Satellite Meteorological Center Research Articles

국내 우주환경 자료 보유 현황: 태양·행성간 공간

The Solar and Space Environment Division of the Korean Space Science Society investigated the use and possession of ground and satellite observations and models of solar and planetary data operated by domestic research institutes and universities. Based on the findings, we would like to introduce observational instruments, data, and models in solar and interplanetary fields in this paper to improve understanding and use of each data and explore opportunities for interdisciplinary research. The ground and satellite observations, which require a lot of investment, were mainly held by research institutes (National Meteorological Satellite Center, Polar Research Institute, Korean Space Weather, Korea Astronomy and Space Science Institute and KAIST Satellite Research Institute), and model development was overwhelmingly carried out at Kyung Hee University. In solar and interplanetary fields, we introduce Fast Imaging Solar Spectrograph (FISS), neutron monitors, and the analysis models [for the Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA) and Hinode/X-Ray Telescope (XRT) obser-vations] in nonequilibrium ionization state as representatives. Survey on solar and interplanetary fields can be downloaded from the website of the Korean Space Science Society (http://ksss.or.kr/). The paper makes know the importance of long-term and continuous management of space science-related materials, and hopes to contribute to enhancing the status of domestic space science data by utilizing locally produced data by various personnel participating in space science research.

Evaluation of GSICS Correction for COMS/MI Visible Channel Using S-NPP/VIIRS

The Global Space-based Inter-Calibration System (GSICS) is an international partnership sponsored by World Meteorological Organization (WMO) to continue and improve climate monitoring and to ensure consistent accuracy between observation data from meteorological satellites operating around the world. The objective for GSICS is to inter-calibration from pairs of satellites observations, which includes direct comparison of collocated Geostationary Earth Orbit (GEO)-Low Earth Orbit (LEO) observations. One of the GSICS intercalibration methods, the Ray-matching technique, is a surrogate approach that uses matched, co-angled and colocated pixels to transfer the calibration from a well calibrated satellite sensor to another sensor. In Korea, the first GEO satellite, Communication Ocean and Meteorological Satellite (COMS), is used to participate in the GSICS program. The National Meteorological Satellite Center (NMSC), which operated COMS/MI, calculated the Radiative Transfer Model (RTM)-based GSICS coefficient coefficients. The L1P reproduced through GSICS correction coefficient showed lower RMSE and Bias than L1B without GSICS correction coefficient applied. The calculation cycles of the GSICS correction coefficients for COMS/MI visible channel are provided annual and diurnal (2, 5, 10, 14-day), but long-term evaluation according to these cycles was not performed. The purpose of this paper is to perform evaluation depending on the annual/diurnal cycles of COMS/MI GSICS correction coefficients based on the ray-matching technique using Suomi-NPP/Visible Infrared Imaging Radiometer Suite (VIIRS) data as reference data. As a result of evaluation, the diurnal cycle had a higher coincidence rate with the reference data than the annual cycle, and the 14-day diurnal cycle was the most suitable for use as the GSICS correction coefficient.

Development of Geo-KOMPSAT-2A Algorithm for Sea-Ice Detection Using Himawari-8/AHI Data

