• Proceedings of the Korea Air Pollution Research Association Conference
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• 2008.04a
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• pp.670-671
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• 2008

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

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.981-996
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• 2018

In agriculture, remote sensing data using earth observation satellites have many advantages over other methods in terms of time, space, and efficiency. This study analyzed the changes of reflectance and vegetation index according to atmospheric correction of images before using satellite images in agriculture. Top OF Atmosphere (TOA) reflectance and surface reflectance through atmospheric correction were calculated to compare the reflectance of each band and Normalized Vegetation difference Index (NDVI). As a result, the NDVI observed from field measurement sensors and satellites showed a higher agreement and correlation than the TOA reflectance calculated from surface reflectance using atmospheric correction. Comparing NDVI before and after atmospheric correction for multi-temporal images, NDVI increased after atmospheric corrected in all images. garlic and onion cultivation area and forest where the vegetation health was high area NDVI increased more 0.1. Because the NIR images are included in the water vapor band, atmospheric correction is greatly affected. Therefore, atmospheric correction is a very important process for NDVI time-series analysis in applying image to agricultural field.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1951-1963
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• 2021

Remote sensing data using earth observation satellites in agricultural environment monitoring has many advantages over other methods in terms of time, space, and efficiency. Since the sensor mounted on the satellite measures the energy that sunlight is reflected back to the ground, noise is generated in the process of being scattered, absorbed, and reflected by the Earth's atmosphere. Therefore, in order to accurately measure the energy reflected on the ground (radiance), atmospheric correction, which must remove noise caused by the effect of the atmosphere, should be preceded. In this study, atmospheric correction sensitivity analysis, inter-satellite cross-analysis, and comparative analysis with ground observation data were performed to evaluate the application of KOMPSAT-3 satellite's atmospheric correction for agricultural application. As a result, in all cases, the surface reflectance after atmospheric correction showed a higher mutual agreement than the TOA reflectance before atmospheric correction, and it is possible to produce the time series vegetation index of the same standard. However, additional research is needed for quantitative analysis of the sensitivity of atmospheric input parameters and the tilt angle.

• 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.

• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 2001.11a
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• pp.90-90
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• 2001

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.331-333
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• 2003

KMA successfully began to receive and utilize the GOES-9 GVAR data since May 22nd 2003 when GOES-9 replaced the long-lived GMS-5 for Western Pacific and East Asian region until operation of MTSAT-1R in 2004. To take advantage of improvements of the GOES-9 data over the GMS-5 data, such as the increase of the temporal and spat ial resolution and addition of 3.9${\mu}$m channel, we have improved several algorithms to derive the meteorological products. Here we show two examples of algorithms, sea surface temperature and atmospheric motion vector, and preliminary results of validation of the improved algorithm.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2008.10a
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• pp.498-499
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• 2008

• Korean Journal of Environmental Agriculture
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• v.40 no.4
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• pp.330-334
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• 2021

BACKGROUND: More recently, it has been shown that atmospheric ammonia (NH₃) plays a primary role in the formation of secondary particulate matter by reacting with the acidic species, e.g. SO₂, NOx, to form PM2.5 aerosols in the atmosphere. The Jeonbuk region is an area with high concentration of particulate matter. Due to environmental changes in the Saemangeum reclaimed land with an area of 219 km², it is necessary to evaluate the impact of the particulate matter and atmospheric ammonia in the Jeonbuk region. METHODS AND RESULTS: Atmospheric ammonia concentrations were measured from June 2020 to May 2021 using a passive sampler and CRDS analyzer. Seasonal and annual atmospheric ammonia concentration measured using passive sampler was significantly lower in Jangjado (background concentration), and the concentration ranged from 11.4 ㎍/m³ to 18.2 ㎍/m³. Atmospheric ammonia concentrations in Buan, Gimje, Gunsan, and Wanju regions did not show a significant difference, although there was a slight seasonal difference. The maximum atmospheric ammonia concentration measured using the CRDS analyzer installed in the IAMS near the Saemangeum reclaimed land was 51.5 ㎍/m³ in autumn, 48.0 ㎍/m³ in summer, 37.6 ㎍/m³ in winter, and 32.7 ㎍/m³ in spring. The minimum concentration was 4.9 ㎍/m³ in spring, 4.2 ㎍/m³ in summer, and 3.5 ㎍/m³ in autumn and winter. The annual average concentration was 14.6 ㎍/m³. CONCLUSION(S): Long term monitoring of atmospheric ammonia in agricultural areas is required to evaluate the formation of fine particulate matter and its impact on the environment. In addition, continuous technology development is needed to reduce ammonia emitted from farmland.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2007.10a
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• pp.507-508
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• 2007