GEO-KOMPSAT-2A Research Articles

Improvement of High-Resolution Daytime Fog Detection Algorithm Using GEO-KOMPSAT-2A/Advanced Meteorological Imager Data with Optimization of Background Field and Threshold Values

This study aimed to improve the daytime fog detection algorithm GK2A_HR_FDA using the GEO-KOMPSAT-2A (GK2A) satellite by increasing the resolution (2 km to 500 m), improving predicted surface temperature by the numerical model, and optimizing some threshold values. GK2A_HR_FDA uses numerical model prediction temperature to distinguish between fog and low clouds and evaluates the fog detection level using ground observation visibility data. To correct the errors of the numerical model prediction temperature, a dynamic bias correction (DBC) technique was developed that reflects the geographic location, time, and altitude in real time. As the numerical model prediction temperature was significantly improved after DBC application, the fog detection level improved (FAR: −0.02–−0.06; bias: −0.07–−0.23) regardless of the training and validation cases and validation method. In most cases, the fog detection level was improved due to DBC and threshold adjustment. Still, the detection level was abnormally low in some cases due to background reflectance problems caused by cloud shadow effects and navigation errors. As a result of removing navigation errors and cloud shadow effects, the fog detection level was greatly improved. Therefore, it is necessary to improve navigation accuracy and develop removal techniques for cloud shadows to improve fog detection levels.

Characteristics of the East Asian Summer Monsoon Using GK2A Satellite Data

In East Asia, where concentrated summer precipitation often leads to climate disasters, understanding the factors that cause such extreme rainfall is crucial for effective forecasting and preparedness. The western North Pacific subtropical high (WNPSH) is a key driver of summer precipitation variability, and therefore, its monitoring is critical to predicting the wet or dry periods during the East Asian summer monsoon. Using the Geo-KOMPSAT 2A (GK2A) satellite cloud amount data and ERA5 reanalysis data during the years 2020–2023, this study identified three leading empirical orthogonal function (EOF) modes and investigated the associated WNPSH variability at synoptic and subseasonal scales. The analysis includes a linear regression of meteorological fields onto the principal component (PC) time series. All three modes play a role in the spatiotemporal variability of the WNPSH, exhibiting lead–lag relationships. In particular, the second mode is responsible for its northwestward shift and intensification. As the WNPSH moves northwestward, the position of the monsoon rain band also shifts, and its intensity is modulated mainly by the moisture transport along the WNPSH boundary. Our results highlight the potential of high-resolution, real-time data from the GK2A satellite to elucidate WNPSH variability and its impact on the East Asian summer monsoon. By addressing the variability of the WNSPH using GK2A data, we pave the way for the development of a real-time monitoring framework with GK2A, which will improve our predictability and readiness for extreme weather events in East Asia.

Spatio-Temporal Deep Learning-Based Forecasting of Surface Solar Irradiance: Leveraging Satellite Data and Feature Selection

Spatio-Temporal Deep Learning-Based Forecasting of Surface Solar Irradiance: Leveraging Satellite Data and Feature Selection

Improving Dust Aerosol Optical Depth (DAOD) Retrieval from the GEOKOMPSAT-2A (GK-2A) Satellite for Daytime and Nighttime Monitoring.

