• Journal of the Korean earth science society
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• v.37 no.7
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• pp.448-464
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• 2016

The purpose of this study is to optimize the parameters related to consensus coherency within the PCL 1300, the operating program of wind profiler, from a validation of wind data between rawinsonde and wind profiler at Chupungryeong ($36^{\circ}13^{\prime}$, $127^{\circ}59^{\prime}$) site in Korea. It is then to analyze the diurnal and seasonal characteristics of the turbulence energy dissipation rate (${\varepsilon}$) in clear and rainy days from March 2009 to February 2010. In comparison of the wind data between wind profiler and rawinsonde during April 22-23, 2010, it was shown in a big error more than $10ms^{-1}$ over the height of 3,000 meters in the zonal (u) and meridional (v) wind components. When removing more than $10ms^{-1}$ in each wind speed difference of u an v components between the two instruments, the correlation coefficients of these wind components were 0.92 and 0.88, respectively, and the root mean square errors were 3.07 and $1.06ms^{-1}$. Based on these results, when the data processing time and the minimum available data within the PCL 1300 program were adjusted as 30 minutes and 60%, respectively, the bias errors were small. In addition, as a result of an analysis of sensitivity to consensus coherency of u and v components within the PCL1300 program, u components were underestimated in radial coherency, instantaneous and winbarbs coherency, whereas v components were overestimated. Finally by optimizing parameters of the PCL1300 program, the diurnal and seasonal means of ${\varepsilon}$ at each height were higher in rainy days than those in clear days because of increasing in the vertical wind speed due to upward and downward motions. The mean ${\varepsilon}$ for clear and rainy days in winter was lower than those of other seasons, due to stronger horizontal wind speed in winter than those in other seasons. Consequently, when the turbulence energy dissipation rates in the vertical wind speed of more than ${\pm}10cm\;s^{-1}$ were excluded for clear and rainy days, the mean ${\varepsilon}$ in rainy days was 6-7 times higher than that in clear days, but when considering them, it was 4-5 times higher.

• Korean Journal of Agricultural and Forest Meteorology
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• v.11 no.1
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• pp.13-18
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• 2009

Accurate solar radiation data are critical to evaluate major physiological responses of plants. For most upland crops and orchard plants growing in complex terrain, however, it is not easy for farmers or agronomists to access solar irradiance data. Here we suggest a simple method using a sun-slope geometry based topographical coefficient to estimate daily solar irradiance on any sloping surfaces from global solar radiation measured at a nearby weather station. An hourly solar irradiance ratio ($W_i$) between sloping and horizontal surface is defined as multiplication of the relative solar intensity($k_i$) and the slope irradiance ratio($r_i$) at an hourly interval. The $k_i$ is the ratio of hourly solar radiation to the 24 hour cumulative radiation on a horizontal surface under clear sky conditions. The $r_i$ is the ratio of clear sky radiation on a given slope to that on a horizontal reference. Daily coefficient for slope correction is simply the sum of $W_i$ on each date. We calculated daily solar irradiance at 8 side slope locations circumventing a cone-shaped parasitic volcano(c.a., 570m diameter for the bottom circle and 90m bottom-to-top height) by multiplying these coefficients to the global solar radiation measured horizontally. Comparison with the measured slope irradiance from April 2007 to March 2008 resulted in the root mean square error(RMSE) of $1.61MJ\;m^{-2}$ for the whole period but the RMSE for April to October(i.e., major cropping season in Korea) was much lower and satisfied the 5% error tolerance for radiation measurement. The RMSE was smallest in October regardless of slope aspect, and the aspect dependent variation of RMSE was greatest in November. Annual variation in RMSE was greatest on north and south facing slopes, followed by southwest, southeast, and northwest slopes in decreasing order. Once the coefficients are prepared, global solar radiation data from nearby stations can be easily converted to the solar irradiance map at landscape scales with the operational reliability in cropping season.

