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We construct a continuous 24-h daily fine particulate matter (PM2.5)record with 2×2 km2 resolution over eastern China, South Korea, and Japan for 2011–2022 by applying a random forest (RF) algorithm to aerosol optical depth (AOD) observations from the Geostationary Ocean Color Imager (GOCI) I and II satellite instruments. The RF uses PM2.5 observations from the national surface networks as training data. PM2.5 network data starting in 2015 in South Korea are extended to pre-2015 with a RF trained on other air quality data available from the network including PM10. PM2.5 network data starting in 2014 in China are supplemented by pre-2014 data from the US embassy and consulates. Missing AODs in the GOCI data are gap-filled by a separate RF fit. We show that the resulting GOCI PM2.5 dataset is successful in reproducing the surface network observations including extreme events, and that the network data in the different countries are representative of population-weighted exposure. We find that PM2.5 peaked in 2014 (China) and 2013 (South Korea, Japan), and has been decreasing steadily since with no region left behind. We quantify the population in each country exposed to annual PM2.5 in excess of national ambient air quality standards and how this exposure evolves with time. The long record for the Seoul Metropolitan Area (SMA) shows a steady decrease from 2013 to 2022 that was not present in the first five years of AirKorea network PM2.5 measurements. Mapping of an extreme pollution event in Seoul with GOCI PM2.5 shows a predicted distribution indistinguishable from the dense urban network observations, while our previous 6×6 km2 product smoothed local features. Our product should be useful for public health studies where long-term spatial continuity of PM2.5 information is essential. [ABSTRACT FROM AUTHOR]

Instruments for air quality observations on geostationary satellites provide multiple observations per day and allow for the analysis of the diurnal variation of important air pollutants such as nitrogen dioxide (NO2) over large areas. The South Korean instrument GEMS on the GK2B satellite was launched in February 2020 and is the first instrument in geostationary orbit that delivers hourly daytime observations of NO2. The measurements with a spatial resolution of 3.5 km x 8 km cover a large part of Asia. This study compares one year of tropospheric NO2 vertical column density (VCD) observations of the operational GEMS L2 product, the scientific GEMS IUP-UB product, the operational TROPOMI product, and ground-based DOAS measurements in South Korea. The GEMS L2 tropospheric NO2 VCDs overestimate the VCDs retrieved from the ground-based observations with a median relative difference of +64 % and a correlation coefficient of 0.75. The median relative difference is -1 % for the GEMS IUP-UB product and -14 % for the TROPOMI product. The evaluation of the GEMS IUP-UB product and the operational TROPOMI product with ground-based measurements is in good agreement with correlation coefficients of 0.82 and 0.88. The scatter in the GEMS products can be reduced when observations are limited to the TROPOMI overpass time. The observed diurnal variations of the tropospheric NO2 VCDs show a maximum of NO2 during the late morning for urban sites, whereas rural sites show weak or almost no diurnal changes. Investigations of the seasonal diurnal variability show with a minimum in the observed tropospheric NO2 VCDs around noon the importance of chemical loss of NO2 in summer. Most variability is seen in spring and autumn, which dominate the average annual diurnal cycle. Observations under low wind conditions show strong enhancements of NO2 over the day, especially at polluted sites during winter. This indicates that under calm conditions, dilution and the less effective chemical loss in winter do not balance the accumulating emissions. The impact of transport processes is illustrated by the diurnal variability at a rural site following mean wind patterns for specific seasons and observation times. Analyzing the weekday-weekend effect, good agreement was found between the different products. However, the GEMS L2 product while agreeing with the other data sets during weekdays shows significantly less reduction on weekends. Our investigations show that the observed diurnal evolution of NO2 varies significantly at the different measurement sites, with good agreement between the GEMS IUP-UB and ground-based observations. The diurnal variability of tropospheric NO2 VCDs depends on chemistry, emissions, and transport into and out of the measurement region. To interpret the sources and sinks of NO2 requires that all of these factors are considered. [ABSTRACT FROM AUTHOR]

The continued interest in air pollution and stratospheric ozone variability has motivated the development of a Geostationary Environmental Monitoring Spectrometer (GEMS) for hourly ozone monitoring. This paper provides the atmospheric science community with the world's first assessment of GEMS total column ozone (TCO) retrieval performance and diurnal ozone variation. The algorithm used for GEMS is a more advanced version of its predecessor, the Total Ozone Mapping Spectrometer (TOMS) V8, that incorporates several improvements, including a new lookup table, a simple Lambertian-equivalent reflectivity model, and a spectral dependence correction. The GEMS algorithm also uses the optimal estimation method (OEM) to make error analysis more accessible and robust. The estimated retrieval errors range from 1.5 to 2 DU in September and 2 DU in December, with a constant degree of freedom of the signal (DFS) of 1 in September and a variable DFS of 1.25 to 1.4 in December throughout the day, depending on solar zenith angle (SZA). To assess the performance of the GEMS algorithm, the hourly GEMS total ozone was compared with ground-based measurements from Pandora instruments and other satellite platforms from TROPOMI (TROPOspheric Monitoring Instrument) and OMPS (Ozone Mapping and Profiler Suite Nadir Mapper). GEMS has a high correlation of 0.97 and small RMSE values compared to Pandora TCO at Busan and Seoul in South Korea. It is notable that despite exhibiting seasonal dependence in the mean bias of GEMS with Pandora, GEMS is capable of observing daily variations in ozone that are highly consistent with Pandora measurements, with a bias of approximately 1 %. The comparison of GEMS TCO data with TROPOMI and OMPS TCO data shows a high correlation of 0.99 and low RMSE compared to TROPOMI and OMPS TCO data, but the data have a negative bias of -2.38 % and -2.17 %, with standard deviations of 1.33 % and 1.57 %, respectively. Similar to OMPS, the influence of SO 2 from volcanic eruptions is not properly removed in some regions, leading to GEMS overestimating TCO in those areas. The mean biases of GEMS TCO data with TROPOMI and OMPS TCO are within ±1 % at low latitudes but become negative at midlatitudes, with an increasingly negative dependence on latitude. Furthermore, this dependence becomes more prominent from summer to winter. The empirical correction applied to the GEMS irradiance data improves the dependence of the mean bias on season and latitude, but a consistent bias still remains, and a marginal positive trend was observed in December. Therefore, further investigation into correction methods is needed. The results are a meaningful scientific advance by providing the first validated, hourly UV ozone retrievals from a satellite in geostationary orbit. This experience can be used to advance research with future geostationary environmental satellite missions, including the incoming TEMPO (Tropospheric Emissions: Monitoring of Pollution) and Sentinel-4. [ABSTRACT FROM AUTHOR]

