• Journal of Environmental Science International
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• v.19 no.9
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• pp.1109-1118
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• 2010

Characteristics of precipitation and temperature in Ulleung-do and Dok-do were analyzed with hourly accumulated precipitation and mean temperature data obtained from Automatic Weather System(AWS) for latest four years(2005~2008). In Ulleung-do, total annual mean precipitation for this period is 1,574.4 mm, which shows larger amount than 1434.2 mm of whole Korean peninsula for latest 10 years(1999~2008) and 1,236.2 mm at Ulleung-do on common years(1971~2000), shows that the trend of precipitation gradually increases during the recent years. This amount is also 1.4 times larger than the total annual mean precipitation of 660.1 mm in Dok-do. Mean precipitation intensity(mm $h^{-1}$) at each time of a day in each month at Ulleung-do represents that the maximum values larger than $3.0\;mm\;h^{-1}$ were shown in May and on 0200 LST, whereas these were found in August and 0700 LST with $3.1\;mm\;h^{-1}$ in Dok-do. The difference of the precipitation amount and its intensity between Uleung-do and Dok-do is explained by the topological effect came from each covering area, and this fact is also identified from similar comparison of the precipitation characteristics for the islands in West Sea. The annual mean temperature of $14.0^{\circ}C$ in Dok-do is $1.2^{\circ}C$ higher than that of $12.8^{\circ}C$ in Ulleung-do. Trends of monthly mean temperature in both islands are shown to increase for the observed period.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.1
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• pp.49-60
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• 2004

Northern Gyeonggi Province(NGP), consisting of 3 counties, is the northernmost region in South Korea adjacent to the de-militarized zone with North Korea. To supplement insufficient spatial coverage of official climate data and climate atlases based on those data, high-resolution digital climate models(DCM) were prepared to support weather- related activities of residents in NGP Monthly climate data from 51 synoptic stations across both North and South Korea were collected for 1981-2000. A digital elevation model(DEM) for this region with 30m cell spacing was used with the climate data for spatially interpolating daily maximum and minimum temperatures, solar irradiance, and precipitation based on relevant topoclimatological models. For daily minimum temperature, a spatial interpolation scheme accommodating the potential influences of cold air accumulation and the temperature inversion was used. For daily maximum temperature estimation, a spatial interpolation model loaded with the overheating index was used. Daily solar irradiances over sloping surfaces were estimated from nearby synoptic station data weighted by potential relative radiation, which is the hourly sum of relative solar intensity. Precipitation was assumed to increase with the difference between virtual terrain elevation and the DEM multiplied by an observed rate. Validations were carried out by installing an observation network specifically for making comparisons with the spatially estimated temperature pattern. Freezing risk in January was estimated for major fruit tree species based on the DCMs under the recurrence intervals of 10, 30, and 100 years, respectively. Frost risks at bud-burst and blossom of tree flowers were also estimated for the same resolution as the DCMs.

• Clean Technology
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• v.29 no.1
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• pp.39-45
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• 2023

To investigate the effects of Mg addition at different aging times and temperatures, Cu/MgO/Al₂O₃ catalysts were synthesized for the low-temperature water gas shift (LT-WGS) reaction. The co-precipitation method was employed to prepare the catalysts with a fixed Cu amount of 30 mol% and varied amounts of Mg/Al. Synthesized catalysts were characterized using XRD, BET, and H₂-TPR analysis. Among the prepared catalysts, the highest CO conversion was achieved by the Cu/MgO/Al₂O₃ catalyst (30/40/30 mol%) with a 60 ℃ aging temperature and a 24 h aging time under a CO₂-rich feed gas. Due to it having the lowest reduction temperature and a good dispersion of CuO, the catalyst exhibited around 65% CO conversion with a gas hourly space velocity (GHSV) of 14,089 h-1 at 300 ℃. However, it has been noted that aging temperatures greater or less than 60 ℃ and aging times longer than 24 h had an adverse impact, resulting in a lower surface area and a higher reduction temperature bulk-CuO phase, leading to lower catalytic activity. The main findings of this study confirmed that one of the main factors determining catalytic activity is the ease of reducibility in the absence of bulk-like CuO species. Finally, the long-term test revealed that the catalytic activity and stability remained constant under a high concentration of CO₂ in the feed gas for 19 h with an average CO conversion of 61.83%.