Sea ice is an important meteorological factor affecting the global climate system, but it is difficult to observe in sea ice ground truth data because of its location mainly at high latitudes and in polar regions. Accordingly, sea-ice detection research has been actively conducted using satellites, since the 1970s. Polar-orbiting and geostationary satellites are used for this purpose; notably, geostationary satellites are capable of real-time monitoring of specific regions. In this paper, we introduce the Geo-KOMPSAT-2A (GK-2A)/Advanced Meteorological Imager (AMI) sea-ice detection algorithm using Japan Meteorological Agency (JMA) Himawari-8/Advanced Himawari Imager (AHI) data as proxy data. The GK-2A/AMI, which is Korea Meteorological Administration (KMA)’s next-generation geostationary satellite launched in December 2018 and Himawari-8/AHI have optically similar channel data, and the observation area includes East Asia and the Western Pacific. The GK-2A/AMI sea-ice detection algorithm produces sea-ice data with a 10-min temporal resolution, a 2-km spatial resolution and sets the Okhotsk Sea and Bohai Sea, where the sea ice is distributed during the winter in the northern hemisphere. It used National Meteorological Satellite Center (NMSC) cloud mask as the preceding data and a dynamic threshold method instead of the static threshold method that is commonly performed in existing sea-ice detection studies. The dynamic threshold methods for sea-ice detection are dynamic wavelength warping (DWW) and IST0 method. The DWW is a method for determining the similarity by comparing the pattern of reflectance change according to the wavelength of two satellite data. The IST0 method detects sea ice by using the correlation between 11.2-μm brightness temperature (BT11.2) and brightness temperature difference (BTD) [BT11.2–BT12.3] according to ice surface temperature (IST). In addition, the GK-2A/AMI sea-ice detection algorithm reclassified the cloud area into sea ice using a simple test. A comparison of the sea-ice data derived the GK-2A/AMI sea-ice detection algorithm with the S-NPP/visible infrared imaging radiometer suite (VIIRS) sea ice characterization product indicates consistency of 99.0% and inconsistency of 0.9%. The overall accuracy (OA) of GK-2A/AMI sea-ice data with the sea ice region of interest (ROI) data, which is constructed by photo-interpretation method from RGB images, is 97.2%.

Application of Cumulative Distribution Functions to Compositing Precipitable Water with Low Earth Orbit Satellite Data

Precipitable water (PW) is a leading indicator of severe weather, such as heavy rainfall. The National Meteorological Satellite Center receives observation data directly from Low Earth Orbit (LEO) satellites, including NOAA-18, 19, MetOp-A, B, and Suomi-NPP, and generates level 2 products in near real time for the East Asia region. In this study, PW data were retrieved from the abovementioned sources and composited to determine meteorologically significant PW fields from all available data sources considering the boundary layer (BL), middle layer (ML), high layer (HL), and total column. Cumulative distribution function (CDF) matching was performed for PW retrieved from the five satellites within specific time windows in order to integrate them and minimize their differences. The PW composite data were matched to reference PW CDF data from the Unified Model (UM) Regional Data Assimilation Prediction System (RDAPS) analysis data, which are updated according to selected time windows. PW data were composited for 03:00–09:00, 09:00–15:00, 15:00–21:00, and 21:00–03:00 UTC time windows. PW data composited during the summer and winter seasons in 2015, 2016, and 2017 were then validated. The results show improvements in composite Total Precipitable Water and BL PW from Suomi-NPP of approximately 15% and 40% (RMSE), respectively.

Atmospheric Motion Vectors Derived from an Infrared Window Channel of a Geostationary Satellite Using Particle Image Velocimetry

AbstractAs the new-generation geostationary satellite Himawari-8 provides a greater frequency and more observation channels than its predecessor, the Multifunctional Transport Satellite series (e.g., MTSAT-2), an opportunity arises to generate atmospheric motion vectors (AMVs) with an increased accuracy and extensive distribution over eastern Asia. In this work AMVs were derived from consecutive images of an infrared-window channel (IR1) of the Himawari-8 satellite using particle image velocimetry (PIV) based on the theory of cross-correlation schemes. A multipass scheme and an adaptive interrogation scheme were also employed to increase spatial resolution and accuracy. For height assignment, an infrared-window method was applied for opaque cloud, while an H2O-intercept method was employed for semitransparent cloud. Validation was conducted by comparing the PIV-derived AMVs with wind fields obtained from NWP analysis, radiosonde observations, and the operational system from the Meteorological Satellite Center (MSC) of the Japan Meteorological Agency (JMA) or JMA/MSC. The comparison of wind velocity maps with the NWP data shows that the PIV-derived AMVs are capable of quantitatively depicting full-field wind field maps and strong jets in atmospheric circulation. Through comparisons with radiosonde observations, the root-mean-square error and wind speed bias (4.29 and −1.05 m s−1) of the PIV-derived AMVs are comparable to, although slightly greater than, that of the NWP data (3.88 and −0.26 m s−1). Based on comparison between the PIV-derived AMVs and wind fields obtained from the JMA/MSC operational system, the PIV-derived AMVs are again comparable, producing a slightly lower error but a larger wind speed bias (−1.05 vs 0.20 m s−1). This also implies that a better height assignment algorithm is necessary.

