• Atmosphere
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• v.21 no.2
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• pp.163-172
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• 2011

Numerical forecasting depends on the initial condition error strongly because numerical model is a chaotic system. To calculate the sensitivity of some forecast aspects to the initial condition in the Korea Meteorological Administration (KMA) Unified Model (UM) which is originated from United Kingdom (UK) Meteorological Office (MO), an algorithm to calculate adjoint sensitivities is developed by modifying the adjoint perturbation forecast model in the KMA UM. Then the new algorithm is used to calculate adjoint sensitivity distributions for typhoon DIANMU (201004). Major initial adjoint sensitivities calculated for the 48 h forecast error are located horizontally in the rear right quadrant relative to the typhoon motion, which is related with the inflow regions of the environmental flow into the typhoon, similar to the sensitive structures in the previous studies. Because of the upward wave energy propagation, the major sensitivities at the initial time located in the low to mid- troposphere propagate upward to the upper troposphere where the maximum of the forecast error is located. The kinetic energy is dominant for both the initial adjoint sensitivity and forecast error of the typhoon DIANMU. The horizontal and vertical energy distributions of the adjoint sensitivity for the typhoon DIANMU are consistent with those for other typhoons using other models, indicating that the tools for calculating the adjoint sensitivity in the KMA UM is credible.

• Atmosphere
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• v.22 no.3
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• pp.345-356
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• 2012

The UK Met Office Unified Model at the KMA has been operationally utilized as the next generation numerical prediction system since 2010 after it was first introduced in May, 2008. Researches need to be carried out regarding various physical processes inside the model in order to improve the predictability of the newly introduced Unified Model. We first performed a preliminary experiment for the domain ($170{\times}170$, 10 km, 38 layers) smaller than that of the operating system using the version 7.4 of the UM local model to optimize its physical processes. The result showed that about 7~8% of the improvement ratio was found at each stage by integrating four factors (u, v, th, q), and the final improvement ratio was 25%. Verification was carried out for one month of August, 2008 by applying the optimized combination to the domain identical to the operating system, and the result showed that the precipitation verification score (ETS, equitable threat score) was improved by 9%, approximately.

• Genomics & Informatics
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• v.14 no.4
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• pp.166-172
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• 2016

Although a large number of genetic variants have been identified to be associated with common diseases through genome-wide association studies, there still exits limitations in explaining the missing heritability. One approach to solving this missing heritability problem is to investigate gene-gene interactions, rather than a single-locus approach. For gene-gene interaction analysis, the multifactor dimensionality reduction (MDR) method has been widely applied, since the constructive induction algorithm of MDR efficiently reduces high-order dimensions into one dimension by classifying multi-level genotypes into high- and low-risk groups. The MDR method has been extended to various phenotypes and has been improved to provide a significance test for gene-gene interactions. In this paper, we propose a simple method, called accelerated failure time (AFT) UM-MDR, in which the idea of a unified model-based MDR is extended to the survival phenotype by incorporating AFT-MDR into the classification step. The proposed AFT UM-MDR method is compared with AFT-MDR through simulation studies, and a short discussion is given.

• The Plant Pathology Journal
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• v.36 no.1
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• pp.54-66
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• 2020

This study was conducted to evaluate usefulness of numerical weather prediction data generated by the Unified Model (UM) for plant disease forecast. Using the UM06- and UM18-predicted weather data, which were released at 0600 and 1800 Universal Time Coordinated (UTC), respectively, by the Korea Meteorological Administration (KMA), disease forecast on bacterial grain rot (BGR) of rice was examined as compared with the model output based on the automated weather stations (AWS)-observed weather data. We analyzed performance of BGRcast based on the UM-predicted and the AWS-observed daily minimum temperature and average relative humidity in 2014 and 2015 from 29 locations representing major rice growing areas in Korea using regression analysis and two-way contingency table analysis. Temporal changes in weather conduciveness at two locations in 2014 were also analyzed with regard to daily weather conduciveness (C_i) and the 20-day and 7-day moving averages of C_i for the inoculum build-up phase (C_inc) prior to the panicle emergence of rice plants and the infection phase (C_inf) during the heading stage of rice plants, respectively. Based on C_inc and C_inf, we were able to obtain the same disease warnings at all locations regardless of the sources of weather data. In conclusion, the numerical weather prediction data from KMA could be reliable to apply as input data for plant disease forecast models. Weather prediction data would facilitate applications of weather-driven disease models for better disease management. Crop growers would have better options for disease control including both protective and curative measures when weather prediction data are used for disease warning.