• Ocean and Polar Research
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• v.26 no.2
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• pp.145-154
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• 2004

Surface energy and $CO_2$ fluxes have been measured over an ocean at Ieodo Ocean Research Station of KORDI since May 2003. Eddy covariance technique, which is a direct flux measurement, is used to quantitatively understand the interaction between the ocean surface and the atmospheric boundary layer. Although fluxes were continuously measured during the period from May 2003 to February 2004, the quality control of these data yielded <20% of data retrieval. The atmospheric stability did not show any distinct dirunal patterns and remained near-neutral to stable from May to June but mostly unstable during fall and winter in 2003. Sensible heat flux showed a good correlation with the difference between the sea water temperature and the air temperature. The maximum fluxes of sensible heat and latent heat were $120Wm^{-2}$ and $350Wm^{-2}$ respectively, with an averaged Bowen ratio of 0.2. The ocean around the tower absorbed $CO_2$ from the atmosphere and the uptake rates showed seasonal variations. Based our preliminary results, the daytime $CO_2$ flux was steady with an average of $-0.1 mgCO_2m^{-2}s^{-1}$ in summer and increased in winter. The nighttime $CO_2$ uptake was greater and fluctuating, reaching up to $-0.1 mgCO_2m^{-2}s^{-1}$ but these data require further examination due to weak turbulent mixing at nighttime. The magnitude of $CO_2$ flux was positively correlated with the half hourly changes in horizontal mean wind speed. Due to the paucity of quality data, further data collection is needed for more detailed analyses and interpretation.

• Journal of Korean Society of Forest Science
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• v.94 no.6
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• pp.481-487
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• 2005

In this study, a GIS model to simulate the behavior of surface forest fires was developed on the basis of forest fire growth prediction algorithm. This model consists of three modules for data-handling, simulation and report writing. The data-handling module was designed to interpret such forest fire environment factors as terrain, fuel and weather and provide sets of data required in analyzing fire behavior. The simulation module simulates the fire and determines spread velocity, fire intensity and burnt area over time associated with terrain slope, wind, effective humidity and such fuel condition factors as fuel depth, fuel loading and moisture content for fire extinction. The module is equipped with the functions to infer the fuel condition factors from the information extracted from digital vegetation map sand the fuel moisture from the weather conditions including effective humidity, maximum temperature, precipitation and hourly irradiation. The report writer has the function to provide results of a series of analyses for fire prediction. A performance test of the model with the 2002 Chungyang forest fire showed the predictive accuracy of 61% in spread rate.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6D
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• pp.1033-1041
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• 2006

In this study, we calculated a space-time variation of GPS precipitable water vapor using GPS meteorology technique during a progress of the typhoon EWINIAR had made an effect on Korean peninsular at 10 July, 2006. We estimated tropospheric dry delay and wet delay for one hourly using 22 GPS permanent stations and precipitable water vapor was conversed by using surface meteorological data. The Korean weighted mean temperature and air-pressure of versa-reduction to the mean sea level have been used for an accuracy improvement of GPS precipitable water vapor estimation. Finally, we compared MTSAT water vapor image, radar image and precipitable water vapor map during a passage of the typhoon EWINIAR.

