• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.141-148
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• 2021

Agrivoltaic systems, also called solar sharing, stated from an idea that utilizes sunlight above the light saturation point of crops for power generation using solar panels. It is expected that agrivoltaic systems can realize climate smart agriculture by reducing evapotranspiration and methane emission due to the reduction of incident solar radiation and the consequent surface cooling effect and bring additional income to farms through solar power generation. In this study, to evaluate that agrivoltaic systems are suitable for realization of climate smart agriculture, we conducted agro-environmental observations (i.e., downward/upward shortwave/longwave radiations, air temperature, relative humidity, water temperature, soil temperature, and wind speed) in a rice paddy under an agrivoltaic system and compared with the environment outside the system using automated meteorological observing systems (AMOS). During the observation period, the spatially averaged incoming solar radiation under the agrivoltaic system was about 70% of that in the open paddy field, and clear differences in the soil and water temperatures between the paddy field under the agrivoltaic system and the open paddy field were confirmed, although the air temperatures were similar. It is required in the near future to confirm whether such environmental differences lead to a reduction in water consumption and greenhouse gas emissions by flux measurements.

• Journal of Environmental Science International
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• v.18 no.5
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• pp.539-550
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• 2009

This study found that tropical cyclones (TCs) formed for fall in 2007 over the western North Pacific were distributed in high-latitudes comparing to 56-year (1951-2006) climatological mean. The frequency and latitude of TC genesis became higher than 56-year climatological mean from September onward in 2007 and all the TCs that formed to the north of 20$^{\circ}$N was also distributed after September in 2007. These characteristics of TC genesis for fall in 2007 could be confirmed through analyzing various variables, such as a large-scale atmospheric circulation, outgoing longwave radiation (OLR), vertical zonal wind shear, and sea surface temperature (SST). On the other hand, a frequency of the TC that occurred to the north of 200N showed a clear interdecadal variation and its decreasing trend was distinctive in recent years. Its intensity was also weaker that TCs that did to the south of 20$^{\circ}$N. However, a latitude of TC genesis showed an increasing trend until recent years, whose variation was consistent with trend that through a SST analysis, warm SST went north in recent years.

• Atmosphere
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• v.30 no.1
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• pp.1-15
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• 2020

This study analyzed and compared development mechanisms leading to heat waves of 2016 and 2018 in Korea. The European Centre for Medium-Range Weather Forecasts Reanalysis Interim (ERA Interim) dataset and Automated Surface Observing System data are used for synoptic scale analysis. The synoptic conditions are investigated using geopotential height, temperature, equivalent potential temperature, thickness, potential vorticity, omega, outgoing longwave radiation, and blocking index, etc. Heat waves in South Korea occur in relation to Western North Pacific Subtropical High (WNPSH) pressure system which moves northwestward to East Asia during summer season. Especially in 2018, WNPSH intensified due to strong large-scale circulation associated with convective activities in the Philippine Sea, and moved farther north to Korea when compared to 2016. In addition, the Tibetan high near the tropopause settled over Northern China on top of WNPSH creating a very strong anticyclonic structure in the upper-level over the Korean Peninsula. Unlike 2018, WNPSH was weaker and centered over the East China Sea in 2016. Analysis of blocking indices show wide blocking phenomena over the North Pacific and the Eurasian continent during heat wave event in both years. The strong upper-level ridge which was positioned zonally near 60°N, made the WNPSH over the South Korea stagnant in both years. Analysis of heat wave intensity (HWI) and duration (HWD) show that HWI and HWD in 2018 was both strong leading to extreme high temperatures. In 2016 however, HWI was relatively weak compared to HWD. The longevity of HWD is attributed to atmosphere blocking in the surrounding Eurasian continent.

• Atmosphere
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• v.28 no.4
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• pp.457-467
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• 2018

This study investigated the variability of spring (March-May) forest fire risk in Korea for the period 1991~2017 and analyzed its relationship with large-scale climate factors. The Forest Weather Index (FWI) representing the meteorological risk for forest fire occurrences calculated based on observational data and its relationship with large-scale climate factors were analyzed. We performed the empirical orthogonal function (EOF) analysis on the spring FWI. The leading EOF mode of FWI accounting for about 70% of total variability was found to be highly correlated with total number of forest fire occurrences in Korea. The high FWI, forest fire occurrence risk, in Korea, is associated with warmer atmosphere temperature in midwest Eurasia-China-Korea peninsula, cyclonic circulation anomaly in northeastern China-Korea peninsula-northwest pacific, westerly wind anomaly in central China-Korea peninsula, and low humidity in Korea. These are further related with warmer sea surface temperature and enhanced outgoing longwave radiation over Western Pacific, which represents a typical condition for a La $Ni\tilde{n}a$ episode. This suggests that large-scale climate factors over East Asia and ENSO could have a significant influence on the occurrence of spring forest fires in Korea.

