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In this study, we develop an advanced retrieval algorithm for tropospheric nitrogen dioxide (NO2) from the geostationary satellite instruments and apply it to Geostationary Environment Monitoring Spectrometer (GEMS) observations. Overall, the algorithm follows previous heritage for the polar-orbiting satellites Global Ozone Monitoring Experiment-2 (GOME-2) and Tropospheric Monitoring Instrument (TROPOMI), but several improvements are implemented to account for specific features of geostationary satellites. The DLR GEMS NO2 retrieval employs an extended fitting window compared to the current fitting window used in GEMS operational v2.0 NO2 retrieval, which results in improved spectral fit quality and lower uncertainties. For the stratosphere–troposphere separation in GEMS measurements, two methods are developed and evaluated: (1) STRatospheric Estimation Algorithm from Mainz (STREAM) as used in the DLR TROPOMI NO2 retrieval and adapted to GEMS and (2) estimation of stratospheric NO2 columns from the Copernicus Atmosphere Monitoring Service (CAMS) Integrated Forecast System (IFS) cycle 48R1 model data, which introduce full stratospheric chemistry as it will be used in the operational Sentinel-4 NO2 retrieval. While STREAM provides hourly estimates of stratospheric NO2, it has limitations in describing small-scale variations and exhibits systematic biases near the boundary of the field of view. In this respect, the use of estimated stratospheric NO2 columns from the CAMS forecast model profile demonstrates better applicability by describing not only diurnal variation but also small-scale variations. For the improved air mass factor (AMF) calculation, sensitivity tests are performed using different input data. In our algorithm, cloud fractions retrieved from the Optical Cloud Recognition Algorithm (OCRA) adapted to GEMS level 1 data are applied instead of the GEMS v2.0 cloud fraction. OCRA is used operationally in TROPOMI and Sentinel-4. Compared to the GEMS level 2 cloud fraction which is typically set to around 0.1 for clear-sky scenes, OCRA sets cloud fractions close to or at 0. The OCRA-based cloud corrections result in increased tropospheric AMFs and decreased tropospheric NO2 vertical columns, leading to better agreement with results from existing TROPOMI observations. The effects of surface albedo on GEMS tropospheric NO2 retrievals are assessed by comparing the GEMS v2.0 background surface reflectance (BSR) and TROPOMI Lambertian-equivalent reflectivity (LER) climatology v2.0 product. The differences between the two surface albedo products and their impact on tropospheric AMF are particularly pronounced over snow/ice scenes during winter. A priori NO2 profiles from the CAMS forecast model, applied in the DLR GEMS algorithm, effectively capture variations in NO2 concentrations throughout the day with high spatial resolution and the advanced chemical mechanism, which demonstrates its suitability for geostationary satellite measurements. The retrieved DLR GEMS tropospheric NO2 columns show good capability for capturing hotspot signals at the scale of city clusters and describe spatial gradients from city centres to surrounding areas. Diurnal variations of tropospheric NO2 columns over Asia are well described through hourly sampling of GEMS. Evaluation of DLR GEMS tropospheric NO2 columns against TROPOMI v2.4 and GEMS v2.0 operational products shows overall good agreement. The uncertainty of DLR GEMS tropospheric NO2 vertical columns varies based on observation scenarios. In regions with low pollution levels such as open-ocean and remote rural areas, retrieval uncertainties typically range from 10 % to 50 %, primarily due to uncertainties in slant columns. For heavily polluted regions, uncertainties in tropospheric NO2 columns are mainly driven by errors in tropospheric AMF calculations. Notably, the total uncertainty in GEMS tropospheric NO2 columns is most significant in winter, particularly over heavily polluted regions with low-level clouds below or near the NO2 peak. [ABSTRACT FROM AUTHOR]

