Korea Multi-Purpose Satellite Research Articles

Aerosol optical depth data fusion with Geostationary Korea Multi-Purpose Satellite (GEO-KOMPSAT-2) instruments GEMS, AMI, and GOCI-II: statistical and deep neural network methods

Aerosol optical depth data fusion with Geostationary Korea Multi-Purpose Satellite (GEO-KOMPSAT-2) instruments GEMS, AMI, and GOCI-II: statistical and deep neural network methods

Aerosol radiative forcing of forest fires unprecedented in South Korea (2022) captured by Korean geostationary satellites, GK-2A AMI and GK-2B GEMS

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.

Enhancing ozone nowcasting over East Asia using a data-to-data translation approach with observations from a geostationary environment monitoring spectrometer

The degradation of air quality caused by excessive anthropogenic ozone (O3) concentrations negatively affects ecosystems and the atmosphere. To monitor this issue, the Geostationary Environment Monitoring Spectrometer (GEMS) aboard the Geostationary Korea Multi-Purpose Satellite (GK)-2B provides ozone products over Asia-centered circular regions based on the ozone algorithm derived from representative polar-orbit atmospheric environmental satellites. In this study, we developed a few-hour GEMS O3 nowcasting model using data-to-data (D2D) translation with a conditional generative adversarial network. This model is based on hourly GEMS O3 time-series products and can be used to predict ozone concentrations. The D2D model underwent training and testing employing paired input and output datasets of GEMS O3, with data collected from March 22, 2020, to June 21, 2020, and from March 22, 2021, to June 18, 2021, respectively. The resulting D2D ozone nowcasting model was used to determine ozone concentrations in time zones where GEMS O3 products were unavailable. Test results of the D2D model demonstrated excellent statistical scores, including a bias of 2.162 Dobson Units (DU, where 1 DU corresponds to 2.687 × 1016 molecules/cm2), root-mean-square error (RMSE) of 5.606 DU, a correlation coefficient (CC) of 0.994 for 1-h prediction, a bias of 1.421 DU, RMSE of 5.903 DU, and CC of 0.992 for 2-h prediction, and a bias of 1.169 DU, RMSE of 6.797 DU, and CC of 0.988 for 3-h prediction. Despite the dataset pairing and number limitations, the D2D prediction model accurately forecasted GEMS O3 within 3 h in East Asia.

Geostationary Environment Monitoring Spectrometer (GEMS) polarization characteristics and correction algorithm

Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) is the first geostationary earth orbit (GEO) environmental instrument, onboard the Geostationary Korea Multi-Purpose Satellite–2B (GEO-KOMPSAT-2B) launched on 19 February 2020, and is measuring reflected radiance from the earth's surface and atmosphere system in the range of 300–500 nm in the ultraviolet–visible (UV–Vis) region. The radiometric response of a satellite sensor that measures the UV–Vis wavelength region can depend on the polarization states of the incoming light. To reduce the sensitivity due to polarization, many current low earth orbit (LEO) satellites are equipped with a scrambler to depolarize the signals or a polarization measurement device (PMD) that simultaneously measures the polarization state of the atmosphere, then utilizes it for a polarization correction. However, a novel polarization correction algorithm is required since GEMS does not have a scrambler or a PMD. Therefore, this study aims to improve the radiometric accuracy of GEMS by developing a polarization correction algorithm optimized for GEMS that simultaneously considers the atmosphere's polarization state and the instrument's polarization sensitivity characteristics. The polarization factor and axis were derived by the preflight test on the ground as a function of wavelengths, showing a polarization sensitivity of more than 2 % at some specific wavelengths. The polarization states of the atmosphere are configured as a look-up table (LUT) using the Vector Linearized Discrete Ordinate Radiative-Transfer model (VLIDORT). Depending on the observation geometry and atmospheric conditions, the observed radiance spectrum can include a polarization error of 2 %. The performance of the proposed GEMS polarization algorithm was assessed using synthetic data, and the errors due to polarization were found to be larger in clear regions than in cloudy regions. After the polarization correction, polarization errors were reduced close to zero for almost all wavelengths, including the wavelength regions with high peaks and curvatures in the GEMS polarization factor, which sufficiently demonstrates the effectiveness of the proposed polarization correction algorithm. From the actual observation data after the launch of GEMS, the diurnal variation for the spatial distribution of polarization error was confirmed to be minimum at noon and maximum at sunrise/sunset. This can be used to improve the quality of GEMS measurements, the first geostationary environmental satellite, and then contribute to the retrieved accuracy of various Level-2 products, such as trace gases and aerosols in the atmosphere.

