• Korean Journal of Remote Sensing
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• v.24 no.6
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• pp.571-581
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• 2008

The sea fog occurs frequently in the west coast of Korea in spring and summer. This study focused on the detection of sea fog using MODIS satellite images. We presented a method for sea fog detection based on the homogeneity level between low stratus and sea fog, which was that the top surface of sea fog had a homogeneous aspect while that of low stratus had a heterogenous aspect. The results showed that the both homogeneity of $11{\mu}m$ brightness temperature (BT) and brightness temperature difference (BTD, $BT_{3.7{\mu}m}-BT_{11{\mu}m}$) were available to discriminate sea fog from low stratus. The frequency of difference between BT in fog/stratus area and BT in clear area provided reasonable result. In addition, the threshold values of standard deviations of BT and BTD in the fog/stratus area were applicable to differentiate fog from low stratus.

• 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 the Korean earth science society
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• v.43 no.6
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• pp.712-736
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• 2022

To understand the characteristics of low-level clouds (CLs), environmental variables are composited on each CL using individual surface observations and six-hourly upper-air meteorologies around the globe. Individual CLs has its own distinct environmental conditions. Over the eastern subtropical and western North Pacific Ocean in JJA, stratocumulus (CL5) has a colder sea surface temperature (SST), stronger and lower inversion, and more low-level cloud amount (LCA) than the climatology whereas cumulus (CL12) has the opposite characteristics. Over the eastern subtropical Pacific, CL5 and CL12 are influenced by cold and warm advection within the PBL, respectively but have similar cold advection over the western North Pacific. This indicates that the fundamental physical process distinguishing CL5 and CL12 is not the horizontal temperature advection but the interaction with the underlying sea surface, i.e., the deepening-decoupling of PBL and the positive feedback between shortwave radiation and SST. Over the western North Pacific during JJA, sky-obscuring fog (CL11), no low-level cloud (CL0), and fair weather stratus (CL6) are associated with anomalous warm advection, surface-based inversion, mean upward flow, and moist mid-troposphere with the strongest anomalies for CL11 followed by CL0. Over the western North Pacific during DJF, bad weather stratus (CL7) occurs in the warm front of the extratropical cyclone with anomalous upward flow while cumulonimbus (CL39) occurs on the rear side of the cold front with anomalous downward flow. Over the tropical oceans, CL7 has strong positive (negative) anomalies of temperature in the upper troposphere (PBL), relative humidity, and surface wind speed in association with the mesoscale convective system while CL12 has the opposite anomalies and CL39 is in between.

• Atmosphere
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• v.12 no.4
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• pp.69-90
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• 2002

The infrared and microwave satellite observations have been used to derive the information of hydrometeors (i.e., cloud and precipitation) and atmospheric temperature. The observations were made by the Nimbus-4 Infrared Interferometer Spectrometer (IRIS) in 1970, and by the Microwave Sounding Unit (MSU) during the period 1980-99, which had channel 1~4 (Chl~4). The IRIS, which has a field of view of ~100 km, has been utilized to examine the cirrus and marine stratus clouds. The cirrus and stratus distributions were obtained, respectively, based on the spectral difference in the infrared window region, and the absorption of water vapor and $CO_2$ in the spectral region $870-980cm^{-1}$. The MSU Ch1 data has been used for low tropospheric temperature and hydrometeors, while the Ch2, Ch3 and Ch4, respectively, for the thermal state of midtroposphere, tropopause, and lower stratosphere. The climatic aspects of El Ni$\tilde{n}$o, Quasi-Biennial Oscillation (QBO) and temperature trends over the globe are discussed with the MSU data. This study suggests that the IRIS and MSU data are useful for monitoring the hydrometeors and atmospheric thermal state in climate system.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.527-544
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• 2018

This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the $3.7{\mu}m$ and $10.8{\mu}m$ channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation-maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.

• 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.1
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• pp.29-45
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• 2012

An unusual autumn storm developed rapidly in the western part of the East sea on the early morning of 23 October 2006. This storm produced a record-breaking heavy rain and strong wind in the northern and middle part of the Yeong-dong region; 24-h rainfall of 304 mm over Gangneung and wind speed exceeding 63.7 m $s^{-1}$ over Sokcho. In this study, MTSAT-1R (Multi-fuctional Transport Satellite) water vapor and infrared channel imagery are examined to find out some features which are dynamically associated with the development of the storm. These features may be the precursor signals of the rapidly developing storm and can be employed for very short range forecast and nowcasting of severe storm. The satellite features are summarized: 1) MTSAT-1R Water Vapor imagery exhibited that distinct dark region develops over the Yellow sea at about 12 hours before the occurrence of maximum rainfall about 1100 KST on 23 October 2006. After then, it changes gradually into dry intrusion. This dark region in the water vapor image is closely related with the positive anomaly in 500 hPa Potential Vorticity field. 2) In the Infrared imagery, low stratus (brightness temperature: $0{\sim}5^{\circ}C$) develops from near Bo-Hai bay and Shanfung peninsula and then dissipates partially on the western coast of Korean peninsula. These features are found at 10~12 hours before the maximum rainfall occurrence, which are associated with the cold and warm advection in the lower troposphere. 3) The IR imagery reveals that two convective cloud cells (brightness temperature below $-50^{\circ}C$) merge each other and after merging it grows up rapidly over the western part of East sea at about 5 hours before the maximum rainfall occurrence. These features remind that there must be the upward flow in the upper troposphere and the low-layer convergence over the same region of East sea. The time of maximum growth of the convective cloud agrees well with the time of the maximum rainfall.