The Geostationary Environment Monitoring Spectrometer (GEMS) launched in February 2020 is now providing continuous daytime hourly observations of nitrogen dioxide (NO 2) columns over eastern Asia (5° S–45° N, 75–145° E) with 3.5 × 7.7 km 2 pixel resolution. These data provide unique information to improve understanding of the sources, chemistry, and transport of nitrogen oxides (NO x) with implications for atmospheric chemistry and air quality, but opportunities for direct validation are very limited. Here we correct the operational level-2 (L2) NO 2 vertical column densities (VCDs) from GEMS with a machine learning (ML) model to match the much sparser but more mature observations from the low Earth orbit TROPOspheric Monitoring Instrument (TROPOMI), preserving the data density of GEMS but making them consistent with TROPOMI. We first reprocess the GEMS and TROPOMI operational L2 products to use common prior vertical NO 2 profiles (shape factors) from the GEOS-Chem chemical transport model. This removes a major inconsistency between the two satellite products and greatly improves their agreement with ground-based Pandora NO 2 VCD data in source regions. We then apply the ML model to correct the remaining differences, Δ (GEMS–TROPOMI), using the GEMS NO 2 VCDs and retrieval parameters as predictor variables. We train the ML model with colocated GEMS and TROPOMI NO 2 VCDs, taking advantage of TROPOMI off-track viewing to cover the wide range of effective zenith angles (EZAs) observed by GEMS. The two most important predictor variables for Δ (GEMS–TROPOMI) are GEMS NO 2 VCD and EZA. The corrected GEMS product is unbiased relative to TROPOMI and shows a diurnal variation over source regions more consistent with Pandora than the operational product. [ABSTRACT FROM AUTHOR]

The Geostationary Environment Monitoring Spectrometer (GEMS) over Asia is the first geostationary Earth orbit instrument in the virtual constellation of sensors for atmospheric chemistry and composition air quality research and applications. For the first time, the hourly observations enable studies of diurnal variation in several important trace gas and aerosol pollutants including nitrogen dioxide (NO2), which is the focus of this work. NO2 is a regulated pollutant and an indicator of anthropogenic emissions in addition to being involved in tropospheric ozone chemistry and particulate matter formation. We present new quantitative measures of NO2 tropospheric column diurnal variation which can be greater than 50 % of the column amount, especially in polluted environments. The NO2 distribution is seen to change hourly and can be quite different from what would be seen by a once-a-day low-Earth-orbit satellite observation. We use GEMS data in combination with TROPOspheric Monitoring Instrument (TROPOMI) satellite and Pandora ground-based remote sensing measurements and Multi-Scale Infrastructure for Chemistry and Aerosols (Version 0, MUSICAv0) 3D chemical transport model analysis to examine the NO2 diurnal variation in January and June 2023 over Northeast Asia and Seoul, South Korea, study regions to distinguish the different emissions, chemistry, and meteorological processes that drive the variation. Understanding the relative importance of these processes will be key to including pollutant diurnal variation in models aimed at determining true pollutant exposure levels for air quality studies. The work presented here also provides a path for investigating similar NO2 diurnal cycles in the new Earth Venture Instrument-1 Tropospheric Emissions: Monitoring Pollution (TEMPO) data over North America, and later over Europe with Sentinel-4. [ABSTRACT FROM AUTHOR]

Nitrogen oxide radicals (NOx≡NO+NO2) emitted by fuel combustion are important precursors of ozone and particulate matter pollution, and NO 2 itself is harmful to public health. The Geostationary Environment Monitoring Spectrometer (GEMS), launched in space in 2020, now provides hourly daytime observations of NO 2 columns over East Asia. This diurnal variation offers unique information on the emission and chemistry of NOx , but it needs to be carefully interpreted. Here we investigate the drivers of the diurnal variation in NO 2 observed by GEMS during winter and summer over Beijing and Seoul. We place the GEMS observations in the context of ground-based column observations (Pandora instruments) and GEOS-Chem chemical transport model simulations. We find good agreement between the diurnal variations in NO 2 columns in GEMS, Pandora, and GEOS-Chem, and we use GEOS-Chem to interpret these variations. NOx emissions are 4 times higher in the daytime than at night, driving an accumulation of NO 2 over the course of the day, offset by losses from chemistry and transport (horizontal flux divergence). For the urban core, where the Pandora instruments are located, we find that NO 2 in winter increases throughout the day due to high daytime emissions and increasing NO2/NOx ratio from entrainment of ozone, partly balanced by loss from transport and with a negligible role of chemistry. In summer, by contrast, chemical loss combined with transport drives a minimum in the NO 2 column at 13:00–14:00 local time (LT). Segregation of the GEMS data by wind speed further demonstrates the effect of transport, with NO 2 in winter accumulating throughout the day at low winds but flat at high winds. The effect of transport can be minimized in summer by spatially averaging observations over the broader metropolitan scale, under which conditions the diurnal variation in NO 2 reflects a dynamic balance between emission and chemical loss. [ABSTRACT FROM AUTHOR]

