Synergistic use of hyperspectral uv-visible omi and broadband meteorological imager modis data for a merged aerosol product

The retrieval of optimal aerosol datasets by the synergistic use of hyperspectral ultraviolet (UV)-visible and broadband meteorological imager (MI) techniques was investigated. The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) was used as a proxy for hyperspectral UV-visible instrument data to which the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol algorithm was applied. Moderate-Resolution Imaging Spectroradiometer (MODIS) L1B and dark target aerosol Level 2 (L2) data were used with a broadband MI to take advantage of the consistent time gap between the MODIS and the OMI. First, the use of cloud mask information from the MI infrared (IR) channel was tested for synergy. High-spatial-resolution and IR channels of the MI helped mask cirrus and sub-pixel cloud contamination of GEMS aerosol, as clearly seen in aerosol optical depth (AOD) validation with Aerosol Robotic Network (AERONET) data. Second, dust aerosols were distinguished in the GEMS aerosol-type classification algorithm by calculating the total dust confidence index (TDCI) from MODIS L1B IR channels. Statistical analysis indicates that the Probability of Correct Detection (POCD) between the forward and inversion aerosol dust models (DS) was increased from 72% to 94% by use of the TDCI for GEMS aerosol-type classification, and updated aerosol types were then applied to the GEMS algorithm. Use of the TDCI for DS type classification in the GEMS retrieval procedure gave improved single-scattering albedo (SSA) values for absorbing fine pollution particles (BC) and DS aerosols. Aerosol layer height (ALH) retrieved from GEMS was compared with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data, which provides high-resolution vertical aerosol profile information. The CALIOP ALH was calculated from total attenuated backscatter data at 1064 nm, which is identical to the definition of GEMS ALH. Application of the TDCI value reduced the median bias of GEMS ALH data slightly. The GEMS ALH bias approximates zero, especially for GEMS AOD values of >similar to 0.4 and GEMS SSA values of <similar to 0.95. Finally, the AOD products from the GEMS algorithm and MI were used in aerosol merging with the maximum-likelihood estimation method, based on a weighting factor derived from the standard deviation of the original AOD products. With the advantage of the UV-visible channel in retrieving aerosol properties over bright surfaces, the combined AOD products demonstrated better spatial data availability than the original AOD products, with comparable accuracy. Furthermore, pixel-level error analysis of GEMS AOD data indicates improvement through MI synergy

AHI/Himawari-8 Yonsei Aerosol Retrieval (YAER): Algorithm, Validation and Merged Products

Himawari-8, a next-generation geostationary meteorological satellite, was successfully launched by the Japanese Meteorological Agency (JMA) on 7 October 2014 and has been in official operation since 7 July 2015. The Advanced Himawari Imager (AHI) onboard Himawari-8 has 16 channels from 0.47 to 13.3 μm and performs full-disk observations every 10 min. This study describes AHI aerosol optical property (AOP) retrieval based on a multi-channel algorithm using three visible and one near-infrared channels (470, 510, 640, and 860 nm). AOPs were retrieved by obtaining the visible surface reflectance using shortwave infrared (SWIR) data along with normalized difference vegetation index shortwave infrared (NDVISWIR) categories and the minimum reflectance method (MRM). Estimated surface reflectance from SWIR (ESR) tends to be overestimated in urban and cropland areas. Thus, the visible surface reflectance was improved by considering urbanization effects. Ocean surface reflectance is obtained using MRM, while it is from the Cox and Munk method in ESR with the consideration of chlorophyll-a concentration. Based on validation with ground-based sun-photometer measurements from Aerosol Robotic Network (AERONET) data, the error pattern tends to the opposition between MRMver (using MRM reflectance) AOD and ESRver (Using ESR reflectance) AOD over land. To estimate optimal AOD products, two methods were used to merge the data. The final aerosol products and the two surface reflectances were merged, which resulted in higher accuracy AOD values than those retrieved by either individual method. All four AODs shown in this study show accurate diurnal variation compared with AERONET, but the optimum AOD changes depending on observation time

Aerosol Layer Height Retrieval Over Ocean From the Advanced Himawari Imager Using Spectral Reflectance Sensitivity

Aerosol layer height (ALH) has been retrieved using multi-angle observations or the O2???O2 and O2???A/B absorption bands. This study attempted to retrieve ALH using the Advanced Himawari Imager (AHI), a single passive imager onboard Himawari-8 and -9. ALH retrieval using geostationary Earth orbit (GEO) satellites is advantageous for monitoring diurnal changes in ALH and understanding long-range transport. Before retrieving the ALH, the aerosol optical properties (AOPs) are retrieved using the green-near infrared (NIR) band, which is relatively insensitive to aerosol height. The retrieved AOPs are used as input to the radiative transfer calculation to compute the top-of-atmosphere (TOA) reflectance of a highly sensitive band (the blue band in this study). Then, the ALH is retrieved using the observed and calculated TOA reflectances. Since the retrieval accuracy of the aerosol optical depth (AOD) is better over the ocean, the retrieval was performed only over the ocean during the Korea???United States Air Quality Study (KORUS-AQ) campaign period. The retrieved ALH was validated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and high-spectral-resolution Lidar (HSRL)

