• The Korean Journal of Applied Statistics
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• v.24 no.3
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• pp.535-546
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• 2011

This study develops statistical models for the binary forecast of Asian dust days over South Korea in the winter season. For this study, we used three kinds of data; the rst one is the observed Asian dust days for a period of 31 years (1980 to 2010) as target values, the second one is four meteorological factors(near surface temperature, precipitation, snowfall, ground wind speed) in the source regions of Asian dust based on the NCEP reanalysis data and the third one is the large-scale climate indices. Four kinds of statistical models(multiple regression models, logistic regression models, decision trees, and support vector machines) are applied and compared based on skill scores(hit rate, probability of detection and false alarm rate).

• The Plant Pathology Journal
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• v.35 no.6
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• pp.585-597
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• 2019

A disease forecast model for Marssonina blotch of apple was developed based on field observations on airborne spore catches, weather conditions, and disease incidence in 2013 and 2015. The model consisted of the airborne spore model (ASM) and the daily infection rate model (IRM). It was found that more than 80% of airborne spore catches for the experiment period was made during the spore liberation period (SLP), which is the period of days of a rain event plus the following 2 days. Of 13 rain-related weather variables, number of rainy days with rainfall ≥ 0.5 mm per day (L_day), maximum hourly rainfall (P_max) and average daily maximum wind speed (W_avg) during a rain event were most appropriate in describing variations in airborne spore catches during SLP (S_i) in 2013. The ASM, Ŝ_i = 30.280+5.860×L_day×P_max-2.123×L_day×P_max×W_avg was statistically significant and capable of predicting the amount of airborne spore catches during SLP in 2015. Assuming that airborne conidia liberated during SLP cause leaf infections resulting in symptom appearance after 21 days of incubation period, there was highly significant correlation between the estimated amount of airborne spore catches (Ŝ_i) and the daily infection rate (R_i). The IRM, ${\hat{R}}_i$ = 0.039+0.041×Ŝ_i, was statistically significant but was not able to predict the daily infection rate in 2015. No weather variables showed statistical significance in explaining variations of the daily infection rate in 2013.

• Atmosphere
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• v.21 no.3
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• pp.257-271
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• 2011

The aim of this study is to examine weather modification by urbanization and human activities. The characteristics of the urban heat island (UHI) and precipitation in Seoul metropolitan area of Korea are investigated to demonstrate that cities can change or modify local and nearby weather and climate, and to confirm that cities can initiate convection, change the behavior of convective precipitation, and enhance downstream precipitation. The data used in this study are surface meteorological station data observed in Seoul and its nearby 5 cities for the period of 1960 to 2009, and 162 Automatic Weather System stations data observed in the Seoul metropolitan area from 1998 to 2009. Air temperature and precipitation amount tend to increase with time, and relative humidity decreases because of urbanization. Similar to previous studies for other cities, the average maximum UHI is weakest in summer and is strong in autumn and winter, and the maximum UHI intensity is more frequently observed in the nighttime than in the daytime, decreases with increasing wind speed, and is enhanced for clear skies. Relatively warm regions extend in the east-west direction and relatively cold regions are located near the northern and southern mountains inside Seoul. The satellite cities in the outskirts of Seoul have been rapidly built up in recent years, thus exhibiting increases in near-surface air temperature. The yearly precipitation amount during the last 50 years is increased with time but rainy days are decreased. The heavy rainfall events of more than $20mm\;hr^{-1}$ increases with time. The substantial changes observed in precipitation in Seoul seem to be linked with the accelerated increase in the urban sprawl in recent decades which in turn has induced an intensification of the UHI effect and enhanced downstream precipitation. We also found that the frequency of intense rain showers has increased in Seoul metropolitan area.

