• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.1-15
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• 2018

Passive microwave sea surface temperatures (SST) were validated in the Northwest Pacific using a total of 102,294 collocated matchup data between Global Precipitation Measurement (GPM) / GPM Microwave Sensor(GMI) data and oceanic in-situ temperature measurements from March 2014 to December 2016. A root-mean-square (RMS) error and a bias error of the GMI SST measurements were evaluated to $0.93^{\circ}C$ and $0.05^{\circ}C$, respectively. The SST differences between GMI and in-situ measurements were caused by various factors such as wind speed, columnar atmospheric water vapor, land contamination near coastline or islands. The GMI SSTs were found to be higher than the in-situ temperature measurements at low wind speed (<6 m/s) during the daytime. As the wind speed increased at night, SST errors showed positive bias. In addition, other factors, coming from atmospheric water vapor, sensitivity degradation at a low temperature range, and land contamination, also contributed to the errors. One of remarkable characteristics of the errors was their latitudinal dependence with large errors at high latitudes above $30^{\circ}N$. Seasonal characteristics revealed that the errors were most frequently observed in winter with a significant positive deviation. This implies that SST errors tend to be large under conditions of high wind speeds and low SSTs. Understanding of microwave SST errors in this study is anticipated to compensate less temporal capability of Infrared SSTs and to contribute to increase a satellite observation rate with time, especially in SST composite process.

• Journal of Environmental Science International
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• v.14 no.1
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• pp.91-96
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• 2005

Several samplers using gravimetric methods such as high-volume air sampler, MiniVol portable sampler, personal environmental monitor(PEM) and cyclone were applied to determine the concentrations of fine particles in atmospheric condition. Comparative evaluation between high-volume air sampler and Minivol portable sampler for $PM_{10}$, and between Minivol portable sampler and PEM was undertaken from June, 2003 to January 2004. Simultaneously, meteorological conditions such as wind speed, wind direction, relative humidity and temperature was measured to check the factors affecting the concentrations of fine particles. In addition, particle concen­trations by cyclone with an aerodynamic diameter of $4{\mu}m$ were measured. Correlation coefficient between high­volume air sampler and portable air sampler for $PM_{10}$ was 0.79 (p<0.001). However, the mean concentration for $PM_{10}$ by high-volume air sampler was significantly higher than that by Minivol portable sampler (p=0.018). Correlation coefficient between Minivol portable sampler and PEM for $PM_{2.5}$ as 0.74 (p<0.001), and the measured mean concentrations for $PM_{2.5}$ did not show significant difference. Difference of the measured con­centrations of fine particle might be explained by wind speed and humidity among meteorological conditions. Particle concentration differences by measurement samplers were proportional to the wind speed, but inversely proportional to the relative humidity, though it was not a significant correlation.

• Journal of Korean Society of Forest Science
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• v.77 no.2
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• pp.216-222
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• 1988

This study was conducted to investigate the effect of meteorological factors on the diameter growth and Key-year in Pinus koraiensis and Pinus densiflora trees grown in Chuncheon and Hongcheon. The results obtained were summarized as follows : 1. The diameter growth of Pinus koraiensis was better than that of Pinus densiflora grown in the same meteorological condition and site environment. 2. The influence of meteorological factors on the diameter growth of Pinus koraiensis was the highest in the descending order fog, hours of sunshine, precipitation, relative humidity, warm index, and evaporation. 3. The influence of meteorological factors on the diameter growth of Pinus densiflora was the highest in the descending order fog, hours of sunshine, relative humidity, precipitation, and warm index. But evaporation was not significant. 4. The Key-years for Pinus koraiensis and Pinus densiflora trees appeared in 1964 and 1913 when the diameter growth was influenced by the specific climate change.

