• Korean Journal of Remote Sensing
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• v.12 no.1
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• pp.60-80
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• 1996

AVHRR(Advanced Very High Resolution Radiometer) on NOAA satellite provides data in five spectral, one in visible range, one in near infrared and three in thermal range. In this paper, application of NOAA/AVHRR data is studied for environment monitoring such as cloud top temperature, surface temperature, albedo, sea surface temperature, vegetation index, forest fire, flood, snow cover and so on. The analyses for cloud top temperature, surface temperature, albedo, sea surface temperature, vegetation index and forest fire showed reasonable agreement. But monitoring for flood and snow cover was uneasy due to the limitations such as cloud contamination, low spatial resolution. So this research had only simple purpose to identify well-defined waterbody for dynamic monitoring of flood. Based on development of these basic algorithms, we have a plan to further reseach for environment monitoring using AVHRR data.

• Atmosphere
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• v.24 no.1
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• pp.39-48
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• 2014

This study provides a comparative analysis of cloud top heights observed by a Ka-band cloud radar and the Communication, Ocean and Meteorological Satellite (COMS) at Boseong National Center for Intensive Observation of severe weather (NCIO) from May 25, 2013 (1600 UTC) to May 27. The rainfall duration is defined as the period of rainfall from start to finish, and the no rainfall duration is defined as the period other than the rainfall duration. As a result of the comparative analysis, the cloud top heights observed by the cloud radar have been estimated to be lower than that observed by the COMS for the rainfall duration due to the signal attenuation caused by raindrops. The stronger rainfall intensity gets, the more the difference grows. On the other hand, the cloud top heights observed by the cloud radar have been relatively similar to that observed by the COMS for the no rainfall duration. In this case, the cloud radar can effectively detect cloud top heights within the range of its observation. The COMS indicates the cloud top heights lower than the actual ones due to the upper thin clouds under the influence of ground surface temperature. As a result, the cloud radar can be useful in detecting cloud top heights when there are no precipitation events. The COMS data can be used to correct the cloud top heights when the radar gets beyond the valid range of observation or there are precipitation events.

• Water for future
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• v.28 no.6
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• pp.229-236
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• 1995

The physically based heavy rainfall model developed by Ceon(1994) for storm events is modified in this study. The main parts of this paper are composed of modeling saturation vapor pressure, cloud thickness, cloud top pressure. In a different way from the previous model, cloud top temperature and albedo measured by satellite are used as input data to the model. In this paper, the defect of saturation vapor pressure equation in the previous model was improved. Furthermore, the parameters for temperature and pressure on cloud top are eliminated as well as the time of calculation in the model is decreased. Also, the results show that there are very small gab between the hourly calculated.

• Atmosphere
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• v.25 no.4
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• pp.577-589
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• 2015

An unusual long-period and heavy snowfall occurred in the Yeongdong region from 6 to 14 February 2014. This event produced snowfall total of 194.8 cm and the recordbreaking 9-day snowfall duration in the 103-year local record at Gangneung. In this study, satellite-derived cloud-top brightness temperatures from the infrared channel in the atmospheric window ($10{\mu}m{\sim}11{\mu}m$) are examined to find out the characteristics of clouds related with this heavy snowfall event. The analysis results reveal that a majority of precipitation is related with the low-level stratiform clouds whose cloud-top brightness temperatures are distributed from -15 to $-20^{\circ}C$ and their standard deviations over the analysis domain (${\sim}1,000km^2$, 37 satellite pixels) are less than $2^{\circ}C$. It is also found that in the above temperature range precipitation intensity tends to increase with colder temperature. When the temperatures are warmer than $-15^{\circ}C$, there is no precipitation or light precipitation. Furthermore this relation is confirmed from the examination of some other heavy snowfall events and light precipitation events which are related with the low-level stratiform clouds. This precipitation-brightness temperature relation may be explained by the combined effect of ice crystal growth processes: the maximum in dendritic ice-crystal growth occurs at about $-15^{\circ}C$ and the activation of ice nuclei begins below temperatures from approximately -7 to $-16^{\circ}C$, depending on the composition of the ice nuclei.

