• Atmosphere
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• v.18 no.1
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• pp.71-81
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• 2008

In this study, in order to estimate high resolution precipitation with monthly time scales, Parameter-elevation Regressions on Independent Slopes Model (PRISM) was modified and configured for Korean precipitation based on elevation, distance, topographic facet, and coastal proximity. Applying this statistical downscaling model to Korean precipitation for 5 years from 2001 to 2005, we have compiled monthly grid data with a horizontal resolution of 5-km and evaluated the model using bias, root mean square error (RMSE), and correlation coefficient between the observed and the estimated. Results show that bias, RMSE, and correlation coefficient of the estimated value have a range from 0.2% to 1.0%, 19.6% (June) to 43.9% (January), and 0.73 to 0.84, respectively, indicating that the modified Korean PRISM (K-PRISM) is reasonably worked by weighting factors, i.e., topographic effect and rain shadow effect.

• Journal of the Korean Society of Visualization
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• v.5 no.2
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• pp.9-13
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• 2007

Typhoon simulation based on dynamical forecasting results is demonstrated by utilizing geodesic model GME (operational global numerical weather prediction model of German Weather Service). It is based on uniform icosahedral-hexagonal grid. The GME gridpoint approach avoids the disadvantages of spectral technique as well as the pole problem in latitude-longitude grids and provides a data structure extremely well suited to high efficiency on distributed memory parallel computers. In this study we made an attempt to simulate typhoon 'NARI' that passed over the Korean Peninsula in 2007. GME has attributes of numerical weather prediction model and its high resolution can provide details on fine scale. High resolution of GME can play key role in the study of severe weather phenomenon such as typhoons. Simulation of future typhoon that is assumed to occur under the global warming situation shows that the life time of that typhoon will last for a longer time and the intensity will be extremely stronger.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.446-454
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• 2023

The impact of climate change on agriculture is substantial, especially as global warming is projected to lead to varying temperature and humidity patterns in the future. These changes pose a higher risk for both crops and livestock, exposing them to environmental stressors under altered climatic conditions. Specifically, as temperatures are expected to rise, the risk of heat stress is assessable through the Temperature-Humidity Index (THI), derived from temperature and relative humidity data. This study involved the comparison of THI collected from 10 Korea Meteorological Administration ASOS stations spanning a 60-year period from 1961 to 2020. Moreover, high-resolution temperature and humidity distribution data from 1981 to 2020 were employed to generate high-resolution TH I distributions, analyzing temporal changes. Additionally, the number of days characterized by heat stress, derived from TH I, was compared over different time periods. Generally, TH I showed an upward trend over the past, albeit with varying rates across different locations. As TH I increased, the frequency of heat stress days also rose, indicating potential future cost increases in the livestock industry due to heat-related challenges. The findings emphasize the feasibility of evaluating heat stress risk in livestock using THI and underscore the need for research analyzing THI under future climate change scenarios.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.1-30
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• 2024

The international community adopts the SSP (Shared Socioeconomic Pathways) scenario as a new greenhouse gas emission pathway. As part of efforts to reflect these international trends and support for climate change adaptation measure in the agricultural sector, the National Institute of Agricultural Sciences (NAS) produced high-resolution (1 km) climate change scenarios for the Korean Peninsula based on SSP scenarios, certified as a "National Climate Change Standard Scenario" in 2022. This paper introduces SSP climate change scenario of the NAS and shows the results of the climate change projections. In order to produce future climate change scenarios, global climate data produced from 18 GCM models participating in CMIP6 were collected for the past (1985-2014) and future (2015-2100) periods, and were statistically downscaled for the Korean Peninsula using the digital climate maps with 1km resolution and the SQM method. In the end of the 21^st century (2071-2100), the average annual maximum/minimum temperature of the Korean Peninsula is projected to increase by 2.6~6.1℃/2.5~6.3℃ and annual precipitation by 21.5~38.7% depending on scenarios. The increases in temperature and precipitation under the low-carbon scenario were smaller than those under high-carbon scenario. It is projected that the average wind speed and solar radiation over the analysis region will not change significantly in the end of the 21st century compared to the present. This data is expected to contribute to understanding future uncertainties due to climate change and contributing to rational decision-making for climate change adaptation.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.741-746
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• 2016

