• Journal of the Korean Society of Marine Environment & Safety
• /
• v.24 no.6
• /
• pp.773-784
• /
• 2018

In this study, we examined the UA (upwelling age) using wind data of AWS/ASOS in the East Sea coast and the correlation between UA and SST (sea surface temperature) from May to August in 1995 to 2016. The data used the 6 observations of the wind data of AWS/ASOS and the SST data of the COD/RISA provided by the National Institute and Fisheries Science near the East Sea coast. The UA was calculated quantitatively low but it rose when the actual cold water mass occurred. Correlation analysis between UA and SST showed the negative (-) r (correlation coefficient) predominately. At the time of cold-water mass in June to August 2013, the r had a very high negative value of -0.65 to -0.89 in the 6 observations. It proved that as the UA increases, the SST is lower. By knowing the UA, we were able to evaluate the trend of upwelling in the cold-water mass of the East Sea coast in the long term and it will contribute to minimizing the damage to aquatic organisms according to the size and intensity of the upwelling.

• Korean Journal of Remote Sensing
• /
• v.32 no.6
• /
• pp.603-616
• /
• 2016

In this study, the effects of differential heating by land-use types on flow and air temperature at an Seoul Automated Synoptic Observing Systems (ASOS) located at Songwol-dong, Jongno-gu, Seoul was analyzed. For this, a computation fluid dynamics (CFD) model was coupled to the local data assimilation and prediction system (LDAPS) for reflecting the local meteorological characteristics at the boundaries of the CFD model domain. Time variation of temperatures on solid surfaces was calculated using observation data at El-Oued, Algeria of which latitude is similar to that of the target area. Considering land-use type and shadow, surface temperatures were prescribed in the LDAPS-CFD coupled model. The LDAPS overestimated wind speeds and underestimated air temperature compared to the observations. However, a coupled LDAPS-CFD model relatively well reproduced the observed wind speeds and air temperature, considering complicated flows and surface temperatures in the urban area. In the morning when the easterly was dominant around the target area, both the LDAPS and coupled LDAPS-CFD model underestimated the observed temperatures at the Seoul ASOS. This is because the Kyunghee Palace located at the upwind region was composed of green area and its surface temperature was relatively low. However, in the afternoon when the southeasterly was dominant, the LDAPS still underestimated, on the while, the coupled LDAPS-CFD model well reproduced the observed temperatures at the Seoul ASOS by considering the building-surface heating.

• Journal of Korea Water Resources Association
• /
• v.54 no.10
• /
• pp.747-757
• /
• 2021

Quantification of extreme rainfall is very important in establishing a flood protection plan, and a general measure of extreme rainfall is expressed as an T-year return level. In this study, a method was proposed for quantifying spatial distribution and uncertainty of daily rainfall depths with various return periods using a hierarchical Bayesian model combined with climate and geographical information, and was applied to the Seoul-Incheon-Gyeonggi region. The annual maximum daily rainfall depth of six automated synoptic observing system weather stations of the Korea Meteorological Administration in the study area was fitted to the generalized extreme value distribution. The applicability and reliability of the proposed method were investigated by comparing daily rainfall quantiles for various return levels derived from the at-site frequency analysis and the regional frequency analysis based on the index flood method. The uncertainty of the regional frequency analysis based on the index flood method was found to be the greatest at all stations and all return levels, and it was confirmed that the reliability of the regional frequency analysis based on the hierarchical Bayesian model was the highest. The proposed method can be used to generate the rainfall quantile maps for various return levels in the Seoul-Incheon-Gyeonggi region and other regions with similar spatial sizes.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.2
• /
• pp.122-133
• /
• 2021