• Atmosphere
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• v.24 no.4
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• pp.523-532
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• 2014

In this study, thermal tropopause height defined from WMO (World Meteorological Organization) using temperature profile derived from Advance Microwave Sounding Unit-A (AMSU-A; hereafter named AMSU) onboard EOS (Earth Observing System) Aqua satellite is retrieved. The temperature profile of AMSU was validated by comparison with the radiosonde data observed at Osan weather station. The validation in the upper atmosphere from 500 to 100 hPa pressure level showed that correlation coefficients were in the range of 0.85~0.97 and the bias was less than 1 K with Root Mean Square Error (RMSE) of ~3 K. Thermal tropopause height was retrieved by using AMSU temperature profile. The bias and RMSE were found to be -5~ -37 hPa and 45~67 hPa, respectively. Correlation coefficients were in the range of 0.5 to 0.7. We also analyzed the change of tropopause height and temperature in middle troposphere in the extreme heavy rain event (23 October, 2003) associated with tropopause folding. As a result, the distinct descent of tropopause height and temperature decrease of ~8 K at 500 hPa altitude were observed at the hour that maximum precipitation and maximum wind speed occurred. These results were consistent with ERA (ECMWF Reanalysis)-Interim data (potential vorticity, temperature) in time and space.

• Journal of the Korean earth science society
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• v.38 no.7
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• pp.522-534
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• 2017

The purpose of this study is to verify urban flow and thermal environment by using the simulated Computational Fluid Dynamics (CFD) model in the area of Gangnam Seonjeongneung, and then to compare the CFD model simulation results with that of Seonjeongneung-monitoring networks observation data. The CFD model is developed through the collaborative research project between National Institute of Meteorological Sciences and Seoul National University (CFD_NIMR_SNU). The CFD_NIMR_SNU model is simulated using Korea Meteorological Administration (KMA) Local Data Assimilation Prediction System (LDAPS) wind and potential temperature as initial and boundary conditions from August 4-6, 2015, and that is improved to consider vegetation effect and surface temperature. It is noticed that the Root Mean Square Error (RMSE) of wind speed decreases from 1.06 to $0.62m\;s^{-1}$ by vegetation effect over the Seonjeongneung area. Although the wind speed is overestimated, RMSE of wind speed decreased in the CFD_NIMR_SNU than LDAPS. The temperature forecast tends to underestimate in the LDAPS, while it is improved by CFD_NIMR_SNU. This study shows that the CFD model can provide detailed and accurate thermal and urban area flow information over the complex urban region. It will contribute to analyze urban environment and planning.

• Atmosphere
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• v.29 no.4
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• pp.417-427
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• 2019

The purpose of this study is to build urban canopy model (Met Office Reading Urban Surface Exchange Scheme, MORUSES) based to Unified Model (UM) by using urban building information data in Seoul, and then to compare the improving urban canopy model simulation result with that of Seoul Automatic Weather Station (AWS) observation site data. UM-MORUSES is based on building information database in London, we performed a sensitivity experiment of UM-MOURSES model using urban building information database in Seoul. Geographic Information System (GIS) analysis of 1.5 km resolution Seoul building data is applied instead of London building information data. Frontal-area index and planar-area index of Seoul are used to calculate building height. The height of the highest building in Seoul is 40m, showing high in Yeoido-gu, Gangnam-gu and Jamsil-gu areas. The street aspect ratio is high in Gangnam-gu, and the repetition rate of buildings is lower in Eunpyeong-gu and Gangbuk-gu. UM-MORUSES model is improved to consider the building geometry parameter in Seoul. It is noticed that the Root Mean Square Error (RMSE) of wind speed is decreases from 0.8 to 0.6 m s^-1 by 25 number AWS in Seoul. The surface air temperature forecast tends to underestimate in pre-improvement model, while it is improved at night time by UM-MORUSES model. This study shows that the post-improvement UM-MORUSES model can provide detailed Seoul building information data and accurate surface air temperature and wind speed in urban region.

• Journal of Environmental Science International
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• v.26 no.12
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• pp.1307-1319
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• 2017

In this study, we investigated the impact of different initial data on atmospheric modeling results using the Weather Research and Forecast (WRF) model. Four WRF simulations were conducted with different initialization in March 2015, which showed the highest monthly mean $PM_{10}$ concentration in the recent ten years (2006-2015). The results of WRF simulations using NCEP-FNL and ERA-Interim were compared with observed surface temperature and wind speed data, and the difference of grid nudging effect on WRF simulation between the two data were also analyzed. The FNL simulation showed better accuracy in the simulated temperature and wind speed than the Interim simulation, and the difference was clear in the coastal area. The grid nudging effect on the Interim simulation was larger than that of the FNL simulation. Despite of the higher spatial resolution of ERA-Interim data compared to NCEP-FNL data, the Interim simulation showed slightly worse accuracy than those of the FNL simulation. It was due to uncertainties associated with the Sea Surface Temperature (SST) field in the ERA-Interim data. The results from the Interim simulation with different SST data showed significantly improved accuracy than the standard Interim simulation. It means that the SST field in the ERA-Interim data need to be optimized for the better WRF simulation. In conclusion, although the WRF simulation with ERA-Interim data does not show reasonable accuracy compared to those with NCEP-FNL data, it would be able to be Improved by optimizing the SST variable.