• Atmosphere
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• v.33 no.5
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• pp.519-530
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• 2023

Deep convection can make adverse effects on safe and efficient aviation operations by causing various weather hazards such as convectively-induced turbulence, icing, lightning, and downburst. To prevent such damage, it is necessary to accurately predict spatiotemporal distribution of deep convective area near the airport and airspace. This study developed a new index, the Aviation Convective Index (ACI), for deep convection, using the operational global Unified Model of the Korea Meteorological Administration. The ACI was computed from combination of three different variables: 3-hour maximum of Convective Available Potential Energy, averaged Outgoing Longwave Radiation, and accumulative precipitation using the fuzzy logic algorithm. In this algorithm, the individual membership function was newly developed following the cumulative distribution function for each variable in Korean Peninsula. This index was validated and optimized by using the 1-yr period of radar mosaic data. According to the Receiver Operating Characteristics curve (AUC) and True Skill Score (TSS), the yearly optimized ACI (ACI_YrOpt) based on the optimal weighting coefficients for 1-yr period shows a better skill than the no optimized one (ACI_NoOpt) with the uniform weights. In all forecast time from 6-hour to 48-hour, the AUC and TSS value of ACI_YrOpt were higher than those of ACI_NoOpt, showing the improvement of averaged value of AUC and TSS by 1.67% and 4.20%, respectively.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.19 no.3
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• pp.169-179
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• 2014

Monthly mean surface heat fluxes in the southeastern Yellow Sea are calculated using directly observed airsea variables from an ocean buoy station including short- and longwave radiations, and COARE 3.0 bulk flux algorithm. The calculated monthly mean heat fluxes are then compared with previous estimates of climatological monthly mean surface heat fluxes near the buoy location. Sea surface receives heat through net shortwave radiation ($Q_i$) and loses heat as net longwave radiation ($Q_b$), sensible heat flux ($Q_h$), and latent heat flux ($Q_e$). $Q_e$ is the largest contribution to the total heat loss of about 51 %, and $Q_b$ and $Q_h$ account for 34% and 15% of the total heat loss, respectively. Net heat flux ($Q_n$) shows maximum in May ($191.4W/m^2$) when $Q_i$ shows its annual maximum, and minimum in December ($-264.9W/m^2$) when the heat loss terms show their annual minimum values. Annual mean $Q_n$ is estimated to be $1.9W/m^2$, which is negligibly small considering instrument errors (maximum of ${\pm}19.7W/m^2$). In the previous estimates, summertime incoming radiations ($Q_i$) are underestimated by about $10{\sim}40W/m^2$, and wintertime heat losses due to $Q_e$ and $Q_h$ are overestimated by about $50W/m^2$ and $30{\sim}70W/m^2$, respectively. Consequently, as compared to $Q_n$ from the present study, the amount of net heat gain during the period of net oceanic heat gain between April and August is underestimated, while the ocean's net heat loss in winter is overestimated in other studies. The difference in $Q_n$ is as large as $70{\sim}130W/m^2$ in December and January. Analysis of long-term reanalysis product (MERRA) indicates that the difference in the monthly mean heat fluxes between the present and previous studies is not due to the temporal variability of fluxes but due to inaccurate data used for the calculation of the heat fluxes. This study suggests that caution should be exercised in using the climatological monthly mean surface heat fluxes documented previously for various research and numerical modeling purposes.

• Korean Journal of Agricultural and Forest Meteorology
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• v.9 no.2
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• pp.140-148
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• 2007

The remote sensing observations of land surface properties are inevitably influenced by the landscape heterogeneity. In this paper, we introduce a geostatistical technique to provide a quantitative interpretation of landscape heterogeneity in terms of key land surface parameters. The study areas consist of the two KoFlux sites: (1) the Gwangneung site, covered with temperate mixed forests on a complex terrain, and (2) the Haenam site with mixed croplands on a relatively flat terrain. The semivariogram and fractal analyses were performed for both sites to characterize the spatial heterogeneity of two radiation parameters, i.e., land surface temperature (LST) and albedo. These parameters are the main factors affecting the reflected longwave and shortwave radiation components from the two study sites. We derived them from the high-resolution Landsat ETM+ satellite images collected on 23 Sep. 2001 and 14 Feb. 2002. The results of our analysis show that the characteristic scales of albedo was >1 km at the Gwangneung site and approximately 0.3 km at the Haenam site. For LST, the scale of heterogeneity was also >1 km at the Gwangneung site and >0.6 to 1.0 km at the Haenam site. At both sites, there was little change in the characteristic scales of the two parameters between the two different seasons.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.331-337
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• 2016

