New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS)

GEMS will monitor air quality over Asia at unprecedented spatial and temporal resolution from GEO for the first time, providing column measurements of aerosol, ozone and their precursors (nitrogen dioxide, sulfur dioxide and formaldehyde). Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in late 2019 - early 2020 to monitor Air Quality (AQ) at an unprecedented spatial and temporal resolution from a Geostationary Earth Orbit (GEO) for the first time. With the development of UV-visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO and aerosols) can be obtained. To date, all the UV-visible satellite missions monitoring air quality have been in Low Earth orbit (LEO), allowing one to two observations per day. With UV-visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be onboard the GEO-KOMPSAT-2 satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager (GOCI)-2. These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA's TEMPO and ESA's Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS)

Validation of Deep Learning-Based Artifact Correction on Synthetic FLAIR Images in a Different Scanning Environment

We investigated the capability of a trained deep learning (DL) model with a convolutional neural network (CNN) in a different scanning environment in terms of ameliorating the quality of synthetic fluid-attenuated inversion recovery (FLAIR) images. The acquired data of 319 patients obtained from the retrospective review were used as test sets for the already trained DL model to correct the synthetic FLAIR images. Quantitative analyses were performed for native synthetic FLAIR and DL-FLAIR images against conventional FLAIR images. Two neuroradiologists assessed the quality and artifact degree of the native synthetic FLAIR and DL-FLAIR images. The quantitative parameters showed significant improvement on DL-FLAIR in all individual tissue segments and total intracranial tissues than on the native synthetic FLAIR (p < 0.0001). DL-FLAIR images showed improved image quality with fewer artifacts than the native synthetic FLAIR images (p < 0.0001). There was no significant difference in the preservation of the periventricular white matter hyperintensities and lesion conspicuity between the two FLAIR image sets (p = 0.217). The quality of synthetic FLAIR images was improved through artifact correction using the trained DL model on a different scan environment. DL-based correction can be a promising solution for ameliorating the quality of synthetic FLAIR images to broaden the clinical use of synthetic magnetic resonance imaging (MRI)

NO2 vertical column density retrieval from Pandora and first comparison with GEMS data during the GMAP & SIJAQ campaign 2020

We, for the first time, compared NO2 VCDs obtained from Ground-based Pandora spectrometer which uses direct sun measurements and those obtained from GEMS which is the first UV-VIS hyperspectral sensor onboard the geostationary earth orbit satellite during the GMAP (GEMS Map of the Air pollution) & SIJAQ (Satellite Integrated Joint monitoring of Air Quality) campaign 2020 held in winter. NO2 VCDs were retrieved using DOAS technic from four Pandora spectrometers which were located over four Seosan sites. During the inter-comparison period four Pandora spectrometers, we found 0.99 of correlation coefficient (R) between the four Pandora spectrometers. While slope and intercepts range from 0.90 to 0.99 and from ??? 5.15 1014 to 5.58 1014, respectively. The diurnal patterns show good agreement between NO2 VCDs measured by the Pandora and those measured by GEMS, TROPOMI (TROPOspheric Monitoring Instrument), and OMPS (Ozone Mapping and Profiler Suite). The R between NO2 VCDs obtained from the Pandora spectrometer and NO2 VCDs obtained from the GEMS range from 0.60 to 0.75. The comparison was also carried out accounting for horizontal representativeness of Pandora. This present study discuses a difference of comparison results obtained between Pandora and GEMS in cases with and without application of horizontal representativeness