• Journal of Korean Society for Atmospheric Environment
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• v.10 no.E
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• pp.343-356
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• 1994

In this study, a statistical investigation was carried out for the evaluation of any relationship between polycyclic aromatic hydrocarbons (PAHss) associated with ambient aerosols and other air quality parameters under varying meteorological conditions. Daily measurements for PAHs and air quality/meteorological parameters were selected from a data-base constructed by a comprehensive air monitoring in London during 1985-1987. Correlation coefficients were calculated to examine any significant relationship between the PAHs and other individual variables. Statistical analysis was further Performed for the air quality/meteorological data set using a principal component analysis to derive important factors inherent in the interactions among the variables. A total of six components were identified, representing vehicle emission, photochemical activity/volatilization, space heating, atmospheric humidity, atmospheric stability, and wet deposition. It was found from a stepwise multiple regression analysis that the vehicle emission component is overall the most important factor contributing to the variability of PAHs concentrations at the monitoring site. The photochemical activity/volatilzation component appeared to be also an important factor particularly for the lower molecular weight PAHs. In general, the space heating component was found to be next important factor, while the contributions of other three components to the variance of each PAHs did not appear to be as much important as the first three components in most cases. However, a consistency for these components in their negative correlations with PAHs data was found, indicating their roles in the depletion of PAHs concentrations in the urban atmosphere.

• Atmosphere
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• v.31 no.1
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• pp.73-83
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• 2021

Numerical weather prediction (NWP) models play an essential role in predicting weather factors, but using them is challenging due to various factors. To overcome the difficulties of NWP models, deep learning models have been deployed in weather forecasting by several recent studies. This study adapts long short-term memory (LSTM), which demonstrates remarkable performance in time-series prediction. The combination of LSTM model input of meteorological features and activation functions have a significant impact on the performance therefore, the results from 5 combinations of input features and 4 activation functions are analyzed in 9 Automated Surface Observing System (ASOS) stations corresponding to cities/islands/mountains. The optimized LSTM model produces better performance within eight forecast hours than Local Data Assimilation and Prediction System (LDAPS) operated by Korean meteorological administration. Therefore, this study illustrates that this LSTM model can be usefully applied to very short-term weather forecasting, and further studies about CNN-LSTM model with 2-D spatial convolution neural network (CNN) coupled in LSTM are required for improvement.

• Journal of The Korean Society of Grassland and Forage Science
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• v.43 no.2
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• pp.75-87
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• 2023

This research was conducted to analyze the cultivation performance and meteorological data of winter rye in Suwon, Gyeonggi Province, and Daegu for 11 years. The objective was to compare the growth and yield of domestically cultivated Korean rye cultivar "Gogu" and identify the factors influencing them, to determine suitable cultivation areas for Korean rye cultivar in the country. The results of the study showed that both Daegu and Suwon regions possess favorable climatic conditions for winter rye cultivation, with Suwon exhibiting a superior moisture supply compared to Daegu. Furthermore, the analysis of climate suitability revealed that rainfall days and precipitation were significant factors affecting rye cultivation. Through correlation and principal component analysis, the research evaluated the interrelationship between climate, cultivation factors, and winter rye crop performance, as well as identified variations among winter rye cultivation regions. This study provides valuable insights and information for winter rye cultivation in the country, thereby assisting in the decision-making process for selecting optimal cultivation areas.

• Atmosphere
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• v.20 no.3
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• pp.343-353
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• 2010