• Atmosphere
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• v.30 no.2
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• pp.169-181
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• 2020

We conducted a study on the impact of observation station density; this was done in order to enable the accurate estimation of spatial meteorological variables. The purpose of this study is to help operate an efficient observation network by examining distributions of temperature, relative humidity, and wind speed in a test area of a three-dimensional meteorological observation project in the Yeongdong region in 2019. For our analysis, we grouped the observation stations as follows: 41 stations (for Step 4), 34 stations (for Step 3), 17 stations (for Step 2), and 10 stations (for Step 1). Grid values were interpolated using the kriging method. We compared the spatial accuracy of the estimated meteorological grid by using station density. The effect of increased observation network density varied and was dependent on meteorological variables and weather conditions. The temperature is sufficient for the current weather observation network (featuring an average distance about 9.30 km between stations), and the relative humidity is sufficient when the average distance between stations is about 5.04 km. However, it is recommended that all observation networks, with an average distance of approximately 4.59 km between stations, be utilized for monitoring wind speed. In addition, this also enables the operation of an effective observation network through the classification of outliers.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1029-1037
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• 2020

The machine learning algorithm has been widely used in water-related fields such as water resources, water management, hydrology, atmospheric science, water quality, water level prediction, weather forecasting, water discharge prediction, water quality forecasting, etc. However, water quality prediction studies based on the machine learning algorithm are limited compared to other water-related applications because of the limited water quality data. Most of the previous water quality prediction studies have predicted monthly water quality, which is useful information but not enough from a practical aspect. In this study, we predicted the dissolved oxygen (DO) using recurrent neural network with long short-term memory model recurrent neural network long-short term memory (RNN-LSTM) algorithms with hourly- and daily-datasets. Bugok Bridge in Oncheoncheon, located in Busan, where the data was collected in real time, was selected as the target for the DO prediction. The 10-month (temperature, wind speed, and relative humidity) data were used as time prediction inputs, and the 5-year (temperature, wind speed, relative humidity, and rainfall) data were used as the daily forecast inputs. Missing data were filled by linear interpolation. The prediction model was coded based on TensorFlow, an open-source library developed by Google. The performance of the RNN-LSTM algorithm for the hourly- or daily-based water quality prediction was tested and analyzed. Research results showed that the hourly data for the water quality is useful for machine learning, and the RNN-LSTM algorithm has potential to be used for hourly- or daily-based water quality forecasting.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.131-138
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• 2022

Novel Coronavirus (COVID-19) is viewed as one of the main general wellbeing theaters on the worldwide level all over the planet. Because of the abrupt idea of the flare-up and the irresistible force of the infection, it causes individuals tension, melancholy, and other pressure responses. The avoidance and control of the novel Covid pneumonia have moved into an imperative stage. It is fundamental to early foresee and figure of infection episode during this troublesome opportunity to control of its grimness and mortality. The entire world is investing unimaginable amounts of energy to fight against the spread of this lethal infection. In this paper, we utilized machine learning and deep learning techniques for analyzing what is going on utilizing countries shared information and for detecting the climate factors that effect on spreading Covid-19, such as humidity, sunny hours, temperature and wind speed for understanding its regular dramatic way of behaving alongside the forecast of future reachability of the COVID-2019 around the world. We utilized data collected and produced by Kaggle and the Johns Hopkins Center for Systems Science. The dataset has 25 attributes and 9566 objects. Our Experiment consists of two phases. In phase one, we preprocessed dataset for DL model and features were decreased to four features humidity, sunny hours, temperature and wind speed by utilized the Pearson Correlation Coefficient technique (correlation attributes feature selection). In phase two, we utilized the traditional famous six machine learning techniques for numerical datasets, and Dense Net deep learning model to predict and detect the climatic factor that aide to disease outbreak. We validated the model by using confusion matrix (CM) and measured the performance by four different metrics: accuracy, f-measure, recall, and precision.