The Advanced Meteorological Image (AMI) onboard GEOKOMPSAT 2A (GK-2A) enables the retrieval of dust aerosol optical depth (DAOD) from geostationary satellites using infrared (IR) channels. IR observations allow the retrieval of DAOD and the dust layer altitude (24 h) over surface properties, particularly over deserts. In this study, dust events in northeast Asia from 2020 to 2021 were investigated using five GK-2A thermal IR bands (8.7, 10.5, 11.4, 12.3, and 13.3 μm). For the dust cloud, the brightness temperature differences (BTDs) of 10.5 and 12.3 μm were consistently negative, while the BTD of 8.7 and 10.5 μm varied based on the dust intensity. This study exploited these optical properties to develop a physical approach for DAOD lookup tables (LUTs) using IR channels to retrieve the DAOD. To this end, the characteristics of thermal radiation transfer were simulated using the forward model; dust aerosols were explained by BTD (10.5, 12.3 μm)-an intrinsic characteristic of dust aerosol. The DAOD and dust properties were gained from a brightness temperature (BT) of 10.5 μm and BTD of 10.5, 12.3 μm. Additionally, the cumulative distribution function (CDF) was employed to strengthen the continuity of 24-h DAOD. The CDF was applied to the algorithm by calculating the conversion value coefficient for the DAOD error correction of the IR, with daytime visible aerosol optical depth as the true value. The results show that the DAOD product can be successfully applied during the daytime and nighttime to continuously monitor the flow of yellow dust from the GK-2A satellite in northeast Asia. In particular, the validation results for IR DAOD were similar to the active satellite product (CALIPSO/CALIOP) results, which exhibited a tendency similar to that for IR DAOD at night.

Potential Improvement of GK2A Clear-Sky Atmospheric Motion Vectors Using the Convolutional Neural Network Model

In this study, we propose a new approach to improve the accuracy of the horizontal atmospheric motion vector (AMV) in cloud-free skies and its forecasting. We adapted the optical flow of the convolutional neural network (CNN) framework model using two 10-min interval infrared images at water vapor channels (centered at 6.3, 7.0, and 7.3 μm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mu m$$\\end{document}) from the Korean geostationary satellite GEO-KOMPSAT-2A (GK2A). Since all pixels had seamless AMVs calculated by CNN (CNN AMVs), we could also predict AMVs using the linear regression method. The tracking performance of the CNN-based algorithm was validated using AMVs retrieved from GK2A (GK2A AMVs) by estimating the difference between those values and the ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalysis v5 (ERA5) wind data over Korea in 2022. CNN AMVs showed similar or better root-mean-square vector differences (RMSVDs) than GK2A AMVs (12.33–12.86 vs. 15.89–19.96 m/s). The RMSVDs of the forecasted AMVs were 2.74, 2.95, 3.41, and 4.79 m/s at lead times of 10, 20, 30, and 60 min, respectively. Consequently, our method showed higher accuracy for tracking motion in the production of AMVs and succeeded in forecasting AMVs. We expect that such potential improvements in computational accuracy for operational GK2A AMVs will contribute to increased accuracy when forecasting meteorological phenomena related to wind.

Tropical cyclone intensity estimation through convolutional neural network transfer learning using two geostationary satellite datasets

Accurate prediction and monitoring of tropical cyclone (TC) intensity are crucial for saving lives, mitigating damages, and improving disaster response measures. In this study, we used a convolutional neural network (CNN) model to estimate TC intensity in the western North Pacific using Geo-KOMPSAT-2A (GK2A) satellite data. Given that the GK2A data cover only the period since 2019, we applied transfer learning to the model using information learned from previous Communication, Ocean, and Meteorological Satellite (COMS) data, which cover a considerably longer period (2011–2019). Transfer learning is a powerful technique that can improve the performance of a model even if the target task is based on a small amount of data. Experiments with various transfer learning methods using the GK2A and COMS data showed that the frozen–fine-tuning method had the best performance due to the high similarity between the two datasets. The test results for 2021 showed that employing transfer learning led to a 20% reduction in the root mean square error (RMSE) compared to models using only GK2A data. For the operational model, which additionally used TC images and intensities from 6 h earlier, transfer learning reduced the RMSE by 5.5%. These results suggest that transfer learning may represent a new breakthrough in geostationary satellite image–based TC intensity estimation, for which continuous long-term data are not always available.