Structure and variations of global planetary boundary layer top from 2008-2022 multiple GNSS RO observations

The structural changes at the planetary boundary layer (PBL) top are very complex and closely related to climate and environmental changes. With the development of Global Navigation Satellite System Radio Occultation (GNSS RO), it provides a good opportunity to estimate and study PBL variations. In this paper, long-term variations and structures of planetary boundary layer height (PBLH) from 2008 to 2022 are investigated by the Wavelet Covariance Transform (WCT) method based on refractivity profiles from Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) and Korea Multi-purpose Satellite-5 (KOMPSAT-5). Furthermore, the temperature and pressure of PBL top are detected using the temperature and pressure profiles from COSMIC and KOMPSAT-5 occultation data. The results demonstrate the latitudinal distribution of the PBL top height, temperature, and pressure, with the more apparent temperature's latitudinal characteristics. The pressure is more strongly correlated with PBL height than temperature. The PBL height in most land and oceans is high in summer and low in winter, and the PBL pressure is low in summer and high in winter, according to seasonal variation characteristics. The global PBL temperature is high in summer and low in winter, which is more obvious in high latitude region. A positive correlation between latitude and the magnitude of seasonal variation in PBL temperature is observed worldwide. Seasonal variation is less pronounced in the Southern Hemisphere (SH) than in the Northern Hemisphere (NH) due to sea-land differences. The annual average value of the global PBL height, temperature, and pressure do not change significantly over the long term, and also there is no a discernible upward or downward trend.

Prototyping of Utilization Model for KOMPSAT-3/3A Analysis Ready Data Based on the Open Data Cube Platform in Multi-Cloud Computing Environment: A Case Study

This study introduces a multi-cloud model that combines private and public cloud services for processing and managing satellite images. The multi-cloud service is established by incorporating private clouds within organizations and integrating them with external public cloud services to utilize the data. Private clouds can maintain data security within an organization or between organizations, while public clouds offer easy processing options for general users with access accounts. The model for the private cloud service utilizes open-source OpenStack software to create virtual machines, allowing users to manage analysis ready data (ARD) of the Korea Multi-Purpose Satellite (KOMPSAT)-3/3A images simultaneously. The public cloud service through Amazon Web Services (AWS) offers four services and uses the Open Data Cube (ODC) to manage data and provide web-based time-series visualization and processing. The model utilizes OpenStack to create virtual machines, and the public cloud service through AWS offers various services using ODC to manage data. A system that handles large amounts of satellite imagery in a multi-cloud environment has benefits such as improved availability, cost savings through open-source, and enhanced scalability. We present a prototyped utilization model that can be used with the ODC user interface (UI) that applies the proposed multi-cloud model. The multi-cloud model of this study can be applied to constructing a country-scale data cube system, that deals with large-scale satellite image data. It can also be applied to systems that need to be built with data that is tailored to a specific user’s needs at any institution.