Temporal variation of surface reflectance and cloud fraction used to identify background aerosol retrieval information over East Asia

Temporal variation in cloud cover and surface conditions greatly affects estimation errors associated with reference surface reflectance and atmospheric composition retrievals. In this study, to determine an optimal temporal window for clear-sky composition methods that are used to identify surface reflectance for the retrieval of atmospheric properties, we analyzed temporal variation in surface reflectance and cloud fractions using longterm daily observations from the Moderate Resolution Imaing Spectroradiometer (MODIS) satellite. For the temporal variation in surface reflectance, gridded pixels with a standard deviation less than 0.025 represented 87.0%, 84.5%, 80.5%, and 77.3% of the total pixels for periods of 15, 20, 30, and 40 days, respectively. The temporal variability of surface reflectance was lowest in summer and highest in winter due to vegetation and snow cover changes over land surface in East Asia. For the temporal variation in cloud fractions, pixels with a cloud fraction <10% ranged from 91.2% (15-day) to 98.1% (40-day). Only temporal windows of 30 and 40 days satisfied the criterion of 95% cumulative distribution in the 10% cloud fraction range. Thus, and appropriate temporal window for clear-sky composition methods must be selected in consideration of the seasonal dependency of surface types and cloud cover variation. The temporal window for the clear-sky composition must be longer than 30 days considering the temporal variability of cloud cover, and shorter than 30 days considering that of surface reflectance. However, seasonal dependencies of surface reflectance and cloud fraction are also additionally considered to select the appropriated temporal window for the clear-sky composition

Potential improvement of XCO2 retrieval of the OCO-2 by having aerosol information from the A-train satellites

Near-real time observations of aerosol properties could have a potential to improve the accuracy of XCO2 retrieval algorithm in operational satellite missions. In this study, we developed a retrieval algorithm of XCO2 (Yonsei Retrieval Algorithm; YCAR) based on the Optimal Estimation (OE) method that used aerosol information at the location of the Orbiting Carbon Observatory-2 (OCO-2) measurement from co-located measurement of the Afternoon constellation (A-train) such as the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Observation (CALIPSO) and the MODerate-resolution Imaging Spectrometer (MODIS) onboard the Aqua. Specifically, we used optical depth, vertical profile, and optical properties of aerosol from MODIS and CALIOP data. We validated retrieval results to the Total Carbon Column Observing Network (TCCON) ground-based measurements and found general consistency. The impact of observed aerosol information and its constraint was examined by retrieval tests using different settings. The effect of using additional aerosol information was analyzed in connection with the bias correction process of the operational retrieval algorithm. YCAR using a priori aerosol loading parameters from co-located satellite measurements and less constraint of aerosol optical properties made comparable results with operational data with the bias correction process in three of the four cases subject to this study. Our work provides evidence supporting the bias correction process of operational algorithms and quantitatively presents the effectiveness of synergic use of multiple satellites (e.g. A-train) and better treatment of aerosol information

Fine particulate concentrations over East Asia derived from aerosols measured by the advanced Himawari Imager using machine learning

Fine particulate matter with a diameter below 2.5 lim (PM2.5) is deleterious to the cardiovascular and respiratory systems. It is often difficult to assess the effects of PM2.5 on human health over regions with limited ground monitoring sites, especially in East Asia. As an alternative, we estimated near-surface PM2.5 concentrations by analyzing Advanced Himawari Imager (AHI) Yonsei Aerosol Retrieval (YAER) products. This study incorporates daytime data for East Asia covering the Korean Peninsula, China, Japan, Southeast Asia, and southern Mongolia. We collocated AHI YAER product pixels with meteorological, land-cover, and other ancillary data for the period from March 2018 to February 2019. To estimate PM2.5 concentrations over wide areas spanning many countries displaying various relationships between aerosol optical depth and PM2.5, monthly models were developed by considering both the spatial and temporal characteristics of ground-based PM2.5 measurements. Random forest machine learning model estimated ground-level mass concentrations of PM2.5; subsequent 10-fold cross vali-dation (CV) yielded a CV R-2 value of 0.81 and a CV root mean squared error (RMSE) of 12.3 lig m(-3). We investigated the spatial pattern of PM2.5 concentrations over multiple countries and seasonal variation in PM2.5 concentrations. Diurnal variation of a severe PM2.5 event in the Korean Peninsula was investigated as a case study. The model captured the extremely heterogeneous spatial distribution of PM2.5 concentrations peaked around local noon. To measure the capability of the developed model to estimate PM2.5 concentrations in areas with few in-situ data, its predictive performance was evaluated using a dataset independent of the training process with an R-2 of 0.60 and RMSE of 8.18 lig m(-3). This study demonstrates the potential for satellite-based PM2.5 estimation for areas with insufficient measuring stations