Impact of Assimilating GK-2A All-Sky Radiance with a New Observation Error for Summer Precipitation Forecasting

In the assimilation of all-sky radiance (ASR), the non-Gaussian behaviour of observation-minus-background (OMB) departures has been the major issue. Treating observation error properly should give the distribution OMB departures closer to Gaussian on which data assimilation systems are based. This study introduces a look-up-table (LUT) observation error inflation (LOEI) for assimilating ASR from three water vapor channels of GEO-KOMPSAT-2A (GK-2A) geostationary satellite based on a three-dimensional variational data assimilation (3DVAR) framework. The impacts are assessed based on summer precipitation cases over South Korea. To confirm all kinds of radiance observations, the ASRs are assimilated without any quality control procedures. The LOEI adopt a pre-estimated radiance error statistics by using the higher order fitting function of cloud amount (CA) and standard deviation (STD) of OMB departures. This LOEI was produced during the summer period from August 1 to 30, 2020, representing the characteristics of the atmosphere condition during the experimental period. The promising impact of LOEI is demonstrated in comparison with the inflated observation error using a simple linier function proposed by Geer and Bauer (GBOEI). Study results revealed the LOEI normalized OMB departures into much more Gaussian form than the GBOEI. Hence, the assimilation of ASR using LOEI (ExpLOEI) produced BT analysis closer to the observation in four cloud phases in contrast with ASR assimilation using GBOEI (ExpGBOEI), which obviously found in the ice phase. The better BT analysis eventually simulated more realistic moisture and temperature variables in the background field. Consequently, the ExpLOEI exhibited more accuracy in precipitation location and intensity compared to the experiment with ExpGBOEI.

Study on the Slant-Path Effect in the Simulation of Clear-Sky Thermal Radiance for the GK2A AMI

Abstract Taking the slanted satellite viewing geometry into account is important in the simulation of satellite radiances, which vary with the atmospheric conditions along the line of sight. As a first step to take the slanted satellite viewing geometry into account in the numerical weather prediction system operated in the Korean Meteorological Administration, the slant-path modeling is applied for the simulation of clear-sky thermal radiances of a geostationary satellite imager, the Advanced Meteorological Imager (AMI) on board the Geo-KOMPSAT-2A (GK2A). The observations minus simulations (O − B) and the Jacobians before and after the slant-path calculation are compared. Since most infrared channels of AMI are not sensitive to the atmosphere above the tropopause, the size of slant-path effect for AMI is overall smaller than the effect shown in the microwave sounders. Still, the slant-path modeling is found to have a noticeable effect on the three water vapor absorption channels of AMI peaking between 300 and 600 hPa, particularly at large satellite zenith angles and on the regions with high water vapor variabilities in the model field along the line of sight. On average, the slant-path interpolation reduces the standard deviation of O − B of the water vapor channels by around 2.0% on land and 1.4% over the ocean for zenith angles 40°–60°, and it also influences not only the shape and magnitude but also the height of the peak of the Jacobians. In addition, the retrieval experiment based on the optimal estimation also demonstrates the impact of this new approach in the retrieved moisture field, though the improvement is not as significant as shown in the simulation experiment.

Compact front module with integrated miniaturised antenna in package for parabolic reflector antenna in satellite ground terminals

A compact front module with integrated miniaturised antenna in package for parabolic reflector antenna is presented for GEO-KOMPSAT-2A (GK-2A) satellite ground terminal application at 1692 MHz. The active feeder antenna module consists of four parts: a pair of miniaturised slot antennas, a pair of differential feeding lines, a metal shielding box, and front circuits, including a low noise amplifier (LNA) and a surface acoustic wave bandpass filter (SAW BPF). To reduce the lateral size of the feeder antennas and obtain required radiation patterns, meandered radiation slots are suggested and fed by a pair of differential coplanar waveguide (CPW) lines. The antenna substrate serves as the module cover and the electromagnetic shielding with a lateral size of 48 × 48 mm2 (0.27 × 0.27 λ 2 ${\lambda }^{2}$ ). Additionally, the feeder antenna module is proven to work with the specified parabolic reflector, both in theory and simulation. The front module with the packaged antenna is fabricated, and the measured output gain exceeds 20 dBi within a desired narrow band. Moreover, the module also achieves excellent performance in the GK-2A satellite signal-receiving experiment when used to feed the parabolic reflector antenna. The entire front module exhibits good package integration and application performance.

