• Proceedings of the KSRS Conference
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• v.1
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• pp.250-252
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• 2006

The land surface temperature (LST) can be defined as a weighted average temperature of components which constitute a pixel. The coefficients of split-window algorithm for MTSAT-1R were obtained by means of a statistical regression analysis from radiative transfer simulations using MODTRAN 4.0 for a wide range of atmospheric, satellite viewing angle (SVA) and lapse rate conditions. 6 types of atmospheric profile data imbedded in the MODTRAN 4 are used for the radiative transfer simulations. The RMSE is clearly larger on warm and humid profiles than cold and dry profiles, especially when the satellite viewing angle and lapse rate are large. The derivation of LST equations according to the atmospheric profiles clearly decreased the RMSE without regard to the SVA and lapse rate. The bias and RMSE are decreased as the more controls factors included. This preliminary result indicates that the characteristics of atmosphere, SVA and lapse rate should be included in the LST equation.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.385-388
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• 2006

The land surface temperature (LST) can be defined as a weighted average temperature of components which constitute a pixel. The coefficients of split-window algorithm for MTSAT-1R were obtained by means of a statistical regression analysis from radiative transfer simulations using MODTRAN 4.0 for a wide range of atmospheric, satellite viewing angle (SVA) and lapse rate conditions. 6 types of atmospheric profile data imbedded in the MODTRAN 4 are used for the radiative transfer simulations. The RMSE is clearly larger on warm and humid profiles than cold and dry profiles, especially when the satellite viewing angle and lapse rate are large. The derivation of LST equations according to the atmospheric profiles clearly decreased the RMSE without regard to the SVA and lapse rate. The bias and RMSE are decreased as the more controls factors included. This preliminary result indicates that the characteristics of atmosphere, SVA and lapse rate should be included in the LST equation.

• 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.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.227-236
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• 2009

This study investigates the characteristics of cloud top brightness temperature (CTBT) of WV and IR1 from MTSAT-1R when lightning strikes in South Korea. For temporal and spatial collocations, lightnings, occurred only within ${\pm}5$ minutes from the six minutes added official satellite observation time (e.g., not 0600 UTC but 0606 UTC, considering the real scan time over South Korea), were selected. And the CTBTs corresponding to lightning spots were determined using the nearest pixel within 5 km. The brightness temperature difference (BTD, defined as WV - IR1) between two channels is negatively large when no lightning occurrs, whereas it increases up to positive values (sometimes, +5 K) and the largest frequency distributes around 225 K and 205 K in lightning cases. The probablistic approach for lightning frequency forecast, presented by Machado et al. (2008) in Southern America, was applied over South Korea and new exponential equations, with high coefficients of determination around 0.95 to 0.99, were developed using two channels' BTDs when lightning strikes. Moreover, a case study on 10th June, 2006, the largest number of lightning occurred between 2002 and 2006, was made. The major finding is that lightning activity is closely related to the dramatic decreases in BT and the increases in BTD (esp., equal to or larger than 0 K). Lightning frequency increases exponentially when BTD increases up to 0 K. Therefore, lightning forecast skill will be improved when the integrated strategy (synoptic background and satellite-based CTBT and BTD) is applied. It is believed that this study contributes to the application of the Korean first geostationary satellite (COMS), scheduled to launch at the end of this year, to severe weather detections.

• Korean Journal of Remote Sensing
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• v.28 no.1
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• pp.69-77
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• 2012

Surface insolation is one of the major indicators for climate research over the Earth system. For the climate research, long-term data and wide range of spatial coverage from the data observed by two or more of satellites of the same orbit are needed. It is important to improve the continuity and consistency of the derived products, such as surface insolation, from different satellites. In this study, surface insolations based on Geostationary Operational Environmental Satellite (GOES-9) and Multi-functional Transport Satellites (MTSAT-1R) were compared during overlap period using physical model of insolation to find ways to improve the consistency and continuity between two satellites through comparison of each channel data and ground observation data. The thermal infrared brightness temperature of two satellites show a relatively good agreement between two satellites : rootmean square error (RMSE)=5.595 Kelvin; Bias=2.065 Kelvin. Whereas, visible channels shown a quite different values, but it distributed similar tendency. And the surface insolations from two satellites are different from the ground observation data. To improve the quality of retrieved insolations, we have reproduced surface insolation of each satellite through adjustment of the Cloud Factor, and the Cloud Factor for GOES-9 satellite is modified based on the analysis result of difference channel data. As a result, the insolations estimated from GOES-9 for cloudy conditions show good agreement with MTSAT-1R and ground observation : RMSE=$83.439W\;m^{-2}$ Bias=$27.296W\;m^{-2}$. The result improved accuracy confirms that the modification of Cloud Factor for GOES-9 can improve the continuity and consistency of the insolations derived from two or more satellites.