• Korean Journal of Remote Sensing
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• v.23 no.2
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• pp.71-87
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• 2007

In this study, 14 heavy snow events in Yeongdong area which are local phenomena are analyzed using MODIS cloud products provided from NASA/GSFC. The clouds of Yeongdong area at observed at specific time by MODIS are classified into A, B, C Types, based on the characteristic of cloud properties: cloud top temperature, cloud optical thickness, Effective Particle Radius, and Cloud Particle Phase. The analysis of relations between cloud properties and precipitation amount for each cloud type show that there are statistically significant correlations between Cloud Optical Thickness and precipitation amount for both A and B type and also significant correlation is found between Cloud Top Temperature and precipitation amount for A type. However, for C type there is not any significant correlations between cloud properties and precipitation amount. A-type clouds are mainly lower stratus clouds with small-size droplet, which may be formed under the low level cold advection derived synoptically in the East sea. B-type clouds are developed cumuliform clouds, which are closely related to the low pressure center developing over the East sea. On the other hand, C-type clouds are likely multi-layer clouds, which make satellite observation difficult due to covering of high clouds over low level clouds directly related with Yeongdong heavy snow. It is, therefore, concluded that MODIS cloud products may be useful except the multi-layer clouds for understanding the mechanism of heavy snow and estimating the precipitation amount from satellite data in the case of Yeongdong heavy snow.

• Atmosphere
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• v.22 no.2
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• pp.163-174
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• 2012

Brightness temperature (BT) difference between sea fog and sea surface is small, because the top height of fog is low. Therefore, it is very difficult to detect sea fog with infrared (IR) channels in the nighttime. To overcome this difficulty, we have developed a new algorithm for detection of sea fog that consists in three tests. Firstly, both stratus and sea fog were discriminated from the other clouds by using the difference between BTs $3.7{\mu}m$ and $11{\mu}m$. Secondly, stratus occurring at a level higher than sea fog was removed when the difference between cloud top temperature and sea surface temperature (SST) is smaller than 3 K. In this process, we used daily SST data from AMSR-E microwave measurements that is available even in the presence of cloud. Then, the SST was converted to $11{\mu}m$ BT based on the regressed relationship between AMSR-E SST and MTSAT-1R $11{\mu}m$ BT at 1733 UTC over clear sky regions. Finally, stratus was further removed by using the homogeneity test based on the difference in cloud top texture between sea fog and stratus. Comparison between the retrievals from our algorithm and that from Korea Meteorological Administration (KMA) algorithm, shows that the KMA algorithm often misconceived sea fog as stratus, resulting in underestimating the occurrence of sea fog. Monthly distribution of sea fog over northeast Asia in 2008 was derived from the proposed algorithm. The frequency of sea fog is lowest in winter, and highest in summer especially in June. The seasonality of the sea fog occurrence between East and West Sea was comparable, while it is not clearly identified over South Sea. These results would serve to prevent the possible occurrence of marine accidents associated with sea fog.

• Journal of Environmental Science International
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• v.29 no.11
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• pp.1065-1078
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• 2020

The statistical characteristics of aerosol-cloud interactions over East Asia were investigated using Moderate Resolution Imaging Spectroradiometer satellite data. The long-term relationship between various aerosol and cloud parameters was estimated using correlation analysis, principle component analysis, and Aerosol Indirect Effect (AIE) estimation. In correlation analysis, Aerosol Optical Depth (AOD) was positively Correlated with Cloud Condensation Nuclei (CCN) and Cloud Fraction (CF), but negatively correlated with Cloud Top Temperature (CTT) and Cloud Top Pressure (CTP). Fine Mode Fraction (FMF) and CCN were positively correlated over the ocean because of sea spray. In principle component analysis, AOD and FMF were influenced by water vapor. In particular, AOD was positively influenced by CF, and negatively by CTT and CTP over the ocean. In AIE estimation, the AIE value in each cloud layer and type was mostly negative (Twomey effect) but sometimes positive (anti-Twomey effect). This is related to regional, environmental, seasonal, and meteorological effects. Rigorous and extensive studies on aerosol-cloud interactions over East Asia should be conducted via micro- and macro-scale investigations, to determine chemical characteristics using various meteorological instruments.

