• Atmosphere
• /
• v.34 no.1
• /
• pp.23-34
• /
• 2024

The Korea Meteorological Administration has improved medium-range weather forecasts by implementing post-processing methods to minimize numerical model errors. In this study, we employ a statistical correction technique known as the minimum continuous ranked probability score (CRPS) to refine medium-range forecast guidance. This technique quantifies the similarity between the predicted values and the observed cumulative distribution function of the Unified Model Ensemble Prediction System for Global (UM EPSG). We evaluated the performance of the medium-range forecast guidance for surface air temperature and relative humidity, noting significant enhancements in seasonal bias and root mean squared error compared to observations. Notably, compared to the existing the medium-range forecast guidance, temperature forecasts exhibit 17.5% improvement in summer and 21.5% improvement in winter. Humidity forecasts also show 12% improvement in summer and 23% improvement in winter. The results indicate that utilizing the minimum CRPS for medium-range forecast guidance provide more reliable and improved performance than UM EPSG.

• Atmosphere
• /
• v.23 no.2
• /
• pp.171-186
• /
• 2013

Recently, the number of observations used in a data assimilation system is increasing due to the enormous amount of observations, including satellite data. However, it is not clear that all of these observations are always beneficial to the performance of the numerical weather prediction (NWP). Therefore, it is important to evaluate the effect of observations on these forecasts so that the observations can be used more usefully in NWP process. In this study, the adjoint-based Forecast Sensitivity to Observation (FSO) method with the KMA Unified Model (UM) is applied to two high-impact weather events which occurred in summer and winter in Korea in an effort to investigate the effects of observations on the forecasts of these events. The total dry energy norm is used as a response function to calculate the adjoint sensitivity. For the summer case, TEMP observations have the greatest total impact while BOGUS shows the greatest impact per observation for all of the 24-, 36-, and 48-hour forecasts. For the winter case, aircraft, ATOVS, and ESA have the greatest total impact for the 24-, 36-, and 48-hour forecasts respectively, while ESA has the greatest impact per observation. Most of the observation effects are horizontally located upwind or in the vicinity of the Korean peninsula. The fraction of beneficial observations is less than 50%, which is less than the results in previous studies. As an additional experiment, the total moist energy norm is used as a response function to measure the sensitivity of 24-hour forecast error to observations. The characteristics of the observation impact with the moist energy response function are generally similar to those with the dry energy response function. However, the ATOVS observations were found to be sensitive to the response function, showing a positive (a negative) effect on the forecast when using the dry (moist) norm for the summer case. For the winter case, the dry and moist energy norm experiments show very similar results because the adjoint of KMA UM does not calculate the specific humidity of ice properly such that the dry and moist energy norms are very similar except for the humidity in air that is very low in winter.

• Journal of Agricultural Extension & Community Development
• /
• v.30 no.1
• /
• pp.15-29
• /
• 2023

Recently, there has been increasing interest in renewable energy in the agricultural sector. Agrophotovoltaics, producing electricity while farming, has attracted much attention. This study aims to identify the factors influencing the acceptance intention of Agrophotovoltaics. Using the UTAUT2 model, we designed a research model to examine the impact of performance expectancy, effort expectancy, facilitating conditions, social influence, and investment utility on the intention to adopt Agrophotovoltaics through the mediation effect of the user's innovativeness. We conducted a survey on farmers and obtained 212 responses for the final analysis. The results showed that effort expectancy and innovativeness significantly positively affected the intention to adopt Agrophotovoltaics. Furthermore, innovativeness was found to fully mediate the relationship among performance expectancy, facilitating conditions, investment utility, and acceptance intention. This suggests that focusing supportive measures on innovative groups could promote the adoption of Agrophotovoltaics.

• Atmosphere
• /
• v.29 no.4
• /
• pp.355-365
• /
• 2019

Based on in-situ monitoring data produced by National Institute of Meteorological Sciences, we evaluated the performance of Joint UK Land Environment Simulator (JULES) on the surface energy balance for rice-paddy and cropland in Korea with the operational ancillary data used for Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) (CTL) and the high-resolution ancillary data from external sources (EXP). For these experiments, we employed the one-year (March 2015~February 2016) observations of eddy-covariance fluxes and soil moisture contents from a double-cropping rice-paddy in BoSeong and a cropland in AnDong. On the rice-paddy site the model performed better in the CTL experiment except for the sensible heat flux, and the latent heat flux was underestimated in both of experiments which can be inferred that the model represents flood-irrigated surface poorly. On the cropland site the model performance of the EXP experiment was worse than that of CTL experiment related to unrealistic surface type fractions. The pattern of the modeled soil moisture was similar to the observation but more variable in time. Our results shed a light on that 1) the improvement of land scheme for the flood-irrigated rice-paddy and 2) the construction of appropriate high-resolution ancillary data should be considered in the future research.