Cloud Detection for FY Meteorology Satellite Based on Ensemble Thresholds and Random Forests Approach

Cloud detection is the first step for the practical processing of meteorology satellite images, and also determines the accuracy of subsequent applications. For Chinese FY serial satellite, the National Meteorological Satellite Center (NSMC) officially provides the cloud detection products. In practical applications, there still are some misdetection regions. Therefore, this paper proposes a cloud detection method trying to improve NSMC’s products based on ensemble threshold and random forest. The binarization is firstly performed using ten threshold methods of the first infrared band and visible channel of the image, and the binarized images are obtained by the voting strategy. Secondly, the binarized images of the two channels are combined to form an ensemble threshold image. Then the middle part of the ensemble threshold image and the upper and lower margins of NSMC’s cloud detection result are used as the sample collection source data for the random forest. Training samples rely only on source image data at one moment, and then the trained random forest model is applied to images of other times to obtain the final cloud detection results. This method performs well on FY-2G images and can effectively detect incorrect areas of the cloud detection products of the NSMC. The accuracy of the algorithm is evaluated by manually labeled ground truth using different methods and objective evaluation indices including Probability of Detection (POD), False Alarm Rate (FAR), Critical Success Index (CSI) and the average and standard deviation of all indices. The accuracy results show that the proposed method performs better than the other methods with less incorrect detection regions. Though the proposed approach is simple enough, it is a useful attempt to improve the cloud detection result, and there is plenty of room for further improvement.

Intercalibration of Advanced Himawari Imager's Infrared Channels With IASI/Metop-B 1C Data

This work addresses the intercalibration of infrared channels 7 to 16 of the Advanced Himawari Imager (AHI) aboard Himawari-8 against the hyperspectral channels of the Infrared Atmospheric Sounding Interferometer (IASI) on board Metop-B using the hyperspectral convolution method. The matching measurements in January, March, July, and November 2016 are collected over a tropical intercalibration area (100 °E∼180 °E, 10 °S∼10 °N) under the conditions of collocation, absolute observation time difference of less than 5 min ${(\vert \Delta \text{time}\vert } , absolute viewing azimuth angle difference of less than 10° ${(\vert \Delta \text{VAA}\vert } , and ${{\vert \cos}}\,\theta _{1}{{/ \cos}}\,\theta _{2}-{{ 1\vert }} , where $\theta _{1}$ and $\theta _{2}$ are the viewing zenith angles of AHI and IASI measurements, respectively. The daytime matching measurements in the channel 7 (centered at about ${\text{3.88 }} \mu {\text{m}}$ ) contaminated by strong and fast changing specular reflected solar irradiance have weak correlation and consequently are excluded from the intercalibration. The results show that the AHI and IASI matching measurements have strong linear correlation, and the intercalibration coefficients (slope and intercept) are obtained through linear regression on the qualified matching measurements. The linear fit results indicate that the in-orbit calibration of the AHI infrared channels is stable in the four months; however, more or less calibration biases exist against IASI/Metop-B hyperspectral channels. The intercalibration results in this work are basically consistent with that of the Meteorological Satellite Center of Japan Meteorological Agency.

Improvements of a COMS Land Surface Temperature Retrieval Algorithm Based on the Temperature Lapse Rate and Water Vapor/Aerosol Effect

The National Meteorological Satellite Center in Korea retrieves land surface temperature (LST) by applying the split-window LST algorithm (CSW_v1.0) to Communication, Ocean, and Meteorological Satellite (COMS) data. Considerable errors were detected under conditions of high water vapor content or temperature lapse rates during validation with Moderate Resolution Imaging Spectroradiometer (MODIS) LST because of the too simplified LST algorithm. In this study, six types of LST retrieval equations (CSW_v2.0) were developed to upgrade the CSW_v1.0. These methods were developed by classifying “dry,” “normal,” and “wet” cases for day and night and considering the relative sizes of brightness temperature difference (BTD) values. Similar to CSW_v1.0, the LST retrieved by CSW_v2.0 had a correlation coefficient of 0.99 with the prescribed LST and a slightly larger bias of −0.03 K from 0.00K; the root mean square error (RMSE) improved from 1.41 K to 1.39 K. In general, CSW_v2.0 improved the retrieval accuracy compared to CSW_v1.0, especially when the lapse rate was high (mid-day and dawn) and the water vapor content was high. The spatial distributions of LST retrieved by CSW_v2.0 were found to be similar to the MODIS LST independently of the season, day/night, and geographic locations. The validation using one year’s MODIS LST data showed that CSW_v2.0 improved the retrieval accuracy of LST in terms of correlations (from 0.988 to 0.989), bias (from −1.009 K to 0.292 K), and RMSEs (from 2.613 K to 2.237 K).