• Atmosphere
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• v.23 no.4
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• pp.471-483
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• 2013

Nighttime sea fog detection from satellite is very hard due to limitation in using visible channels. Currently, most widely used method for the detection is the Dual Channel Difference (DCD) method based on Brightness Temperature Difference between 3.7 and 11 ${\mu}m$ channel (BTD). However, this method have difficulty in distinguishing between fog and low cloud, and sometimes misjudges middle/high cloud as well as clear scene as fog. Using CALIPSO Lidar Profile measurements, we have analyzed the intrinsic problems in detecting nighttime sea fog from various satellite remote sensing algorithms and suggested the direction for the improvement of the algorithm. From the comparison with CALIPSO measurements for May-July in 2011, the DCD method excessively overestimates foggy pixels (2542 pixels). Among them, only 524 pixel are real foggy pixels, but 331 pixels and 1687 pixels are clear and other type of clouds, respectively. The 514 of real foggy pixels accounts for 70% of 749 foggy pixels identified by CALIPSO. Our proposed new algorithm detects foggy pixels by comparing the difference between cloud top temperature and underneath sea surface temperature from assimilated data along with the DCD method. We have used two types of cloud top temperature, which obtained from 11 ${\mu}m$ brightness temperature (B_S1) and operational COMS algorithm (B_S2). The detected foggy 1794 pixels from B_S1 and 1490 pixel from B_S2 are significantly reduced the overestimation detected by the DCD method. However, 477 and 446 pixels have been found to be real foggy pixels, 329 and 264 pixels be clear, and 989 and 780 pixels be other type of clouds, detected by B_S1 and B_S2 respectively. The analysis of the operational COMS fog detection algorithm reveals that the cloud screening process was strictly enforced, which resulted in underestimation of foggy pixel. The 538 of total detected foggy pixels obtain only 187 of real foggy pixels, but 61 of clear pixels and 290 of other type clouds. Our analysis suggests that there is no winner for nighttime sea fog detection algorithms, but loser because real foggy pixels are less than 30% among the foggy pixels declared by all algorithms. This overwhelming evidence reveals that current nighttime sea fog algorithms have provided a lot of misjudged information, which are mostly originated from difficulty in distinguishing between clear and cloudy scene as well as fog and other type clouds. Therefore, in-depth researches are urgently required to reduce the enormous error in nighttime sea fog detection from satellite.

• Journal of the Korean earth science society
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• v.30 no.1
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• pp.58-68
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• 2009

Three-dimensional distributions of longwave radiation flux for the April-September 1998 period are generated from radiative transfer calculations using the GEWEX Asian Monsoon Experiment (GAME) reanalysis temperature and humidity profiles and International Satellite Cloud Climatology Project (ISCCP) cloudiness as inputs to understand the effect of cloud radiative forcing in the monsoon season. By subtracting the heating of the clear atmosphere from the cloudy radiative heating, cloud-induced atmospheric radiative heating has been obtained. Emphasis is placed on the impact of horizontal gradients of the cloud-generated radiative heating on the Asian monsoon. Cloud-induced heating exhibits its maximum heating areas within the Indian Ocean and minimum heating over the Tibetan Plateau, which establishes the north-south oriented differential heating gradient. Considering that the differential heating is a ultimate source generating the atmospheric circulation, the cloud-induced heating gradient established between the Indian Ocean and the Plateau can enhance the strength of the north-south Hadley-type monsoon circulation. Cooling at cloud top and warming at cloud bottom, which are the vertical distributions of cloud-induced heating, can exert on the monsoon circulation by altering the atmospheric stability.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1287-1292
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• 2018

In case of fire, the information related with the fire need to be provided to fire fighters in order to extinguish the fire efficiently. However, existing fire detection systems have the problem not to provide the data of the fire to fire fighters visually. In this paper, we propose the fire spot system using a cloud server to solve this problem. In the proposed system, the sensors installed in a building collect the gas and temperature data and store them into the cloud database using a wireless network. For fire fighters in the field, the details and history of the fire spot are displayed visually on top of the blue print retrieved from the cloud database using a smart device.