Albedo is one of the climate variables that modulate absorption of solar energy, and its retrieval is important process for climate change study. High spatial resolution and long-term consistent periods are important considerations in order to efficiently use the retrieved albedo data. This study retrieved surface broadband albedo based on Landsat 8 as high resolution which is consistent with Landsat 7. First of all, we analyzed consistency of Landsat 7 channel and Landsat 8 channel. As a result, correlation coefficient(R) on all channels is average 0.96. Based on this analysis, we used multiple linear regression model using Landsat 7 albedo, which is being used in many studies, and Landsat 8 reflectance channel data. The regression coefficients of each channel calculated by regression analysis were used to derive a formula for converting the Landsat 8 reflectance channel data to broadband albedo. After Landsat 8 albedo calculated using the derived formula is compared with Landsat 7 albedo data, we confirmed consistency of two satellite using Root Mean Square Error (RMSE), R-square ($R^2$) and bias. As a result, $R^2$ is 0.89 and RMSE is 0.003 between Landsat 7 albedo and Landsat 8 albedo.

• Korean Journal of Agricultural Science
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• v.48 no.3
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• pp.433-446
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• 2021

In this study, the future flood inundation changes under a climate change were simulated in the Tonle Sap basin in Cambodia, one of the countries with high vulnerability to climate change. For the flood inundation simulation using the rainfall-runoff-inundation (RRI) model, globally available geological data (digital elevation model [DEM]; hydrological data and maps based on Shuttle elevation derivatives [HydroSHED]; land cover: Global land cover facility-moderate resolution imaging spectroradiometer [GLCF-MODIS]), rainfall data (Asian precipitation-highly-resolved observational data integration towards evaluation [APHRODITE]), climate change scenario (HadGEM3-RA), and observational water level (Kratie, Koh Khel, Neak Luong st.) were constructed. The future runoff from the Kratie station, the upper boundary condition of the RRI model, was constructed to be predicted using the long short-term memory (LSTM) model. Based on the results predicted by the LSTM model, a total of 4 cases were selected (representative concentration pathway [RCP] 4.5: 2035, 2075; RCP 8.5: 2051, 2072) with the largest annual average runoff by period and scenario. The results of the analysis of the future flood inundation in the Tonle Sap basin were compared with the results of previous studies. Unlike in the past, when the change in the depth of inundation changed to a range of about 1 to 10 meters during the 1997 - 2005 period, it occurred in a range of about 5 to 9 meters during the future period. The results show that in the future RCP 4.5 and 8.5 scenarios, the variability of discharge is reduced compared to the past and that climate change could change the runoff patterns of the Tonle Sap basin.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.284-301
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• 2023

The changes in rice climatic yield potential (CYP) across the Korean Peninsula are evaluated based on the new climate change scenario produced by the National Institute of Agricultural Sciences with 18 ensemble members at 1 km resolution under a Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathways (RCP) emission scenarios. To overcome the data availability, we utilize solar radiation f or CYP instead of sunshine duration which is relatively uncommon in the climate prediction f ield. The result show that maximum CYP(CYPmax) decreased, and the optimal heading date is progressively delayed under warmer temperature conditions compared to the current climate. This trend is particularly pronounced in the SSP5-85 scenario, indicating faster warming, except for the northeastern mountainous regions of North Korea. This shows the benef its of lower emission scenarios and pursuing more efforts to limit greenhouse gas emissions. On the other hand, the CYPmax shows a wide range of feasible futures, which shows inherent uncertainties in f uture climate projections and the risks when analyzing a single model or a small number of model results, highlighting the importance of the ensemble approach. The f indings of this study on changes in rice productivity and uncertainties in temperature and solar radiation during the 21st century, based on climate change scenarios, hold value as f undamental information for climate change adaptation efforts.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.1
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• pp.63-73
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• 2010