First-time comparison between NO2 vertical columns from Geostationary Environmental Monitoring Spectrometer (GEMS) and Pandora measurements

Abstract. The Geostationary Environmental Monitoring Spectrometer (GEMS) is a UV-visible (UV-Vis) spectrometer on board the GEO-KOMPSAT-2B (Geostationary Korea Multi-Purpose Satellite 2B) satellite launched into a geostationary orbit in February 2020. To evaluate the GEMS NO2 total column data, a comparison was carried out using the NO2 vertical column density (VCD) that measured direct sunlight using the Pandora spectrometer system at four sites in Seosan, South Korea, from November 2020 to January 2021. Correlation coefficients between GEMS and Pandora NO2 data at four sites ranged from 0.35 to 0.48, with root mean square errors (RMSEs) from 4.7×1015 to 5.5×1015 molec. cm−2 for a cloud fraction (CF) <0.7. Higher correlation coefficients of 0.62–0.78 with lower RMSEs from 3.3×1015 to 5.0×1015 molec. cm−2 were found with CF <0.3, indicating the higher sensitivity of GEMS to atmospheric NO2 in less cloudy conditions. Overall, the GEMS NO2 total column data tended to be lower than the Pandora data, owing to differences in the representative spatial coverage, with a large negative bias under high CF conditions. With a correction for horizontal representativeness in the Pandora measurement coverage, correlation coefficients ranging from 0.69 to 0.81, with RMSEs from 3.2×1015 to 4.9×1015 molec. cm−2, were achieved for CF <0.3, showing a better correlation with the correction than without the correction.

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.

KOMPSAT-5 영상에 대한 재초점화(Refocusing) 성능 분석

In this study, we analyzed the performance of a refocusing algorithm that compensates for the blurring effect of a moving target in a synthetic aperture radar image. First, sub-images containing blurred target signals were extracted from the images obtained from the KOrea Multi-Purpose SATellite-5 (KOMPSAT-5). Subsequently, some refocusing algorithms were applied to the sub-images to compensate for the blurring effect of the moving target, and their performance was analyzed.

COSMIC-2 soundings impacts on a RO-based NOAA microwave satellite data quality monitoring system

The Constellation Observing System for Meteorology, Ionosphere and Climate-2/Formosa Satellite Mission 7 (COSMIC-2) Global Navigation Satellite System (GNSS) Radio Occultation (RO) constellation is the follow-on to the highly successful COSMIC-1 program. The GNSS RO atmospheric soundings have historically been used to generate Community Radiative Transfer Model simulated background (B) microwave (MW) brightness temperature data needed to monitor NOAA operational MW sounding instrument observed (O) antenna temperature (Ta) product quality. This study is motivated by the need to determine the impact of COSMIC-2 RO soundings on this critical long-term monitoring capability. This study is based on individual MW sensor O-B Ta bias (∆Ta) statistics and "double-difference" inter-sensor Ta bias (δTa) statistics for two time periods. Time Period 1 (TP1—May 1, 2017, to September 30, 2019) exclusively uses COSMIC-1 and Korea Multi-Purpose Satellite-5 soundings, while Time Period 2 (TP2—October 1, 2019, to December 31, 2020) expands the analysis with COSMIC-2 soundings. The TP1 and TP2 ∆Ta statistics comparisons indicate COSMIC-2 data population augmentation and latitudinal distribution impact the MW sounder performance monitoring tool transition from TP1 to TP2. COSMIC-2 competently supports long-term MW individual sensor and inter-sensor product monitoring for MW radiometer channels with weighting functions that peak between 8 and 30 km. Individual sensor ∆Ta monitoring for other microwave channels that depend on COSMIC-2 data below 8 km and from 30 km to 60 has limitations, but these limitations are shown not to inhibit critical δTa monitoring.