• Journal of the Korean earth science society
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• v.43 no.6
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• pp.691-711
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• 2022

The state-of-the-art Earth system model as a virtual Earth is required for studies of current and future climate change or climate crises. This complex numerical model can account for almost all human activities and natural phenomena affecting the atmosphere of Earth. The Unified Model (UM) from the United Kingdom Meteorological Office (UK Met Office) is among the best Earth system models as a scientific tool for studying the atmosphere. However, owing to the expansive numerical integration cost and substantial output size required to maintain the UM, individual research groups have had to rely only on supercomputers. The limitations of computer resources, especially the computer environment being blocked from outside network connections, reduce the efficiency and effectiveness of conducting research using the model, as well as improving the component codes. Therefore, this study has presented detailed guidance for installing a new version of the UM on high-performance parallel computers (Linux clusters) owned by individual researchers, which would help researchers to easily work with the UM. The numerical integration performance of the UM on Linux clusters was also evaluated for two different model resolutions, namely N96L85 (1.875° ×1.25° with 85 vertical levels up to 85 km) and N48L70 (3.75° ×2.5° with 70 vertical levels up to 80 km). The one-month integration times using 256 cores for the AMIP and CMIP simulations of N96L85 resolution were 169 and 205 min, respectively. The one-month integration time for an N48L70 AMIP run using 252 cores was 33 min. Simulated results on 2-m surface temperature and precipitation intensity were compared with ERA5 re-analysis data. The spatial distributions of the simulated results were qualitatively compared to those of ERA5 in terms of spatial distribution, despite the quantitative differences caused by different resolutions and atmosphere-ocean coupling. In conclusion, this study has confirmed that UM can be successfully installed and used in high-performance Linux clusters.

• Atmosphere
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• v.21 no.4
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• pp.415-427
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• 2011

In this study, the impacts of land surface conditions, land cover (LC) map and leaf area index (LAI), on the short-range weather forecast over the East-Asian region were examined using Unified Model (UM) coupled with the MOSES 2.2 (Met-Office Surface Exchange Scheme). Four types of experiments were performed at 12-km horizontal resolution with 38 vertical layers for two months, July and August 2009 through consecutive reruns of 72-hour every 12 hours, 00 and 12 UTC. The control experiment (CTRL) uses the original IGBP (International Geosphere-Biosphere Programme) LC map and old MODIS (MODerate resolution Imaging Spectroradiometer) LAI, the new LAI experiment (NLAI) uses improved monthly MODIS LAI. The new LC experiment (NLCE) uses KLC_v2 (Kongju National Univ. land cover), and the new land surface experiment (NLSE) uses KLC_v2 and new LAI. The reduced albedo and increased roughness length over southern part of China caused by the increased broadleaf fraction resulted in increase of land surface temperature (LST), air temperature, and sensible heat flux (SHF). Whereas, the LST and SHF over south-eastern part of Russia is decreased by the decreased needleleaf fraction and increased albedo. The changed wind speed induced by the LC and LAI changes also contribute the LST distribution through the change of vertical mixing and advection. The improvement of LC and LAI data clearly reduced the systematic underestimation of air temperature over South Korea. Whereas, the impacts of LC and LAI conditions on the simulation skills of precipitation are not systematic. In general, the impacts of LC changes on the short range forecast are more significant than that of LAI changes.