The Geostationary Environment Monitoring Spectrometer (GEMS) over Asia is the first geostationary Earth orbit instrument in the virtual constellation of sensors for atmospheric chemistry and composition air quality research and applications. For the first time, the hourly observations enable studies of diurnal variation of several important trace gas and aerosol pollutants including nitrogen dioxide (NO2) which is the focus of this work. NO2 is a regulated pollutant and an indicator of anthropogenic emissions in addition to being involved in tropospheric ozone chemistry and particulate matter formation. We present new quantitative measures of NO2 tropospheric column diurnal variation which can be greater than 50 % of the column amount especially in polluted environments. The NO2 distribution is seen to change hourly and can be quite different from what would be seen by a once-a-day low Earth orbit satellite observation. We use GEMS data in combination with TROPOMI satellite and Pandora ground-based remote sensing measurements and MUSICAv0 3D chemical transport model analysis to examine the NO2 diurnal variation in January and June 2023 over Northeast Asia and Seoul, South Korea, study regions to distinguish the different emissions, chemistry, and meteorological processes that drive the variation. Understanding the relative importance of these processes will be important for including pollutant diurnal variation in models aimed at determining true pollutant exposure levels for air quality studies. The work presented here also provides a path for investigating similar NO2 diurnal cycles in the new TEMPO data over North America, and later over Europe with S-4. [ABSTRACT FROM AUTHOR]

Nitrogen oxide radicals (NOx ≡ NO + NO2) emitted by fuel combustion are important precursors of ozone and particulate matter pollution, and NO2 itself is harmful to public health. The Geostationary Environment Monitoring Spectrometer (GEMS), launched in space in 2020, now provides hourly daytime observations of NO2 columns over East Asia. This diurnal variation offers unique information on the emission and chemistry of NOx,but it needs to be carefully interpreted. Here we investigate the drivers of the diurnal variation of NO2 observed by GEMS during winter and summer over Beijing and Seoul. We place the GEMS observations in the context of ground-based column observations (Pandora instruments) and GEOS-Chem chemical transport model simulations. We find good agreement between the diurnal variations of NO2 columns in GEMS, Pandora, and GEOS-Chem, and we use GEOS-Chem to interpret these variations. NOx emissions are four times higher in the daytime than at night, driving an accumulation of NO2 over the course of the day, offset by losses from chemistry and transport (horizontal flux divergence). For the urban core, where the Pandora instruments are located, we find that NO2 in winter increases throughout the day due to high daytime emissions and increasing NO2/NOx ratio from entrainment of ozone, partly balanced by loss from transport and with negligible role of chemistry. In summer, by contrast, chemical loss combined with transport drives a minimum in the NO2 column at 13–14 local time. Segregation of the GEMS data by wind speed further demonstrates the effect of transport, with NO2 in winter accumulating throughout the day at low winds but flat at high winds. The effect of transport can be minimized in summer by spatially averaging observations over the broader metropolitan scale, under which conditions the diurnal variation of NO2 reflects a dynamic balance between emission and chemical loss. [ABSTRACT FROM AUTHOR]