Cultivar parameter calibration can be affected by the reliability of the input data to a crop growth model. In South Korea, two sets of weather stations, which are included in the automated synoptic observing system (ASOS) or the automatic weather system (AWS), are available for preparation of the weather input data. The objectives of this study were to estimate the cultivar parameter using those sets of weather data and to compare the uncertainty of these parameters. The cultivar parameters of CERES-Rice model for Shindongjin cultivar was calibrated using the weather data measured at the weather stations included in either ASO S or AWS. The observation data of crop growth and management at the experiment farms were retrieved from the report of new cultivar development and research published by Rural Development Administration. The weather stations were chosen to be the nearest neighbor to the experiment farms where crop data were collected. The Generalized Likelihood Uncertainty Estimation (GLUE) method was used to calibrate the cultivar parameters for 100 times, which resulted in the distribution of parameter values. O n average, the errors of the heading date decreased by one day when the weather input data were obtained from the weather stations included in AWS compared with ASO S. In particular, reduction of the estimation error was observed even when the distance between the experiment farm and the ASOS stations was about 15 km. These results suggest that the use of the AWS stations would improve the reliability and applicability of the crop growth models for decision support as well as parameter calibration.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.4
• /
• pp.268-279
• /
• 2021

The geospatial schemes based on topo-climatology have been developed to produce digital climate maps at a site-specific scale. Their development processes are reviewed here to derive the needs for new schemes in the future. Agricultural and forestry villages in Korea are characterized by complexity and diversity in topography, which results in considerably large spatial variations in weather and climate over a small area. Hence, the data collected at a mesoscale through the Automated Synoptic Observing System (ASOS) operated by the Korea Meteorological Administration (KMA) are of limited use. The geospatial schemes have been developed to estimate climate conditions at a local scale, e.g., 30 m, lowering the barriers to deal with the processes associated with production in agricultural and forestry industries. Rapid enhancement of computing technologies allows for near real-time production of climate information at a high-resolution even in small catchment areas and the application to future climate change scenarios. Recent establishment of the early warning service for agricultural weather disasters can provide growth progress and disaster forecasts for cultivated crops on a farm basis. The early warning system is being expanded worldwide, requiring further advancement in geospatial schemes and digital climate mapping.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.24 no.3
• /
• pp.155-163
• /
• 2022

The optimization of long-range ensemble climate prediction for rice phenology model with advanced bias correction method is conducted. The daily long-range forecast(6-month) of mean/ minimum/maximum temperature and observation of January to October during 1991-2021 is collected for rice phenology prediction. In this study, the concept of "buffer period" is newly introduced to reduce the problem after bias correction by quantile mapping with constructing the transfer function by month, which evokes the discontinuity at the borders of each month. The four experiments with different lengths of buffer periods(5, 10, 15, 20 days) are implemented, and the best combinations of buffer periods are selected per month and variable. As a result, it is found that root mean square error(RMSE) of temperatures decreases in the range of 4.51 to 15.37%. Furthermore, this improvement of climatic variables quality is linked to the performance of the rice phenology model, thereby reducing RMSE in every rice phenology step at more than 75~100% of Automated Synoptic Observing System stations. Our results indicate the possibility and added values of interdisciplinary study between atmospheric and agriculture sciences.

• Journal of Korean Society of Occupational and Environmental Hygiene
• /
• v.32 no.4
• /
• pp.402-413
• /
• 2022

Objectives: In this study, the scale of exceeding the extreme heat exposure standard at the construction site was estimated using the nationally approved statistical data and wet bulb globe temperature modeling method. By comparing and analyzing the modeling results with the existing work environment monitoring results, the risk of heat exposure at outdoor construction sites was considered. Methods: Using the coordinates of second level administrative districts and meteorological observatories as the key, the automated synoptic observing system data and building permit data for 2021 were matched. The wet-bulb temperature was obtained using Stull's formula, and the globe temperature was obtained using the Tg_KMA2006 model. WBGT was calculated using these. Excess rates were obtained compared to exposure limits for heavy work-continuous work and moderate work-25% rest. It was compared with the results of the work environment monitoring in 2020. Results: As a result, 1,827,536 cases were estimated for 11,052 workplaces in one year. This is much higher than the 5,116 cases of 3818 workplaces of the existing work environment monitoring results. It is confirmed that the exposure limit was exceeded in 10.6~24.0% of the entire period and 70.2~84.1% of the peak period of the heat wave. It is very high compared to 0.9% of the existing work environment monitoring result. Conclusions: It is necessary to improve the system of monitoring and statistics related to extreme heat. Additional considerations are needed regarding WBGT estimation methods, meteorological data, and evaluation time. Various follow-up risk assessment studies for other industries and time series need to be continued.

