LESO: A ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrations.

This study presents a novel ensemble of surface ozone (O3) generated by the LEarning Surface Ozone (LESO) framework. The aim of this study is to investigate the spatial and temporal variation of surface O3. The LESO ensemble provides unique and accurate hourly (daily/monthly/yearly as needed) O3 surface concentrations on a fine spatial resolution of 0.1◦ × 0.1◦ across China, Europe, and the United States over a period of 10 years (2012-2021). The LESO ensemble was generated by establishing the relationship between surface O3 and satellite-derived O3 total columns together with high-resolution meteorological reanalysis data. This breakthrough overcomes the challenge of retrieving O3 in the lower atmosphere from satellite signals. A comprehensive validation indicated that the LESO datasets explained approximately 80% of the hourly variability of O3, with a root mean squared error of 19.63 µg/m3. The datasets convincingly captured the diurnal cycles, weekend effects, seasonality, and interannual variability, which can be valuable for research and applications related to atmospheric and climate sciences.

Estimation of ground-level O3 concentration in the Yangtze River Delta region based on a high-performance spatiotemporal model MixNet.

In recent years, the escalating ozone (O3) concentration has significantly damaged human health. The machine learning models are widely used to estimate ground-level O3 concentrations, but the spatial and temporal features in the data are less considered. To address the issue, this study proposed a novel framework named MixNet to estimate daily O3 concentration from 2020 to 2021 over the Yangtze River Delta. The MixNet utilized image convolution to extract the potential spatial information related to O3 fully. The temporal features were extracted by a Long Short-Term Memory (LSTM). A U-Net, a new jump connection method with an attention mechanism and residual blocks, facilitated a more comprehensive extraction of spatial features in the data. The extracted temporal and spatial features were fused to estimate ground-level O3. Meanwhile, a novel training method was proposed to enhance the accuracy of MixNet. The daily mean O3 maps have high validation results in comparison with ground-level O3 measurement, with R2 (RMSE) of 0.903 (14.511 µg/m3) for sample-based validation, 0.831 (19.036 µg/m3) for site-based validation, and 0.712 (25.108 µg/m3) for time-based validation. The season-average maps indicate that O3 concentration is summer > autumn > spring > winter. The highest value was 137.41 µg/m3 in the summer of 2021 over the Yangtze River Delta urban agglomeration, and the lowest value was 52.73 µg/m3 in winter 2020. The MixNet showed better performance compared with other models, and thus the "point-plane image thinking" will contribute to future studies in developing better methods to estimate atmospheric pollutants.

Assessment of Retrieved N2O, NO2, and HF Profiles from the Atmospheric Infrared Ultraspectral Sounder Based on Simulated Spectra.

The Atmospheric Infrared Ultraspectral Sounder (AIUS), the first high-resolution (0.02 cm−1) solar occultation sounder, aboard GF5, was launched in May 2018 from China. However, relevant studies about vertical profiles of atmospheric constituents based on its operational data were not conducted until half a year later. Due to an urgent need for Hin-orbit tests, the real spectra (called reference spectra hereafter) were substituted with simulated spectra calculated from the reference forward model (RFM) plus different random noises at different altitudes. In the generation process of the reference spectra for N2O, NO2, and HF species, ACE-FTS (Atmospheric Chemistry Experiment⁻Fourier Transform Spectrometer instrument on the SCISAT satellite) level 2 products replace corresponding profiles included in the atmospheric background profiles. The optimal estimation method is employed to extract N2O, NO2, and HF profiles in this study. Comparing the retrieved results with ACE-FTS level 2 products, the relative deviations for these three species are calculated. For N2O, the average relative deviation is less than 6% at altitudes below 25 km, while larger deviations are observed in the range of 25⁻45 km, with the maximum being at ~25%. Additionally, the difference for NO2 is less than 5% in the 20⁻45 km range, with a larger discrepancy found below 20 km and above 45 km; the maximum deviation reaches ±40%. For HF, the relative deviation is less than 6% for all tangent heights, implying satisfactory retrieval. The vertical resolution, averaging kernel, and number of degrees of freedom are used to assess the retrieval algorithm, which indicate that the retrieved information content is much more attributable to the reference spectra contribution than to the a priori profile. Finally, a large number of retrieval tests are performed for N2O, NO2, and HF in selected areas covering the Arctic region, northern middle latitude, tropics, southern middle latitude, and Antarctic region, and reliable results are obtained. Thus, to a great extent, the algorithm adopted in the AIUS system can process retrievals reliably and precisely.