Concentrations of ambient particulate matter (such as PM2.5 and PM10) have come to represent a serious environmental problem worldwide, causing many deaths and economic losses. Because of the detrimental effects of PM2.5 on human health, many countries and international organizations have developed and operated regional and global short-term PM2.5 prediction systems. The short-term predictability of PM2.5 (and PM10) is determined by two main factors: the performance of the air quality model and the precision of the initial states. While specifically focusing on the latter factor, this study attempts to demonstrate how information from classical ground observation networks, a state-of-the-art geostationary (GEO) satellite sensor, and an advanced air quality modeling system can be synergistically combined to improve short-term PM2.5 predictability over South Korea. Such a synergistic combination of information can effectively overcome the major obstacle of scarcity of information, which frequently occurs in PM2.5 prediction systems using low Earth orbit (LEO) satellite-borne observations. This study first presents that the scarcity of information is mainly associated with cloud masking, sun-glint effect, and ill-location of satellite-borne data, and it then demonstrates that an advanced air quality modeling system equipped with synergistically-combined information can achieve substantially improved performances, producing enhancements of approximately 10%, 19%, 29%, and 10% in the predictability of PM2.5 over South Korea in terms of index of agreement (IOA), correlation coefficient (R), mean biases (MB), and hit rate (HR), respectively, compared to PM2.5 prediction systems using only LEO satellite-derived observations. [ABSTRACT FROM AUTHOR]

Forest fire frequency is increasing owing to climate change. Therefore, better forest fire monitoring strategies are required, as they can start unexpectedly and spread rapidly. Earth observation satellites can efficiently prompt rapid responses to forest fires. In this study, Burned Area Index (BAI) and difference Normalized Burn Ratio (dNBR) were analysed to detect and monitor a forest fire in Korea using data from four sun-synchronous satellites and one geostationary satellite, and the results were compared in terms of their spatial, temporal, and spectral resolutions. KOMPSAT-3A efficiently estimated detailed information of the fire on a local-scale for its spatial resolution but was limited to only observing the local-scale fire due to its narrow swath. Sentinel-2 and Landsat-8 were adequate for observing the forest fire on both local- and large-scales and provided more spectral bands and temporal information, which increased the accuracy of detecting the fire damage. Visible Infrared Imaging Radiometer Suite (VIIRS) and GK-2A showed the highest temporal resolution and enabled early detection of the wildfire and its duration, but their low spatial resolutions limited damage estimates to the local-scale. Thus, satellites worldwide may be used synergistically to ensure efficient responses to frequent and massive forest fires. [ABSTRACT FROM AUTHOR]

Aerosol particles can be emitted, transported, removed, or transformed, leading to aerosol variability at scales impacting the climate (days to years and over hundreds of kilometers) or the air quality (hours to days and from meters to hundreds of kilometers). We present the temporal and spatial scales of changes in AOD (aerosol optical depth) and aerosol size (using Ångström exponent – AE; fine-mode fraction – FMF) over Korea during the 2016 KORUS-AQ (KORea–US Air Quality) atmospheric experiment. We use measurements and retrievals of aerosol optical properties from airborne instruments for remote sensing (4STAR; Spectrometers for Sky-Scanning Sun-Tracking Atmospheric Research) and in situ (LARGE; NASA Langley Aerosol Research Group Experiment) on board the NASA DC-8 and geostationary satellites (GOCI; Geostationary Ocean Color Imager; Yonsei aerosol retrieval – YAER, version 2) as well as from reanalysis (MERRA-2; Modern-Era Retrospective Analysis for Research and Applications, version 2). Measurements from 4STAR when flying below 1000 m show an average AOD at 501 nm of 0.36 and an average AE of 1.11 with large standard deviation (0.12 and 0.15 for AOD and AE, respectively), likely due to mixing of different aerosol types (fine and coarse mode). The majority of AOD due to fine-mode aerosol is observed at altitudes lower than 2 km. Even though there are large variations, for 18 out of the 20 flight days, the column AOD measurements by 4STAR along the NASA DC-8 flight trajectories match the South Korean regional average derived from GOCI. GOCI-derived FMF, which was found to be slightly low compared to AErosol RObotic NETwork (AERONET) sites (Choi et al., 2018), is lower than 4STAR's observations during KORUS-AQ. Understanding the variability of aerosols helps reduce uncertainties in the aerosol direct radiative effect by quantifying the errors due to interpolating between sparse aerosol observation sites or modeled pixels, potentially reducing uncertainties in the upcoming observational capabilities. We observed that, contrary to the prevalent understanding, AE and FMF are more spatially variable than AOD during KORUS-AQ, even when accounting for potential sampling biases by using Monte Carlo resampling. Averaging between measurements and models for the entire KORUS-AQ period, the reduction in correlation by 15 % is 65.0 km for AOD and shorter at 22.7 km for AE. While there are observational and model differences, the predominant factor influencing spatial–temporal homogeneity is the meteorological period. High spatiotemporal variability occurs during the dynamic period (25–31 May), and low spatiotemporal variability occurs during the blocking pattern (1–7 June). While AOD and FMF / AE are interrelated, the spatial variability and relative variability of these parameters in this study indicate that microphysical processes vary at scales shorter than aerosol concentration processes at which microphysical processes such as aerosol particle formation, growth, and coagulation mostly impact the dominant aerosol size (characterized by, e.g., FMF / AE) and to some degree AOD. In addition to impacting aerosol size, aerosol concentration processes such as aerosol emission, transport, and removal mostly impact the AOD. [ABSTRACT FROM AUTHOR]