Hypothetical Visible Bands of Advanced Meteorological Imager Onboard the Geostationary Korea Multi-Purpose Satellite -2A Using Data-To-Data Translation

True-color imagery of satellites provides various atmospheric and surface information for intuitive understanding and visualization. This study presents a conditional generative adversarial network method for generating daytime and nighttime hypothetical visible (VIS) bands of the Advanced Meteorological Imager (AMI) sensor onboard the Geostationary Korea Multi-Purpose Satellite. The AMI datasets in the form of albedo and brightness temperature ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{\boldsymbol{T}}_{\boldsymbol{B}}}$</tex-math></inline-formula> ) were normalized and denormalized between 0 and 1 data-to-data (D2D) translation. The D2D model was trained and tested using data pair of the albedos at AMI VIS bands and AMI infrared (IR) bands or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{\boldsymbol{T}}_{\boldsymbol{B}}}$</tex-math></inline-formula> differences between two AMI IR bands. The constructed D2D model showed that the statistical results of bias, root-mean-square-error, and correlation coefficient between the observed and D2D-generated AMI VIS bands during daytime were -0.006 and 0.047 in albedo, and 0.941 for the blue band; -0.007 and 0.05 in albedo and 0.939 for the green band; -0.01 and 0.061 in albedo, and 0.917 for the red band, respectively. The proposed D2D method is being officially used by the Korea Meteorological Administration. Except for simulating desert areas as clouds at night, the D2D model demonstrated excellent performance, generating hypothetical AMI VIS bands at day and night. Consequently, this study could significantly contribute to the monitoring and understanding of meteorological phenomena over one-third of the Earth. Additionally, the method can be extended to other geostationary weather satellites, including Himawari-8, Fengyun-4A, Meteosat Third Generation, and Geostationary Operational Environmental Satellites.

An Implementation of Open Source-Based Software as a Service (SaaS) to Produce TOA and TOC Reflectance of High-Resolution KOMPSAT-3/3A Satellite Image

The majority of cloud applications are created or delivered to provide users with access to system resources or prebuilt processing algorithms for efficient data storage, management, and production. The number of cases linking cloud computing to the use of global observation satellite data continues to rise, owing to the benefits of cloud computing. This study aims to develop a cloud software as a service (SaaS) that yields reflectance products in high-resolution Korea Multi-Purpose Satellite (KOMPSAT)-3/3A satellite images. The SaaS model was designed as three subsystems: a Calibration Processing System (CPS), a Request System for CPS supporting RESTful application programming interface (API), and a Web Interface Application System. Open-source components, libraries, and frameworks were used in this study’s SaaS, including an OpenStack for infrastructure as a service. An absolute atmospheric correction scheme based on a Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer code with atmospheric variable inputs was used to generate the top-of-atmosphere (TOA) and top-of-canopy (TOC) reflectance products. The SaaS implemented in this study provides users with the absolute atmospheric calibration functionality to apply their KOMPSAT-3/3A satellite image set through a web browser and obtain output directly from this service. According to experiments to check the total performance time for images, bundled with four bands of red, green, blue, and near-infrared, it took approximately 4.88 min on average for the execution time to obtain all reflectance results since satellite images were registered into the SaaS. The SaaS model proposed and implemented in this study can be used as a reference model for the production system to generate reflectance products from other optical sensor images. In the future, SaaS, which offers professional analysis functions based on open source, is expected to grow and expand into new application fields for public users and communities.

Daytime Cloud Detection Algorithm Based on a Multitemporal Dataset for GK-2A Imagery

Cloud detection is an essential and important process in remote sensing when surface information is required for various fields. For this reason, we developed a daytime cloud detection algorithm for GEOstationary KOrea Multi-Purpose SATellite 2A (GEO-KOMPSAT-2A, GK-2A) imagery. For each pixel, the filtering technique using angular variance, which denotes the change in top of atmosphere (TOA) reflectance over time, was applied, and filtering technique by using the minimum TOA reflectance was used to remove remaining cloud pixels. Furthermore, near-infrared (NIR) and normalized difference vegetation index (NDVI) images were applied with dynamic thresholds to improve the accuracy of the cloud detection results. The quantitative results showed that the overall accuracy of proposed cloud detection was 0.88 and 0.92 with Visible Infrared Imaging Radiometer Suite (VIIRS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), respectively, and indicated that the proposed algorithm has good performance in detecting clouds.

