• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.1
• /
• pp.434-439
• /
• 2019

Recently, interest in urban temperature change and ground surface temperature change has been increasing due to weather phenomenon due to global warming, heat island phenomenon caused by urbanization in urban areas. In Korea, weather data such as temperature and precipitation have been collected since 1904. In recent years, there are 96 ASOS stations and 494 AWS weather observation stations. However, in the case of terrestrial networks, terrestrial meteorological data except measurement points are predicted through interpolation because they provide point data for each installation point. In this study, to improve the resolution of ground surface temperature measurement, the surface temperature using satellite image was calculated and its applicability was analyzed. For this purpose, the satellite images of Landsat 8 OLI TIRS were obtained for Seoul Metropolitan City by seasons and transformed to surface temperature by applying NASA equation to the thermal bands. The ground measurement data was based on the temperature data measured by AWS. Since the AWS temperature data is station based point data, interpolation is performed by Kriging interpolation method for comparison with Landsat image. As a result of comparing the satellite image base surface temperature with the AWS temperature data, the temperature difference according to the season was calculated as fall, winter, summer, based on the RMSE value, Spring, in order of applicability of Landsat satellite image. The use of that attribute and AWS support starts at $2.11^{\circ}C$ and RMSE ${\pm}3.84^{\circ}C$, which reflects information from the extended NASA.

• Korean Journal of Remote Sensing
• /
• v.30 no.2
• /
• pp.315-329
• /
• 2014

In order to analyze the Land Surface Temperature (LST) in metropolitan area including Seoul, Landsat and MODIS land surface temperature, Automatic Weather Station (AWS) temperature, digital elevation model and landuse are used. Analysis method among the Landsat and MODIS LST and AWS temperature is basic statistics using by correlation coefficient, root-mean-square error and linear regression etc. Statistics of Landsat and MODIS LST are a correlation coefficient of 0.32 and Root Mean Squared Error (RMSE) of 4.61 K, respectively. And statistics of Landsat and MODIS LST and AWS temperature have the correlations of 0.83 and 0.96 and the RMSE of 3.28 K and 2.25 K, respectively. Landsat and MODIS LST have relatively high correlation with AWS temperature, and the slope of the linear regression function have 0.45 (Landsat) and 1.02 (MODIS), respectively. Especially, Landsat 5 has lower correlation about 0.5 or less in entire station, but Landsat 8 have a higher correlation of 0.5 or more despite of lower match point than other satellites. Landsat 7 have highly correlation of more than 0.8 in the center of Seoul. Correlation between satellite LSTs and AWS temperature with landuse (urban and rural) have 0.8 or higher. Landsat LST have correlation of 0.84 and RMSE of more than 3.1 K, while MODIS LST have correlation of more than 0.96 and RMSE of 2.6 K. Consequently, the difference between the LSTs by two satellites have due to the difference in the optical observation and detection the radiation generated by the difference in the area resolution.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2010.04a
• /
• pp.395-397
• /
• 2010

AWS that exist in Pusan is watching local meteorological phenomena established in place that the weather observatory does not exist by real time, and is used usefully to early input data of numerical weather forecasting model. I wished to display downtown of Pusan and air temperature change of peripheral area using this AWS data. Analyzed volatility using AWS observation data for 5 years to recognize air temperature change of Pusan area through data about temperature among them. Drew air temperature distribution chart by season of recapitulative Pusan area applying IDW linear interpolation with this.

• Korean Journal of Remote Sensing
• /
• v.17 no.1
• /
• pp.15-31
• /
• 2001

The relationship between GMS-5 IR1 brightness temperature (CTT:cloud top temperature) and AWS (automatic weather station) rainfall is investigated on a heavy rain event over the mid-western part of Korea for August 5-6, 1998. It is found that a temporal variability of the heavy rain can be described in detail y the time series of rain area and rain rates over the study area that are calculated from AWS accumulated rainfalls for 15 minutes. A time period of 0030-0430 LST 6 August 1998 is chosen in the time series as a heavy rain period which has relatively small rain area (20~25%) and very strong rain rates(6~9 mm/15 min.) with a good time continuity. In the heavy rain period, CTT of a point and AWS 15-minute rainfall beneath that point are compared. From the comparison, AWS rainfalls are shown to be not closely correlated with CTT. In the range of CTT lower than -5$0^{\circ}C$ where most AWS with rain are distributed, the probability of rain is at most about 30%. However, when the satellite images are shifted by 2~3 pixels southward and 3 pixels westward for the geometric correction of images, AWS rainfalls are shown to be statistically correlated with CTT (correlation coefficient:-0.46). Most AWS with rain are distributed in the much lower CTT range(lower than -58$^{\circ}C$), but there is still not much change in the rain probability. Even though a temporal change of CTT is taken into account, the rain probability amount to at most 50~55% in the same range.

• Atmosphere
• /
• v.15 no.3
• /
• pp.179-186
• /
• 2005

Due to several difficulties, a number of Automatic Weather Systems (AWS) operated by Korea Meteorological Administration (KMA) are located on the rooftop so that the forming of standard observation environment to obtain the accuracy is needed. Therefore, the air temperature of AWSs on the synthetic lawn and the concrete of the rooftop is compared with the standard observation temperature. The hourly mean temperature is obtained by monthly and hourly mean value and the difference of temperature is calculated according to the location, the weather phenomenon, and cloud amount. The maximum and the minimum temperatures are compared by the conditions, such as cloud amount, the existence of precipitation or not. Consequently, the temperature on the synthetic lawn is higher than it on the concrete so that it is difficult to obtain same effect from ASOS, on the contrary the installation of AWS on the synthetic lawn seem to be inadequate due to heat or cold source of the building.