A regional wind network with complex surface conditions must be designed with sufficient space and time resolution to resolve the local circulations. In this study, the spatial variations of the wind field observed in the Seoul and Jeju regional networks were evaluated in terms of annual, seasons, and months to assess the spatial homogeneity of wind fields within the regional networks. The coherency of the wind field as a function of separation distance between stations indicated that significant coherency was sometimes not captured by the network, as inferred by low correlations between adjacent stations. A meso-velocity scale was defined in terms of the spatial variability of the wind within the network. This problem is predictably most significant with weak winds, dull prevailing wind, clear skies and significant topography. The relatively small correlations between stations imply that the wind at a given point cannot be estimated by interpolating winds from the nearest stations. For the Seoul and Jeju regional network, the meso-velocity scale has typically a same order of magnitude as the speed of the network averaged wind, revealing the large spatial variability of the Jeju network station imply topography and weather. Significant scatter in the relationship between spatial variability of the wind field and the wind speed is thought to be related to thermally-generated flows. The magnitude of the mesovelocity scale was significantly different along separation distance between stations, wind speed, intensity of prevailing wind, clear and cloudy conditions, topography. Resultant wind vectors indicate much different flow patterns along condition of contributing factors. As a result, the careful considerations on contributing factors such as prevailing wind in season, weather, and complex surface conditions with topography and land/sea contrast are required to assess the spatial variations of wind field on a regional network. The results in the spatial variation from the mesovelocity scale are useful to represent the characteristics of regional wind speed including lower surface conditions over the grid scale of large scale atmospheric model.

• Journal of the Korean Society of Tobacco Science
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• v.26 no.1
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• pp.17-26
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• 2004

This study was conducted to get the informations for reducing the variation of chemical contents of leaf tobacco. The contents and variations of some chemical constituents of leaf(Leaf, Grade 2) produced in various growing areas from 1999 to 2003 and the effects of meteorological factors on the chemical constituents of leaf were analysed. The contents of analysed constituents of leaf showed high significant differences among crop years in flue-cured and burley, particularly the variation among crop years were higher in chlorine and nicotine contents while lower in total nitrogen content. There were significant differences among growing areas in nicotine and total sugar contents of flue-cured leaf and chlorine content of burley leaf. The total sugar content were negatively correlated to the nicotine and total nitrogen contents in flue-cured leaf. The average air temperature in June and July were positively correlated to the nicotine content of leaf while negatively to total sugar, and the precipitation in May were negatively correlated to the nicotine while positively to total sugar.

• Journal of Environmental Science International
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• v.26 no.12
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• pp.1363-1374
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• 2017

Global climate change caused by industrialization has caused abnormal weather conditions such as urban temperatures and tropical nights, urban heat waves, heat waves, and heavy rains. Therefore, the study tried to analyze climate conditions and weather conditions in the streets and analyze climate factors and meteorological factors that lead to inconvenience to citizens. In the case of trees, the overall temperature, surface temperature, solar irradiance, and net radiation were measured low, and the temperature was lower in the Pedestrian road than in roads. The dry bulb temperature, the black bulb temperature, and the wet bulb temperature for the thermal evaluation showed the same tendency. In the case of thermal evaluation, there was a similar tendency to temperature in WBGT, MRT, and UTCI, and varied differences between types. Although the correlation between the meteorological environment and the thermal environment showed a statistically significant significance, the difference between the measured items was not significant. The study found that the trees were generally pleasant to weather and thermal climate in the form of trees, and the differences were mostly documented.

• Journal of Environmental Science International
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• v.27 no.11
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• pp.1141-1154
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• 2018

Numerical experiments were carried out to investigate the effect of data assimilation of observational data on weather and PM (particulate matter) prediction. Observational data applied to numerical experiment are aircraft observation, satellite observation, upper level observation, and AWS (automatic weather system) data. In the case of grid nudging, the prediction performance of the meteorological field is largely improved compared with the case without data assimilations because the overall pressure distribution can be changed. So grid nudging effect can be significant when synoptic weather pattern strongly affects Korean Peninsula. Predictability of meteorological factors can be expected to improve through a number of observational data assimilation, but data assimilation by single data often occurred to be less predictive than without data assimilation. Variation of air pressure due to observation nudging with high prediction efficiency can improve prediction accuracy of whole model domain. However, in areas with complex terrain such as the eastern part of the Korean peninsula, the improvement due to grid nudging were only limited. In such cases, it would be more effective to aggregate assimilated data.