Combined Dust Detection Algorithm for Asian Dust Events Over East Asia Using GK2A/AMI: a Case Study in October 2019

A combined algorithm comprising multiple dust detection methods was developed using infrared (IR) channels onboard the GEOstationary Korea Multi-Purpose SATellite 2A equipped with the Advanced Meteorological Imager (GK2A/AMI). Six cloud tests using brightness temperature difference (BTD) were utilized to reduce errors caused by clouds. For detecting dust storms, three standard BTD tests (i.e., {BT}_{12.3}-{BT}_{10.5}, {BT}_{8.7}-{BT}_{10.5}, and {BT}_{11.2}-{BT}_{10.5}) were combined with the polarized optical depth index (PODI). The combined algorithm normalizes the indices for cloud and dust detection, and adopts weighted combinations of dust tests depending on the observation time (day/night) and surface type (land/sea). The dust detection results were produced as quantitative confidence factors and displayed as false color imagery, applying a dynamic enhancement background reduction algorithm (DEBRA). The combined dust detection algorithm was qualitatively assessed by comparing it with dust RGB imageries and ground-based lidar data. The combined algorithm especially improved the discontinuity in weak dust advection to the sea and considerably reduced false alarms as compared to previous dust monitoring methods. For quantitative validation, we used aerosol optical thickness (AOT) and fine mode fraction (FMF) derived from low Earth orbit (LEO) satellites in daytime. For both severe and weakened dust cases, the probability of detection (POD) ranged from 0.667 to 0.850 and it indicated that the combined algorithm detects more potential dust pixels than other satellites. In particular, the combined algorithm was advantageous in detecting weak dust storms passing over the warm and humid Yellow Sea with low dust height and small AOT.

AI Training Dataset for Cloud Detection of KOMPSAT Images

Clouds that appear inevitably when acquiring optical satellite images hinder the interpretation of surface information, so removing them is a crucial procedure to increase the utilization of satellite images. Currently, for KOMPSAT (Korea Multi-purpose Satellite) images, only the cloud amount by visual measurement is proved for the entire scene and detailed cloud masks are not provided. Since cloud detection is a time-consuming task, we built a cloud dataset for KOMPSAT images so as to develop an algorithm that expedites the task with state-of-the-art artificial intelligent techniques. In the dataset, satellite images were selected from various regions considering that clouds have different characteristics depending on the region, and masks were classified into thin clouds, thick clouds, cloud shadows, and clear sky. The size of dataset is over 4,000 image/mask pairs by an image size of 1000x1000 and one of the largest among publicly available cloud datasets, as of this writing. The dataset is built by a government AI (artificial intelligent) training dataset building program and will be available through the website, aihub.or.kr.

A Validation Experiment of the Reflectance Products of KOMPSAT-3A Based on RadCalNet Data and Its Applicability to Vegetation Indexing

Surface reflectance products obtained through the absolute atmospheric correction of multispectral satellite images are useful for precise scientific applications. For broader applications, the reflectance products computed using high-resolution images need to be validated with field measurement data. This study dealt with 2.2-m resolution Korea Multi-Purpose Satellite (KOMPSAT)-3A images with four multispectral bands, which were used to obtain top-of-atmosphere (TOA) and top-of-canopy (TOC) reflectance products. The open-source Orfeo Toolbox (OTB) extension was used to generate these products. Next, these were subsequently validated by considering three sites (i.e., Railroad Valley Playa, NV, USA (RVUS), Baotou, China (BTCN), and La Crau, France (LCFR)) in RadCalNet, as well as a calibration and validation portal for remote sensing. We conducted the validations comparing satellite image-based reflectance products and field measurement reflectance based on data sets acquired at different times. The experimental results showed that the overall trend of validation accuracy of KOPSAT-3A was well fitted in all the RadCalNet sites and that the accuracy remained quite constant. Reflectance bands showing the minimum and maximum differences between the sets of experimental data are presented in this paper. The vegetation indices (i.e., the atmospherically resistant vegetation index (ARVI) and the structure insensitive pigment index (SIPI)) and three TOC reflectance bands obtained from KOMPSAT-3A were computed as a case study and used to achieve a detailed vegetation interpretation; finally, the correspondent results were compared with those obtained from Landsat-8 images (downloaded from the Google Earth Engine (GEE)). The validation and the application scheme presented in this study can be potentially applied to the generation of analysis ready data from high-resolution satellite sensor images.