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We aim to reduce uncertainties in CH2O and other volatile organic carbon (VOC) emissions through assimilation of remote sensing data. We first update a three‐dimensional (3D) chemical transport model, GEOS‐Chem with the KORUSv5 anthropogenic emission inventory and inclusion of chemistry for aromatics and C2H4, leading to modest improvements in simulation of CH2O (normalized mean bias (NMB): −0.57 to −0.51) and O3 (NMB: −0.25 to −0.19) compared against DC‐8 aircraft measurements during KORUS‐AQ; the mixing ratio of most VOC species are still underestimated. We next constrain VOC emissions using CH2O observations from two satellites (OMI and OMPS) and the DC‐8 aircraft during KORUS‐AQ. To utilize data from multiple platforms in a consistent manner, we develop a two‐step Hybrid Iterative Finite Difference Mass Balance and four‐dimensional variational inversion system (Hybrid IFDMB‐4DVar). The total VOC emissions throughout the domain increase by 47%. The a posteriori simulation reduces the low biases of simulated CH2O (NMB: −0.51 to −0.15), O3 (NMB: −0.19 to −0.06), and VOCs. Alterations to the VOC speciation from the 4D‐Var inversion include increases of biogenic isoprene emissions in Korea and anthropogenic emissions in Eastern China. We find that the IFDMB method alone is adequate for reducing the low biases of VOCs in general; however, 4D‐Var provides additional refinement of high‐resolution emissions and their speciation. Defining reasonable emission errors and choosing optimal regularization parameters are crucial parts of the inversion system. Our new hybrid inversion framework can be applied for future air quality campaigns, maximizing the value of integrating measurements from current and upcoming geostationary satellite instruments. Key Points: A two step Hybrid IFDMB‐4DVar inversion systemVOC DC‐8 emissions in Northeast Asia were constrained using CH2O concentrations observed by Ozone Monitoring Instrument, Ozone Mapping and Profiler Suite, and DC‐8 aircraft during KORUS‐AQVOC emissions were increased by 47% leading to improved performance in simulation of O3 and speciated VOCs [ABSTRACT FROM AUTHOR]

We use 2011–2019 aerosol optical depth (AOD) observations from the Geostationary Ocean Color Imager (GOCI) instrument over East Asia to infer 24 h daily surface fine particulate matter (PM 2.5) concentrations at a continuous 6 × 6 km 2 resolution over eastern China, South Korea, and Japan. This is done with a random forest (RF) algorithm applied to the gap-filled GOCI AODs and other data, including information encoded in GOCI AOD retrieval failure and trained with PM 2.5 observations from the three national networks. The predicted 24 h GOCI PM 2.5 concentrations for sites entirely withheld from training in a 10-fold cross-validation procedure correlate highly with network observations (R2 = 0.89) with a single-value precision of 26 %–32 %, depending on the country. Prediction of the annual mean values has R2 = 0.96 and a single-value precision of 12 %. GOCI PM 2.5 is only moderately successful for diagnosing local exceedances of the National Ambient Air Quality Standard (NAAQS) because these exceedances are typically within the single-value precisions of the RF and also because of RF smoothing of extreme PM 2.5 concentrations. The area-weighted and population-weighted trends of GOCI PM 2.5 concentrations for eastern China, South Korea, and Japan show steady 2015–2019 declines consistent with surface networks, but the surface networks in eastern China and South Korea underestimate population exposure. Further examination of GOCI PM 2.5 fields for South Korea identifies hot spots where surface network sites were initially lacking and shows 2015–2019 PM 2.5 decreases across the country, except for flat concentrations in the Seoul metropolitan area. Inspection of the monthly PM 2.5 time series in Beijing, Seoul, and Tokyo shows that the RF algorithm successfully captures observed seasonal variations in PM 2.5 , even though AOD and PM 2.5 often have opposite seasonalities. The application of the RF algorithm to urban pollution episodes in Seoul and Beijing demonstrates high skill in reproducing the observed day-to-day variations in air quality and spatial patterns on the 6 km scale. A comparison to a Community Multiscale Air Quality (CMAQ) simulation for the Korean peninsula demonstrates the value of the continuous GOCI PM 2.5 fields for testing air quality models, including over North Korea, where they offer a unique resource. [ABSTRACT FROM AUTHOR]