Tropospheric vertical column densities (VCDs) of nitrogen dioxide (NO2) retrieved from sun-synchronous satellite instruments have provided abundant NO2 data for environmental studies, but such data are limited by retrieval uncertainties and insufficient temporal sampling (e.g., once a day). The Geostationary Environment Monitoring Spectrometer (GEMS) launched in February 2020 monitors NO2 at an unprecedented hourly resolution during the daytime. Here we present a research product for tropospheric NO2 VCDs, referred to as POMINO–GEMS (where POMINO is the Peking University OMI NO2 algorithm). We develop a hybrid retrieval method combining GEMS, TROPOMI (TROPOspheric Monitoring Instrument) and GEOS-CF (Global Earth Observing System Composition Forecast) data to generate hourly tropospheric NO2 slant column densities (SCDs). We then derive tropospheric NO2 air mass factors (AMFs) with explicit corrections for surface reflectance anisotropy and aerosol optical effects through parallelized pixel-by-pixel radiative transfer calculations. Prerequisite cloud parameters are retrieved with the O2 – O2 algorithm by using ancillary parameters consistent with those used in NO2 AMF calculations. The initial retrieval of POMINO–GEMS tropospheric NO2 VCDs for June–August 2021 exhibits strong hotspot signals over megacities and distinctive diurnal variations over polluted and clean areas. POMINO–GEMS NO2 VCDs agree with the POMINO–TROPOMI v1.2.2 product (R=0.98 ; NMB = 4.9 %) over East Asia, with slight differences associated with satellite viewing geometries and cloud and aerosol properties affecting the NO2 retrieval. POMINO–GEMS also shows good agreement with the following: OMNO2 (Ozone Monitoring Instrument (OMI) NO2 Standard Product) v4 (R=0.87 ; NMB = - 16.8 %); and GOME-2 (Global Ozone Monitoring Experiment-2) GDP (GOME Data Processor) 4.8 (R=0.83 ; NMB = - 1.5 %) NO2 products. POMINO–GEMS shows small biases against ground-based MAX-DOAS (multi-axis differential optical absorption spectroscopy) NO2 VCD data at nine sites (NMB = - 11.1 %), with modest or high correlation in diurnal variation at six urban and suburban sites (R from 0.60 to 0.96). The spatiotemporal variation in POMINO–GEMS correlates well with mobile car MAX-DOAS measurements in the Three Rivers source region on the Tibetan Plateau (R=0.81). Surface NO2 concentrations estimated from POMINO–GEMS VCDs are consistent with measurements from the Ministry of Ecology and Environment of China for spatiotemporal variation (R=0.78 ; NMB = - 26.3 %) and diurnal variation at all, urban, suburban and rural sites (R≥0.96). POMINO–GEMS data will be made freely available for users to study the spatiotemporal variations, sources and impacts of NO2. [ABSTRACT FROM AUTHOR]

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 NO 2 total column data, a comparison was carried out using the NO 2 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 NO 2 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 NO 2 in less cloudy conditions. Overall, the GEMS NO 2 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. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jenvman.2008.11.025 Byline: Hanlim Lee (a), Young J. Kim (a), Jinsang Jung (a), Chulkyu Lee (b), Klaus-Peter Heue (c), Ulrich Platt (c), Min Hu (d), Tong Zhu (d) Abstract: During the CAREBEIJING campaign in 2006, imaging differential optical absorption spectroscopy (I-DOAS) measurements were made from 08:00 to 16:00 on September 9 and 10 over Beijing, China. Detailed images of the near-surface NO.sub.2 differential slant column density (DSCD) distribution over Beijing were obtained. Images with less than a 30-min temporal resolution showed both horizontal and vertical variations in NO.sub.2 distributions. For DSCD to mixing ratio conversion, path length along the lines of I-DOAS lines of sight was estimated using the light-extinction coefficient and a'ngstrom exponent data obtained by a transmissometer and a sunphotometer, respectively. Mixing ratios measured by an in-situ NO.sub.2 analyzer were compared with those estimated by the I-DOAS instrument. The obtained temporal and spatial variations in NO.sub.2 distributions measured by I-DOAS for the two days are interpreted with consideration of the locations of the major NO.sub.x sources and local wind conditions. I-DOAS measurements have been applied in this study for estimating NO.sub.2 distribution over an urban area with multiple and distributed emission sources. Results are obtained for estimated temporal and spatial NO.sub.2 distributions over the urban atmosphere; demonstrating the capability of the I-DOAS technique. We discuss in this paper the use of I-DOAS measurements to estimate the NO.sub.2 distribution over an urban area with multiple distributed emission sources. Author Affiliation: (a) Advanced Environmental Monitoring Research Center (ADEMRC), Department of Environmental Science and Engineering, Gwangju Institute of Science and Technology (GIST), 1 Oryong-dong, Bukgu, Gwangju 500-712, Republic of Korea (b) Institute of Environmental Physics and Remote Sensing, University of Bremen, P.O. Box 33 04 40, D-28334 Bremen, Germany (c) Institute of Environmental Physics, University of Heidelberg, INF 229, D-69120 Heidelberg, Germany (d) State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences, Peking University, Beijing 100871, China Article History: Received 5 April 2008; Revised 9 November 2008; Accepted 23 November 2008