Spatial Sharpening of KOMPSAT-3A MIR Images Using Optimal Scaling Factor

This paper present efficient methods for merging KOMPSAT-3A (Korea Multi-Purpose Satellite) medium wave Infrared (MIR) and panchromatic (PAN) images. Spatial sharpening techniques have been developed to create an image with both high spatial and high spectral resolution by combining the desired qualities of a PAN image with high spatial and low spectral resolution and an MS/MIR image with low spatial and high spectral resolution. The proposed methods can extract an optimal scaling factor, and uses the tactics of appropriately controlling the balance between the spatial and spectral resolutions. KOMPSAT-3A PAN and MIR images were used to test and evaluate the performance of the proposed methods. A qualitative assessment were performed using the image quality index (Q4), the cross correlation index (CC) and the relative global dimensional synthesis error (Spectral/Spatial ERGAS). These tests indicate that the proposed methods preserve the spectral and spatial characteristics of the original MIR and PAN images. Visual analysis reveals that the spectral and spatial information derived from the proposed methods were well retained in the test images. A comparison of the results of the proposed methods with those obtained from applying existing ones such as the Multi Sensor Fusion (MSF) technique or the Guide Filter Based Fusion (GF) show the efficiency of the new fusion process to be superior to the one of the others. The results showed a significant improvement in fusion capability for KOMPSAT-3A MIR imagery.

Simultaneous Radio Occultation for Intersatellite Comparison of Bending Angles toward More Accurate Atmospheric Sounding

AbstractGlobal Navigation Satellite System (GNSS) radio occultation (RO) is a remote sensing technique that uses International System of Units (SI) traceable GNSS signals for atmospheric limb soundings. The retrieved atmospheric temperature profile is believed to be more accurate and stable than those from other remote sensing techniques, although rigorous comparison between independent measurements is difficult because of time and space differences between individual RO events. Typical RO comparisons are based on global statistics with relaxed matchup criteria (within 3 h and 250 km) that are less than optimal given the dynamic nature and spatial nonuniformity of the atmosphere. This study presents a novel method that allows for direct comparison of bending angles when simultaneous RO measurements occur near the simultaneous nadir overpasses (SNO) of two low-Earth-orbit satellites receiving the same GNSS signal passing through approximately the same atmosphere, within minutes in time and less than 125 km in distance. Using this method, we found very good agreement between Formosa Satellite 7 (FORMOSAT-7)/second Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC-2) satellite measurements and those from MetOp-A/B/C, COSMIC-1, Korea Multi-Purpose Satellite 5 (KOMPSAT-5), and Paz, although systematic biases are also found in some of the intercomparisons. Instrument and processing algorithm performances at different altitudes are also characterized. It is expected that this method can be used for the validation of GNSS RO measurements for most missions and would be a new addition to the tools for intersatellite calibration. This is especially important given the large number of RO measurements made available both publicly and commercially, and the expansion of receiver capabilities to all GNSS systems.

