• Transactions of the Korean Society of Mechanical Engineers B
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• v.23 no.3
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• pp.352-363
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• 1999

Target detection by the infrared imager depends on the apparent temperature difference between the target and the background, so it is essential to predict apparent temperature variations for this purpose. In this study, thermal analysis program Including conduction, convection and radiation is developed and applied to a representative geometry adequate for examining the apparent temperature characteristics. The results show that the longwave emissivity in association with the background temperature affects the apparent temperature strongly but does not affect the physical temperature. It is revealed that the background temperature plays a role of tuning the apparent temperature. As the longwave emissivity decreases, the apparent temperature decreases when the target is hotter than the background, whereas it increases in the reversed situation. These findings imply that an effective surface treatment, such as painting of a less emissive material, may provide a less detection probability and contribute to preventing the target from being detected at night.

• 한국해양학회지
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• v.30 no.2
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• pp.91-115
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• 1995

Air-sea heat fluxes in the East Sea were estimated from the various ship's data observed from 1961 to 1990 and the JMA buoy #6 data from 1976 to 1985. The oceanic heat transport in the sea was also determined from the fluxes above and the heat storage rate of the upper layer of 200m from the sea surface. In winter, The incoming solar radiation is almost balanced with the outgoing longwave radiation. but the sea loses her heat through the sea surface mainly due to the latent and sensible heat fluxes. The spatial variation of the net surface heat flux is about 100 Wm/SUP -2/, and the maximum loss of heat is occurred near the Tsugaru Strait. There are also lots of heat losses in the southern part of the East Sea, Korea Strait and Ulleung Basin. Particularly, the heat strong loss in the south-western part of the sea might be concerned with the formation of her Intermediate Homogeneous Water. In summer, the sea is heated up to about 120∼140 Wm/SUP -2/ sue to strong incoming solar radiation and weak turbulent heat fluxes and her spatial variation is only about 20 Wm/SUP -2/. The oceanic heat flux is positive in the southeasten part f the sea and the magnitude of the flux is larger than that of the net surface heat flux. This shows the importance of the area. In the southwestern part of the sea, however, the oceanic heat flux is negative. This fact implies cold water inflow, the North Korean Cold Water. The sigh of net surface heat flux is changed from negative to positive in March and from positive to negative in September. The heat content in the upper surface 200 m from the sea surface reaches its minimum in March and maximum in October. The annual variation of the net surface heat flux is 580 Wm/SUP -2/ in southwestern part of the sea. The annual mean values of net surface heat fluxes are negative, which mean the net heat transfer from the sea to the atmosphere. The magnitude of the flux is about 130 Wm/SUP -2/ near the Tsugaru Strait. The net surface fluxes in the Korea Strait and the Ulleung Basin are relatively larger than those of the rest areas. The spatial mean values of surface heat fluxes from 35$^{\circ}C$ to 39$^{\circ}$N are 129, -90, -58, and -32 Wm/SUP -2/ for the incoming solar radiation, latent hear flux, outgoing longwave radiation, and sensible heat flux, respectively.

• Journal of Wetlands Research
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• v.15 no.4
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• pp.509-518
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• 2013

Understanding of the exchange between the water and energy which is happening between the surface and atmosphere is the basic of studying water resources. To study these, lots of researches using Noah Land Surface Model(LSM) are in progress. Noah LSM is based on energy and water balance equation and simulates various hydrological factors. There are diverse researches with Noah LSM are ongoing in overseas, on the other hand not enough study has been done. Especially there is almost no study using uncoupled Noah LSM in Korea. In this study we used data from Korea Flux Tower in Haenam(HFK) and Gwangneung(GDK) as forcing data to simulate the model and compared its result of net radiation, sensible heat flux and latent heat flux with the observation data to assess the applicability of Noah LSM in Korea. Regression coefficients of the comparison results of Noah LSM and observation show good agreement with the value of 0.83~0.99 at Haenam and 0.64~0.99 at Gwangneung which means Noah LSM can be trusted.

• 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.