• International Journal of Advanced Culture Technology
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• 제7권4호
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• pp.327-333
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• 2019

Wind speed data constitute important weather information for aircrafts flying at low altitudes, such as drones. Currently, the accuracy of low altitude wind predictions is much lower than that of high-altitude wind predictions. Deep neural networks are proposed in this study as a method to improve wind speed forecast information. Deep neural networks mimic the learning process of the interactions among neurons in the brain, and it is used in various fields, such as recognition of image, sound, and texts, image and natural language processing, and pattern recognition in time-series. In this study, the deep neural network model is constructed using the wind prediction values generated by the numerical model as an input to improve the wind speed forecasts. Using the ground wind speed forecast data collected at the Boseong Meteorological Observation Tower, wind speed forecast values obtained by the numerical model are compared with those obtained by the model proposed in this study for the verification of the validity and compatibility of the proposed model.

• 대기
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• 제24권2호
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• pp.159-171
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• 2014

This study investigated the temporal and spatial characteristics of summertime (June-August) precipitation over Korean peninsula, using Korea Meteorological Administration (KMA)is Automated Synoptic Observing System (ASOS) data for the period of 1973-2010 and Automatic Weather System (AWS) data for the period of 1998-2010.The authors looked through climatological features of the summertime precipitation, then examined the degree of locality of the precipitation, and probable precipitation amount and its return period of 100 years (i.e., an extreme precipitation event). The amount of monthly total precipitation showed increasing trends for all the summer months during the investigated 38-year period. In particular, the increasing trends were more significant for the months of July and August. The increasing trend of July was seen to be more attributable to the increase of precipitation intensity than that of frequency, while the increasing trend of August was seen to be played more importantly by the increase of the precipitation frequency. The e-folding distance, which is calculated using the correlation of the precipitation at the reference station with those at all other stations, revealed that it is August that has the highest locality of hourly precipitation, indicating higher potential of localized heavy rainfall in August compared to other summer months. More localized precipitation was observed over the western parts of the Korean peninsula where terrain is relatively smooth. Using the 38-years long series of maximum daily and hourly precipitation as input for FARD2006 (Frequency Analysis of Rainfall Data Program 2006), it was revealed that precipitation events with either 360 mm $day^{-1}$ or 80 mm $h^{-1}$ can occur with the return period of 100 years over the Korean Peninsula.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2019년도 학술발표회
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• pp.147-147
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• 2019

The Numerical Weather Prediction (NWP) models determine the future state of the weather by forcing current weather conditions into the atmospheric models. The NWP models approximate mathematically the physical dynamics by nonlinear differential equations; however these approximations include uncertainties. The errors of the NWP estimations can be related to the initial and boundary conditions and model parameterization. Development in the meteorological forecast models did not solve the issues related to the inevitable biases. In spite of the efforts to incorporate all sources of uncertainty into the forecast, and regardless of the methodologies applied to generate the forecast ensembles, they are still subject to errors and systematic biases. The statistical post-processing increases the accuracy of the forecast data by decreasing the errors. Error prediction of the NWP models which is updating the NWP model outputs or model output statistics is one of the ways to improve the model forecast. The regression methods (including linear, polynomial and scaling regression) are applied to the present study to improve the real time forecast skill. Such post-processing consists of two main steps. Firstly, regression is built between forecast and measurement, available during a certain training period, and secondly, the regression is applied to new forecasts. In this study, the WRF real-time forecast data, in comparison with the observed data, had systematic biases; the errors related to the NWP model forecasts were reflected in the underestimation of the meteorological data forecast by the WRF model. The promising results will indicate that the post-processing techniques applied in this study improved the meteorological forecast data provided by WRF model. A comparison between various bias correction methods will show the strength and weakness of the each methods.

• 한국인터넷방송통신학회논문지
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• 제15권2호
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• pp.129-135
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• 2015

본 논문에서는 가로등 운용에 있어서 일기예보 및 일조량을 고려한 알고리즘을 제안하였다. 이 알고리즘에 의해 생성된 Weather Factor를 적용하여 보행자가 있을 시에는 가로등의 광량을 최대로 유지하고 보행자가 없을 경우 최대전력을 사용하지 않고 일정한 밝기를 유지하는 대기전력모드를 사용하여 전력소비를 줄였다. 이렇게 함으로써 배터리의 잔량을 확보할 수 있으며 이를 이용하여 부조일이 지속될 경우 운용일수를 최대한 연장하기 위한 적절한 알고리즘을 제안하였다. 또한 이러한 알고리즘에 필요한 Weather Factor의 값을 실험을 통하여 결정하였으며. 모의실험을 통해 알고리즘의 적합성을 확인하였다.