• Korean Chemical Engineering Research
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• v.53 no.6
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• pp.818-823
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• 2015

In this study, FT reaction in a microchannel was simulated using computational fluid dynamics(CFD), and sensitivity analyses conducted to see effects of channel geometry variables, namely, process channel width, height, gap between process channel and cooling channel, and gap between process channels on the channel temperature profile. Microchannel reactor considered in the study is composed of five reaction channels with height and width ranging from 0.5 mm to 5.0 mm. Cooling surfaces is assumed to be in isothermal condition to account for the heat exchange between the surface and process channels. A gas mixture of $H_2$ and CO($H_2/CO$ molar ratio = 2) is used as a reactant and operating conditions are the following: GHSV(gas hourly space velocity) = $10000h^{-1}$, pressure = 20 bar, and temperature = 483 K. From the simulation study, it was confirmed that heat removal in an FT microchannel reactor is affected channel geometry variables. Of the channel geometry variables considered, channel height and width have significant effect on the channel temperature profile. However, gap between cooling surface and process channel, and gap between process channels have little effect. Maximum temperature in the reaction channel was found to be proportional to channel height, and not affected by the width over a particular channel width size. Therefore, microchannels with smaller channel height(about less than 2 mm) and bigger channel width (about more than 4 mm), can be attractive design for better heat removal and higher production.

• Journal of Environmental Science International
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• v.29 no.3
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• pp.257-272
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• 2020

This study investigates the temporal and spatial variations of marine meterological elements (air temperature (Temp), Sea Surface Temperature (SST), and Significant Wave Height (SWH)) in seven coastal waters of South Korea, using hourly data observed at marine meteorological buoys (10 sites), Automatic Weather System on lighthouse (lighthouse AWS) (9 sites), and AWS (20 sites) during 2013-2017. We also compared the characteristics of Temp, SST, and air-sea temperature difference (Temp-SST) between sea fog and non-sea-fog events. In general, annual mean values of Temp and SST in most of the coastal waters were highest (especially in the southern part of Jeju Island) in 2016, due to heat waves, and lowest (especially in the middle of the West Sea) in 2013 or 2014. The SWH did not vary significantly by year. Wind patterns varied according to coastal waters, but their yearly variations for each coastal water were similar. The maximum monthly/seasonal mean values of Temp and SST occurred in summer (especially in August), and the minimum values in winter (January for Temp and February for SST). Monthly/seasonal mean SWH was highest in winter (especially in December) and lowest in summer (June), while the monthly/seasonal variations in wind speed over most of the coastal waters (except for the southern part of Jeju Island) were similar to those of SWH. In addition, sea fog during spring and summer was likely to be in the form of advection fog, possibly because of the high Temp and low SST (especially clear SST cooling in the eastern part of South Sea in summer), while autumn sea fog varied between different coastal waters (either advection fog or steam fog). The SST (and Temp-SST) during sea fog events in all coastal waters was lower (and more variable) than during non-sea-fog events, and was up to -5.7℃ for SST (up to 5.8℃ for Temp-SST).

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.201-217
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• 2022

The United States is one of the largest producers of major crops such as wheat, maize, and soybeans, and is a major exporter of these crops. Therefore, it is important to estimate the crop production of the country in advance based on reliable long- term weather forecast information for stable crops supply and demand in Korea. The purpose of this study is to improve the seasonal predictability of the agro-climatic indices over the United States by using regional-scale daily temperature. For long-term numerical weather prediction, a dynamical downscaling is performed using Weather Research and Forecasting (WRF) model, a regional climate model. As the initial and lateral boundary conditions of WRF, the global hourly prediction data obtained from the Pusan National University Coupled General Circulation Model (PNU CGCM) are used. The integration of WRF is performed for 22 years (2000-2021) for period from June to December of each year. The empirical quantile mapping, one of the bias correction methods, is applied to the timeseries of downscaled daily mean, minimum, and maximum temperature to correct the model biases. The uncorrected and corrected datasets are referred WRF_UC and WRF_C, respectively in this study. The daily minimum (maximum) temperature obtained from WRF_UC presents warm (cold) biases over most of the United States, which can be attributed to the underestimated the low (high) temperature range. The results show that WRF_C simulates closer to the observed temperature than WRF_UC, which lead to improve the long- term predictability of the temperature- based agro-climatic indices.