Application of FY-4B Geostationary Meteorological Satellite in Grassland Fire Dynamic Monitoring

In this study, the channel data related to fire point identification of Advanced Geostationary Radiation Imager on Fengyun-4B (FY-4B/AGRI) and Advanced Meteorological Imager on GEO-KOMPSAT 2A (GK-2A/AMI) were cross-compared. A total of 267 sampling points in China (Guangdong, Guangxi, Guizhou, Yunnan, Hainan) were selected to carry out fine positioning correction on the data in different elevation intervals. Then, a fire monitoring algorithm based on FY-4B was proposed, in which the self-adaptive threshold adjustment of the underlying surface parameters and the reprocessing module of fire point identification were added. The algorithm can realize the high-precision and stable monitoring of fire. The continuous dynamic monitoring was carried out using the grassland fire Mongolia from April 18 to 20, 2022 as an example. The results showed that the parameters of FY-4B/AGRI and GK2A/AMI channels have high consistency. The root mean square error (RMSE) of reflection channel was 1.33%, and the maximum RMSE of brightness temperature channel was less than 1.3 Kelvin (K). Through the positioning analysis in different elevation intervals, the average longitude offset of FY-4B satellite data was -0.5°–0° and the average latitude offset was -0.9°–0.6°. Overall, these findings indicate that the high-frequency observations of FY-4B can be fully utilized to monitor forest and grassland fires, which can continuously track the dynamic evolution of fire and can distinguish the spatial distribution of different fire intensities in large-scale fire field.

Estimation of Geometric Factors for the Particle Detecting Instruments of the Geostationary Satellite GK2A at 128.2°E Longitude Based on Observations of the Outer Radiation Belt During Geomagnetically Quiet Periods

AbstractWe present a method to determine energy‐dependent geometric factors for charged particle instruments in electron energy range of 125–2,200 keV aboard the geostationary satellite GEO‐KOMPSAT‐2A (GK2A), stationed at 128.2°E longitude. As deduced from the GEANT4 Monte Carlo simulation, the response function of the instrument in a matrix form was employed to undertake forward‐fitting of the differential energy spectra against various functions such as the double Maxwellian (DM), relativistic kappa (KP), and power‐law (PL). Thereby, we determined the energy‐dependent geometric factors for each energy channel and instrument. The forward‐fitting method was performed for a selected period of geomagnetically quiet days from May to November 2019, by applying stringent conditions to geomagnetic indices and the residual from the fit. It was found that the KP and DM functions provided better fits overall, whereas the PL function yielded considerably worse fits to the measurements. For the KP and DM functions, the validity of the geometric factors was examined against the distributions of the measured count rates and inferred model fluxes. The estimated model parameters, such as the spectral index from the KP function or the temperature from the DM functions, were also analyzed. The present study shows that the instruments provided quantitative observations of the outer radiation belt at a vantage point of 128.2°E longitude and allowed for a simple and reliable means to obtain electron fluxes based on combinations of results from numerical simulation, ground calibration, and space measurements.

Hypothetical Ground Radar-Like Rain Rate Generation of Geostationary Weather Satellite Using Data-to-Data Translation

This study proposes a deep-learning-based data-to-data (D2D) translation framework to simulate a radar-like retrieval of rainfall rates using the advantages of spatial coverage and temporal resolution of geostationary (GEO) satellite observation. The D2D method comprises normalization and denormalization in pre- and post-processing and an adversarial learning structure for an inter-domain conversion between physical values of data such as albedo and brightness temperature (BT) unlike the image-to-image translation using digital number values in image data. The GEO-KOMPSAT-2A (GK2A) and radar hybrid surface rainfall (HSR) datasets over the Korean Peninsula from September 2019 to September 2021 were used as the source and target domains for training and testing the D2D model. The constructed D2D model for ground radar-like rainfall generation was validated using the ground radar-observed rainfall data and compared to the GK2A rainfall rate (RR), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), and Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) rainfall products. The D2D model exhibited excellent performance for various rain types in the study area compared to the GK2A RR, PERSIANN-CCS, and IMERG data. Consequently, the D2D model can provide valuable and accurate radar-like rainfall intensity and distribution data with a high temporal resolution and complementary rainfall information over lands and oceans without radar observation.

