• Journal of Korean Society for Atmospheric Environment
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• v.26 no.6
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• pp.666-682
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• 2010

The forecasting system for Today's and Tomorrow's PM10 was developed based on the statistical model and the forecasting was performed at 9 AM to predict Today's 24 hour average PM10 concentration and at 5 PM to predict Tomorrow's 24 hour average PM10. The Today's forecasting model was operated based on measured air quality and meteorological data while Tomorrow's model was run by monitored data as well as the meteorological data calculated from the weather forecasting model such as MM5 (Mesoscale Meteorological Model version 5). The observed air quality data at ambient air quality monitoring stations as well as measured and forecasted meteorological data were reviewed to find the relationship with target PM10 concentrations by the regression analysis. The PM concentration, wind speed, precipitation rate, mixing height and dew-point deficit temperature were major variables to determine the level of PM10 and the wind direction at 500 hpa height was also a good indicator to identify the influence of long-range transport from other countries. The neural network, regression model, and decision tree method were used as the forecasting models to predict the class of a comprehensive air quality index and the final forecasting index was determined by the most frequent index among the three model's predicted indexes. The accuracy, false alarm rate, and probability of detection in Tomorrow's model were 72.4%, 0.0%, and 42.9% while those in Today's model were 80.8%, 12.5%, and 77.8%, respectively. The statistical model had the limitation to predict the rapid changing PM10 concentration by long-range transport from the outside of Korea and in this case the chemical transport model would be an alternative method.

• Atmosphere
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• v.22 no.1
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• pp.29-45
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• 2012

An unusual autumn storm developed rapidly in the western part of the East sea on the early morning of 23 October 2006. This storm produced a record-breaking heavy rain and strong wind in the northern and middle part of the Yeong-dong region; 24-h rainfall of 304 mm over Gangneung and wind speed exceeding 63.7 m $s^{-1}$ over Sokcho. In this study, MTSAT-1R (Multi-fuctional Transport Satellite) water vapor and infrared channel imagery are examined to find out some features which are dynamically associated with the development of the storm. These features may be the precursor signals of the rapidly developing storm and can be employed for very short range forecast and nowcasting of severe storm. The satellite features are summarized: 1) MTSAT-1R Water Vapor imagery exhibited that distinct dark region develops over the Yellow sea at about 12 hours before the occurrence of maximum rainfall about 1100 KST on 23 October 2006. After then, it changes gradually into dry intrusion. This dark region in the water vapor image is closely related with the positive anomaly in 500 hPa Potential Vorticity field. 2) In the Infrared imagery, low stratus (brightness temperature: $0{\sim}5^{\circ}C$) develops from near Bo-Hai bay and Shanfung peninsula and then dissipates partially on the western coast of Korean peninsula. These features are found at 10~12 hours before the maximum rainfall occurrence, which are associated with the cold and warm advection in the lower troposphere. 3) The IR imagery reveals that two convective cloud cells (brightness temperature below $-50^{\circ}C$) merge each other and after merging it grows up rapidly over the western part of East sea at about 5 hours before the maximum rainfall occurrence. These features remind that there must be the upward flow in the upper troposphere and the low-layer convergence over the same region of East sea. The time of maximum growth of the convective cloud agrees well with the time of the maximum rainfall.

• Atmosphere
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• v.29 no.3
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• pp.325-342
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• 2019