• Atmosphere
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• v.29 no.4
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• pp.381-390
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• 2019

This study investigates the impacts of urban land-use fraction and temperature advection on the urban heat island intensity over the Seoul metropolitan area using the UM (Unified Model) with the MORUSES (Met Office Reading Urban Surface Exchange Scheme) during the heat wave over the region from 2 to 8, August 2016. Two simulations are performed with two different land-use type, the urban (urban simulation) and the urban surfaces replaced with grass (rural simulation), in order to calculate the urban heat island intensity defined as the 1.5-m temperature difference between the urban and the rural simulations. The land-use type for the urban simulation is obtained from Korea Ministry of Environment (2007) land-use data after it is converted into the types used in the UM. It is found that the urban heat island intensity over high urban-fraction regions in the metropolitan area is as large as 1℃ in daytime and 3.2℃ in nighttime, i.e., the effects of urban heat island is much larger for night than day. It is also found that the magnitude of urban heat island intensity increases linearly with urban land-use fraction. Spatially, the estimated the urban heat island intensities are systematically larger in the downwind regions of the metropolitan area than in the upwind area due to the effects of temperature advection. Results of this study indicate that urban surface fraction in the city area and temperature advection play a key role in determining the spatial distribution and magnitude of urban heat island intensity.

• Atmosphere
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• v.34 no.2
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• pp.217-231
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• 2024

The Korea Integrated Model (KIM)-based real-time volcanic ash dispersion prediction system, which employs the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, has been developed to quantitatively predict volcanic ash dispersion in East Asia and the Northwest Pacific airspace. This system, known as KIM-HYSPLIT, automatically generates forecasts for the vertical and horizontal spread of volcanic ash up to 72 hours. These forecasts are initiated upon the receipt of a Volcanic Ash Advisory (VAA) from the Tokyo Volcanic Ash Advisory Center by the server at the Korea Meteorological Administration (KMA). This system equips KMA forecasters with diverse volcanic ash prediction information, complemented by the Unified Model (UM)-based HYSPLIT (UM-HYSPLIT) system. Extensive experiments have been conducted using KIM-HYSPLIT across 128 different volcanic scenarios, along with qualitative comparisons with UM-HYSPLIT. The results indicate that the ash direction predictions from KIM-HYSPLIT are consistent with those from UM-HYSPLIT. However, there are slight differences in the horizontal extent and movement speed of the volcanic ash. Additionally, quantitative verifications of the KIM-HYSPLIT forecasts have been performed, including threat score evaluations, based on recent eruption cases. On average, the KIMHYSPLIT forecasts for 6 and 12 hours show better quantitative alignment with the VAA forecasts compared to UM-HYSPLIT. Nevertheless, both models tend to predict a broader horizontal spread of the ash cloud than indicated in the VAA forecasts, particularly noticeable in the 6-hour forecast period.

• Journal of the Korean Solar Energy Society
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• v.40 no.5
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• pp.13-22
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• 2020

Day ahead forecast is necessary for the electricity market to stabilize the electricity penetration. Numerical weather prediction is usually employed to produce the solar irradiance as well as electric power forecast for longer than 12 hours forecast horizon. Korea Meteorological Administration operates the UM-LDAPS model to produce the 36 hours forecast of hourly total irradiance 4 times a day. This study interpolates the hourly total irradiance into 15 minute instantaneous irradiance and then compare them with observed solar irradiance at four ground stations at 1 minute resolution. Numerical weather prediction model employed here was produced at 00 UTC or 18 UTC from January to December, 2018. To compare the statistical model for the forecast horizon less than 3 hours, smart persistent model is used as a reference model. Relative root mean square error of 15 minute instantaneous irradiance are averaged over all ground stations as being 18.4% and 19.6% initialized at 18 and 00 UTC, respectively. Numerical weather prediction is better than smart persistent model at 1 hour after simulation began.

• Journal of the Korean Solar Energy Society
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• v.39 no.2
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• pp.71-80
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• 2019

Daily ahead forecast is necessary for the electricity balance between load and supply due to the variability renewable energy. Numerical weather prediction is usually employed to produce the solar irradiance as well as electric power forecast for more than 12 hours forecast horizon. UM-LDAPS model is the numerical weather prediction operated by Korea Meteorological Administration and it generates the 36 hours forecast of hourly total irradiance 4 times a day. This study attempts to evaluate the model performance against the in situ measurements at 37 ground stations from January to May, 2013. Relative mean bias error, mean absolute error and root mean square error of hourly total irradiance are averaged over all ground stations as being 8.2%, 21.2% and 29.6%, respectively. The behavior of mean bias error appears to be different; positively largest in Chupoongnyeong station but negatively largest in Daegu station. The distinct contrast might be attributed to the limitation of microphysics parameterization for thick and thin clouds in the model.