GEO-KOMPSAT-2A/2B AMI/GOCI-II/GEMS Data & Products

Two geostationary satellites developed by the Korea Aerospace Research Institute and currently in operation are the GEO-KOMPSAT-2A (GK-2A) and the GEO-KOMPSAT-2B (GK-2B). The main instruments mounted on these satellites are the Advanced Meteorological Imager (AMI), the Geostationary Ocean Color Imager (GOCI-II) and the Geostationary Environment Monitoring Spectrometer (GEMS). This paper briefly introduced the GK-2A and GK-2B programs including measurement principles and elements of the instruments. Moreover, the data formats, operational products, and applications are summarized.

Adequacy of the GK-2A AMI Land Surface Temperature Product According to Geographic Factors and Compared with Other Satellite Products (MODIS and S-VIRRS)

In this study, we evaluated the land surface temperatures (LSTs) retrieved from the GEO-KOMPSAT-2A (GK-2A) satellite AMI sensor by comparing them to LSTs measured at 106 ground-based stations (95 ASOS and 11 AAOS stations). In addition, the statistical performance of the GK-2A AMI LST was compared with those of LSTs retrieved from the MODIS and S-VISSR satellites. LSTs were evaluated in terms of inland versus coastal regions and at different inland elevations. A time series analysis of the entire study period confirmed that the GK-2A satellite yielded the smallest bias among four satellite-based LST products. The satellite-based LSTs also exhibited higher correlations with ground-based measurements in the inland region than in the coastal region. In addition, the difference in LSTs at the highest and lowest elevations was approximately 1 ℃. A comparison of the annual mean LST and observation frequency revealed that geostationary satellite-based LSTs tend to be overestimated because of the lower observation frequency (1/3 of the polar orbit satellite frequency). Overall, the GK-2A AMI LSTs can be used to analyze the spatiotemporal variations in the urban heat island effect and heat waves at fine-scale resolutions, while further topographical correction is recommended to improve their accuracy.

Fire Monitoring Algorithm and Its Application on the Geo-Kompsat-2A Geostationary Meteorological Satellite

Geo-Kompsat-2A (GK-2A) is the third new-generation geostationary meteorological satellite that orbits Asia and monitors China and its surrounding areas, following the Himawari-8 and Fengyun-4A satellites. The nadir point positioning and satellite channel parameters of the GK-2A are better than those of the Himawari-8 and FY-4A, which are more conducive to fire monitoring in China. In this study, a new fire detection algorithm is proposed based on GK-2A satellite data. That is, considering the large solar zenith angle correction for reflectance and the proportion information of background pixels in the existing spatial threshold method, fires under the different underlying surface types and solar radiation states can be automatically identified. Moreover, the accuracy of the Himawari-8 fire monitoring algorithm and the present algorithm of GK-2A is compared and analyzed through the ground truth fire spot data. The results show that compared with the original fire monitoring algorithm with fixed parameter thresholds, the brightness temperature difference of this algorithm is reduced by 0.55 K, and the correction coefficient is reduced by 0.6 times, the fire can be found earlier, and the monitoring sensitivity is improved. According to the practical fire case, the present fire monitoring algorithm of GK-2A has better monitoring accuracy than the fire monitoring algorithm of Himawari-8. The present fire monitoring algorithm of GK-2A can meet the fire monitoring requirements under different sun angles, different cloud cover ratios and vegetation ratios with good versatility.