We show that it is possible to launch a satellite to Geostationary Equatorial orbit (GEO) from the non-equatorial launch site (Naro Space Center in South Korea) even though that is located in the mid-latitudes of the northern hemisphere. When launched from this site, the equatorial inclination after separation will be 80°. We use a lunar gravity assist (LGA) transfer to avoid the excessive ∆V costs of plane change maneuvers. There are eight possible paths for the LGA; there are four paths consisting of Earth departures and free-return types, and there are two nodes of the Moon's orbit (ascending and descending). We analyze trajectories over five launch periods for each path using a high-fidelity orbit propagation model. We show that the LGA changes the orbital energy of the "cislunar" free-returns more than for the "circumlunar" free-returns, resulting in less geostationary insertion ∆V for the cislunar free-returns. We also show that the geometrical ∆V variation over the different paths is greater than the seasonal ∆V variation. Our results indicate that an ascending departure and cislunar free-return at the descending node have lower ∆V requirements than the other paths, and lower than described in several previous studies. [ABSTRACT FROM AUTHOR]

We use 2011-2019 aerosol optical depth (AOD) observations from the Geostationary Ocean Color Imager (GOCI) instrument over East Asia to infer 24-h daily surface fine particulate matter (PM2.5) concentrations at continuous 6x6 km² resolution over eastern China, South Korea, and Japan. This is done with a random forest (RF) algorithm applied to the gap-filled GOCI AODs and other data and trained with PM2.5 observations from the three national networks. The predicted 24-h PM2.5 concentrations for sites entirely withheld from training in a ten-fold crossvalidation procedure correlate highly with network observations (R² = 0.89) with single-value precision of 26-32% depending on country. Prediction of annual mean values has R² = 0.96 and single-value precision of 12%. The RF algorithm is only moderately successful for diagnosing local exceedances of the National Ambient Air Quality Standard (NAAQS) because these exceedances are typically within the single-value precisions of the RF, and also because of RF smoothing of extreme PM2.5 concentrations. The area-weighted and population-weighted trends of RF PM2.5 concentrations for eastern China, South Korea, and Japan show steady 2015-2019 declines consistent with surface networks, but the surface networks in eastern China and South Korea underestimate population exposure. Further examination of RF PM2.5 fields for South Korea identifies hotspots where surface network sites were initially lacking and shows 2015-2019 PM2.5 decreases across the country except for flat concentrations in the Seoul metropolitan area. Inspection of monthly PM2.5 time series in Beijing, Seoul, and Tokyo shows that the RF algorithm successfully captures observed seasonal variations of PM2.5 even though AOD and PM2.5 often have opposite seasonalities. Application of the RF algorithm to urban pollution episodes in Seoul and Beijing demonstrates high skill in reproducing the observed day-to-day variations in air quality as well as spatial patterns on the 6 km scale. Comparison to a CMAQ simulation for the Korean peninsula demonstrates the value of the continuous RF PM2.5 fields for testing air quality models, including over North Korea where they offer a unique resource. [ABSTRACT FROM AUTHOR]

Sub-grid variability (SGV) in atmospheric trace gases within satellite pixels is a key issue in satellite design and interpretation and validation of retrieval products. However, characterizing this variability is challenging due to the lack of independent high-resolution measurements. Here we use tropospheric NO2 vertical column (VC) measurements from the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument with a spatial resolution of about 250m×250m to quantify the normalized SGV (i.e., the standard deviation of the sub-grid GeoTASO values within the sampled satellite pixel divided by the mean of the sub-grid GeoTASO values within the same satellite pixel) for different hypothetical satellite pixel sizes over urban regions. We use the GeoTASO measurements over the Seoul Metropolitan Area (SMA) and Busan region of South Korea during the 2016 KORUS-AQ field campaign and over the Los Angeles Basin, USA, during the 2017 Student Airborne Research Program (SARP) field campaign. We find that the normalized SGV of NO2 VC increases with increasing satellite pixel sizes (from ∼10 % for 0.5km×0.5km pixel size to ∼35 % for 25km×25km pixel size), and this relationship holds for the three study regions, which are also within the domains of upcoming geostationary satellite air quality missions. We also quantify the temporal variability in the retrieved NO2 VC within the same hypothetical satellite pixels (represented by the difference of retrieved values at two or more different times in a day). For a given satellite pixel size, the temporal variability within the same satellite pixels increases with the sampling time difference over the SMA. For a given small (e.g., ≤4 h) sampling time difference within the same satellite pixels, the temporal variability in the retrieved NO2 VC increases with the increasing spatial resolution over the SMA, Busan region, and the Los Angeles Basin. The results of this study have implications for future satellite design and retrieval interpretation and validation when comparing pixel data with local observations. In addition, the analyses presented in this study are equally applicable in model evaluation when comparing model grid values to local observations. Results from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model indicate that the normalized satellite SGV of tropospheric NO2 VC calculated in this study could serve as an upper bound to the satellite SGV of other species (e.g., CO and SO2) that share common source(s) with NO2 but have relatively longer lifetime. [ABSTRACT FROM AUTHOR]

The worst forest fires in Korean history broke out on March 4, 2022 and lasted for ten days. In order to monitor the catastrophic forest fires, Geostationary Korea Multi-Purpose Satellite (GK)-2 A Advanced Meteorological Imager (AMI) and GK-2B Geostationary Environment Monitoring Spectrometer (GEMS) data were used in this study. Aerosol optical depth (AOD) irretrievable for the biomass-burning aerosols produced with water vapor classified as could-contaminated, was reconstructed by ultraviolet aerosol index (UVAI). Afterward, aerosol radiative forcing (ARF) at TOA was finally estimated by the correlation of AOD and surface albedo with ARF. Most of the aerosols drifted toward the East Sea by the prevailing westerly winds, and caused a cooling effect on the atmosphere with a maximum daily average radiative forcing of −69.28 Wm-2. Furthermore, the fire-prone conditions for the unprecedented forest fires were discussed in detail as following aspects; 1) the most severe drought caused by a "triple-dip" La Niña; 2) pressure patterns and topographical features that generate strong winds; 3) coniferous forests prone to fires; and 4) increased human activity following the nationwide COVID-19 vaccination. This study demonstrated that the rapid and effective ARF estimation based on the satellite remote sensing can contribute to a better understanding of ARF in the Earth's radiation budget for the global forest fires that will be more frequent, intense, and longer-lasting due to the human-caused climate and environment changes. [Display omitted] • The worst human-caused forest fires in South Korea occurred from 4 to March 13, 2022. • New Korean satellites effectively captured the aerosol plume and its climate impact. • Daily radiative forcing of biomass-burning was assessed from −28.36 to −69.28 Wm-2. [ABSTRACT FROM AUTHOR]