• Atmosphere
• /
• v.25 no.1
• /
• pp.67-83
• /
• 2015

Predicting the location and intensity of precipitation still remains a main issue in numerical weather prediction (NWP). Resolution is a very important component of precipitation forecasts in NWP. Compared with a lower resolution model, a higher resolution model can predict small scale (i.e., storm scale) precipitation and depict convection structures more precisely. In addition, an ensemble technique can be used to improve the precipitation forecast because it can estimate uncertainties associated with forecasts. Therefore, NWP using both a higher resolution model and ensemble technique is expected to represent inherent uncertainties of convective scale motion better and lead to improved forecasts. In this study, the limited area ensemble prediction system for the convective-scale (i.e., high resolution) operational Unified Model (UM) in Korea Meteorological Administration (KMA) was developed and evaluated for the ensemble forecasts during August 2012. The model domain covers the limited area over the Korean Peninsula. The high resolution limited area ensemble prediction system developed showed good skill in predicting precipitation, wind, and temperature at the surface as well as meteorological variables at 500 and 850 hPa. To investigate which combination of horizontal resolution and ensemble member is most skillful, the system was run with three different horizontal resolutions (1.5, 2, and 3 km) and ensemble members (8, 12, and 16), and the forecasts from the experiments were evaluated. To assess the quantitative precipitation forecast (QPF) skill of the system, the precipitation forecasts for two heavy rainfall cases during the study period were analyzed using the Fractions Skill Score (FSS) and Probability Matching (PM) method. The PM method was effective in representing the intensity of precipitation and the FSS was effective in verifying the precipitation forecast for the high resolution limited area ensemble prediction system in KMA.

• Journal of the Korean earth science society
• /
• v.35 no.1
• /
• pp.19-28
• /
• 2014

The radiative transfer for TIROS operational vertical sounder (RTTOV) and the community radiative transfer model (CRTM) are two fast radiative transfer models (RTM) that are used as observation operators in numerical weather prediction (NWP) systems. This study compares the basic structure and input data of the two models. With data from Advanced Microwave Sounding Unit-A (AMSU-A), which has channels of various frequencies, observed brightness temperature ($T_B$) and simulated $T_B$s from the two models are compared over the ocean surface in two cases-one where cloud information is included and the other without it. Regarding AMSU-A sounding channels (5-14), the two models produce no large significant differences in their calculated $T_B$, but RTTOV produces smaller first guess (FG) departures (i.e., better results) in window and near-surface sounding channels than does CRTM. When adding cloud water and ice particles from Unified Model (UM), the $T_B$ bias between observations and simulations are reduced in both models and the bias at 31.4 and 89 GHz is substantially decreased in CRTM compared to those of RTTOV.

• Journal of Korean Society for Atmospheric Environment
• /
• v.30 no.2
• /
• pp.119-127
• /
• 2014

A very high resolution weather analysis system (VHRAS) of 50 m horizontal resolution is established based on LAPS. VHRAS utilizes the 3 hourly forecast data of the Unified Model (UM) of the Korea Meteorological Administration (KMA) with the horizontal resolution of 12 km as initial guess fields. The analysis system ingests the automatic weather station (AWS) data as input observations. The analysis system operates every hour for Seoul, Korea region in real time basis. It takes less than 10 minutes for one analysis cycle. The size of grid of the analysis domain is $800{\times}660$, respectively. The analysis results from December 2010 to February 2011 showed that the mean biases of temperature, maximum and minimum temperature were -0.07, 1.6, $0.2^{\circ}C$, respectively. The temperature in the central part of the city revealed relatively higher value than that of the surrounding mountainous areas, which showed a heat island feature. The heat island appears in zonal direction since the central city region is developed along a large river. Along the heat island, the eastern region was warmer than the western region. The warmer temperature in the western part of the heat island was caused by anthropogenic heat change in conjunction with the change of land use. This system will provide more reliable weather data and information in Seoul.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2012.05a
• /
• pp.71-71
• /
• 2012