To validate the Geostationary Environment Monitoring Spectrometer (GEMS), the GEMS Map of Air Pollution (GMAP) campaign was conducted during 2020–2021 by integrating Pandora Asia Network, aircraft, and in situ measurements. In the present study, GMAP-2020 measurements were applied to evaluate urban air quality and explore the synergy of Pandora column (PC) NO2 measurements and surface in situ (SI) NO2 measurements for Seosan, South Korea, where large point source (LPS) emissions are densely clustered. Due to the difficulty of interpreting the effects of LPS emissions on air quality downwind of Seosan using SI monitoring networks alone, we explored the combined analysis of both PC- NO2 and SI- NO2 measurements. Agglomerative hierarchical clustering using vertical meteorological variables combined with PC- NO2 and SI- NO2 yielded three distinct conditions: synoptic wind-dominant (SD), mixed (MD), and local wind-dominant (LD). These results suggest meteorology-dependent correlations between PC- NO2 and SI- NO2. Overall, yearly daytime mean (11:00–17:00 KST) PC- NO2 and SI- NO2 statistical data showed good linear correlations (R=∼0.73); however, the differences in correlations were largely attributed to meteorological conditions. SD conditions characterized by higher wind speeds and advected marine boundary layer heights suppressed fluctuations in both PC- NO2 and SI- NO2 , driving a uniform vertical NO2 structure with higher correlations, whereas under LD conditions, LPS plumes were decoupled from the surface or were transported from nearby cities, weakening correlations through anomalous vertical NO2 gradients. The discrepancies suggest that using either PC- NO2 or SI- NO2 observations alone involves a higher possibility of uncertainty under LD conditions or prevailing transport processes. However, under MD conditions, both pollution ventilation due to high surface wind speeds and daytime photochemical NO2 loss contributed to stronger correlations through a decline in both PC- NO2 and SI- NO2 towards noon. Thus, Pandora Asia Network observations collected over 13 Asian countries since 2021 can be utilized for detailed investigation of the vertical complexity of air quality, and the conclusions can be also applied when performing GEMS observation interpretation in combination with SI measurements. [ABSTRACT FROM AUTHOR]

A two-dimensional visibility estimation model was developed, based on random forest (RF), a machine learning-based technique. A geostatistical method was introduced into the visibility estimation model for the first time to interpolate point measurement data to gridded data spatially with a pixel size of 10 km. The RF-based model was trained using gridded visibility data, as well as meteorological and air pollution input variable data, for each location in South Korea, which were characterized by complex geographical features and high air pollution levels. Generally, relative humidity was the most important input variable for the visibility estimation (average mean decrease accuracy: 35%). However, PM2.5 tended to be the most crucial variable in polluted regions. The spatial interpolation was found to result in an additional visibility estimation error of 500 m in locations where no adjacent visibility observations within 0.2° were available. The performance of the proposed model was preliminarily assessed. Generally, the best detection performance was achieved in good visibility conditions (visibility range: 10 to 20 km). This study is the first to demonstrate a visibility estimation model based on a geostatistical method and machine learning, which can provide visibility information in locations for which no observations exist. [ABSTRACT FROM AUTHOR]

A large scale dynamic model of electroabsorption modulator integrated distributed feedback lasers is presented.

A study has been conducted to determine frequency chirping by considering facet reflectivity, the extinction ratio, the alpha parameter along the longitudinal direction, and amplified spontaneous emission noise. Facet reflection and the variation of the alpha parameter along the cavity was used to accurately calculate chirp. Findings indicated that a large injection current, a large confinement factor, a long cavity and high input power are necessary to improve modulation bandwidth obtained from eye opening ratio.