• Journal of Environmental Health Sciences
• /
• v.42 no.2
• /
• pp.85-91
• /
• 2016

Objectives: The objective of this study was to assess heat stress, compare heat stress indices, and evaluate the usefulness of wet bulb globe temperature (WBGT) among outdoor workers exposed to heat during the summer season. Methods: WBGT, dry temperature, and heat index were measured using WBGT measurers (QUESTemp 32 model and QUESTemp 34 model, QUEST, WI, USA) by industrial hygienists from August 27 to September 16, 2015. Heat stress indices were measured at the workplaces of a shipbuilder in Ulsan and a construction site in Daegu. The dry temperature observed by the Automated Synoptic Observing System (ASOS) of the Korea Meteorological Administration was also compared. Results: Dry temperature measured by WBGT is different from that by ASOS. The temperature obtained from ASOS was less than $33^{\circ}C$, above which point a heat wave is forecast by the Korea Meteorological Administration. A heat index above $32.8^{\circ}C$ as a moderate risk was not observed during measurement. WBGT was consistently higher than $22^{\circ}C$, above which the risk of heat-related illness is increased in unacclimated workers involved in work with a high metabolic rate. WBGT was sometimes higher than $28^{\circ}C$, above which the risk of heat-related illness is increased in acclimated workers involved in work with a moderate metabolic rate in September. Conclusion: According to the measurement of heat stress indices, WBGT was more sensitive than heat index and temperature. Thus, general measures to prevent heat-related diseases should be implemented in workplaces during the summer season according to WBGT.

• Journal of Korea Water Resources Association
• /
• v.50 no.10
• /
• pp.691-701
• /
• 2017

The objective of this study is to evaluate the long-term drought outlook information based on long-term forecast data for the 2015 drought event. In order to estimate the Standardized Precipitation Index (SPI) for different durations (3-, 6-, 9-, 12-months), we used the observation precipitation of 59 Automated Synoptic Observing System (ASOS) sites, forecast and hindcast data of GloSea5. The Receiver Operating Characteristic (ROC) analysis and statistical analysis (Correlation Coefficient, CC; Root Mean Square Error, RMSE) were used to evaluate the utilization of drought outlook information for the forecast lead-times (1~6months). As a result of ROC analysis, ROC scores of SPI(3), SPI(6), SPI(9) and SPI(12) were estimated to be over 0.70 until the 2-, 3-, 4- and 5-months. The CC and RMSE values of SPI(3), SPI(6), SPI(9) and SPI(12) for forecast lead-time were estimated as (0.60, 0.87), (0.72, 0.95), (0.75, 0.95) and (0.77, 0.89) until the 2-, 4-, 5- and 6-months respectively.

• Korean Journal of Remote Sensing
• /
• v.37 no.5_2
• /
• pp.1317-1328
• /
• 2021

This study analyzes the early satellite mission marine fog detection results from Geostationary Ocean Color Imager-II (GOCI-II). We investigate optical characteristics of the GOCI-II spectral bands for marine fog between October 2020 and March 2021 during the overlapping mission period of Geostationary Ocean Color Imager (GOCI) and GOCI-II. For Rayleigh-corrected reflection (R_rc) at 412 nm band available for the input of the GOCI-II marine fog algorithm, the inter-comparison between GOCI and GOCI-II data showed a small Root Mean Square Error (RMSE) value (0.01) with a high correlation coefficient (0.988). Another input variable, Normalized Localization Standard (NLSD), also shows a reasonable correlation (0.798) between the GOCI and GOCI-II data with a small RMSE value (0.007). We also found distinctive optical characteristics between marine fog and clouds by the GOCI-II observations, showing the narrower distribution of all bands' R_rc values centered at high values for cloud compared to marine fog. The GOCI-II marine fog detection distribution for actual cases is similar to the GOCI but more detailed due to the improved spatial resolution from 500 m to 250 m. The validation with the automated synoptic observing system (ASOS) visibility data confirms the initial reliability of the GOCI-II marine fog detection. Also, it is expected to improve the performance of the GOCI-II marine fog detection algorithm by adding sufficient samples to verify stable performance, improving the post-processing process by replacing real-time available cloud input data and reducing false alarm by adding aerosol information.