국가기상위성센터 영상처리 시스템 구축을 위한 국내외 기상위성 영상처리 시스템 아키텍처 분석

In this paper, we have surveyed the existing architectures of the image processing systems for several meteorological satellites and identified issues which are taken into consideration to construct the advanced meteorological satellite image processing system that is being developed by NMSC(National Meteorological Satellite Center). Most of the existing systems provide the functionalities of the image acquisition, the image processing, the data management, and the data dissemination. Those systems have some common problems with respect to system integration and system maintenance. To solve these problems, NOAA, NWS and ESA suggest new system architectures to improve the existing systems. This paper introduces domestic and foreign approaches to build the satellite image processing systems and studies some issues and strategies for developing those systems.

기상위성 영상처리 기본체계 아키텍처 설계

This paper suggests a system architecture for the advanced meteorological satellite image processing system that is developing by NMSC(National Meteorological Satellite Center). The meteorology satellite image processing system has basically the image acquisition, the image processing, the data management, and the data dissemination functionalities. However, the existing system has some problems with respect to system integration as well as maintenance to accommodate new satellites and/or the new image processing systems for them which will be developed in the near future. To cope with these problems we propose a new system architecture for the advanced meteorology satellite image processing system. To do this we select as the architectural drivers the quality attributes such as modifiability, inter-operability, extendability, reusability, and platform independency and design the architecture to achieve such quality attributes. We expect that the new approach will solve current issues such as system integration, system dependency, or data management problems and will provide easy ways to incorporate new systems and to maintain them.

South Korea's renewed focus on space weather

South Korea's renewed focus on space weather

Verification of satellite-derived aerosol optical thickness over land with AERONET data

The Meteorological Satellite Center (MSC) of the Japan Meteorological Agency began retrieving aerosol optical thickness over land in March 2007 using the AVHRR/NOAA visible channel. Exact nonspherical scattering with realistic particle shape distribution is fully considered. We compared the MSC optical thickness with AERONET version 1.5 optical thickness at nine AERONET stations in northeastern Asia. Although we found statistically significant correlations at all stations, the dispersions were large, especially at southern stations. We thus examined the samples with large errors at each station to investigate the causes of the errors. The following suggestions were obtained:(1) The MSC optical thickness shows overestimation for severe Yellow Sand events and underestimation for very thick gray haze. This suggests that the absorption property of aerosol particles used in our theoretical calculation is unrealistic. A revision of the complex refractive index of aerosol used in making the look-up table may be required.(2) The influence of small cumuli or cloud edges is seen in the medium range of optical thickness (< ∼2). When cloud-contaminated samples are removed by reference to MODIS high-resolution images, the correlations between the MSC and AERONET optical thickness are notably improved and are as high as 0.6 at the most of the stations. However, systematic underestimations were found at many stations.(3) These underestimations are likely to be caused by the aerosol-free assumption used in the land surface reflectance modelling because the samples on adjacent ocean grids showed excellent correlations and no systematic bias. This is confirmed by the fact that the monthly minimums of optical thickness at AERONET stations show notable positive values.

Near‐Real‐Time Auroral Electrojet Index: An International Collaboration Makes Rapid Delivery of Auroral Electrojet Index

Near‐Real‐Time Auroral Electrojet Index: An International Collaboration Makes Rapid Delivery of Auroral Electrojet Index

Improved Estimates of Hourly Insolation from GMS S-VISSR Data

Special images from the Geostationary Meteorological Satellite (GMS) were used by the Meteorological Satellite Center (MSC) to watch a typhoon for the period from 1 August 1997 through 19 August 1997. We use three successive images with 15-minute time differences of the northern hemisphere from 03:31 UTC to 04:15 UTC throughout the period. A simple physical model is applied to the visible image of GMS-5 to estimate the insolation at the ground in 0.01° grids. The estimates of hourly insolation have been improved at 63 of the 67 validation points using three well-navigated successive images. It has been found that the increase of observation frequency results in better estimates of the hourly insolation. Therefore, as the combination of geostationary meteorological satellites, for example, GMS-5 and FY-2 (Chinese Geostationary Meteorological Satellite), running in two shifts provides twice as frequent observation than each of them. The improvement for hourly insolation estimate can be operational.