We retrieved aerosol effective height (AEH) values from TROPOspheric monitoring instrument (TROPOMI) measurements based on the O4 absorption at 477 nm. As the first step in the AEH retrieval algorithm, TROPOMI carbon monoxide and aerosol index products were used for the first time to determine aerosol type. An online radiative transfer calculation method was introduced to the AEH retrieval process. The AEHs were retrieved over northeast Asia (various aerosol types), South Africa (biomass-burning smoke), and the Sahara Desert (dust). The retrieved AEHs were compared with AEHs derived using aerosol extinction profiles from ground and satellite-based lidar measurements. A preliminary evaluation based on comparison with lidar suggests reasonable performance, but shows that cloud masking is the main source of uncertainty. The errors associated with the online-based AEH retrieval algorithm were calculated using synthetic radiance. The total AEH retrieval errors were 0.91 km (0.4 ≤ AOD < 1) and 0.30 km (2 ≤ AOD < 3), highlighting the increased sensitivity of the AEH retrieval algorithm under high AOD conditions. This study demonstrates that the O4 absorption band at 477 nm is applicable to aerosol height retrieval from TROPOMI with a high spatial resolution. [ABSTRACT FROM AUTHOR]

In the present study, we investigate the effects of signal to noise (SNR), slit function (FWHM), and aerosol optical depth (AOD) on the accuracy of formaldehyde (HCHO) vertical column density (HCHOVCD) using the ground-based direct-sun synthetic radiance based on differential optical absorption spectroscopy (DOAS). We found that the effect of SNR on HCHO retrieval accuracy is larger than those of FWHM and AOD. When SNR = 650 (1300), FWHM = 0.6, and AOD = 0.2, the absolute percentage difference (APD) between the true HCHOVCD values and those retrieved ranges from 54 (30%) to 5% (1%) for the HCHOVCD of 5.0 x 1015 and 1.1 x 1017 molecules cm-2, respectively. Interestingly, the maximum AOD effect on the HCHO accuracy was found for the HCHOVCD of 3.0 x 1016 molecules cm-2. In addition, we carried out the first ground-based direct-sun measurements in the ultraviolet (UV) wavelength range to retrieve the HCHOVCD using Pandora in Seoul. The HCHOVCD was low at 12:00 p.m. local time (LT) in all seasons, whereas it was high in the morning (10:00 a.m. LT) and late afternoon (4:00 p.m. LT), except in winter. The maximum HCHOVCD values were 2.68 x 1016, 3.19 x 1016, 2.00 x 1016, and 1.63 x 1016 molecules cm-2 at 10:00 a.m. LT in spring, 10:00 a.m. LT in summer, 1:00 p.m. LT in autumn, and 9:00 a.m. LT in winter, respectively. The minimum values of Pandora HCHOVCD were 1.63 x 1016, 2.23 x 1016, 1.26 x 1016, and 0.82 x 1016 molecules cm-2 at around 1:45 p.m. LT in spring, summer, autumn, and winter, respectively. This seasonal pattern of high values in summer and low values in winter implies that photo-oxidation plays an important role in HCHO production. The correlation coefficient (R) between the monthly HCHOVCD values from Pandora and those from the Ozone Monitoring Instrument (OMI) is 0.61, and the slope is 1.25. [ABSTRACT FROM AUTHOR]

Fire radiative power (FRP), which is the power radiated by fire within a unit area, is a fundamental component for estimation of fire emissions. Successive information of FRP is provided by instruments on geostationary satellites, such as the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat, and the Imager on board Geostationary Operational Environmental Satellite (GOES) for Europe, Africa and America. In East Asia, however, the geostationary satellites such as Multifunctional Transport Satellite, Himawari, Fengyun, and Communication, Ocean and Meteorological Satellite (COMS) do not provide official FRP products yet. This article describes the first retrieval of COMS FRP using the mid-infrared radiance method with an optimal sensor coefficient derived from our experimental simulations. The COMS FRP retrievals were compared with Moderate Resolution Imaging Spectroradiometer (MODIS) FRP products in East Asia for each April during 2011–2014. The mean absolute percentage difference was approximately 17%, which is comparable with the results of SEVIRI (30%) and GOES (17%) against MODIS FRP even if their experiment conditions were slightly different. The retrieval accuracies of the COMS FRP had almost no dependence on land-cover types and the size of fire, which can be interpreted a stable outcome covering most wildfire situations, although parts of the pixels showed somewhat low accuracies according to viewing and solar zenith angles. The results of our study can be useful to understand spatiotemporal variations of wildfire emissions. Furthermore, it can be a reference for FRP retrievals of the next-generation geostationary satellites such as the latest Himawari-8 and the forthcoming Geostationary Korean Multipurpose Satellite 2A. [ABSTRACT FROM AUTHOR]

