분석하고자 한다. 다양한 위성영상 기반 기상자료인 Climate Hazards Groups InfraRed Precipitation with Station (CHIRPS), Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Global Precipitation Climatology Centre (GPCC), Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) 4개의 강우위성영상을 수집하여, 1991년부터 2020년까지 30년 데이터를 활용하였다. 강수량 변동성 비교를 위하여 기상청의 종관기상관측장비 (Automated Synoptic Observation System, ASOS), 자동기상관측시설 (Automatic Weather System, AWS) 데이터와 상관 분석을 수행하고, 강우위성영상의 국내 적합성을 판단하고자 한다.

• Atmosphere
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• v.26 no.2
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• pp.277-288
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• 2016

Polar lows are intense mesoscale cyclones that mainly occur over the sea in polar regions. Owing to their small spatial scale of a diameter less than 1000 km, simulating polar lows is a challenging task. At King Sejong station in West Antartica, polar lows are often observed. Despite the recent significant climatic changes observed over West Antarctica, adequate validation of regional simulations of extreme weather events such as polar lows are rare for this region. To address this gap, simulation results from a recent version of the Polar Weather Research and Forecasting model (Polar WRF) covering Antartic Peninsula at a high horizontal resolution of 3 km are validated against near-surface meteorological observations. We selected a case of high wind speed event on 7 January 2013 recorded at Automatic Meteorological Observation Station (AMOS) in King Sejong station, Antarctica. It is revealed by in situ observations, numerical weather prediction, and reanalysis fields that the synoptic and mesoscale environment of the strong wind event was due to the passage of a strong mesoscale polar low of center pressure 950 hPa. Verifying model results from 3 km grid resolution simulation against AMOS observation showed that high skill in simulating wind speed and surface pressure with a bias of $-1.1m\;s^{-1}$ and -1.2 hPa, respectively. Our evaluation suggests that the Polar WRF can be used as a useful dynamic downscaling tool for the simulation of Antartic weather systems and the near-surface meteorological instruments installed in King Sejong station can provide invaluable data for polar low studies over West Antartica.

• Journal of Korean Society of Forest Science
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• v.86 no.1
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• pp.25-34
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• 1997

The optimum global natural vegetation mapping(GNVM) system was selected as a series of the study to estimate potential forest area of the globe. To select the system, three types of GNVM systems which are simple system with Light Climatic Dataset(LCD), altitude-allowed system with LCD and altitude-allowed system with Heavy Climatic Dataset(HCD) were established and compared. The three GNVM systems spherically interpolate such spotty climate data as those observed at weather stations the world over onto $1^{\circ}{\times}1^{\circ}$ grid points, product vegetation type classification, and produce a potential natural vegetation(PNV) map and a PNV area. As a result of comparison with three GNVM systems, altitude-allowed LCD system represented natural vegetation distribution better than other versions. The difference between the simple system versus the one with altitude allowance indicated that the simple version tends to over-represent the warmer climate areas and under-represent cold and hostile climate areas. In the difference between altitude-allowed versions of LCD and HCD, HCD version tended to overestimate moist climate areas and to underestimate dry climate areas.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.1
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• pp.40-49
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• 2013

Current service system of the Korea Meteorological Administration (KMA) for blooming date forecasting in spring depends on regression equations derived from long term observations in both temperature and phenology at a given station. This regression based system does not allow a timely correction or update of forecasts that are highly sensitive to fluctuating weather conditions. Furthermore, the system cannot afford plant responses to climate extremes which were not observed before. Most of all, this method may not be applicable to locations other than that which the regression equations were derived from. This note suggests a way to replace the location restricted regression equations with a thermal time based phenology model to complement the KMA blooming forecast system. Necessary parameters such as reference temperature, chilling requirement and heating requirement were derived from phenology data for forsythia, azaleas and Japanese cherry at 29 KMA stations for the 1951-1980 period to optimize spring phenology prediction model for each species. Best fit models for each species were used to predict blooming dates and the results were compared with the observed dates to produce a correction grid across the whole nation. The models were driven by the KMA's daily temperature data at a 5km grid spacing and subsequently adjusted by the correction grid to produce the blooming date maps. Validation with the 1971-2012 period data showed the RMSE of 2-3 days for Japanese cherry, showing a feasibility of operational service; whereas higher RMSE values were observed with forsythia and azaleas.