The application of solar energy as a renewable energy source has significantly escalated owing to its abundance and availability worldwide. However, the intermittent behavior of solar irradiance is a serious disadvantage for electricity grids using photovoltaic (PV) systems. Thus, reliable solar irradiance data are vital to achieve consistent energy production. Geostationary satellite images have become a solution to this issue, as they represent a database for solar irradiance on a massive spatiotemporal scale. The estimation of global horizontal irradiance (GHI) using satellite images has been developed based on physical and semi-empirical models, but only a few studies have been dedicated to modeling GHI using semi-empirical models in Korea. Therefore, this study conducted a comparative analysis to determine the most suitable semi-empirical model of GHI in Korea. Considering their applicability, the Beyer, Rigollier, Hammer, and Perez, models were selected to estimate the GHI over Seoul, Korea. After a comparative evaluation, the Hammer model was determined to be the best model. This study also introduced a hybrid model and applied a long short-term memory (LSTM) model in order to improve prediction accuracy. The hybrid model exhibited a smaller root-mean-square error (RMSE), 97.08 W/m2, than that of the Hammer model, 103.92 W/m2, while producing a comparable mean-bias error. Meanwhile, the LSTM model showed the potential to further reduce the RMSE by 11.2%, compared to the hybrid model. [ABSTRACT FROM AUTHOR]

The Particle Detector (PD) experiment aboard the geostationary satellite GEO-KOMPSAT-2A (GK2A) measures populations of electrons and positive ions in the Earth's geostationary orbit at a geographic longitude of 128.2 ∘ E , inclination of 0 ∘ and a mean orbital radius of 6.6 Earth radii ( R E ). The PD experiment consists of three sensors with different viewing angles relative to the spacecraft. Each sensor consists of two telescopes that are mechanically configured back-to-back with a field-of-view of 20 ∘ × 20 ∘ and measures electrons and ions, using silicon detectors equipped with foils and magnets for the separation of ions and electrons. The energy ranges of the sensor for electrons and ions are 100–3800 keV and 148–22500 keV, respectively. A particular emphasis on electron measurement is given by allocating 48 energy bins in the measured energy range, whereas 22 energy bins are allocated for ion measurements. This unprecedented energy resolution of Δ E / E in the range 5–25% for the electron and ion flux measurements is acquired every three seconds with cyclic polling of each sensor every second to provide an effective temporal resolution of one second. Together with the magnetometer aboard the spacecraft, the PD experiment will provide quantitative observations that will enable improved understanding of the adiabatic and nonadiabatic dynamics of the Earth's magnetosphere for space weather studies at geostationary orbits from the vantage point of a far-east longitude. [ABSTRACT FROM AUTHOR]

A new algorithm has been developed to retrieve total column ozone (TOZ) from the infrared measurements of a geostationary satellite imager such as the Advanced Himawari Imager (AHI) aboard Himawari-8 and the advanced meteorological imager flying with the second generation geostationary satellite of South Korea. Based on a nonlinear optimal estimation method, this operational algorithm iteratively retrieves TOZ from AHI thermal measurements under clear-sky conditions as well as over low clouds at 10-min intervals with 6-km spatial resolutions. The first guess for ozone is prepared by combining a monthly climatology with the daily updated TOZ from polar orbiting satellites, while the first guess for the temperature and humidity profiles are from a numerical weather prediction model. AHI contains only one ozone absorption channel ($9.6~\mu \text{m}$), providing information contents (degrees of freedom for signal) of about 0.98 for the ozone retrieval. However, the study shows that the retrieved TOZ successfully captures the temporal and latitudinal variations of ozone flow particularly at mid- and high-latitudes. A validation using the Ozone Monitoring Instrument, Infrared Atmospheric Sounding Interferometer (IASI), and ozonesondes shows that the AHI TOZ are in a good agreement with the three references with mean difference of 2.5%, −0.5%, and 2.6%, respectively. This impressive performance is attributed to the improvements in both first-guess information and the accuracy of the retrieved temperature and humidity profiles. In particular, the good agreement shown in the comparison with the night-time IASI TOZ suggests potential benefits of using the retrieved TOZ for a continuous monitoring of global ozone evolution with high spatial resolution. [ABSTRACT FROM AUTHOR]