Precipitation and no-precipitation events under the influence of the Siberian high pressure system in Yeondong region, were analysed and classified as four types [obvious precipitation event (OP) type, obvious no-precipitation event (ON) type, ambiguous precipitation event (AP) type and ambiguous no-precipitation event (AN) type], according to the easiness in determining whether to have precipitation or not in Yeongdong region, to help in improving the forecast skill. Concerning the synoptic pressure pattern, for OP type, the ridge of Siberian high extends from Lake Baikal toward Northeast China, and there is a northerly wind upstream of the northern mountain complex (located near the Korean-Chinese border). On the other hand, for ON type, the ridge of Siberian high extends southeastward from Lake Baikal, and there is a westerly wind upstream of the northern mountain complex. The pressure pattern of AP type was similar to the OP type and that of AN type was also similar to ON type. Thus it was difficult to differentiate AP type and OP type and AN type and ON type based on the synoptic pressure pattern only. The four types were determined by U (wind speed normal to the Taebaek mountains) and Froude number (FN). That is, for OP type, average FN and U at Yeongdong coast are ~2.0 and ${\sim}6m\;s^{-1}$, and those at Yeongseo region are 0.0 and $0.1m\;s^{-1}$, respectively. On the contrary, for ON type, average FN and U at Yeongdong coast are 0.0 and $0.2m\;s^{-1}$, and those at Yeongseo region are ~1.0 and ${\sim}4m\;s^{-1}$, respectively. For AP type, average FN and U at Yeongdong coast are ~1.0 and ${\sim}4m\;s^{-1}$, and those at Yeongseo region are 0.0 and $0.2m\;s^{-1}$, whereas for AN type, average FN and U at Yeongdong coast are 0.1 and $0.6m\;s^{-1}$ and those at Yeongseo region are ~1.0 and ${\sim}3m\;s^{-1}$, respectively. Based on the result, a schematic diagram for each type was suggested.

• Journal of the Korean earth science society
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• v.41 no.6
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• pp.575-587
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• 2020

Meteorological phenomena are observed by the Korea Meteorological Administration in a variety of ways (e.g., surface, upper-air, marine, ocean, and aviation). However, there are limits to the meteorological observation of the planetary boundary layer (PBL) that greatly affects human life. In particular, observations using a sonde or aircraft require significant observational costs in economic terms. Therefore, the goal of this study was to measure and analyze the meteorological factors of the vertical distribution of the see-land breeze among local meteorological phenomena using meteorological drones. To investigate the spatial distribution of the see-land breeze, a same integrated meteorological sensor was mounted on each drone at three different points (seaside, bottom of mountain, and mountainside), including the Boseong tall tower (BTT) at the Boseong Standard Weather Observatory (BSWO) in the Boseong region. Vertical profile observations for air temperature, relative humidity, wind direction, wind speed, and air pressure were conducted up to 400 m every 30 minutes from 1100 LST to 1800 LST on August 4, 2018. The spatial characteristics of meteorological phenomena for temperature, relative humidity, and atmospheric pressure were not shown at the four points. Strong winds (~8 m s-1) were observed from the midpoint (~100 m) at strong solar radiation hour, and in the afternoon the wind direction changed from the upper layer at the inland area to the west wind. It is expected that the analysis results of the lower atmospheric layer observed using the meteorological drone may help to improve the weather forecast more accurately.

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.4
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• pp.344-353
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• 2016

The cause of abnormal tidal residuals was examined by analyzing sea levels, sea surface atmospheric pressures, winds at ten tide stations, and current, measured at the coast of the Yellow Sea from the night of November $24^{th}$ to the morning of the $25^{th}$ in 2013, along with weather chart. Additionally, the cross-correlations among the measured data were also examined. The 'abnormal tidal residuals' mentioned in this study refer to differences between maximum and minium tidal residuals. The largest abnormal tidal residual was identified to be a difference of 176 cm occurring over 4 hours and 1 minute at YeongJongDo (YJD) with a maximum tidal residual of 111 cm and minimum of -65 cm. The smallest abnormal tidal residual was 68 cm at MoSeulPo (MSP) during 8 hours 52 minutes. The cause of these abnormal tidal residuals was not a meteo-tsunami generated by an atmospheric pressure jump but wind generated by the pressure patterns. The flow speed due to these abnormal tidal residuals as measured at ten tide stations was not negligible, representing 16 ~ 41 % of the annual average ebb current speed. From the cross correlation among the tidal residuals, winds, and tidal residual currents, we learned the northern flow, due to southerly winds, raised the sea level at Incheon when a low pressure center located on the left side of the Korean Peninsula. After passing the Korean Peninsula, a southern flow due to northerly winds decreased the sea level.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.89-92
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• 2015