• Journal of the Korean earth science society
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• v.39 no.2
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• pp.139-153
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• 2018

The sea surface wind field has long been obtained from satellite scatterometers or passive microwave radiometers. However, the importance of satellite altimeter-derived wind speed has seldom been addressed because of the outstanding capability of the scatterometers. Satellite altimeter requires the accurate wind speed data, measured simultaneously with sea surface height observations, to enhance the accuracy of sea surface height through the correction of sea state bias. This study validates the wind speeds from the satellite altimeters (GFO, Jason-1, Envisat, Jason-2, Cryosat-2, SARAL) and analyzes characteristics of errors. In total, 1504 matchup points were produced using the wind speed data of Ieodo Ocean Research Station (IORS) and of Korea Meteorological Administration (KMA) buoys at Marado and Oeyeondo stations for 10 years from December 2007 to May 2016. The altimeter wind speed showed a root mean square error (RMSE) of about $1.59m\;s^{-1}$ and a negative bias of $-0.35m\;s^{-1}$ with respect to the in-situ wind speed. Altimeter wind speeds showed characteristic biases that they were higher (lower) than in-situ wind speeds at low (high) wind speed ranges. Some tendency was found that the difference between the maximum and minimum value gradually increased with distance from the buoy stations. For the improvement of the accuracy of altimeter wind speed, an equation for correction was derived based on the characteristics of errors. In addition, the significance of altimeter wind speed on the estimation of sea surface height was addressed by presenting the effect of the corrected wind speeds on the sea state bias values of Jason-1.

• Korean Journal of Agricultural and Forest Meteorology
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• v.13 no.4
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• pp.176-184
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• 2011

Cold air on sloping surfaces flows down to the valley bottom in mountainous terrain at calm and clear nights. Based on the assumption that the cold air flow may be the same as the water flow, current models estimate temperature drop by regarding the cold air accumulation at a given location as the water-like free drainage. At a closed catchment whose outlet is blocked by man-made obstacles such as banks and roads, however, the water-like free drainage assumption is no longer valid because the cold air accumulates from the bottom first. We developed an empirical model to estimate quantitatively the effect of cold pool on nocturnal temperature in a closed catchment. In our model, a closed catchment is treated like a "vessel", and a digital elevation model (DEM) was used to calculate the maximum capacity of the cold pool formed in a closed catchment. We introduce a topographical variable named "shape factor", which is the ratio of the cold air accumulation potential across the whole catchment area to the maximum capacity of the cold pool to describe the relative size of temperature drop at a wider range of catchment shapes. The shape factor is then used to simulate the density profile of cold pool formed in a given catchment based on a hypsometric equation. The cold lake module was incorporated with the existing model (i.e., Chung et al., 2006), generating a new model and predicting distribution of minimum temperature over closed catchments. We applied this model to Akyang valley (i.e., a typical closed catchment of 53 $km^2$ area) in the southern skirt of Mt. Jiri National Park where 12 automated weather stations (AWS) are operational. The performance of the model was evaluated based on the feasibility of delineating the temperature pattern accurately at cold pool forming at night. Overall, the model's ability of simulating the spatial pattern of lower temperature were improved especially at the valley bottom, showing a similar pattern of the estimated temperature with that of thermal images obtained across the valley at dawn (0520 to 0600 local standard time) of 17 May 2011. Error in temperature estimation, calculated with the root mean square error using the 10 low-lying AWSs, was substantially decreased from $1.30^{\circ}C$ with the existing model to $0.71^{\circ}C$ with the new model. These results suggest the feasibility of the new method in predicting the site-specific freeze and frost warning at a closed catchment.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.23 no.1
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• pp.1-19
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• 2018

In order to compare significant wave height (SWH) data from multi-satellites (GFO, Jason-1, Envisat, Jason-2, Cryosat-2, SARAL) and SWH measurements from Ieodo Ocean Research Station (IORS), we constructed a 12 year matchup database between satellite and IORS measurements from December 2004 to May 2016. The satellite SWH showed a root mean square error (RMSE) of about 0.34 m and a positive bias of 0.17 m with respect to the IORS wave height. The satellite data and IORS wave height data did not show any specific seasonal variations or interannual variability, which confirmed the consistency of satellite data. The effect of the wind field on the difference of the SWH data between satellite and IORS was investigated. As a result, a similar result was observed in which a positive biases of about 0.17 m occurred on all satellites. In order to understand the effects of topography and the influence of the construction structures of IORS on the SWH differences, we investigated the directional dependency of differences of wave height, however, no statistically significant characteristics of the differences were revealed. As a result of analyzing the characteristics of the error as a function of the distance between the satellite and the IORS, the biases are almost constant about 0.14 m regardless of the distance. By contrast, the amplitude of the SWH differences, the maximum value minus the minimum value at a given distance range, was found to increase linearly as the distance was increased. On the other hand, as a result of the accuracy evaluation of the satellite SWH from the Donghae marine meteorological buoy of Korea Meteorological Administration, the satellite SWH presented a relatively small RMSE of about 0.27 m and no specific characteristics of bias such as the validation results at IORS. In this paper, we propose a conversion formula to correct the significant wave data of IORS with the satellite SWH data. In addition, this study emphasizes that the reliability of data should be prioritized to be extensively utilized and presents specific methods and strategies in order to upgrade the IORS as an international world-wide marine observation site.