Estimating Hourly Surface Solar Irradiance from GK2A/AMI Data Using Machine Learning Approach around Korea

Surface solar irradiance (SSI) is a crucial component in climatological and agricultural applications. Because the use of renewable energy is crucial, the importance of SSI has increased. In situ measurements are often used to investigate SSI; however, their availability is limited in spatial coverage. To precisely estimate the distribution of SSI with fine spatiotemporal resolutions, we used the GEOstationary Korea Multi-Purpose SATellite 2A (GEO-KOMPSAT 2A, GK2A) equipped with the Advanced Meteorological Imager (AMI). To obtain an optimal model for estimating hourly SSI around Korea using GK2A/AMI, the convolutional neural network (CNN) model as a machine learning (ML) technique was applied. Through statistical verification, CNN showed a high accuracy, with a root mean square error (RMSE) of 0.180 MJ m−2, a bias of −0.007 MJ m−2, and a Pearson’s R of 0.982. The SSI obtained through a ML approach showed an accuracy higher than the GK2A/AMI operational SSI product. The CNN SSI was evaluated by comparing it with the in situ SSI from the Ieodo Ocean Research Station and from flux towers over land; these in situ SSI values were not used for training the model. We investigated the error characteristics of the CNN SSI regarding environmental conditions including local time, solar zenith angle, in situ visibility, and in situ cloud amount. Furthermore, monthly and annual mean daily SSI were calculated for the period from 1 January 2020 to 31 January 2022, and regional characteristics of SSI around Korea were analyzed. This study addressed the availability of satellite-derived SSI to resolve the limitations of in situ measurements. This could play a principal role in climatological and renewable energy applications.

Estimation of electron fluxes in the Earth's geostationary orbit with Tikhonov regularization during a magnetically quiet period

Electron energy fluxes in the Earth's outer radiation belt are estimated using an inverse theory of the Tikhonov regularization based upon observations in geostationary orbits. Particle Detector (PD) experiment aboard a geostationary satellite GEO-KOMPSAT-2A (GK2A) at a geographic longitude of 128.2°E provided observations of electrons within a 150–2,400 keV energy range with an unprecedented energy resolution of ΔE/E in the range of 5–25%. Instrument response functions, calculated with Monte-Carlo simulations, are deconvoluted with electron observations. Using regularization parameters determined from Generalized Cross Validation (GCV), the Tikhonov method was applied to observations made during a geomagnetically quiet period. This Tikhonov regularization method, now possible for observations in the Earth's radiation belt for the first time, allows direct inference of electron fluxes without resorting to predetermined functional forms. Comparisons of our results with those from conventional methods indicate differences among the results as large as ∼200%.

Estimation of Daily Potential Evapotranspiration in Real-Time from GK2A/AMI Data Using Artificial Neural Network for the Korean Peninsula

Evapotranspiration (ET) is a fundamental factor in energy and hydrologic cycles. Although highly precise in-situ ET monitoring is possible, such data are not always available due to the high spatiotemporal variability in ET. This study estimates daily potential ET (PET) in real-time for the Korean Peninsula, via an artificial neural network (ANN), using data from the GEO-KOMPSAT 2A satellite, which is equipped with an Advanced Meteorological Imager (GK2A/AMI). We also used passive microwave data, numerical weather prediction (NWP) model data, and static data. The ANN-based PET model was trained using data for the period 25 July 2019 to 24 July 2020, and was tested by comparing with in-situ PET for the period 25 July 2020 to 31 July 2021. In terms of accuracy, the PET model performed well, with root-mean-square error (RMSE), bias, and Pearson’s correlation coefficient (R) of 0.649 mm day−1, −0.134 mm day−1, and 0.954, respectively. To examine the efficiency of the GK2A/AMI-derived PET data, we compared it with in-situ ET measured at flux towers and with MODIS PET data. The accuracy of the GK2A/AMI-derived PET, in comparison with the flux tower-measured ET, showed RMSE, bias, and Pearson’s R of 1.730 mm day−1, 1.212 mm day−1, and 0.809, respectively. In comparison with the in-situ PET, the ANN model produced more accurate estimates than the MODIS data, indicating that it is more locally optimized for the Korean Peninsula than MODIS. This study advances the field by applying an ANN approach using GK2A/AMI data and could play an important role in examining hydrologic energy for air-land interactions.