Drought is one of the main constraints on vegetation growth and crop yields, although land ecosystems differ in their sensitivity to drought. Satellite-based vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), have been used in many drought studies, but they may not sufficiently represent the water content of vegetated land. Alternatively, the Normalized Difference Drought Index (NDDI) has been developed by integrating the NDVI and Normalized Difference Water Index (NDWI). In this letter, we examine how drought affects crop growth by quantifying the relationships between NDDI and the Gross Primary Production (GPP) derived from the moderate-resolution imaging spectroradiometer. In the North Korean croplands, NDDI had a strong negative correlation with GPP during 2000–2012. The relationships were more significant under relatively dry conditions (e.g., dry seasons or dry regions). The impacts of NDDI on GPP was greater in summer than in spring, which indirectly shows summer drought may be more critical to crop productivity. The NDDI–GPP relationship was slightly time-lagged in spring, which indicates that vegetation productivity may not always respond instantly to surface dryness. The NDDI can be a viable option for measuring the impacts of drought on vegetation and agriculture over a wide area. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we introduce a new method to estimate the change in mean mixing ratio of a target species at a receptor site due to the contribution of the long-range transport (CLRT). We applied our method to determine inter-annual and inter-seasonal variations in the CLRT of SO2 in Seoul, a major megacity in northeast Asia, during the period from 2001 to 2010. The major potential source areas of SO2 for the 2001–2010 period were located in East China according to the potential source contribution function (PSCF) maps. The CLRT of SO2 in Seoul was estimated to range from 0.40 to 1.03 ppb, which accounted for 8–21% of the ambient mean SO2 mixing ratio in Seoul. The inter-annual variations of estimated CLRT of SO2 was well correlated with those of the total emissions in China during the period of 2001–2008 (R = 0.85). We found that both local emissions from around Seoul and long-range transport from East China, especially the Shandong peninsula, affected the SO2 mixing ratio in Seoul throughout the decade of study. The CLRT of SO2 in Seoul increased after 2007 even though the total emissions of SO2 by China have been decreasing since 2006. The CLRT of SO2 in Seoul was high in spring and winter, which can be attributed to enhanced SO2 emissions in East China during these seasons and a dominant westerly wind. The CLRTs of SO2 accounted for 15, 11, 4, and 12% of the seasonal mean SO2 mixing ratio in spring, summer, fall, and winter, respectively. The uncertainty ranged from 24 to 62% of the estimated CLRT values. [Copyright &y& Elsevier]

Abstract: Topographic Target Light scattering Differential Optical Absorption Spectroscopy (ToTaL-DOAS) measurements were performed in a street canyon in Gwangju, Korea (35°10′51.43″N, 126°52′53.74″E) during a period of 10 days in October 2010. NO2 slant volume mixing ratios (VMRs) at the lowest elevation angle (EL) of 1°, as obtained from ToTaL-DOAS measurements, were validated via comparisons with collocated in situ ground data, showing good agreement within a scatter range of 20% during the entire measurement period, and within a scatter range of 15% when cloudy days were excluded. For the first time, based on inversion calculations using ToTaL-DOAS data, we retrieved NO2 vertical distributions that consist of five layers (from 6 to 118m) for three measurement days. We obtained generally decreasing NO2 VMRs with altitude, whereas increased NO2 VMRs at two elevated layers (6–19m and 19–33m) were observed on 26 and 27 October. We obtained a coefficient of determination (R 2) of 0.87 (0.61) between slant VMRs at 1° EL (retrieved NO2 VMRs in the layer from 6m to 19m) and in situ data, with the scatter of the correlation being within the 15% (10%) range. The relation between retrieved NO2 VMRs at 6–19m and slant VMRs at 1° EL yielded an R 2 value of 0.97, with the scatter of the correlation being within the 5% range. The results demonstrate that the ToTaL-DOAS technique is a useful tool in identifying the temporal and vertical characteristics of NO2 VMRs in an urban street canyon with a complex geometry. [Copyright &y& Elsevier]