• Journal of the Korean earth science society
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• v.43 no.3
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• pp.367-385
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• 2022

This study evaluates the temperature and precipitation results in East Asia simulated from the Hadley Centre Global Environmental Model version 3 regional climate model (HadGEM3-RA) developed by the UK Met Office. The HadGEM3-RA is conducted in the Coordinated Regional climate Downscaling Experiment-East Asia (CORDEX-EA) Phase II domain for 15 year (2000-2014). The spatial distribution of rainbands produced from the HadGEM3-RA by the summer monsoon is in good agreement with the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of water resources (APRODITE) data over the East Asia. But, precipitation amount is overestimated in Southeast Asia and underestimated over the Korean Peninsula. In particular, the simulated summer rainfall and APRODITE data show the least correlation coefficient and the maximum value of root mean square error in South Korea. Prediction of temperature in Southeast Asia shows underestimation with a maximum error during winter season, while it appears the largest underestimation in South Korea during spring season. In order to evaluate local predictability, the time series of temperature and precipitation compared to the ASOS data of the Seoul Meteorological Station is similar to the spatial average verification results in which the summer precipitation and winter temperature underestimate. Especially, the underestimation of the rainfall increases when the amounts of precipitation increase in summer. The winter temperature tends to underestimate at low temperature, while it overestimates at high temperature. The results of the extreme climate index comparison show that heat wave is overestimated and heavy rainfall is underestimated. The HadGEM3-RA simulated with a horizontal resolution of 25 km shows limitations in the prediction of mesoscale convective system and topographic precipitation. This study indicates that improvement of initial data, horizontal resolution, and physical process are necessary to improve predictability of regional climate model.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.28-36
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• 2023

Solar radiation that is measured at relatively small number of weather stations is one of key inputs to crop models for estimation of crop productivity. Solar radiation products derived from GK-2A and Himawari 8 satellite data have become available, which would allow for preparation of input data to crop models, especially for assessment of crop productivity under an agrivoltaic system where crop and power can be produced at the same time. The objective of this study was to compare the degree of agreement between the solar radiation products obtained from those satellite data. The sub hourly products for solar radiation were collected to prepare their daily summary for the period from May to October in 2020 during which both satellite products for solar radiation were available. Root mean square error (RMSE) and its normalized error (NRMSE) were determined for daily sum of solar radiation. The cumulative values of solar radiation for the study period were also compared to represent the impact of the errors for those products on crop growth simulations. It was found that the data product from the Himawari 8 satellite tended to have smaller values of RMSE and NRMSE than that from the GK-2A satellite. The Himawari 8 satellite product had smaller errors at a large number of weather stations when the cumulative solar radiation was compared with the measurements. This suggests that the use of Himawari 8 satellite products would cause less uncertainty than that of GK2-A products for estimation of crop yield. This merits further studies to apply the Himawari 8 satellites to estimation of solar power generation as well as crop yield under an agrivoltaic system.

