• Journal of Preventive Medicine and Public Health
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• v.43 no.5
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• pp.436-444
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• 2010

Objectives: Infectious diseases are known to be affected by climate change. We investigated if the infectious diseases were related to meteorological factors in Korea. Methods: Scrub typhus, hemorrhagic fever with renal syndrome (HFRS), leptospirosis, malaria and Vibrio vulnificus sepsis among the National Notifiable Infectious Diseases were selected as the climate change-related infectious diseases. Temperature, relative humidity and precipitation were used as meteorological factors. The study period was from 2001 through 2008. We examined the seasonality of the diseases and those correlations with meteorological factors. We also analyzed the correlations between the incidences of the diseases during the outbreak periods and monthly meteorological factors in the hyper-endemic regions. Results: All of the investigated diseases showed strong seasonality; malaria and V. vulnificus sepsis were prevalent in summer and scrub typhus, HFRS and leptospirosis were prevalent in the autumn. There were significant correlations between the monthly numbers of cases and all the meteorological factors for malaria and V. vulnificus sepsis, but there were no correlation for the other diseases. However, the incidence of scrub typhus in hyper-endemic region during the outbreak period was positively correlated with temperature and humidity during the summer. The incidences of HFRS and leptospirosis had positive correlations with precipitation in November and temperature and humidity in February, respectively. V. vulnificus sepsis showed positive correlations with precipitation in April/May/July. Conclusions: In Korea, the incidences of the infectious diseases were correlated with meteorological factors, and this implies that the incidences could be influenced by climate change.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.4
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• pp.3-12
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• 2007

The purpose of this paper is to analyze the relationship between meteorological factors and agricultural reservoir storage, and predict the reservoir storage by multiple regression equation selected by high correlated meteorological factors. Two agricultural reservoirs (Geumgwang and Gosam) located in the upsteam of Gongdo water level gauging station of Anseong-cheon watershed were selected. Monthly reservoir storage data and meteorological data in Suwon weather station of 21 years (1985-2005) were collected. Three cases of correlation (case 1: yearly mean, case 2: seasonal mean dividing a year into 3 periods, and case 3: lagging the reservoir storage from 1 month to 3 months under the condition of case 2) were examined using 8 meteorological factors (precipitation, mean/maximum/minimum temperature, relative humidity, sunshine hour, wind velocity and evaporation). From the correlation analysis, 4 high correlated meteorological factors were selected, and multiple regression was executed for each case. The determination coefficient ($R^{2}$) of predicted reservoir storage for case 1 showed 0.45 and 0.49 for Geumgwang and Gosam reservoir respectively. The predicted reservoir storage for case 2 showed the highest $R^{2}$ of 0.46 and 0.56 respectively in the period of April to June. The predicted reservoir storage for 1 month lag of case 3 showed the $R^{2}$ of 0.68 and 0.85 respectively for the period of April to June. The results showed that the status of agricultural reservoir storage could be expressed with couple of meteorological factors. The prediction enhanced when the storage data are divided into periods rather than yearly mean and especially from the beginning time of paddy irrigation (April) to high decrease of reservoir storage (June) before Jangma.

• Atmosphere
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• v.30 no.3
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• pp.209-220
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• 2020

Seasonal predictability and variability of tropical storms (TCs) simulated in the Global Seasonal Forecast System version 5 (GloSea5) of the Korea Meteorological Administration (KMA) is assessed in Northern Hemisphere in 1996~2009. In the KMA, the GloSea5-Global Atmosphere version 3.0 (GloSea5-GA3) that was previously operated was switched to the GloSea5-Global Coupled version 2.0 (GloSea5-GC2) with data assimilation system since May 2016. In this study, frequency, track, duration, and strength of the TCs in the North Indian Ocean, Western Pacific, Eastern Pacific, and North Atlantic regions derived from the GloSea5-GC2 and GloSea5-GA3 are examined against the best track data during the research period. In general, the GloSea5 shows a good skill for the prediction of seasonally averaged number of the TCs in the Eastern and Western Pacific regions, but underestimation of those in the North Atlantic region. Both the GloSea5-GA3 and GC2 are not able to predict the recurvature of the TCs in the North Western Pacific Ocean (NWPO), which implies that there is no skill for the prediction of landfalls in the Korean peninsula. The GloSea5-GC2 has higher skills for predictability and variability of the TCs than the GloSea5-GA3, although continuous improvements in the operational system for seasonal forecast are still necessary to simulate TCs more realistically in the future.