• Spatial Information Research
• /
• v.22 no.1
• /
• pp.55-63
• /
• 2014

Near surface air temperature data which are one of the essential factors in hydrology, meteorology and climatology, have drawn a substantial amount of attention from various academic domains and societies. Meteorological observations, however, have high spatio-temporal constraints with the limits in the number and distribution over the earth surface. To overcome such limits, many studies have sought to estimate the near surface air temperature from satellite image data at a regional or continental scale with simple regression methods. Alternatively, we applied various Kriging methods such as ordinary Kriging, universal Kriging, Cokriging, Regression Kriging in search of an optimal estimation method based on near surface air temperature data observed from automatic weather stations (AWS) in South Korea throughout 2010 (365 days) and MODIS land surface temperature (LST) data (MOD11A1, 365 images). Due to high spatial heterogeneity, auxiliary data have been also analyzed such as land cover, DEM (digital elevation model) to consider factors that can affect near surface air temperature. Prior to the main estimation, we calculated root mean square error (RMSE) of temperature differences from the 365-days LST and AWS data by season and landcover. The results show that the coefficient of variation (CV) of RMSE by season is 0.86, but the equivalent value of CV by landcover is 0.00746. Seasonal differences between LST and AWS data were greater than that those by landcover. Seasonal RMSE was the lowest in winter (3.72). The results from a linear regression analysis for examining the relationship among AWS, LST, and auxiliary data show that the coefficient of determination was the highest in winter (0.818) but the lowest in summer (0.078), thereby indicating a significant level of seasonal variation. Based on these results, we utilized a variety of Kriging techniques to estimate the surface temperature. The results of cross-validation in each Kriging model show that the measure of model accuracy was 1.71, 1.71, 1.848, and 1.630 for universal Kriging, ordinary Kriging, cokriging, and regression Kriging, respectively. The estimates from regression Kriging thus proved to be the most accurate among the Kriging methods compared.

• Journal of Environmental Science International
• /
• v.29 no.10
• /
• pp.1003-1010
• /
• 2020

In this study, the necessity for a village unit Automatic Weather System (AWS) was suggested to obtain correct agricultural weather information by comparing the data of AWS of the weather station with the data of AWS installed in agricultural villages 7 km away. The comparison sites are Hyogyo-ri and Hongseong weather station. The seasonal and monthly averaged and cumulative values of data were calculated and compared. The annual time series and correlation was analyzed to determine the tendency of variation in AWS data. The average values of temperature, relative humidity and wind speed were not much different in comparison with each season. The difference in precipitation was ranged from 13.2 to 91.1 mm. The difference in monthly precipitation ranged from 1.2 to 75.4 mm. The correlation coefficient between temperature, humidity and wind speed was ranged from 0.81 to 0.99 and it of temperature was the highest. The correlation coefficient of precipitation was 0.63 and the lowest among the observed elements. Through this study, precipitation at the weather station and village unit area showed the low correlation and the difference for a quantitative comparison, while the elements excluding precipitation showed the high correlation and the similar annual variation pattern.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.6
• /
• pp.7-12
• /
• 2022

This paper introduces the implementation of the AWS IoT service and MQTT based smart home system. The smart home system implemented in this study can monitor temperature and humidity, and can manually adjust the air conditioner heating, and can check the visitors with the camera and remotely control the door lock. The implemented smart home system controls door locks, heating and air conditioners using Arduino, and manages the collected data and control information using the AWS IoT service. In this study, the Android app has been developed to allow users to control IoT devices remotely, and the MQTT protocol was used for data communication and control between the app and the AWS IoT server and Arduino. The implemented smart home system has been implemented based on AWS IoT service, which has scalability to add sensors and devices.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.26 no.3
• /
• pp.182-189
• /
• 2016

For high accuracy of forecast using numerical weather prediction models, we need to get weather observation data that are large and high dense. Korea Meteorological Administration (KMA) mantains Automatic Weather Stations (AWSs) to get weather observation data, but their installation and maintenance costs are high. Mini-AWS is a very compact automatic weather station that can measure and record temperature, humidity, and pressure. In contrast to AWS, costs of Mini-AWS's installation and maintenance are low. It also has a little space restraints for installing. So it is easier than AWS to install mini-AWS on places where we want to get weather observation data. But we cannot use the data observed from Mini-AWSs directly, because it can be affected by surrounding. In this paper, we suggest a correcting method for using pressure data observed from Mini-AWS as weather observation data. We carried out preconditioning process on pressure data from Mini-AWS. Then they were corrected by using machine learning methods with the aim of adjusting to pressure data of the AWS closest to them. Our experimental results showed that corrected pressure data are in regulation and our correcting method using SVR showed very good performance.

• KIEAE Journal
• /
• v.16 no.6
• /
• pp.69-75
• /
• 2016

Climate change has resulted in the urban heat island (UHI) effect throughout the globe, contributing to heat-related illness and fatalities. In order to reduce such damage, it is necessary to improve the climate observation network for precise observation of the urban thermal environment and quick UHI forecasting system. Purpose: This study analyzed the effective range of the climate observation network and the distribution of the existing Automatic Weather Stations (AWS) in Seoul to propose optimal locations for additional installment of AWS. Method: First, we performed quality analysis to pinpoint missing values and outliers within the high-density temperature data measured. With the result from the analysis, a spatial autocorrelation structure in the temperature data was tested to draw the effective range and correlation distance for each major time period. Result: As a result, it turned out that the optimal effective range for the climate observation network in Seoul in July was a radius of 2.8 kilometers. Based on this result, population density, and temperature data, we selected the locations for additional installment of AWS. This study is expected to be used to generate urban temperature maps, select and move measurement locations since it is able to suggest valid, specific spatial ranges when the data measured in point is converted into surface data.