• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.457-460
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• 2011

For the mobile meteorological services, sensed data should be gathered at a server from various clients like as Ubiquitous Sensor Network, mobile phone or public traffic vehicle by wireless network. The gathered data at server have huge volume and increase continuously. Therefore, a special query method and data structure should be considered. This paper studies on all possible query type on the data and processing steps for the mobile meteorological services. Some query spaces will be discussed. After that, this paper proposes effective data structure for the sensed data to support the query types.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.4
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• pp.751-758
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• 2000

In this paper, we discuss frequency allocation and sharing for high speed radio access network in domestic 5GHz the band. In order to evaluate the possibility of frequency sharing between meteorological radar and high speed radio access network, we analyses radio interference of meteorological radar by means of minimum coupling loss method and Monte Carlo simulation. And simulations show that it is necessary to use DFS(Dynamic Frequency Selection) scheme for frequency sharing between meteorological radar and high speed radio access network.

• Atmosphere
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• v.30 no.3
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• pp.237-248
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• 2020

In order to effectively prepare for damage caused by weather events, it is important to proactively identify the possible impacts of weather phenomena on the domestic society and economy. Text mining and Network analysis are used in this paper to build a database of damage types and levels caused by heat wave. We collect news articles about heat wave from the SBS news website and determine the primary and secondary effects of that through network analysis. In addition to that, based on the frequency with which each impact keyword is mentioned, we estimate how much influence each factor has. As a result, the types of impacts caused by heat wave are efficiently derived. Among these types of impacts, we find that people in South Korea are mainly interested in algae and heat-related illness. Since this technique of analysis can be applied not only to news articles but also to social media contents, such as Twitter and Facebook, it is expected to be used as a useful tool for building weather impact databases.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.117-127
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• 2020

The Early Warning System for agrometeorological hazard of the Rural Development Administration (Korea) forecasts detailed weather for each farm based on the meteorological information provided by the Korea Meteorological Administration, and estimates the growth of crops and predicts a meteorological hazard that can occur during the growing period by using the estimated detailed meteorological information. For verification of early warning system, automated weather observation network was constructed in the study area. Moreover, a real-time web display system was built to deliver near real-time weather data collected from the observation network. The meteorological observation system collected diverse meteorological variables including temperature, humidity, solar radiation, rainfall, soil moisture, sunshine duration, wind velocity, and wind direction. These elements were collected every minute and transmitted to the server every ten minutes. The data display system is composed of three phases: the first phase builds a database of meteorological data collected from the meteorological observation system every minute; the second phase statistically analyzes the collected meteorological data at ten-minutes, one-hour, or one-day time step; and the third phase displays the collected and analyzed meteorological data on the web. The meteorological data collected in the database can be inquired through the webpage for all data points or one data point in the unit of one minute, ten minutes, one hour, or one day. Moreover, the data can be downloaded in CSV format.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.1077-1080
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• 2009

Dense meteorological data are hard to be collected on the limited urban areas because of vest cost which is required to install the corresponding sensors on the areas. Recently, to overcome this problem, the sensor network technique comes to the fore. This paper studies an application to service the meteorological map using mobile phone sensors. A design results for system implementation are introduced in this paper.

• Journal of Environmental Science International
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• v.6 no.3
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• pp.205-211
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• 1997

For the prediction of multi-site rainfall with radar data and ground meteorological data, a rainfall prediction model was proposed, which uses the neural network theory, a kind of artifical Intelligence technique. The Input layer of the prediction model was constructed with current ground meteorological data, their variation, moving vectors of rain- fall field and digital terrain of the measuring site, and the output layer was constructed with the predicted rainfall up to 3 hours. In the application of the prediction model to the Pyungchang river basin, the learning results of neural network prediction model showed more Improved results than the parameter estimation results of an existing physically based model. And the proposed model comparisonally well predicted the time distribution of ralnfall.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.11
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• pp.2569-2575
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• 2015

Network RTK generally uses a linear interpolation method by using the corrections from reference stations. This minimizes the spatial decorrelation error caused by the increase of distance between the reference station's baseline and user's baseline. However, tropospheric delay, a function of the meteorological data can cause a spatial decorrelation characteristic among reference stations within a network by local meteorological difference. A non-linear characteristic of tropospheric delay can deteriorate Network RTK performance. In this paper, the modeling of tropospheric delay irregularity is made from the data when the typhoon is occurred. By using this modeling, analyzing the effect of meteorological difference between reference stations on correction is performed. Finally, we analyze an effect of non-linear characteristics of tropospheric delay among reference stations to Network RTK user.

• Atmosphere
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• v.16 no.4
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• pp.259-267
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• 2006

In the present study, the neural network (NN) model with cluster analysis method was developed to predict storm surge in the whole Korean coastal regions with special focuses on the regional extension. The model used in this study is NN model for each cluster (CL-NN) with the cluster analysis. In order to find the optimal clustering of the stations, agglomerative method among hierarchical clustering methods was used. Various stations were clustered each other according to the centroid-linkage criterion and the cluster analysis should stop when the distances between merged groups exceed any criterion. Finally the CL-NN can be constructed for predicting storm surge in the cluster regions. To validate model results, predicted sea level value from CL-NN model was compared with that of conventional harmonic analysis (HA) and of the NN model in each region. The forecast values from NN and CL-NN models show more accuracy with observed data than that of HA. Especially the statistics analysis such as RMSE and correlation coefficient shows little differences between CL-NN and NN model results. These results show that cluster analysis and CL-NN model can be applied in the regional storm surge prediction and developed forecast system.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.286-289
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• 2007

This study is to develop a cloud detection algorit1un for COMS and it is currently tested by using MODIS level 2B and MTSAT-1R satellite radiance data. Unlike many existing cloud detection schemes which use a threshold method and traditional statistical methods, in this study a feed-forward neural network method with back-propagation algorit1un is used. MODIS level 2B products are matched with feature information of five-band MTSAT 1R image data to form the training dataset. The neural network is trained over the global region for the period of January to December in 2006 with 5 km spatial resolution. The main results show that this model is capable to detect complex cloud phenomena. And when it is applied to seasonal images, it shows reliable results to reflect seasonal characteristics except for snow cover of winter. The cloud detection by the neural network method shows 90% accuracy compared to the MODIS products.

• Atmosphere
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• v.16 no.4
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• pp.343-350
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• 2006

The rainfall amount measured by Chugugi at Gongju was found in "Gaksadeungnok". Gaksadeungnok is ancient documents from governmental offices in Joseon dynasty. Rainfall data at Gongju are restored for 18 years of 19th century. In 1871, total rainfall amount is 1,338 mm. It is different by about 11% in the amount compared with Seoul Chugugi rainfall in 1871 and Daejeon modern raingauge measurement result during the 30 years (1971-2000). Annual march of monthly rainfall data at Gongju is similar with that of Seoul. Based on the results, restored rainfall at Gongju is consistent with Seoul Chugugi rainfall data. The rainfall amount restored in this study is measured by Chugugi which was installed at Gongju, in Chung-Cheong province. Furthermore, Gaksadeungnok includes rainfall amount reports by agricultural tool measurement in addition to Chugugi measurement. These facts prove a network of rain gauge in Joseon dynasty.