현재 전 세계적으로 극한강우의 발생빈도가 점차 높아지고 있으며 홍수량 또한 강도가 커지고 있는 것이 현실이다. 하지만 과거의 홍수발생 빈도에 따라 설계된 홍수방어시설들이 점차 한계를 보이고 있으므로 이를 대비하기위한 구조적 대책뿐만 아니라 홍수피해 발생 가능지역에 사전 예경보를 시행하는 비구조적 대책마련 또한 필요하다. 기존의 홍수예측은 확정적인 하나의 유량예측값만을 제공함으로써 신속하고 편리하였지만 이에 대한 불확실성이 큰 경우 예상치 못한 큰 인적 물적 피해를 가져올 수 있다. 이처럼 확률론적 홍수예측의 필요성이 대두되어 지면서 유럽이나 미국등 선진국에서는 EFFS(European Flood Forecasting System)과 NWSRFS(National Water Service River Forecast System)같이 이미 확률론적 홍수예측에 대한 연구 및 기술개발이 활발하게 진행되어지고 있다. 하지만 홍수예측의 확률론적 접근에 있어서는 많은 불확실성들이 내포되어 있으므로 예측시스템에서 생성된 앙상블 유량예측 결과의 신뢰도 분석과 올바른 불확실성 정보의 제공이 필요하다. 본 연구는 확률론적 홍수예측 방법을 국내에 적용시켜서 기상청의 예측시스템 KLAPS(Korea Local Analysis and Prediction System), MAPLE(McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation), UM(Unified Model) 그리고 MOGREPS(Met Office Global Regional Ensemble Prediction System)으로부터 생성된 기상앙상블을 현재 국토해양부 홍수통제소에서 사용하고 있는 강우-유출모형인 저류함수모형(Storage Function Method)의 입력 자료로 사용한다. 확률론적 홍수예측에서 오는 불확실성을 분석하기 위해서 첫 번째로 제공되는 기상예측 시스템의 시 공간적 스케일 및 대상유역의 공간특성에 따라 어떠한 형태로 전파되어지는지를 분석하였다. 두 번째는 각각의 예측시스템들이 선행기간(Lead time)에 따라 불확실성의 특성이 어떻게 나타나게 되는지를 확인하였다. 이러한 불확실성의 특성을 정확하게 파악하게 된다면 예측에 있어서 현재 갖고 있는 문제점들로부터 개선해 나가야 할 방향을 제시해주어 향후연구에 유용하게 활용될 수 있을 것이다.

• Atmosphere
• /
• v.31 no.4
• /
• pp.395-404
• /
• 2021

In order to investigate downslope windstorm by using more detailed observation, we observed 6 cases at 3 sites - Inje, Yongpyeong, and Bukgangneung - during "3-D Meteorological Observation Project in Yeongdong region of Gangwon province, South Korea in 2020." The results from analysis of the project data were as follows. First, AWS data showed that a subsidence inversion layer appeared in 800~700 hPa on the windward side and 900~850 hPa on the leeward side. Second, before strong wind occurred, the inversion layer had descended to about 880~800 hPa. Third, with mountain wave breaking, downslope wind was intensified at the height of 2~3 km above sea level. After the downslope wind began to descend, the subsidence inversion layer developed. When the subsidence inversion layer got close to the ground, wind peak occurred. In general, UM (Unified Model) GDAPS (Global Data Assimilation Prediction System) have had negative bias in wind speed around peak area of Taebaek mountain range, and positive bias in that of East Sea coast area. The stronger wind blew, the larger the gap between observed and predicted wind speed by GDAPS became. GDAPS predicted strong p-velocity at 0600 LST 25 Apr 2020 (4^th case) and weak p-velocity at 2100 LST 01 Jun 2020 (6^th case) on the lee-side of Taebaek mountain range near Yangyang. As hydraulic jump theory was proved, which is known as a mechanism of downslope windstorm in Yeongdong region, it was confirmed that there is a relationship between p-velocity of lee-side and wind speed of eastern slope of Taebaek mountain range.

• Atmosphere
• /
• v.25 no.2
• /
• pp.221-233
• /
• 2015

Pollen is closely related to health issues such as allergenic rhinitis and asthma as well as intensifying atopic syndrome. Information on current and future spatio-temporal distribution of allergenic pollen is needed to address such issues. In this study, the Community Multiscale Air Quality Modeling (CMAQ) was utilized as a base modeling system to forecast pollen dispersal from oak trees. Pollen emission is one of the most important parts in the dispersal modeling system. Areal emission factor was determined from gridded areal fraction of oak trees, which was produced by the analysis of the tree type maps (1:5000) obtained from the Korea Forest Service. Daily total pollen production was estimated by a robust multiple regression model of weather conditions and pollen concentration. Hourly emission factor was determined from wind speed and friction velocity. Hourly pollen emission was then calculated by multiplying areal emission factor, daily total pollen production, and hourly emission factor. Forecast data from the KMA UM LDAPS (Korea Meteorological Administration Unified Model Local Data Assimilation and Prediction System) was utilized as input. For the verification of the model, daily observed pollen concentration from 12 sites in Korea during the pollen season of 2014. Although the model showed a tendency of over-estimation in terms of the seasonal and daily mean concentrations, overall concentration was similar to the observation. Comparison at the hourly output showed distinctive delay of the peak hours by the model at the 'Pocheon' site. It was speculated that the constant release of hourly number of pollen in the modeling framework caused the delay.