The Geostationary Ocean Color Imager II (GOCI-II) deployed on board GEO-KOMPSAT 2B (GK2B) satellite can observe the earth with high frequency, which is of great significance to the dynamic monitoring of regional aerosol optical depth (AOD). However, the lack of short-wave infrared bands makes the classical methods based on radiative transfer analysis difficult to achieve retrieval of aerosols. The data-driven methods based on learning from massive samples can achieve high-accuracy retrieval of aerosols on the local, regional and global scale. However, the samples required for the data-driven method are difficult to obtain due to the short operation time of the GK2B satellite and insufficient ground-based measurements. Aiming to solve such a problem, the Simplified and Robust Surface Reflectance Estimation and Simplified Aerosol Retrieval Algorithm (SEMARA) were aggregated to be applied in the study. The SEMARA method using ground-based observation can retrieve local AOD with high reliability and accuracy, which can be used to construct high-quality and high-quantity samples for training with higher efficiency compared with traditional sample construct methods. Four data-driven methods: Fully Connected Neural Network (FCNN), Random Forest (RF), Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost), were chosen to retrieve the AOD over South Korea in 2021 and 2022 based on GOCI-II. The results were validated and compared using ground-based measurements from the Aerosol Robotic Network (AENRONET) sites and show that the FCNN method has high accuracy and stability, followed by, the RF and XGBoost methods, while the SVM results significantly overestimate the spatial distribution of the AOD. • High-precision AOD retrieval methods with high time frequency based GOCI-II over South Korea were proposed. • A sample construction method based on SEMARA was proposed for four data-driven models. • The retrieval accuracy and reliability of these four AOD retrieval methods were verified and compared. • The FCNN has high accuracy and stability, followed by, the RF and XGBoost, while the SVM shows clear overestimation. [ABSTRACT FROM AUTHOR]

Abstract Recent studies in the application of geostationary satellite sensors, such as the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the European Union’s meteorological satellite, and the Geostationary Ocean Color Imager (GOCI) from South Korea, to monitor the Total Suspended Sediment (TSS) in coastal waters have shown that the higher temporal dynamics in the coastal processes can be better achieved with high temporal resolution geostationary satellite sensors. The availability of the geostationary satellite, Himawari-8 carrying the Advanced Himawari Imager (AHI) sensor with visible, Near InfraRed (NIR) and Shortwave InfraRed (SWIR) bands over the coastal waters of Australia has prompted this study to test the feasibility of AHI in mapping TSS concentration. In this study, we present an atmospheric correction method for AHI data using two different aerosol correction methods, a combination of two SWIR bands, and a combination of one NIR and one SWIR band. The outcome from this study indicated that the atmospherically corrected AHI data at 10 min temporal resolution can be used to discern TSS concentrations greater than ∼0.15 mg/L when solar zenith angles (θ 0) are less than 60°. The result of the cross-validation of AHI derived TSS concentration, with MODIS-Aqua and Lansat-8 Operational Land Imager (OLI) derived TSS concentration showed good correlation, with correlation coefficients r = 0.71 and r = 0.91, respectively. [ABSTRACT FROM AUTHOR]

Surface currents in Korean coastal regions were obtained using the maximum cross-correlation method applied to hourly suspended particulate matter images from the Geostationary Ocean Color Imager. Preliminary current vectors were filtered out by applying a series of quality-control procedures. The current vectors resulting from the tests were compared with the currents from a numerical model with tide and wind field. It was found that the estimated currents were more similarly to the currents caused by both tide and wind. A high degree of discrepancy was detected in regions of strong tidal currents, where the fundamental assumption of horizontal movement was limited due to the dominant vertical tidal mixing in the shallow region. The hourly rotations of the current vectors within a day were clarified by a comparison of the time-varying orientation angles of tidal ellipses. This study emphasized how to understand the short-term surface flows from hourly high-resolution geostationary satellite images. [ABSTRACT FROM PUBLISHER]

To produce real-time ground-level information on particulate matter with a diameter equal to or less than 2.5 μm (PM 2.5), many studies have explored the applicability of satellite data, particularly aerosol optical depth (AOD). However, many of the techniques used are computationally demanding; to overcome these challenges, machine learning(ML)-based research has been on the rise. Here, we used ML techniques to directly estimate ground-level PM 2.5 concentrations over South Korea using top-of-atmosphere (TOA) reflectance from the Geostationary Ocean Color Imager I (GOCI-I) and its next generation GOCI-II with improved spatial, spectral, and temporal resolutions. Three ML techniques were used to estimate ground-level PM 2.5 concentrations: random forest, light gradient boosting machine (LGBM), and artificial neural network. Three schemes were examined based on the input feature composition of the GOCI spectral bands: scheme 1 using all GOCI-I bands, scheme 2 using only GOCI-II bands that overlap with GOCI-I bands, and scheme 3 using all GOCI-II bands. The results showed that LGBM performed better than the other ML models. GOCI–II–based schemes 2 and 3 (determination of coefficient (R2) = 0.85 and 0.85 and root-mean-square-error (RMSE) = 7.69 and 7.82 μg/m3, respectively) performed slightly better than GOCI-I-based scheme 1 (R2 = 0.83 and RMSE = 8.49 μg/m3). In particular, TOA reflectance at a new channel (380 nm) of GOCI-II was identified as the most contributing variable, given its high sensitivity to aerosols. The long-term estimation of PM 2.5 concentrations using the proposed models was examined for ground stations located in two major cities. GOCI–II–based models produced a more detailed spatial distribution of PM 2.5 concentrations owing to their higher spatial resolution (i.e., 250 m). The use of TOA reflectance data, instead of AOD and other aerosol products commonly used in previous studies, reduced the missing rate of the estimated ground-level PM 2.5 concentrations by up to 50%. Our results indicate that the proposed approach using TOA reflectance data from geostationary satellite sensors has great potential for estimating ground-level PM 2.5 concentrations for operational purposes. [Display omitted] • Hourly PM 2.5 estimation was conducted directly using TOA reflectance from GOCI series. • TOA-based PM 2.5 estimation greatly reduced missing rates compared to AOD-based one. • New spectral bands of GOCI-II contributed significantly to PM 2.5 estimation. • Continuity of GOCI-I/II models enabled stable long-term monitoring of PM 2.5. [ABSTRACT FROM AUTHOR]