A cloud type classification with NOAA 7 split‐window measurements

A simple objective cloud type classification method has been developed, based on split‐window measurements of the Advanced Very High Resolution Radiometer on board the NOAA 7 satellite. Brightness temperature difference between the split‐window data is a good parameter for the detection of cirrus and blackbody clouds. Two‐dimensional histograms of brightness temperature of the 11‐μm channel and the brightness temperature difference between the split‐window data over (64 km)2 subareas are constructed. By selecting appropriate thresholds in the two‐dimensional histogram, cirrus, dense cirrus, cumulonimbus, and cumulus clouds are classified over the tropical ocean. Cloud type classification maps were generated by this method for the western Pacific Ocean and were compared with the nephanalysis chart constructed at the Japan Meteorological Satellite Center from GMS data collected within 1 hour of the NOAA 7 observations. The comparison shows reasonable agreement. Fractional cloud cover for cirrus over each (64 km)2 subarea is calculated as the ratio of the number of samples which belong to the cirrus cloud type in the two‐dimensional histogram to the number of total samples in the subarea. Fractional cloud cover estimations for cumulonimbus and low‐level cumulus are also presented.

多重チャネルによる軌道衛星からの海面温度遠隔測定

A theoretical assessment has been made on the relationship between the number of chan-nels used and the accuracy of remotely measured sea surface temperature (SST) by multi-channel radiance observations from satellite. It has been shown that the accuracy of SST continuously increases with the increase of the number of channels. The accuracy also depends on the accuracy of they clear radiance, accuracy of the initial guess of the SST and transmittance of the atmosphere.A procedure of the objective analysis for the SST has been developed and applied to the analysis of the SST that is obtained in Meteorological Satellite Center (MSC) on routine base from NOAA-6 meteorological satellite. The results has been compared with buoy and ship reports of SST and the GOSSTCOMP sea surface temperature made by NOAA/NESS. Root-mean-square deviation of satellite derived SST from ship reports is about 0.8°K.

Cloud height determination by satellite stereography.

A feasibility study of deriving cloud height was conducted by the use of two geosynchronous meteorological satellites (GMS-1 and GMS-2). The GMS-1 was located at about 140°E on the equator and the GMS-2 was at about 160°E on the equator. The cloud height was observed by the GMS-1 through the visible and IR channels at 23:32 GMT on Sept. 17, '81 (23:36 GMT for the GMS-2), but at equator the time difference between the data acquisitions by these two satellites is less than a minute due to their spin rate and attitude difference. These observations were made by the Meteorological Satellite Center, JMA. To obtain the good quality of anaglyphic photographs, apparent image distortions were reduced through the mapping process by which all the pixels in the observational coordinates were converted to those in the longitude-latitude coordinates. Upon deriving the cloud height, the earth was assumed to be spheroid. It was found from the analysis that it is possible to obtain the fine structure of cloud in a relative accuracy of 200 m, but a large number of dependable reference points around the target cloud are required as well as the precise orbit parameters, to obtain a high accuracy of absolute cloud height. This analysis was practically impossible by the use of infrared technique due to the unknown emissivity of cloud and the vertical temperature profiles.

Measurement of sea surface temperature from the geostationary meteorological satellite ‘Himawari’ : Abe, Katsuhiro, 1979. (In Japanese; English abstract.) Umi to Sora, 55(1): 23–39. Meteorological Satellite Center, Japan J. atmos. Sci., 37(4):717–732

Measurement of sea surface temperature from the geostationary meteorological satellite ‘Himawari’ : Abe, Katsuhiro, 1979. (In Japanese; English abstract.) Umi to Sora, 55(1): 23–39. Meteorological Satellite Center, Japan J. atmos. Sci., 37(4):717–732