A comprehensive mapping project for agroclimatic zoning in South Korea will end by April 2010, which has required 4 years, a billion won (ca. 0.9 million US dollars) and 22 experts from 7 institutions to complete it. The map database from this project may be categorized into primary, secondary and analytical products. The primary products are called "high definition" digital climate maps (HD-DCMs) and available through the state of the art techniques in geospatial climatology. For example, daily minimum temperature surfaces were prepared by combining the climatic normals (1971-2000 and 1981-2008) of synoptic observations with the simulated thermodynamic nature of cold air by using the raster GIS and microwave temperature profiling which can quantify effects of cold air drainage on local temperature. The spatial resolution of the gridded climate data is 30m for temperature and solar irradiance, and 270m for precipitation. The secondary products are climatic indices produced by statistical analysis of the primary products and includes extremes, sums, and probabilities of climatic events relevant to farming activities at a given grid cell. The analytical products were prepared by driving agronomic models with the HD-DCMs and dates of full bloom, the risk of freezing damage, and the fruit quality are among the examples. Because the spatial resolution of local climate information for agronomic practices exceeds the current weather service scale, HD-DCMs and the value-added products are expected to supplement the insufficient spatial resolution of official climatology. In this lecture, state of the art techniques embedded in the products, how to combine the techniques with the existing geospatial information, and agroclimatic zoning for major crops and fruits in South Korea will be provided.

• Atmosphere
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• v.29 no.5
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• pp.567-583
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• 2019

In this study, the high-resolution numerical simulations considering detailed anthropogenic heat, albedo, emission and roughness length are analyzed by using single layer Urban Canopy Model (UCM) in Weather Research Forecast (WRF). For this, improved urban parameter data for Seoul Metropolitan Area (SMA) was collected from global data. And then the parameters were applied to WRF-UCM model after it was processed into 2-dimensional topographical data. The 6 experiments were simulated by using the model with each parameter and verified against observation from Automated Weather Station (AWS) and flux tower for the temperature and sensible heat flux. The data for sensible heat flux of flux towers on Jungnang and Bucheon, the temperature of AWS on Jungnang, Gangnam, Bucheon and Neonggok were used as verification data. In the case of summer, the improvement of simulation by using detailed anthropogenic heat was higher than the other experiments in sensible flux simulation. The results of winter case show improved in all simulations using each advanced parameters in temperature and sensible heat flux simulation. Improvement of urban parameters in this study are possible to reflect the heat characteristics of urban area. Especially, detailed application of anthropogenic heat contributed to the enhancement of predicted value for sensible heat flux and temperature.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.156-156
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• 2022

Explicitly spatially distributed and reliable data on industrial water demand is very much important for both policy makers and researchers in order to carry a region-specific analysis of water resources management. However, such type of data remains scarce particularly in underdeveloped and developing countries. Current research is limited in using different spatially available socio-economic, climate data and geographical data from different sources in accordance to predict industrial water demand at finer resolution. This study proposes a random forest regression (RFR) model to predict the industrial water demand at 0.50× 0.50 spatial resolution by combining various features extracted from multiple data sources. The dataset used here include National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) night-time light (NTL), Global Power Plant database, AQUASTAT country-wise industrial water use data, Elevation data, Gross Domestic Product (GDP), Road density, Crop land, Population, Precipitation, Temperature, and Aridity. Compared with traditional regression algorithms, RF shows the advantages of high prediction accuracy, not requiring assumptions of a prior probability distribution, and the capacity to analyses variable importance. The final RF model was fitted using the parameter settings of ntree = 300 and mtry = 2. As a result, determinate coefficients value of 0.547 is achieved. The variable importance of the independent variables e.g. night light data, elevation data, GDP and population data used in the training purpose of RF model plays the major role in predicting the industrial water demand.