Temperature Vegetation Dryness Index-Based Soil Moisture Retrieval Algorithm Developed for Geo-KOMPSAT-2A

The Korea Meteorological Administration (KMA) has developed many product algorithms including that for soil moisture (SM) retrieval for the geostationary satellite Geo-Kompsat-2A (GK-2A) launched in December 2018. This was developed through a five-year research project owing to the significance of SM information for hydrological and meteorological applications. However, GK-2A’s visible and infrared sensors lack direct SM sensitivity. Therefore, in this study, we developed an SM algorithm based on the conversion relationships between SM and the temperature vegetation dryness index (TVDI) estimated for various land types in the full disk area using two of GK-2A’s level 2 products, land surface temperature (LST) and normalized difference vegetation index (NDVI), and the Global Land Data Assimilation System (GLDAS) SM data for calibration. Methodologically, various coefficients were obtained between TVDI and SM and used to estimate the GK-2A-based SM. The GK-2A SM algorithm was validated with GLDAS SM data during different periods. Our GK-2A SM product showed seasonal and spatial agreement with GLDAS SM data, indicating a dry-wet pattern variation. Quantitatively, the GK-2A SM showed annual validation results with a correlation coefficient (CC) >0.75, bias <0.1%, and root mean square error (RMSE) <4.2–4.7%. The monthly averaged CC values were higher than 0.7 in East Asia and 0.5 in Australia, whereas RMSE and unbiased RMSE values were <0.5% in East Asia and Australia. Discrepancies between GLDAS and GK-2A TVDI-based SMs often occurred in dry Australian regions during dry seasons due to the high LST sensitivity of GK-2A TVDI. We determined that relationships between TVDI and SM had positive or negative slopes depending on land cover types, which differs from the traditional negative slope observed between TVDI and SM. The KMA is currently operating this GK-2A SM algorithm.

Enhanced Accuracy of Airborne Volcanic Ash Detection Using the GEOKOMPSAT-2A Satellite.

In this study, a technique facilitating the enhanced detection of airborne volcanic ash (VA) has been developed, which is based on the use of visible (VIS), near-infrared (NIR), and infrared (IR) bands by meteorological satellite systems. Channels with NIR and IR bands centered at ~3.8, 7.3, 8.7, 10.5, and 12.3 μm are utilized, which enhances the accuracy of VA detection. The technique is based on two-band brightness temperature differences (BTDs), two-band brightness temperature ratios (BTRs), and background image BTDs. The physical effects of the observed BTDs and BTRs, which can be used to distinguish VA from meteorological clouds based on absorption differences, depend on the channel and time of day. The Advanced Meteorological Imager onboard the GEOKOMPSAT-2A (GK-2A) satellite has several advantages, including the day- and nighttime detection of land and ocean. Based on the GK-2A data on several volcanic eruptions, multispectral data are more sensitive to volcanic clouds than ice and water clouds, ensuring the detection of VA. They can also be used as an input to provide detailed information about volcanoes, such as the height of the VA layer and VA mass. The GK-2A was optimized, and an improved ash algorithm was established by focusing on the volcanic eruptions that occurred in 2020. In particular, the 3.8 μm band was utilized, the threshold was changed by division between day and night, and efforts were made to reduce the effects of clouds and the discontinuity between land and ocean. The GK-2A imagery was used to study volcanic clouds related to the eruptions of Taal, Philippines, on 12 January and Nishinoshima, Japan, from 30 July–2 August to demonstrate the applicability of this product during volcanic events. The improved VA product of GK-2A provides vital information, helping forecasters to locate VA as well as guidance for the aviation industry in preventing dangerous and expensive interactions between aircrafts and VA.