• Atmosphere
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• v.30 no.3
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• pp.257-276
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• 2020

Faithful evaluation of the meteorological input is a prerequisite for a better understanding of air quality model performance. Despite the importance, the preliminary meteorological assessment has rarely been concerned. In this study, we aim to evaluate the performance of the Weather Research and Forecasting (WRF) model conducting a year-long high-resolution meteorological simulation in 2016 over the Seoul metropolitan area. The WRF model was configured based on a series of sensitivity simulations of initial/boundary meteorological conditions, land use mapping data, reanalysis grid nudging method, domain nesting method, and urban canopy model. The simulated results of winds, air temperature, and specific humidity in the atmospheric boundary layer (ABL) were evaluated following statistical evaluation guidance using the surface and upper meteorological measurements. The statistical evaluation results are presented. The model performance was interpreted acceptable for air quality modeling within the statistical criteria of complex conditions, showing consistent overestimation in wind speeds. Further statistical analysis showed that the meteorological model biases were highly systematic with systematic bias fractions (f_SB) of 20~50%. This study suggests that both the momentum exchange process of the surface layer and the ABL entrainment process should be investigated for further improvement of the model performance.

• Atmosphere
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• v.21 no.1
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• pp.85-93
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• 2011

UK Met Office Unified Model (UM) is a grid model applicable for both global and regional model configurations. The Met Office has developed a 4D-Var data assimilation system, which was implemented in the global forecast system on 5 October 2004. In an effort to improve its Numerical Weather Prediction (NWP) system, Korea Meteorological Administration (KMA) has adopted the UM system since 2008. The aim of this study is to provide the basic information on the effects of satellite data assimilation on UM performance by conducting global satellite data denial experiments. Advanced Tiros Operational Vertical Sounder (ATOVS), Infrared Atmospheric Sounding Interferometer (IASI), Special Sensor Microwave Imager Sounder (SSMIS) data, Global Positioning System Radio Occultation (GPSRO) data, Air Craft (CRAFT) data, Atmospheric Infrared Sounder (AIRS) data were assimilated in the UM global system. The contributions of assimilation of each kind of satellite data to improvements in UM performance were evaluated using analysis data of basic variables; geopotential height at 500 hPa, wind speed and temperature at 850 hPa and mean sea level pressure. The statistical verification using Root Mean Square Error (RMSE) showed that most of the satellite data have positive impacts on UM global analysis and forecasts.

• Journal of Integrative Natural Science
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• v.11 no.1
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• pp.30-38
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• 2018

CALPUFF is one of the recommended air pollution models by EPA with AERMOD. It has been used to simulate the ambient concentration of critical air pollutants as well as non-critical pollutants such as persistent organic matters and the organic materials causing odor. In this model, the air pollutants go through dispersion, transportation, chemical reaction, and deposition process. These mechanisms are significantly influenced by meteorological condition. This study produces the meteorological field in three different methods for the simulation of $SO_2$ using CALPUFF: 1) CALMET model by using both ground-level and aerological observation, 2) CALMET model by using MM5 results with NCEP/NCAR reanalyzed data, 3) CALMET model by using MM5 results in which FDDA is applied with NCEP/NCAR reanalyzed data as well as the meteorological data of Korea Meteorological Administration. As a result of CALPUFF model, the resolved concentration of $SO_2$ showed different behaviors in three cases. For the first case, the fluctuation of SO2 concentration was frequently observed while the fluctuation is reduced in the second and third cases. In addition, the maximum concentration of $SO_2$ in the first case was about 2~3 times higher than the second case, and about 4~6 times higher than the third case. These results can be caused by the accuracy of the resolved meteorological field. It is inferred that the meteorological field of the first case could be less accurate than other two cases. These results show that the use of correct meteorological data can improve the result of dispersion model. Moreover, the contribution of various sources such as point, line, and area sources on the ambient concentration of air pollutant can be roughly estimated from the sensitivity analysis.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.361-365
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• 2002

Rain-rate retrieval using the NOAA/AMSU (Advanced Microwave Sounding Unit) (Zaho et al., 2001) has been implemented at METRI/KMA since 2001. Here, we present the results of the AMSU derived rain-rate and validation result, especially for the rainfall associated with the tropical cyclone for 2001. For the validation, we use rain-rate derived from the ground based radar and/or rainfall observation from the rain gauge in Korea. We estimate the bias score, threat score, bias, RMSE and correlation coefficient for total of 16 tropical cyclone cases. Bias score shows around 1.3 and it increases with the increasing threshold value of rain-rate, while the threat score extends from 0.4 to 0.6 with the increasing threshold value of precipitation. The averaged rain-rate for at all 16 cases is 3.96mm/hr and 1.41mm/hr for the retrieved from AMSU and the ground observation, respectively. On the other hand, AMSU rain-rate shows a much better agreement with the ground based observation over inner part of tropical cyclone than over the outer part (Correlation coefficient for convective region is about 0.7, while it is only about 0.3 over the stratiform region). The larger discrepancy of tile correlation coefficient with the different part of the tropical cyclone is partly due to the time difference in between ice water path and surface rainfall. This results indicates that it might be better to develop the algorithm for different rain classes such as convective and stratiform.

