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The Yonsei AErosol Retrieval (YAER) algorithm for the Geostationary Ocean Color Imager (GOCI) retrieves aerosol optical properties only over dark surfaces, so it is important to mask pixels with bright surfaces. The Advanced Himawari Imager (AHI) is equipped with three shortwave-infrared and nine infrared channels, which is advantageous for bright-pixel masking. In addition, multiple visible and near-infrared channels provide a great advantage in aerosol property retrieval from the AHI and GOCI. By applying the YAER algorithm to 10 minutes AHI or 1 hour GOCI data at 6 km x 6 km resolution, diurnal variations and aerosol transport can be observed, which has not previously been possible from low-earth-orbit satellites. This study attempted to estimate the optimal aerosol optical depth (AOD) for East Asia by data fusion, taking into account satellite retrieval uncertainty. The data fusion involved two steps: (1) analysis of error characteristics of each retrieved result with respect to the ground-based Aerosol Robotic Network (AERONET), and bias correction based on normalized difference vegetation indexes; and (2) estimation of the fused product using ensemble-mean and maximum-likelihood estimation methods. Fused results show a better statistics in terms of fraction within the expected error, correlation coefficient, root-mean-square error, median bias error than the retrieved result for each product. [ABSTRACT FROM AUTHOR]

Remote sensing of atmospheric state variables typically relies on the inverse solution of the radiative transfer equation. An adequately characterized retrieval provides information on the uncertainties of the estimated state variables as well as on how any constraint or a priori assumption affects the estimate. Reported characterization data should be intercomparable between different instruments, empirically validatable, grid-independent, usable without detailed knowledge of the instrument or retrieval technique, traceable, and still have reasonable data volume. The latter condition of adequacy may force one to work with representative rather than individual characterization data. The main sources of uncertainty are measurement noise, calibration errors, simplifications and idealizations in the radiative transfer model and retrieval scheme, auxiliary data errors, and uncertainties in atmospheric or instrumental parameters. Some of these errors affect the result in a random way, while others chiefly cause a bias or are of mixed character. Beyond this, it is of utmost importance to know the influence of any constraint and prior information on the solution. While different instruments or retrieval schemes may require different error estimation schemes, we provide a list of recommendations which shall help to unify retrieval error reporting. [ABSTRACT FROM AUTHOR]

Acetonitrile (CH3CN) is one of the volatile organic compounds (VOC) and a potential tracer of biomass burning. We evaluated the capability of using observations derived from the Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES) on the International Space Station (ISS) to measure CH3CN profiles. The error in a CH3CN vertical profile from the Level-2 research (L2r) product version 3.0.0 was estimated by both theoretical error analysis and compared with other instrumental measurements. We estimated the systematic and random errors to be ~5.8 ppt (7.8 %) and 25 ppt (60 %) for a single observation at 15.7 hPa, respectively, in the Tropics, where the CH3CN measurements are enhanced. The major source of systematic error was a pressure broadening, and its contribution to the total systematic error was approximately 60 % in the middle stratosphere (15.7–4.8 hPa). The random error decreased to less than 40 % after averaging 10 profiles in the pressure range of 28.8–1.6 hPa. The total error due to uncertainties in other molecular spectroscopic parameters was comparable (2.8 ppt) to those of CH3CN spectroscopic parameters. We compared the SMILES CH3CN profiles with those of the Microwave Limb Sounder (MLS) on the Aura satellite (version 4.2). The SMILES CH3CN values were consistent with those from MLS within the standard deviation (1 σ) of the MLS observations. The difference between the SMILES and MLS CH3CN profiles increased with altitude and was within 20–35 ppt (20–260 %) at 15.7–1.6 hPa. We observed discrepancies of 5–10 ppt (10–30 %) between the SMILES CH3CN profiles observed by different spectrometers, so we do not recommend merging SMILES CH3CN profiles derived from the different spectrometers. We found that SMILES CH3CN VMR in the upper stratosphere has a seasonal maximum in February, which is consistent with the fact that biomass burning events are highest from December–March. [ABSTRACT FROM AUTHOR]

Time series of stratospheric and lower mesospheric water vapour using 33 data sets from 15 different satellite instruments were compared in the framework of the second SPARC (Stratosphere-troposphere Processes And their Role in Climate) water vapour assessment (WAVAS-II). This comparison aimed to provide a comprehensive overview of the typical uncertainties in the observational database that can be considered in the future in observational and modelling studies addressing e.g stratospheric water vapour trends. The time series comparisons are presented for the three latitude bands, the Antarctic (80°-70° S), the tropics (15° S-15° N) and the northern hemisphere mid-latitudes (50° N-60° N) at four different altitudes (0.1, 3, 10 and 80 hPa) covering the stratosphere and lower mesosphere. The combined temporal coverage of observations from the 15 satellite instruments allowed considering the time period 1986-2014. In addition to the qualitative comparison of the time series, the agreement of the data sets is assessed quantitatively in the form of the spread (i.e. the difference between the maximum and minimum volume mixing ratio among the data sets), the (Pearson) correlation coefficient and the drift (i.e. linear changes of the difference between time series over time). Generally, good agreement between the time series was found in the middle stratosphere while larger differences were found in the lower mesosphere and near the tropopause. Concerning the latitude bands, the largest differences were found in the Antarctic while the best agreement was found for the tropics. From our assessment we find that all data sets can be considered in the future in observational and modelling studies addressing e.g. stratospheric and lower mesospheric water vapour variability and trends when data set specific characteristics (e.g. a drift) and restrictions (e.g. temporal and spatial coverage) are taken into account. [ABSTRACT FROM AUTHOR]

We performed a quantitative feasibility study of constraining the vertical profile of the amount of ozone in the troposphere by using a synergetic retrieval method on multiple spectra, i.e., ultraviolet (UV), thermal infrared (TIR) and microwave (MW) ranges, measured from space. Twenty atmospheric scenarios for East Asia in summer and winter seasons were assumed in this study. Geometry of line-of-sight was nadir down-looking for UV and TIR measurements, and limb-sounding for MW measurement. The sensitivities of retrieved ozone in the upper troposphere (UT), middle troposphere (MT) and lowermost troposphere (LMT) were estimated using values of the degree of freedom for signal (DFS), partial column error, and averaging kernel matrix, derived based on the optimal estimation method. The measurement noises were assumed at the same level as the currently available instruments. The weighting functions for the UV, TIR and MW ranges were calculated using the SCIATRAN radiative transfer model, the Line-By-Line Radiative Transfer Model, and the Advanced Model for Atmospheric Terahertz Radiation Analysis and Simulation, respectively. In the UT region, the DFS value was enhanced by about 200% by adding the MW measurements to the combination of UV and TIR measurements. We found that the DFS value of the LMT ozone was increased by approximately 40% by adding the MW measurements to the combination of UV and TIR measurements; nevertheless, the MW measurement alone has no sensitivity for the LMT ozone. Better information of the LMT ozone can be educed by adding constraints on the UT and MT ozone from the MW measurement. The results of this study will be implemented in the Japanese air-quality monitoring missions, APOLLO, GMAP-Asia and uvSCOPE. [ABSTRACT FROM AUTHOR]