Abstract: Multi Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements were taken during 12 hazy days in December 2006 in Fresno, USA. We obtained vertical distributions of aerosol extinction coefficients (AECs) and aerosol optical depths (AODs) at UV wavelengths of 353 and 380 nm by applying a recently developed aerosol retrieval algorithm based on O4 slant column densities (SCDs) from MAX-DOAS measurements. A linear correlation coefficient (R) of 0.74 (0.73) was obtained between surface PM2.5 concentrations and AECs at 353 (380) nm within the 0–1 km layer under hazy conditions. AODs obtained from MAX-DOAS measurements were validated by comparison with those measured by a collocated sunphotometer. AODs derived at both wavelengths were overestimated under the hazy atmospheric conditions compared with those retrieved from sunphotometer measurements. A coefficient of determination (R 2) of 0.58 (0.79) was obtained for AODs at 353 (380) nm from MAX-DOAS and sunphotometer during the hazy period, and AODs obtained using MAX-DOAS agreed with sunphotometer data within 50% and 40% for the different wavelengths, respectively. [Copyright &y& Elsevier]

The contributions of long range transported aerosol in East Asia to carbonaceous aerosol and particulate matter (PM) concentrations in Seoul, Korea were estimated with potential source contribution function (PSCF) calculations. Carbonaceous aerosol (organic carbon (OC) and elemental carbon (EC)), PM2.5, and PM10concentrations were measured from April 2007 to March 2008 in Seoul, Korea. The PSCF and concentration weighted trajectory (CWT) receptor models were used to identify the spatial source distributions of OC, EC, PM2.5, and coarse particles. Heavily industrialized areas in Northeast China such as Harbin and Changchun and East China including the Pearl River Delta region, the Yangtze River Delta region, and the Beijing–Tianjin region were identified as high OC, EC and PM2.5source areas. The conditional PSCF analysis was introduced so as to distinguish the influence of aerosol transported from heavily polluted source areas on a receptor site from that transported from relatively clean areas. The source contributions estimated using the conditional PSCF analysis account for not only the aerosol concentrations of long range transported aerosols but also the number of transport days effective on the measurement site. Based on the proposed algorithm, the condition of airmass pathways was classified into two types: one condition where airmass passed over the source region (PS) and another condition where airmass did not pass over the source region (NPS). For most of the seasons during the measurement period, 249.5–366.2% higher OC, EC, PM2.5, and coarse particle concentrations were observed at the measurement site under PS conditions than under NPS conditions. Seasonal variations in the concentrations of OC, EC, PM2.5, and coarse particles under PS, NPS, and background aerosol conditions were quantified. The contributions of long range transported aerosols on the OC, EC, PM2.5, and coarse particle concentrations during several Asian dust events were also estimated. We also investigated the performance of the PSCF results obtained from combining highly time resolved measurement data and backward trajectory calculations viacomparison with those from data in low resolutions. Reduced tailing effects and the larger coverage over the area of interest were observed in the PSCF results obtained from using the highly time resolved data and trajectories. [ABSTRACT FROM AUTHOR]

A recently developed aerosol retrieval algorithm based on O4 slant column densities (SCDs) measured at a visible wavelength (476 nm) was utilized to derive aerosol information (e.g., aerosol optical depth (AOD) and vertical distributions of aerosol extinction coefficients (AECs)) in the lower troposphere during a severe Asian dust period. The MAX-DOAS measurements were carried out at Gwangju, Korea for nearly three months from February through May 2008. Comparison with AOD and surface PM10, measured by collocated sunphotometer and beta gauge sampler, were made to validate the retrieved AODs and AECs in the atmospheric layer surface to 1 km height above ground. On the Asian dust days, temporal variations of the AODs retrieved fromMAX-DOAS measurements show similar patterns, but with reduced magnitudes, to those measured by sunphotometer whereas similar AOD magnitude and temporal variation was observed between MAX-DOAS and sunphotometer measurements during the non-episodic days. Smaller correlation was observed between the surface PM10 and AECs at 0.5 km during the Asian dust period compared to the correlation obtained for the non episodic days. This study demonstrates the ability ofMAX-DOAS as a remote sensing technique for surface aerosol measurements under conditions of homogeneously distributed pollution in the planetary boundary layer. However, for the measurement of significantly enhanced aerosol loads with heterogeneous vertical distribution (e.g., Asian dust), measured AODs and AECs are underestimated at altitudes above 1 km due to decreased sensitivity of MAX-DOAS measurements at high altitudes. [ABSTRACT FROM AUTHOR]