• Atmosphere
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• v.31 no.4
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• pp.461-472
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• 2021

Yeongdong has frequently suffered from severe snowstorms, which generally give rise to societal and economic damages to the region in winter. In order to understand its mechanism, there has been a long-term measurement campaign, based on the rawinsonde measurements for every snowfall event at Gangneung since 2014. The previous observations showed that a typical heavy snowfall is generally accompanied with northerly or northeasterly flow below the snow clouds, generated by cold air outbreak over the relatively warmer East Sea. An intensive and multi-institutional measurement campaign has been launched in 2019 mainly in collaboration with Gangwon Regional Office of Meteorology and National Institute of Meteorological Studies of Korean Meteorological Administration, with a special emphasis on winter snowfall and spring windstorm altogether. The experiment spanned largely from February to April with comprehensive measurements of frequent rawinsonde measurements at a super site (Gangneung) with continuous remote sensings of wind profiler, microwave radiometers and weather radar etc. Additional measurements were added to the campaign, such as aircraft dropsonde measurements and shipboard rawinsonde soundings. One of the fruitful outcomes is, so far, to identify a couple of cold air damming occurrences, featuring lowest temperature below 1 km, which hamper the convergence zone and snow clouds from penetrating inland, and eventually make it harder to forecast snowfall in terms of its location and timing. This kind of comprehensive observation campaign with continuous remote sensings and intensive additional measurement platforms should be conducted to understand various orographic precipitation in the complex terrain like Yeongdong.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.169-176
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• 2021

The Global Space-based Inter-Calibration System (GSICS) is an international partnership sponsored by World Meteorological Organization (WMO) to continue and improve climate monitoring and to ensure consistent accuracy between observation data from meteorological satellites operating around the world. The objective for GSICS is to inter-calibration from pairs of satellites observations, which includes direct comparison of collocated Geostationary Earth Orbit (GEO)-Low Earth Orbit (LEO) observations. One of the GSICS inter-calibration methods, the Ray-matching technique, is a surrogate approach that uses matched, co-angled and co-located pixels to transfer the calibration from a well calibrated satellite sensor to another sensor. In Korea, the first GEO satellite, Communication Ocean and Meteorological Satellite (COMS), is used to participate in the GSICS program. The National Meteorological Satellite Center (NMSC), which operated COMS/MI, calculated the Radiative Transfer Model (RTM)-based GSICS coefficient coefficients. The L1P reproduced through GSICS correction coefficient showed lower RMSE and Bias than L1B without GSICS correction coefficient applied. The calculation cycles of the GSICS correction coefficients for COMS/MI visible channel are provided annual and diurnal (2, 5, 10, 14-day), but long-term evaluation according to these cycles was not performed. The purpose of this paper is to perform evaluation depending on the annual/diurnal cycles of COMS/MI GSICS correction coefficients based on the ray-matching technique using Suomi-NPP/Visible Infrared Imaging Radiometer Suite (VIIRS) data as reference data. As a result of evaluation, the diurnal cycle had a higher coincidence rate with the reference data than the annual cycle, and the 14-day diurnal cycle was the most suitable for use as the GSICS correction coefficient.

• Atmosphere
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• v.32 no.4
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• pp.307-322
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• 2022

A drone has recently got attention as an instrument for weather observation in lower atmosphere because it can produce the high spatiotemporal resolution weather data even though the weather phenomenon is inaccessible. Sea fog is a weather phenomenon occurred in lower atmosphere, and has observational limitations because it occurs on the sea. Therefore, goal of this study is to analyze the vertical structures about inflow, development and dispersion of sea fog using the high-resolution weather data with the meteorological sensor-equipped drone. This study observed sea fogs in the west coast of the Korean peninsula from March to October 2021 and investigated one sea fog inflowed into the coast on June 8th 2021. θ_e - q_v diagrams (θ_e: equivalent potential temperature, q_v: water vapor ratio) and vertical wind structures were analyzed. At inflow of sea fog, moist adiabatically stable layer was formed in 0-300 m and prevailing wind was switched from south-southwesterly to west-southwesterly under 120 m. Both changes are favorable for sea fog on the location. θ_e and q_v plummeted in a layer 0-183 m. The inflowed sea fog developed from 183 m to 327 m by mixing with ambient atmosphere on top of sea fog. Also, strong mechanical turbulence near ground drove a vertical mixing under stable layer. At dispersion of sea fog, as θ_e on ground gradually increased, air condition was changed to neutral. Evaporation occurred on both bottom and top in sea fog. These results induced dissipation of sea fog.