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  • Copernicus GmbH  (4)
  • 1
    Online Resource
    Online Resource
    A three-dimensional variational data assimilation system for aerosol optical properties based on WRF-Chem v4.0: design, development, and application of assimilating Himawari-8 aerosol observations
    Copernicus GmbH ; 2022
    In:  Geoscientific Model Development Vol. 15, No. 4 ( 2022-03-03), p. 1821-1840
    Details
    In: Geoscientific Model Development, Copernicus GmbH, Vol. 15, No. 4 ( 2022-03-03), p. 1821-1840
    Abstract: Abstract. This paper presents a three-dimensional variational (3DVAR) data assimilation (DA) system for aerosol optical properties, including aerosol optical thickness (AOT) retrievals and lidar-based aerosol profiles, developed for the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) within the Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) model. For computational efficiency, 32 model variables in the MOSAIC_4bin scheme are lumped into 20 aerosol state variables that are representative of mass concentrations in the DA system. To directly assimilate aerosol optical properties, an observation operator based on the Mie scattering theory was employed, which was obtained by simplifying the optical module in WRF-Chem. The tangent linear (TL) and adjoint (AD) operators were then established and passed the TL/AD sensitivity test. The Himawari-8 derived AOT data were assimilated to validate the system and investigate the effects of assimilation on both AOT and PM2.5 simulations. Two comparative experiments were performed with a cycle of 24 h from 23 to 29 November 2018, during which a heavy air pollution event occurred in northern China. The DA performances of the model simulation were evaluated against independent aerosol observations, including the Aerosol Robotic Network (AERONET) AOT and surface PM2.5 measurements. The results show that Himawari-8 AOT assimilation can significantly improve model AOT analyses and forecasts. Generally, the control experiments without assimilation seriously underestimated AOTs compared with observed values and were therefore unable to describe real aerosol pollution. The analysis fields closer to observations improved AOT simulations, indicating that the system successfully assimilated AOT observations into the model. In terms of statistical metrics, assimilating Himawari-8 AOTs only limitedly improved PM2.5 analyses in the inner simulation domain (D02); however, the positive effect can last for over 24 h. Assimilation effectively enlarged the underestimated PM2.5 concentrations to be closer to the real distribution in northern China, which is of great value for studying heavy air pollution events.
    Type of Medium: Online Resource
    ISSN: 1991-9603
    URL: Article
    DOI: 10.5194/gmd-15-1821-2022
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2022
    detail.hit.zdb_id: 2456725-5
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  • 2
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    Online Resource
    Vertical profiles of black carbon measured by a micro-aethalometer in summer in the North China Plain
    Copernicus GmbH ; 2016
    In:  Atmospheric Chemistry and Physics Vol. 16, No. 16 ( 2016-08-19), p. 10441-10454
    Details
    In: Atmospheric Chemistry and Physics, Copernicus GmbH, Vol. 16, No. 16 ( 2016-08-19), p. 10441-10454
    Abstract: Abstract. Black carbon (BC) is a dominant absorber in the visible spectrum and a potent factor in climatic effects. Vertical profiles of BC were measured using a micro-aethalometer attached to a tethered balloon during the Vertical Observations of trace Gases and Aerosols (VOGA) field campaign, in summer 2014 at a semirural site in the North China Plain (NCP). The diurnal cycle of BC vertical distributions following the evolution of the mixing layer (ML) was investigated for the first time in the NCP region. Statistical parameters including identified mixing height (Hm) and average BC mass concentrations within the ML (Cm) and in the free troposphere (Cf) were obtained for a selected dataset of 67 vertical profiles. Hm was usually lower than 0.2 km in the early morning and rapidly rose thereafter due to strengthened turbulence. The maximum height of the ML was reached in the late afternoon. The top of a full developed ML exceeded 1 km on sunny days in summer, while it stayed much lower on cloudy  days. The sunset triggered the collapse of the ML, and a stable nocturnal boundary layer (NBL) gradually formed. Accordingly, the highest level Cm was found in the early morning and the lowest was found in the afternoon. In the daytime, BC was almost uniformly distributed within the ML and significantly decreased above the ML. During the field campaign, Cm averaged about 5.16 ± 2.49 µg m−3, with a range of 1.12 to 14.49 µg m−3, comparable with observational results in many polluted urban areas such as Milan in Italy and Shanghai in China. As evening approached, BC gradually built up near the surface and exponentially declined with height. In contrast to the large variability found both in Hm and Cm, Cf stayed relatively unaffected through the day. Cf was less than 10 % of the ground level under clean conditions, while it amounted to half of the ground level in some polluted cases. In situ measurements of BC vertical profiles would hopefully have an important implication for accurately estimating direct radiative forcing by BC and improving the retrieval of aerosol optical properties by remote sensing in this region.