Cross-evaluation of GEMS tropospheric ozone retrieval performance using OMI data and the use of an ozonesonde dataset over East Asia for validation

Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled to be launched in 2019–2020 on board the GEO-KOMPSAT (GEOstationary KOrea Multi-Purpose SATellite)-2B, contributing as the Asian partner of the global geostationary constellation of air quality monitoring. To support this air quality satellite mission, we perform a cross-evaluation of simulated GEMS ozone profile retrievals from OMI (Ozone Monitoring Instrument) data based on the optimal estimation and ozonesonde measurements within the GEMS domain, covering from 5∘ S (Indonesia) to 45∘ N (south of the Russian border) and from 75 to 145∘ E. The comparison between ozonesonde and GEMS shows a significant dependence on ozonesonde types. Ozonesonde data measured by modified Brewer–Mast (MBM) at Trivandrum and New Delhi show inconsistent seasonal variabilities in tropospheric ozone compared to carbon–iodine (CI) and electrochemical condensation cell (ECC) ozonesondes at other stations in a similar latitude regime. CI ozonesonde measurements are negatively biased relative to ECC measurements by 2–4 DU; better agreement is achieved when simulated GEMS ozone retrievals are compared to ECC measurements. ECC ozone data at Hanoi, Kuala Lumpur, and Singapore show abnormally worse agreements with simulated GEMS retrievals than other ECC measurements. Therefore, ECC ozonesonde measurements at Hong Kong, Pohang, Naha, Sapporo, and Tsukuba are finally identified as an optimal reference dataset. The accuracy of simulated GEMS retrievals is estimated to be ∼5.0 % for both tropospheric and stratospheric column ozone with the precision of 15 % and 5 %, which meets the GEMS ozone requirements.

Development and Intercomparison Study of an Atmospheric Motion Vector Retrieval Algorithm for GEO-KOMPSAT-2A

We derived an atmospheric motion vector (AMV) algorithm for the Geostationary Korea Multipurpose Satellite (GEO-KOMPSAT-2A; GK-2A) launched on 4 December 2018, using the Advanced Himawari Imager (AHI) onboard Himawari-8, which is very similar to the Advanced Meteorological Imager onboard GK-2A. This study clearly describes the main steps in our algorithm and optimizes it for the target box size and height assignment methods by comparing AMVs with numerical weather prediction (NWP) and rawinsonde profiles for July 2016 and January 2017. Target box size sensitivity tests were performed from 8 × 8 to 48 × 48 pixels for three infrared channels and from 16 × 16 to 96 × 96 pixels for one visible channel. The results show that the smaller box increases the speed, whereas the larger one slows the speed without quality control. The best target box sizes were found to be 16 × 16 for CH07, 08, and 13, and 48 × 48 pixels for CH03. Height assignment sensitivity tests were performed for several methods, such as the cross-correlation coefficient (CCC), equivalent blackbody temperature (EBBT), infrared/water vapor (IR/WV) intercept, and CO2 slicing methods for a cloudy target as well as normalized total contribution (NTC) and normalized total cumulative contribution (NTCC) for a clear-air target. For a cloudy target, the CCC method is influenced by the quality of the cloud’s top pressure. Better results were found when using EBBT and IR/WV intercept methods together rather than individually. Furthermore, CO2 slicing had the best statistics. For a clear-air target, the combined use of NTC and NTCC had the best statistics. Additionally, the mean vector difference, root-mean-square (RMS) vector difference, bias, and RMS error (RMSE) between GK-2A AMVs and NWP or rawinsonde were smaller by approximately 18.2% on average than in the case of the Communication, Ocean and Meteorology Satellite (COMS) AMVs. In addition, we verified the similarity between GK-2A and Meteosat Third Generation (MTG) AMVs using the AHI of Himawari-8 from 21 July 2016. This similarity can provide evidence that the GK-2A algorithm works properly because the GK-2A AMV algorithm borrows many methods of the MTG AMV algorithm for geostationary data and inversion layer corrections. The Pearson correlation coefficients in the speed, direction, and height of the prescribed GK-2A and MTG AMVs were larger than 0.97, and the corresponding bias/RMSE were0.07/2.19 m/s, 0.21/14.8°, and 2.61/62.9 hPa, respectively, considering common quality indicator with forecast (CQIF) &gt; 80.