Conference Agenda

The upcoming ESA mission Copernicus Anthropogenic CO2 Monitoring Mission (CO2M) will deliver more frequent observations of atmospheric carbon dioxide (CO2) than previous missions, such as OCO-2 and TanSat. This generates orders of magnitude more data which needs to be processed with more efficient algorithms. In this poster we present two methods for boosting the computational capabilities of simulating radiative transfer in the atmosphere.
The atmospheric CO2 is observed by measuring near and shortwave infrared (NIR-SWIR) solar radiation reflected off the Earth's surface. In this wavelength regime, CO2, among other atmospheric gases, have tens of thousands of spectral absorption lines, which need to be measured to get accurate enough data for estimating atmospheric constituents. The estimation procedure includes simulations of the spectral NIR-SWIR radiation, which is computationally expensive. To alleviate this, certain wavelengths can be skipped in the computation and they are interpolated in the final simulated spectrum. We present an adaptive method for wavelength skipping, which can increase the speed of the radiative transfer computations considerably. This method is currently being tested on the Bremen IUP's FusionalP retrieval algorithm to examine if the retrieval can be sped up without loss in accuracy.
In satellite observations with clouds, emission plumes, small ground pixel sizes or large solar zenith angles, 3D effects become pronounced and they can affect atmospheric observations. The 3D radiative transfer in the context of satellite remote sensing of CO2 is a relatively unexplored topic. In this poster we present a novel approach for solving the radiative transfer in a 3D atmosphere and example simulated observations using a GPU implementation of the solver.
After rigorous testing, the developments introduced here may contribute to the speed-up of CO2 observations from space, and may be applicable to the retrievals of other gases.

In this study, we examine the distribution and variations of CO in the Upper Troposphere and Lower Stratosphere (UTLS) considering the controlling effects of Global Monsoon System (GMS). In order to understand the impacts of each factors, by applying the 3-D atmospheric chemical transport model (CTM) simulations, and the satellite observations, we focus on the chemical composition and processes, the temporal and spatial distribution and variation of CO in UTLS, and the complex mechanism induced by GMS. Moreover, we investigate the controlling effects of individual surface emission regions and varieties, and of dynamical transports of each and combined GMS.

Monitoring and accurately quantifying greenhouse gas (GHG) emissions from point sources via satellite measurements is crucial for validating emission inventories. Numerous studies have utilised varied methods to estimate emission intensities from both natural and anthropogenic point sources, highlighting the potential of satellites for point source quantification. To promote the development of the space-based GHGmonitoring system, it is pivotal to assess the satellite's capacity to quantify emissions from distinct sources before its design and launch. However, no universal method currently exists for quantitatively assessing the ability of satellites to quantify point source emissions. This paper presents a parametric conceptual model and database for efficiently evaluating the quantification capabilities of satellites and optimizing their technical characteristics for particular detection missions. Using the model and database, we evaluated how well various satellites can detect and quantify GHG emissions. Our findings indicate that accurate estimation of point source emissions requires both high spatial resolution and measurement precision. The requirement for satellite spatial resolution and measurement precision to achieve unbiased emission estimation gradually decreases with increasing emission intensity. The model and database developed in this study can serve as a reference for harmonious satellite configuration that balances measurement precision and spatial resolution. Furthermore, to progress the evaluation model of satellites for low-intensity emission point sources, it is imperative to implement a more precise simulation model and estimate method with a refined mask-building approach.

The Arctic stratospheric polar vortex was exceptional strong, cold and persistent in the winter and spring of 2019–2020. Based on reanalysis data from the National Centers for Environmental Prediction/National Center for Atmospheric Research and ozone observations from the Ozone Monitoring Instrument, the authors investigated the dynamical variation of the stratospheric polar vortex during winter 2019–2020 and its influence on surface weather and ozone depletion. This strong stratospheric polar vortex was affected by the less active upward propagation of planetary waves. The seasonal transition of the stratosphere during the stratospheric final warming event in spring 2020 occurred late due to the persistence of the polar vortex. A positive Northern Annular Mode index propagated from the stratosphere to the surface, where it was consistent with the Arctic Oscillation and North Atlantic Oscillation indices. As a result, the surface temperature in Eurasia and North America was generally warmer than the climatology. In some places of Eurasia, the surface temperature was about 10K warmer during the period from January to February 2020. The most serious Arctic ozone depletion since 2004 has been observed since February 2020. The mean total column ozone within 60°–90°N from March to 15 April was about 80DU less than the climatology.

We use the superposition column model to estimate NOx emissions over Wuhan from 2018 to 2023 based on TROPOMI NO₂ observations. The operational TROPOMI version 2.4-2.6 data is employed. We compare the results with other emission inventories and find a ~20% lower estimation compared to the ABACAS bottom-up inventory while ~50% below the EDGAR v6.1 NO_x emissions. From 2018 to 2023, NOx emissions over Wuhan is highest in the year 2019 and lowest in 2020 due to the COVID restrictions. Annual NOx emissions in 2020 is 24% lower than those in 2019, and NOx emissions in 2023 is 7.1% higher than 2022 and 3.9% lower than 2019. Seasonally, we find a 7% higher NOx emissions in winter than in summer and find no obvious weekly cycle, but a distinct NOx emissions reduction in the Chinese New Year periods.

During the Research on the Simulation and Mechanism of the impacts of Black Carbon on Climate and Environment atmospheric measurement campaign carried out near Nanjing, China in June 2018, a lightweight, accurate nitrogen dioxide (NO2) sensor was attached to a quadcopter to measure vertical profiles of NO2. Between 1 and 14 June 2018, ∼50 vertical NO2 profiles were measured inside the planetary boundary layer up to an altitude of 900-1300 meters during 13 subsequent measurement days. Six NO2 soundings were conducted on a daily basis at approximately 8 AM (morning), 12 & 4 PM (afternoon), 8 PM (evening) and 12 & 4 AM (night). The NO2 measurements were calibrated using a scaling factor derived from a side-by-side inter comparison with a commercial NO2 analyzer operated by NUIST prior to the start of the campaign. These measurements clearly demonstrate the diurnal cycle of NO2, including the emergence of elevated concentrations close to the surface during the night and early morning and the mixing of the boundary layer from sunrise onward resulting in flat NO2 vertical profile shapes with lower concentrations. The in-situ NO2 vertical profile shapes were compared to NO2 profile information retrieved from nearby MAX-DOAS observations as well as computed using the CHIMERE chemistry-transport model. This comparison demonstrates that in-situ quadcopter measurements could play an important role in the validation of future geostationary satellites since the diurnal cycle of NO2 will have an impact on the accuracy of the satellite retrievals and is not always flawlessly captured by commonly used measurement techniques and models.