• Proceedings of the KSRS Conference
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• v.1
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• pp.216-219
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• 2006

This paper analyzes on LHGS (LRIT/HRIT Generation Subsystem) processing timeline for COMS LHGS design. The LHGS shall transmit LRIT/HRIT (Low Rate Information Transmission/ High Rate Information Transmission) data to the users within 15 minutes after the end of the image acquisition. So, this paper performs experiment using MTSAT-1R LRIT/HRIT (11 days) and calculates minimum LHGS processing time. Only HRIT FD (Full Disk) image is considered in this paper because data size of HRIT FD image is the largest. As a result of experiment, COMS LHGS should be able to receive MI Level 1B product within 157 seconds at least.

• Atmosphere
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• v.17 no.2
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• pp.125-133
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• 2007

Cloud amount calculation algorithm was developed using MTSAT-1R satellite data. The cloud amount is retrieved at 5 km ${\times}$ 5 km over the Korean Peninsula and adjacent sea area. The algorithm consists of three steps that are cloud detection, cloud type classification, and cloud amount calculation. At the first step, dynamic thresholds method was applied for detecting cloud pixels. For using objective thresholds in the algorithm, sensitivity test was performed for TBB and Albedo variation with temporal and spatial change. Detected cloud cover was classified into 3 cloud types (low-level cloud, cirrus or uncertain cloud, and cumulonimbus type high-level cloud) in second step. Finally, cloud amount was calculated by the integration method of the steradian angle of each cloud pixel over $3^{\circ}$ elevation. Calculated cloud amount was compared with measured cloud amount with eye at surface observatory for the validation. Bias, RMSE, and correlation coefficient were 0.4, 1.8, and 0.8, respectively. Validation results indicated that calculated cloud amount was a little higher than measured cloud amount but correlation was considerably high. Since calculated cloud amount has 5km ${\times}$ 5km resolution over Korean Peninsula and adjacent sea area, the satellite-driven cloud amount could show the possibility which overcomes the temporal and spatial limitation of measured cloud amount with eye at surface observatory.

• Korean Journal of Remote Sensing
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• v.27 no.6
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• pp.653-662
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• 2011

Land surface temperature (LST) is a one of the key variables of land surface which can be estimated from geostationary meteorological satellite. In this study, we have developed the three sets of LST retrieval algorithm from MTSAT-2 data through the radiative transfer simulations under various atmospheric profiles (TIGR data), satellite zenith angle, spectral emissivity, and surface lapse rate conditions using MODTRAN 4. The three LST algorithms are daytime, nighttime and total LST algorithms. The weighting method based on the solar zenith angle is developed for the consistent retrieval of LST at the early morning and evening time. The spectral emissivity of two thermal infrared channels is estimated by using vegetation coverage method with land cover map and 15-day normalized vegetation index data. In general, the three LST algorithms well estimated the LST without regard to the satellite zenith angle, water vapour amount, and surface lapse rate. However, the daytime LST algorithm shows a large bias especially for the warm LST (> 300 K) at day time conditions. The night LST algorithm shows a relatively large error for the LST (260 ~ 280K) at the night time conditions. The sensitivity analysis showed that the performance of weighting method is clearly improved regardless of the impacting conditions although the improvements of the weighted LST compared to the total LST are quite different according to the atmospheric and surface lapse rate conditions. The validation results of daytime (nighttime) LST with MODIS LST showed that the correlation coefficients, bias and RMSE are about 0.62~0.93 (0.44~0.83), -1.47~1.53 (-1.80~0.17), and 2.25~4.77 (2.15~4.27), respectively. However, the performance of daytime/nighttime LST algorithms is slightly degraded compared to that of the total LST algorithm.

• 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.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.294-297
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• 2007

Steam and advection fogs are frequently observed in the Yellow Sea located between Korea and China during the periods of March-April and June-July respectively. This study uses the remote sensing (RS) data for monitoring sea fog. Meteorological data obtained from the Ieodo Ocean Research Station provided an informative synopsis for the occurrence of steam and advection fogs through a ground truth. The RS data used in this study was GOES-9, MTSAT-1R images and QuikSCAT wind data. A dual channel difference (DCD) approach using IR and near-IR channel of GOES-9 and MTSAT-1R satellites was applied to estimate the extension of the sea fog. For the days examined, it was found that not only the DCD but also the texture-related measurement and the weak wind condition are required to separate the sea fog from the low cloud. The QuikSCAT wind is used to provide a weak wind area less than threshold under stable condition of the surface wind around a fog event. The Laplacian computation for a measurement of the homogeneity was designed. A new combined method of DCD, QuikSCAT wind speed and Laplacian was applied in the twelve cases with GOES-9 and MTSAT-1R. The threshold values for DCD, QuikSCAT wind speed and Laplacian are -2.0 K, 8 m $s^{-1}$ and 0.1, respectively. The validation methods such as Heidke skill score, probability of detection, probability of false detection, true skill score and odds ratio show that the new combined method improves the detection of sea fog rather than DCD method.