The diurnal sampling capability of geostationary satellites provides unprecedented opportunities for monitoring canopy photosynthesis at multiple temporal scales. At the diurnal scale, only geostationary satellites can currently provide sub-daily data at regular intervals, also it can help to minimize data gaps due to clouds at the seasonal scale. However, the potential of geostationary satellites for monitoring photosynthesis has not been explored in depth. In this study, we tracked diurnal to seasonal variations in gross primary production (GPP) using the product of near-infrared reflectance of vegetation and photosynthetically active radiation (PAR) (NIRvP) over deciduous forests, mixed forests and a rice paddy during the growing season. For this purpose, we generated three levels of reflectance and PAR from Geostationary Korea Multi-Purpose Satellite-2A (GK-2A). We examined how NIRvP derived from GK-2A tracked in-situ GPP data collected from five flux tower sites in South Korea. Bi-directional Reflectance Distribution Function (BRDF) normalized NIRvP agreed well with in-situ GPP over the course of the growing season at hourly (R2 = 0.68–0.77) and daily timesteps (R2 = 0.71–0.83). Atmospheric correction and BRDF normalization improved the performance of NIRvP in tracking GPP at both the diurnal and seasonal time scales. Also, GK-2A showed a much higher percentage of available high-quality BRDF data over the whole growing season for all study sites than the Moderate Resolution Imaging Spectroradiometer (MODIS) (GK-2A: 85%; MODIS: 39%), especially during the cloudy monsoon period. Our findings demonstrated that the unique observation characteristics of geostationary satellites can contribute to large-scale monitoring of diurnal to seasonal GPP dynamics. [Display omitted] • Demonstrated that GK-2A NIRvP well captured diurnal to seasonal variations in GPP. • Quantified greater high-quality BRDF data availability of GK-2A (85%) than MODIS (39%). • Showed importance of atmospheric correction and BRDF normalization for tracking GPP. [ABSTRACT FROM AUTHOR]

The Cheollian Satellite 2A called Geo-Kompsat-2A is a geostationary meteorological satellite operated by Korea Meteorological Administration for weather and space-weather observation purposes. This article seeks to evaluate the public value arising from improving the accuracy of early detection of cumulonimbus using Geo-Kompsat-2A. To this end, an economic technique called contingent valuation (CV) is adopted taking a three step approach. In step 1, a CV questionnaire was prepared for deriving the willingness to pay (WTP) for the improvement. In step 2, a nationwide survey of 1000 households was conducted using the CV questionnaire and employing person-to-person interviews with household visits. In step 3, the average WTP was estimated. The yearly household average WTP was KRW 2143 (USD 1.91), securing statistical significance. Adjusting this value to suit the entire population of South Korea produces a yearly public value of KRW 44.1 billion (USD 39.3 million). Although it is not easy to accurately evaluate the costs involved in the improvement, the results of conducting a pilot cost-benefit analysis suggest that the improvement secures social desirability. [ABSTRACT FROM AUTHOR]

Early warning of severe weather caused by intense convective weather systems is challenging. To help such activities, meteorological satellites with high temporal and spatial resolution have been utilized for the monitoring of instability trends along with water vapor variation. The current study proposes a retrieval algorithm based on an artificial neural network (ANN) model to quickly and efficiently derive total precipitable water (TPW) and convective available potential energy (CAPE) from Korea's second geostationary satellite imagery measurements (GEO-KOMPSAT-2A/Advanced Meteorological Imager (AMI)). To overcome the limitations of the traditional static (ST) learning method such as exhaustive learning, impractical, and not matching in a sequence data, we applied an ANN model with incremental (INC) learning. The INC ANN uses a dynamic dataset that begins with the existing weight information transferred from a previously learned model when new samples emerge. To prevent sudden changes in the distribution of learning data, this method uses a sliding window that moves along the data with a window of a fixed size. Through an empirical test, the update cycle and the window size of the model are set to be one day and ten days, respectively. For the preparation of learning datasets, nine infrared brightness temperatures of AMI, six dual channel differences, temporal and geographic information, and a satellite zenith angle are used as input variables, and the TPW and CAPE from ECMWF model reanalysis (ERA5) data are used as the corresponding target values over the clear-sky conditions in the Northeast Asia region for about one year. Through the accuracy tests with radiosonde observation for one year, the INC NN results demonstrate improved performance (the accuracy of TPW and CAPE decreased by approximately 26% and 26% for bias and about 13% and 12% for RMSE, respectively) when compared to the ST learning. Evaluation results using ERA5 data also reveal more stable error statistics over time and overall reduced error distribution compared with ST ANN. [ABSTRACT FROM AUTHOR]

The Korea Institute of Energy Research builds Korean solar irradiance datasets, using gridded solar insolation estimates derived using the University of Arizona solar irradiance based on Satellite–Korea Institute of Energy Research (UASIBS–KIER) model, with the incorporation of geostationary satellites over the Korean Peninsula, from 1996 to 2019. During the investigation period, the monthly mean of daily total irradiance was in a good agreement with the in situ measurements at 18 ground stations; the mean absolute error is also normalized to 9.4%. It is observed that the irradiance estimates in the datasets have been gradually increasing at a rate of 0.019 kWh m−2 d−1 per year. The monthly variation in solar irradiance indicates that the meteorological conditions in the spring season dominate the annual solar insolation. In addition, the local distribution of solar irradiance is primarily affected by the geographical environment; higher solar insolation is observed in the southern part of Korea, but lower solar insolation is observed in the mountainous range in Korea. The annual capacity factor is the secondary output from the Korean solar irradiance datasets. The reliability of the estimate of this factor is proven by the high correlation coefficient of 0.912. Thus, in accordance with the results from the spatial distribution of solar irradiance, the southern part of Korea is an appropriate region for establishing solar power plants exhibiting a higher annual capacity factor than the other regions. [ABSTRACT FROM AUTHOR]