• Journal of Korean Society of Forest Science
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• v.112 no.4
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• pp.472-489
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• 2023

This study was conducted to derive the optimal estimation equations for deriving the green and dry weights of Larix kaempferi (Japanese larch) and Pinus rigida (Rigida pine), which are major coniferous tree species in South Korea. The equations were then used to develop weight tables. Table development began with the sampling of 150 L. kaempferi and 90 P. rigida trees distributed throughout the national scale, after which green weights were measured on-site. Samples from each stand were then collected, and their dry weights were measured in a laboratory. The equation used to calculate green and dry weights was divided into a one-variable formula that uses only the diameter at breast height (DBH) and a two-variable equation that employs DBH and height. The equations used to estimate the green and dry weights of logs were divided into one- and two-variable equations using DBH. Statistical data, such as the fitness index (FI), root mean square error, standard error of estimation, and residual diagram, were used to verify the suitability of the estimation equations. Applicability was examined by calculating weights using the derived optimal equations. The equation W = bD+cD² was used in measurements involving only DBH, whereas the equation W = aD^bH^c was employed in cases involving both diameter and height at breast height. The FI of W = bD+cD² was 0.91, while that of W = aD^bH^c was 0.95, both of which are high values. With these estimation formulas, weight tables for the green and dry weights of L. kaempferi and P. rigida were prepared and compared with weight tables created 20 years ago. The green and dry weight tables of both species were larger.

• Journal of Food Hygiene and Safety
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• v.28 no.1
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• pp.7-12
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• 2013

This study presents the influence on growth properties determined using a novel predictive growth model of wild-type Salmonella spp. KSC 101 by variations in the temperature and time during cut packaging in cold, uncooked pork meat. The experiment performed for model development included an arrangement of different temperatures ($0^{\circ}C$, $5^{\circ}C$, $10^{\circ}C$, $15^{\circ}C$, and $20^{\circ}C$) and time durations (0, 1, 2, and 3 hours) that reflect actual pork-cut and packaging processes. No growth was observed at $0^{\circ}C$ and $5^{\circ}C$, whereas some growth was observed at $10^{\circ}C$, $15^{\circ}C$, and $20^{\circ}C$, with a mean increase of only 0.34 log CFU/g. The growth observed at $20^{\circ}C$ was more robust than that observed at $15^{\circ}C$, but the difference was not statistically significant (p > 0.05). However, compared with PMP (Pathogen Modeling Program), the wild-type Salmonella spp. KSC 101 showed a more rapid growth. We used the Gompertz 4 parameter equation as the primary model, and the exponential decay formula as the secondary model. The estimated $R^2$ values were 0.99 or higher. The developed model was evaluated by comparison of the experimental and predictive values, and the values were in agreement with the ${\pm}0.5$ log CFU/g, although the RMSE (Root mean square error) value was 0.103, which indicates a slight overestimation. Therefore, we suggest that the developed predictive growth model would be useful as a tool for evaluating sanitation criteria in pork cut-packaging processes.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.21 no.1
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• pp.1-10
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• 2016

Currently available surface seawater partial pressure carbon dioxide ($pCO_2$) data sets in the East Sea are not enough to quantify statistically the carbon dioxide flux through the air-sea interface. To complement the scarcity of the $pCO_2$ measurements, we construct a neural network (NN) model based on satellite data to map $pCO_2$ for the areas, which were not observed. The NN model is constructed for the Ulleung Basin, where $pCO_2$ data are best available, to map and estimate the variability of $pCO_2$ based on in situ $pCO_2$ for the years from 2003 to 2012, and the sea surface temperature (SST) and chlorophyll data from the MODIS (Moderate-resolution Imaging Spectroradiometer) sensor of the Aqua satellite along with geographic information. The NN model was trained to achieve higher than 95% of a correlation between in situ and predicted $pCO_2$ values. The RMSE (root mean square error) of the NN model output was $19.2{\mu}atm$ and much less than the variability of in situ $pCO_2$. The variability of $pCO_2$ with respect to SST and chlorophyll shows a strong negative correlation with SST than chlorophyll. As SST decreases the variability of $pCO_2$ increases. When SST is lower than $15^{\circ}C$, $pCO_2$ variability is clearly affected by both SST and chlorophyll. In contrast when SST is higher than $15^{\circ}C$, the variability of $pCO_2$ is less sensitive to changes in SST and chlorophyll. The mean rate of the annual $pCO_2$ increase estimated by the NN model output in the Ulleung Basin is $0.8{\mu}atm\;yr^{-1}$ from 2003 to 2014. As NN model can successfully map $pCO_2$ data for the whole study area with a higher resolution and less RMSE compared to the previous studies, the NN model can be a potentially useful tool for the understanding of the carbon cycle in the East Sea, where accessibility is limited by the international affairs.