• Korean Journal of Agricultural and Forest Meteorology
• /
• v.6 no.4
• /
• pp.272-289
• /
• 2004

The spatial resolution of local weather and climate information for agronomic practices exceeds the current weather service scale. To supplement the insufficient spatial resolution of official forecasts and observations, gridded climate data are frequently generated. Most ecological models can be run using gridded climate data to produce ecosystem responses at landscape scales. In this lecture, state of the art techniques derived from geospatial climatology, which can generate gridded climate data by spatially interpolating point observations at synoptic weather stations, will be introduced. Removal of the urban effects embedded in the interpolated surfaces of daily minimum temperature, incorporation of local geographic potential for cold air accumulation into the minimum temperature interpolation scheme, and solar irradiance correction for daytime hourly temperature estimation are presented. Some experiences obtained from their application to real landscapes will be described.

• Journal of Korean Society for Atmospheric Environment
• /
• v.30 no.6
• /
• pp.600-618
• /
• 2014

The metadata for urban meteorological observation is standardized through comparison with those established at the World Meteorological Organization and the Korea Meteorological Administration to understand the surrounding environment around the sites exactly and maintain the networks and sites efficiently. It categorizes into metadata for an observational network and observational sites. The latter is again divided into the metadata for station general information, local scale information, micro scale information, and visual information in order to explain urban environment in detail. The metadata also contains the static information such as urban structure, surface cover, metabolism, communication, building density, roof type, moisture/heat sources, and traffic as well as the update information on the environment change, maintenance, replacement, and/or calibration of sensors. The standardized metadata for urban meteorological observation is applied to the Weather Information Service Engine (WISE) integrated meteorological sensor network and sites installed at Incheon area. It will be very useful for site manager as well as researchers in fields of urban meteorology, radiation, surface energy balance, anthropogenic heat, turbulence, heat storage, and boundary layer processes.

• Journal of Korea Multimedia Society
• /
• v.16 no.11
• /
• pp.1239-1249
• /
• 2013

According to development of weather radar, researches about observation, analysis or forecast of weather phenomena such as tornado, flash-flood etc. were encouraged by reducing frequency interferences, transmission noises, attenuations of radar signal. In contrast, there is a growing interest in the visualization and expression methods for weather radar data but weather radar manufacturers or the organs of government for weather are just busy interpreting expressed weather images projected on GIS. We propose an effective high definition weather radar information visualization method able to apply various GIS platforms to observe and take actions against rapid local weather changes effectively. In this paper, first we change information acquired from weather radar to raster or vector type high definition data structures using specific algorithms. And then, we quadrate our processed raster/vector type weather data with various GIS platforms accurately to make observers can recognize and check weather situations over exact geographical positions and elevations intuitively. Experimental results verify that our method make observers can recognize and analyze weather changes, tornados, local downpours or flash floods accurately by analyzing high definition weather radar data combined with GIS platform including detailed target locations and elevations.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.13 no.5
• /
• pp.917-923
• /
• 2010

The local area weather information is very important element to estimate where the air-pollutant will flow. But the existing NBC hazard prediction model does not consider the local area weather information. So, in this paper, we present SN-HPM that uses the local area wether information to perform more accurate and reliable estimate, and embody it to program.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.8
• /
• pp.3567-3582
• /
• 2020

Human activities are often affected by weather conditions. Automatic weather recognition is meaningful to traffic alerting, driving assistance, and intelligent traffic. With the boost of deep learning and AI, deep convolutional neural networks (CNN) are utilized to identify weather situations. In this paper, a three-channel convolutional neural network (3C-CNN) model is proposed on the basis of ResNet50.The model extracts global weather features from the whole image through the ResNet50 branch, and extracts the sky and ground features from the top and bottom regions by two CNN5 branches. Then the global features and the local features are merged by the Concat function. Finally, the weather image is classified by Softmax classifier and the identification result is output. In addition, a medium-scale dataset containing 6,185 outdoor weather images named WeatherDataset-6 is established. 3C-CNN is used to train and test both on the Two-class Weather Images and WeatherDataset-6. The experimental results show that 3C-CNN achieves best on both datasets, with the average recognition accuracy up to 94.35% and 95.81% respectively, which is superior to other classic convolutional neural networks such as AlexNet, VGG16, and ResNet50. It is prospected that our method can also work well for images taken at night with further improvement.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.1
• /
• pp.233-240
• /
• 2011

A field server was developed by using ubiquitous sensor network technology to monitor the abrupt weather variation in local or mountain area. The data transmissions between deployed field servers in local terrain are very important technology in disaster prevention monitoring system. Weather related information such as temperature, humidity, illumination, atmospheric pressure, dew point and meteorological data are collected from the designated field at a regular interval. The received information from the multiple sensors located at the sensor field is used flooding routing protocol transmission techniques and the sensing data is transferred to gateway through multi-hop method. Telosb sensor node are programmed by nesC language in TinyOS platform to monitor the weather parameters of the local terrain.

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

• Atmosphere
• /
• v.25 no.2
• /
• pp.339-351
• /
• 2015

The purpose of this study is for the fundamental understandings about building morphological parameters and aerodynamic roughness parameters of Seoul, Korea using the detailed urban geographic information datasets. Applied roughness parameter calculations are based on a digital map of buildings with lot area polygons. The quality of the developed roughness length ($z_0$) of Seoul was evaluated with densely installed 107 automatic weather stations. The correlation coefficient results between averaged wind speeds of AWS data and averaged $z_0$ is -0.303 in night and -0.398 in day (200 m radii circles case). Further $z_0$ enhancement should follow by considering other surface features such as high tree and orography of Seoul. However, this study would meet the needs to for local- or meso-scale meteorological modeling applications of Seoul. However, further studies would require for enhancing the $z_0$ applications of Seoul.

• Journal of Advanced Navigation Technology
• /
• v.21 no.5
• /
• pp.435-442
• /
• 2017

The runway visual range affected by fog and so on is one of the important indicators to determine whether aircraft can take off and land at the airport or not. In the case of airports where transportation airplanes are operated, major weather forecasts including the runway visual range for local area have been released and provided to aviation workers for recognizing that. This paper proposes a runway visual range estimation model with a deep neural network applied recently to various fields such as image processing, speech recognition, natural language processing, etc. It is developed and implemented for estimating a runway visual range of local airport with a deep neural network. It utilizes the past actual weather observation data of the applied airfield for constituting the learning of the neural network. It can show comparatively the accurate estimation result when it compares the results with the existing observation data. The proposed model can be used to generate weather information on the airfield for which no other forecasting function is available.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.05a
• /
• pp.223-225
• /
• 2010

In order to minimize the casualties and damages from natural disasters, local terrain and weather phenomena need to be constantly monitored. Various weather monitoring systems are designed to collect and monitor the weather information for disaster prevention. Nowadays, wireless sensor networks have been widely used to transmit the weather information and collected by the base station at a regular interval. In this paper, disaster prevention monitoring system for efficient data transfer of weather information such as temperature, humidity and illumination are designed. Weather information is able to burst the data transmission based on storage of flash memory. Telosb sensor node are used in the research; programmed by nesC language used by TinyOS.