This study calculated the augmentation of water resources that can be achieved through precipitation enhancement and the ensuing economic benefits by conducting precipitation enhancement experiments using atmospheric aircraft in the catchment areas of 21 multipurpose dams in Korea. The maximum number of precipitation enhancement experiments to be carried out was estimated based on the frequency of occurrence of seedable clouds near each dam, using geostationary satellite data. The maximum quantity of water that can be obtained was calculated considering the mean precipitation enhancement and probability of success, as determined from the results of experiments conducted in South Korea during 2018–2019. The effective area of seeding was assumed 300 km2. In addition, the amount of hydroelectric power generation possible was determined from the quantity of water thus calculated. In conclusion, it was established that an approximate increase of 12.89 million m3 (90% confidence interval: 7.83–17.95 million m3) of water, and 4.79 (2.91–6.68) million kWh of electric power generation will be possible through approximately 96 precipitation enhancement operations in a year at the catchment area of Seomjin River (SJ) dam which has a high frequency of occurrence of seedable clouds, a large drainage area, and a high net head. An economic benefit of approximately 1.01 (0.61–1.40) million USD can be anticipated, the benefit/cost ratio being 1.46 (0.89–2.04). [ABSTRACT FROM AUTHOR]

Leaf wetness duration (LWD) and plant diseases are strongly associated with each other. Therefore, LWD is a critical ecological variable for plant disease risk assessment. However, LWD is rarely used in the analysis of plant disease epidemiology and risk assessment because it is a non-standard meteorological variable. The application of satellite observations may facilitate the prediction of LWD as they may represent important related parameters and are particularly useful for meteorologically ungauged locations. In this study, the applicability of geostationary satellite observations for LWD prediction was investigated. GEO-KOMPSAT-2A satellite observations were used as inputs and six machine learning (ML) algorithms were employed to arrive at hourly LW predictions. The performances of these models were compared with that of a physical model through systematic evaluation. Results indicated that the LWD could be predicted using satellite observations and ML. A random forest model exhibited larger accuracy (0.82) than that of the physical model (0.79) in leaf wetness prediction. The performance of the proposed approach was comparable to that of the physical model in predicting LWD. Overall, the artificial intelligence (AI) models exhibited good performances in predicting LWD in South Korea. [ABSTRACT FROM AUTHOR]

Statistical approaches for quantitative precipitation nowcasts (QPNs) have emerged with recent advances in sensors in geostationary orbits, which provide more frequent observations at higher spatial resolutions. Several investigations have reported that extrapolation-based prediction can be more effective than numerical weather model-based prediction for a short lead time of less than a few hours. This study introduces the algorithm based on extrapolation method to produce QPN products for Advanced Meteorological Imager (AMI) onboard South Korea's second geostationary satellite (GEO-KOMPSAT-2A). The algorithm first provides the 3-hour potential accumulated rainfall (PAR) for a very-short-range forecast using extrapolation and then calculates the probability of rainfall (POR) during the same time period through statistical methods. The algorithm for PAR first identifies rainfall features and computes the motion vectors of the identified rainfall features through tracking between two consecutive GEO-KOMPSAT-2A rain rate images. The algorithm can then extrapolate future rainfall rates from current and previous rainfall rates outputs. In addition, the extrapolated rainfall fields are used as inputs for the POR estimation process, which produces the rainfall probability during the same 3-h period. POR is estimated based on rainfall frequency and adjusted by neighboring rainfall feature information. POR can provide a quantitative measure for the uncertainty in the PAR estimates. Retrieved PAR and POR from the QPN algorithm are validated with 3-h accumulated rainfalls obtained from the GEO-KOMPSAT-2A rain rate products. Forecasting accuracy of the PAR algorithm with time has also been investigated. The accuracy tends to decrease with increasing lead time, as expected. It may be due to the limitations of the extrapolation technique and degradation of the accuracy of the PAR estimates. The details on the improvements from the prototype algorithm and validation statistics will be discussed.AcknowledgementThis work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 18AWMP-B127555-02). [ABSTRACT FROM AUTHOR]

During the EMeRGe-Asia campaign (from 12 March to 8 April, 2018), we detected several cases for the long-range transboundary transport of airborne aerosols from the west to the Korean peninsula. To investigate the characteristic of this transport, representative 4 cases were selected for this study at first: 12-13 March, 24-25 March, 28-29 March, and 7-8 April. In these cases, we could find the high aerosol optical depth (AOD) in all west coastal regions in Korea based on the Aerosol Robotic Network (AERONET) measurement. The satellite Geostationary Ocean Color Imager (GOCI) and Advanced Himawari Imager (AHI) well indicated that this high AOD is attributed to the transport of thick aerosol plumes from eastern China. For the 24-25 March case, highly enhanced AOD was found over the Yellow Sea with low-level cloud, meaning the potential increase of atmospheric turbidity due to the hygroscopic growth of particle. Volume size distribution provided from the AERONET observations revealed the size increase of fine mode particle in this case. For 7-8 April, there was a large transport of Asian dust and PM10 level in Korea reached ~ 400 μg/m3 at maximum. For all these cases, we tried to estimate the flux rate of transported aerosol masses using the regional wind field information. Comparison with surface particulate matter (PM) observations showed the moderate consistency with AOD in general, but a discrepancy in terms of a diurnal variation. Further analysis will be required for better understanding of this issue. [ABSTRACT FROM AUTHOR]