Water vapor is main absorption factor of outgoing longwave radiation. So, it is essential to monitoring the changes in the amount of water vapor and to understanding the causes of such changes. In this study, we monitor temporal variability of Total Precipitable Water (TPW) which observed by satellite. Among climate variables, precipitation play an important part to analyze temporal variability of water vapor because it is produced by water vapor. And El $Ni{\tilde{n}}o$ is one of climate variables which appear regularly in comparison with the others. Through them, we analyze relationship between temporal variability of TPW and climate variable. In this study, we analyzed long-term change of TPW from Moderate-Resolution Imaging Spectroadiometer (MODIS) data and change of precipitation in middle area of Korea peninsula quantitatively. After these analysis, we compared relation of TPW and precipitation with El $Ni{\tilde{n}}o$. The aim of study is to research El $Ni{\tilde{n}}o$ has an impact on TPW and precipitation change in middle area of Korea peninsula. First of all, we calculated TPW and precipitation from time series analysis quantitatively, and anomaly analysis is performed to analyze their correlation. As a result, TPW and precipitation has correlation mostly but the part had inverse correlation was found. This was compared with El $Ni{\tilde{n}}o$ of anomaly results. As a result, TPW and precipitation had inverse correlation after El $Ni{\tilde{n}}o$ occurred. It was found that El $Ni{\tilde{n}}o$ have a decisive effect on change of TPW and precipitation.

• 한국해양학회지
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• v.29 no.4
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• pp.325-337
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• 1994

The distributions of heat and momentum fluxes on the surface over the oceans around the Korean Peninsula are obtained based on the surface-layer flux model of Kim and Kang (1994), and their seasonal variations are examined in the present study. the input data of the model is the oceanatmosphere data with a grid interval of 2$^{\circ}$ in longitude and latitude. The atmosphere data, which are the pressure, temperature, and specific humidity on the 1000 mb level for 3 year period of 1985∼1987, are obtained from the European center for Medium Range Forecast. The sea surface temperature (SST) is obtained from National Meteorological Center (NMC). The solar insolation and longwave radiation on the ocean surface are obtained, respectively, from the NASA satellite data and based on an emprical formula. It is shown from the net heat flux that the oceans near Korea lose heat to the atmosphere in January and October with the rates of 200∼ 400 Wm/SUP -2/ and 100 Wm/SUP -2/, respectively. But the oceans are heated by the atmosphere in April and July with about the same rate of 100 Wm/SUP -2/. The annualmean net heat flux is negative over the entire domain except the northern part of the Yellow Sea. The largest annual-mean cooling rate of about 120 Wm/SUP -2/ is appeared off the southwest of Japan. In the East Sea, the annual-mean cooling rate is 60∼90 Wm/SUP -2/ in the southern and northern parts and about 30 Wm/SUP -2/ in the middle part. The magnitude of wind stress in january is 3∼ 5 times bigger than those of the other months. As a result, the spatial pattern of annual-mean wind stress is similar to that of January. It is also shown that the annual-mean wind stress curl is negative. in the East China Sea and the South Sea,but it is positive in the northern part of the Yellow Sea.In the East sea,the stress curl is positive in the southeast and northern parts and negative in the northwestern part.

• Korean Journal of Remote Sensing
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• v.9 no.2
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• pp.51-69
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• 1993

Precipitable Water(PW) are retrieved over the tropical and subtropical Pacific Ocean from TOVS infrared and microwave channel brightness temperature and OLR observations by means of stepwise linear regression. The retrieved TOVS PW fields generated by PW$_{sfc}$(71.1 % of the variance and 0.62 g cm$^{-2}$ standard error over the surface) and PW$_{700500}$(71.7 % and 0.17 g cm$^{-2}$ over the 700 - 500 hPa layer) revealed more evolving synoptic signals over the tropical and subtropical Pacific Ocean. The PW$_{sfc}$ dose not show significantly the TP feature because of the representation of the lower PW for high-level clouds not associated with deep convection. There exists some elusion to trace the TP on the PW$_{sfc}$ field if any supplementary information does not provide. But ECMWF analysis has a general tendency of drying the subtropics and moistening the ITCZ (InterTropical Convergence Zone) and SPCZ(South Pacific Convergence Zone). However, although ECMWF analysis is fairly successful in capturing mean patterms, it is unsuccessful in following active synoptic signal like a tropical plume. Similarly, SMMR-PW does not represent the TP well which consists of the highand middle-level clouds, but PW$_{sfc}$ shows underestimated moistness of TP and does not depict significant signal of TP. In the PW field derived from microwave observations, the TP can not be recognized well. Furthermore, the signature of PW$_{sfc}$ was different from OLR for the TP, which implies the presence of high- and middle-layer thin clouds, but in a closer agreement for deep and active convection areas which contain thick middle- and lower-layer clouds; though OLR represented the cloudiness in the tropics well. In synoptically active regions, it differed from OLR analysis, primarily bacause of actual differences in water vapor and cloud features. The signature of PW$_{sfc}$ was different from OLR for the TP.