• 대기
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• 제28권2호
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• pp.201-209
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• 2018

Benefits of the next generation geostationary meteorological satellite observation (e.g., GEO-KOMPSAT-2A) are qualitatively and comprehensively described and discussed. Main beneficial phenomena for application can be listed as tropical cyclones (typhoon), high impact weather (heavy rainfall, lightning, and hail), ocean, air pollution (particulate matter), forest fire, fog, aircraft icing, volcanic eruption, and space weather. The next generation satellites with highly enhanced spatial and temporal resolution images, expanding channels, and basic and additional products are expected to create the new valuable benefits, including the contribution to the reduction of socioeconomic losses due to weather-related disasters. In particular, the new satellite observations are readily applicable to early warning and very-short time forecast application of hazardous weather phenomena, global climate change monitoring and adaptation, improvement of numerical weather forecast skill, and technical improvement of space weather monitoring and forecast. Several policy plans for expanding the application of the next generation satellite data are suggested.

• 대기
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• 제31권1호
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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.

• 대기
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• 제23권4호
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• pp.485-500
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• 2013

This study conducted WRF-Fire simulations in order to investigate sensitivities of the resolution of fire fuel and terrain data sets, and the surface wind to simulated fire area. The sensitivity simulations were consisted of 8 different WRF-Fire runs, each of which used different combination of data sets of fire fuel and terrain with different resolution. From the results it was turned out that the surface wind was most sensitive. The next was fire fuel and then fire terrain. Unfortunately, every run produced too much fire area. In other words no simulations succeeded in simulating such proper fire area so as for the WRF-Fire to be used realistically. It was verified that the errors of fire area from each runs were contributed by 41%, 53%, and 6% from surface wind, fire fuel, and fire terrain, respectively. Finally this study suggested that the selection of Anderson fuel category in the area of interest seemed to be very critical in the performance of WRF-Fire simulations.

• 한국환경과학회지
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• 제27권7호
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• pp.531-541
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• 2018

Road freezing caused by snowfall during wintertime causes traffic congestion and many accidents. To prevent such problems, we developed, in this study, a system to predict road freezing based on weather forecast data and the freezing generation modules. The weather forecast data were obtained from a high-resolution model with 1 km resolution for Jeju Island from 00:00 KST on December 1, 2017, to 23:00 KST on February 28, 2018. The results of the weather forecast data show that index of agreement (IOA) temperature was higher than 0.85 at all points, and that for wind speed was higher than 0.7 except in Seogwipo city. In order to evaluate the results of the freezing predictions, we used model evaluation metrics obtained from a confusion matrix. These metrics revealed that, the Imacho module showed good performance in precision and accuracy and that the Karlsson module showed good performance in specificity and FP rate. In particular, Cohen's kappa value was shown to be excellent for both modules, demonstrating that the algorithm is reliable. The superiority of both the modules shows that the new system can prevent traffic problems related to road freezing in the Jeju area during wintertime.

• 천문학회보
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• 제36권2호
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• pp.101.2-101.2
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• 2011

We have developed solar and space weather monitoring system for space weather users since 2007 as a project named 'Construction of Korea Space Weather Prediction Center'. In this presentation we will introduce space weather monitoring system for Geostationary Satellites and Polar Routes. These were developed for satisfying demands of space weather user groups. 'Space Weather Monitoring System for Geostationary Satellites' displays integrated space weather information on geostationary orbit such as magnetopause location, nowcast and forecast of space weather, cosmic ray count rate, number of meteors and x-ray solar flux. This system is developed for space weather customers who are managing satellite systems or using satellite information. In addition, this system provides space weather warning by SMS in which short message is delivered to users' cell phones when space weather parameters reach a critical value. 'Space Weather Monitoring System for Polar Routes' was developed for the commercial airline companies operating polar routes. This provides D-region and polar cap absorption map, aurora and radiation particle distribution, nowcast and forecast of space weather, proton flux, Kp index and so on.

• 대기
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• 제28권2호
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• pp.187-200
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• 2018

The heavy snowfall phenomenon with thunder and lightning occurred in Yeongdong coastal region on 20 January 2017. Amount of snow on that day was a maximum of 47 cm and was concentrated in a short time (2 hours) at the Yeongdong coastal area. The mechanism of thundersnow was investigated to describe in detail using observational data and numerical simulation (Weather Research and Forecast, WRF) applied lightning option. The results show that a convective cloud occurred at the Yeongdong coastal area. The east wind flow was generated and the pressure gradient force was maximized by the rapidly developed cyclone. The cold and dry air in the upper atmosphere has descended (so called tropopause folding) atmospheric lower layer at precipitation peak time (1200 LST). In addition, latent heat in the lower atmosphere layer and warm sea surface temperature caused thermal instability. The convective cloud caused by the strong thermal instability was developed up to 6 km at that time. And the backdoor cold front was determined by the change characteristics of meteorological elements and shear line in the east sea. Instability indexes such as Total totals Index (TT) and Lightning Potential Index (LPI) are also confirmed as one of good predictability indicates for the explosive precipitation of convective rainfall.