In this paper, we propose a method to reduce the fire suppression time. Our suggestions can secure a safe area according to the diffusion path and speed of the fire, forest fire prediction minimize casualties and property damage forests. The existing path prediction method wildfire spread predict the wildfire spread model and speed through topography, weather, fuel factor and the image information. In this case, however, occur to control a large mountain huge costs. Also Focus on the diffusion model predictions and the path identified by the problem arises that insufficient efforts to ensure the safe area. In this paper, we estimate the moving direction and speed of fire at a lower cost, and proposes an algorithm to ensure the safety zone for fire suppression. The proposed algorithm is a technique to analyze the attribute information that temperature, wind, smoke measured over time. According to our algorithm forecast wildfire moving direction and ensure the safety zone. By analyzing the moving speed and the moving direction of the simulated fire in a given environment is expected to be able to quickly reduce the damage to the forest fire fighters.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.12
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• pp.2348-2354
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• 2016

In recent days, IoT (Internet of Things) technology has been widely used in the field of agriculture, which enables the collection of environmental data and biometric data into the database. The availability of big data on agriculture results in the increase of the machine learning based analysis. Through the analysis, it is possible to forecast agricultural production and the diseases of livestock, thus helping the efficient decision making in the management of smart farm. Herein, we use the environmental and biometric data of Smart Pig farm to derive the accurate relationship model between the environmental information and the daily weight increase of swine and verify the accuracy of the derived model. To this end, we applied the M5P tree algorithm of machine learning which reveals that the wind speed is the major factor which affects the daily weight increase of swine.

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.3
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• pp.156-162
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• 2001

In order to monitor local climatic information, twelve automated weather stations (AWS) were installed in alpine area by the Alpine Agricultural Experiment Station, Rural Development Administration (RDA), at the field of major crop located in around highland area, and collected data from 1993 to 2000. Hourly measurements of air and soil temperature (underground 10 cm,20 cm), relative humidity, wind speed and direction, precipitation, solar radiation and leaf wetness were automatically performed and the data could be collected through a public phone line. Datalogger was selected as CR10X (Campbell scientific, LTD, USA) out of consideration for sensers' compatibility, economics, endurance and conveniences. All AWS in alpine area were combined for net work and daily climatic data were analyzed in text and graphic file by program (Chumsungdae, LTD) on 1 km $\times$ 1 km grid tell basis. In this analysis system, important multi-functionalities, monitoring and analysis of local climatic information in alpine area was emphasized. The first objective was to obtain the output of a real time data from AWS. Secondly, daily climatic normals for each grid tell were calculated from geo-statistical relationships based on the climatic records of existing weather stations as well as their topographical informations. On 1 km $\times$ 1 km grid cell basis, real time climatic data from the automated weather stations and daily climatic normals were analyzed and graphed. In the future, if several simulation models were developed and connected with this system it would be possible to precisely forecast crop growth and yield or plant disease and pest by using climatic information in alpine area.

• Journal of agriculture & life science
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• v.45 no.4
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• pp.29-35
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• 2011

A forest fire was one of the huge disasters and damaged human lifes and a properties. Therefore, many countries operated forest fire forecasting systems which developed from forest fire records, weather data, fuel models and etc. And many countries also estimated future state of forest fire using a long-term climate forecasting like GCMs and prepared resources for future huge disasters. In this study, we analyzed relationships between forest fire occurrence and meteorological factors (the minimum temperature ($^{\circ}C$), the relative humidity (%), the precipitation (mm), the duration of sunshine (hour) and etc.) for developing a estimating tools, which could forecast forest fire regime under future climate change condition. Results showed that forest fires in this area were mainly occurred when the maximum temperature was $10{\sim}200^{\circ}C$, when the relative humidity was 40~60%, and when the average wind speed was under 2m/s. And forest fires mainly occurred at 2~3 day after rainfall.

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