    Type of Medium: Online Resource
    ISSN: 1680-7324
    URL: Article
    URL: Article
    DOI: 10.5194/acp-16-10441-2016
    DOI: 10.5194/acp-16-10441-2016-supplement
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2016
    detail.hit.zdb_id: 2092549-9
    detail.hit.zdb_id: 2069847-1
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  • 3
    Online Resource
    Online Resource
    Four-dimensional variational assimilation for SO 2 emission and its application around the COVID-19 lockdown in the spring 2020 over China
    Copernicus GmbH ; 2022
    In:  Atmospheric Chemistry and Physics Vol. 22, No. 19 ( 2022-10-14), p. 13183-13200
    Details
    In: Atmospheric Chemistry and Physics, Copernicus GmbH, Vol. 22, No. 19 ( 2022-10-14), p. 13183-13200
    Abstract: Abstract. Emission inventories are essential for modelling studies and pollution control, but traditional emission inventories are usually updated after a few years based on the statistics of “bottom-up” approach from the energy consumption in provinces, cities, and counties. The latest emission inventories of multi-resolution emission inventory in China (MEIC) was compiled from the statistics for the year 2016 (MEIC_2016). However, the real emissions have varied yearly, due to national pollution control policies and accidental special events, such as the coronavirus disease (COVID-19) pandemic. In this study, a four-dimensional variational assimilation (4DVAR) system based on the “top-down” approach was developed to optimise sulfur dioxide (SO2) emissions by assimilating the data of SO2 concentrations from surface observational stations. The 4DVAR system was then applied to obtain the SO2 emissions during the early period of COVID-19 pandemic (from 17 January to 7 February 2020), and the same period in 2019 over China. The results showed that the average MEIC_2016, 2019, and 2020 emissions were 42.2×106, 40.1×106, and 36.4×106 kg d−1. The emissions in 2020 decreased by 9.2 % in relation to the COVID-19 lockdown compared with those in 2019. For central China, where the lockdown measures were quite strict, the mean 2020 emission decreased by 21.0 % compared with 2019 emissions. Three forecast experiments were conducted using the emissions of MEIC_2016, 2019, and 2020 to demonstrate the effects of optimised emissions. The root mean square error (RMSE) in the experiments using 2019 and 2020 emissions decreased by 28.1 % and 50.7 %, and the correlation coefficient increased by 89.5 % and 205.9 % compared with the experiment using MEIC_2016. For central China, the average RMSE in the experiments with 2019 and 2020 emissions decreased by 48.8 % and 77.0 %, and the average correlation coefficient increased by 44.3 % and 238.7 %, compared with the experiment using MEIC_2016 emissions. The results demonstrated that the 4DVAR system effectively optimised emissions to describe the actual changes in SO2 emissions related to the COVID lockdown, and it can thus be used to improve the accuracy of forecasts.
    Type of Medium: Online Resource
    ISSN: 1680-7324
    URL: Article
    DOI: 10.5194/acp-22-13183-2022
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2022
    detail.hit.zdb_id: 2092549-9
    detail.hit.zdb_id: 2069847-1
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  • 4
    Online Resource
    Online Resource
    Background error covariance with balance constraints for aerosol species and applications in variational data assimilation
    Copernicus GmbH ; 2016
    In:  Geoscientific Model Development Vol. 9, No. 8 ( 2016-08-10), p. 2623-2638
    Details
    In: Geoscientific Model Development, Copernicus GmbH, Vol. 9, No. 8 ( 2016-08-10), p. 2623-2638
    Abstract: Abstract. Balance constraints are important for background error covariance (BEC) in data assimilation to spread information between different variables and produce balance analysis fields. Using statistical regression, we develop a balance constraint for the BEC of aerosol variables and apply it to a three-dimensional variational data assimilation system in the WRF/Chem model; 1-month forecasts from the WRF/Chem model are employed for BEC statistics. The cross-correlations between the different species are generally high. The largest correlation occurs between elemental carbon and organic carbon with as large as 0.9. After using the balance constraints, the correlations between the unbalanced variables reduce to less than 0.2. A set of data assimilation and forecasting experiments is performed. In these experiments, surface PM2.5 concentrations and speciated concentrations along aircraft flight tracks are assimilated. The analysis increments with the balance constraints show spatial distributions more complex than those without the balance constraints, which is a consequence of the spreading of observation information across variables due to the balance constraints. The forecast skills with the balance constraints show substantial and durable improvements from the 2nd hour to the 16th hour compared with the forecast skills without the balance constraints. The results suggest that the developed balance constraints are important for the aerosol assimilation and forecasting.
    Type of Medium: Online Resource
    ISSN: 1991-9603
    URL: Article
    DOI: 10.5194/gmd-9-2623-2016
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2016
    detail.hit.zdb_id: 2456725-5
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