AS3.24

CO2Image is a satellite demonstration mission, now in phase B, to be launched by the German Aerospace Center (DLR ) in 2026. The satellite will carry a next generation imaging spectrometer for measuring atmospheric column concentrations of carbon dioxide (CO₂). The instrument concept reconciles compact design with fine ground resolution (50-100 m) with decent spectral resolution (1.0-1.3 nm) in the shortwave infrared spectral range (2,000 nm). Thus, CO2Image will enable quantification of point sources with CO₂ emission rates of less than 1 MtCO₂/a. This will complement global monitoring missions such as CO2M, which are less sensitive to point sources due to their coarser ground resolution, and hyperspectral imagers, which suffer from spectroscopic interference errors that limit the quantification of small sources due to their coarser spectral resolution. Further, CO2Image is sufficiently compact to envision, after successful demonstration, a fleet of sensors operated by public bodies to support and evaluate greenhouse gas emission reduction strategies on community-scales.

Here, we will focus on the design choices and performance analyses carried out for building the mission. We have developed an end-to-end simulator that starts out with realistic atmospheric CO₂ concentration fields simulated by large-eddy-simulations of CO₂ plumes emitted by various point-sources in a 50x50 km² tile. The latter represents a typical individual target which CO2Image will sample in less than 100 ms in a pushbroom configuration using forward motion compensation. The simulated concentration fields will be used to create synthetic measurements from instrument parameters. These measurements will then be fed into our retrieval and we will use mass balance methods to estimate the compatible emission rates. We evaluate various performance parameters and hypothetical error sources such as calibration errors in terms of their impact on the emission estimates. 

How to cite: Butz, A., Scheidweiler, L., Baumgartner, A., Feist, D. G., Gottschaldt, K.-D., Jöckel, P., Kern, B., Köhler, C., Krutz, D., Lichtenberg, G., Marshall, J., Paproth, C., Slijkhuis, S., Sebastian, I., Strandgren, J., Wilzewski, J. S., and Roiger, A.: CO2Image: a next generation imaging spectrometer for CO2 point source quantification, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6324, https://doi.org/10.5194/egusphere-egu22-6324, 2022.

The status and most recent developments of the operational L2 Cloud product will be presented in this contribution for the ongoing Sentinel-5 Precursor and upcoming Sentinel-4 missions. These Copernicus missions are focused on atmospheric composition, operate in the UV/VIS/NIR/(SWIR) spectral region and comprise the retrieval of trace gases, greenhouse gases, aerosol and cloud properties. A good knowledge about the latter, i.e. the presence and characteristics of clouds, is a pre-requisite for an accurate retrieval of the aforementioned trace gases and greenhouse gases. Additionally, clouds are by themselves an interesting indicator to measure and monitor because of their contribution to the radiation budget, and hence, impact on climatological applications. 
The algorithms for retrieving the operational cloud products from TROPOMI onboard Sentinel-5 Precursor and the UVN spectrometer onboard Sentinel-4 are called OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) and both have their heritage with GOME/ERS-2 and GOME-2 MetOp-A/B/C, where they have already been successfully implemented in an operational environment. OCRA applies a broad band color space approach to the measured reflectance in order to retrieve a radiometric cloud fraction that is used as an a priori input to ROCINN, which retrieves cloud top height, cloud optical thickness and cloud albedo from measurements of sun-normalized radiances in the NIR in and around the oxygen A-band. The cloud parameters retrieved by ROCINN are provided for two different cloud models. The Clouds-as-Layers (CAL) model treats clouds as layers of scattering water droplets, which is physically more realistic than the second model, Clouds-as-Reflecting Boundaries (CRB), which treats a cloud as a simple Lambertian reflector.
In addition, this contribution will cover initial results of applying the OCRA algorithm to the recently launched Korean geostationary GEMS instrument. Applying the algorithm to GEMS does provide a great opportunity to test the performance under a geostationary configuration and to transfer lessons learned in a synergetic way to the Sentinel-4 development. Also, initial comparisons of the S5P and GEMS cloud products will be shown.

How to cite: Lutz, R., Molina Garcia, V., del Aguila, A., Romahn, F., and Loyola, D.: The Operational Cloud Products for Sentinel-5 Precursor and Sentinel-4 and comparisons with GEMS, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9384, https://doi.org/10.5194/egusphere-egu22-9384, 2022.

This study considers usage of high-resolution data on CO and NO₂ of newest orbital spectrometer TROPOMI.
First, using our own software package Level 2 data (15 NetCdf files for every day) were converted to Level 3 (one daily mat-file) while maintaining spatial resolution. Then CO total content (CO TC) and NO₂ tropospheric (NO₂ TrC) and total content (NO₂ TC) OFFL diurnal data were compared with OIAP ground-based measurements in Moscow and at Zvenigorod Scientific station (ZSS, Moscow province, 53 km West from Moscow) as well as with AIRS CO TC observations.
The character of dependence between ground-based and satellite data was chosen as linear:
U_gr = KU_stl + A, were U_gr and U_stl are ground-based and satellite contents of an impurity, respectively; A is the constant.
High correlation between TROPOMI CO OFFL and ground-based CO TC measurements was obtained, with R~ 0.79-0.85, K~0.78-0.91, A~2.3-5.0*10¹⁷ molec/cm² in dependence on orbital data resolution.
Correlation parameters depend on viewing azimuth angle VZA (the best correlation was obtained for VZA≤50º) and do not significantly depende on the Earth albedo.
For diurnal NO₂ TrC OFFL for ZSS were obtained R~ 0.41-0.48, K~3.3-4.3, A~0.9-1.1*10¹⁵ molec/cm²and for NO₂ TC OFFL R~0.59-0.61, K~6.7-7.0, A~ (-)0.76-0.86 *10¹⁵ molec/cm².
Additional possibilities to calculate averaged spatial and trend distributions of different atmospheric species based on TROPOMI data using our original software are presented.  
The study was supported by Russian Science Foundation under grant №21-17-00210. NO₂ data comparison was supported by the Russian Foundation for Basic Research, grant №20-05-00274.

How to cite: Rakitin, V., Gruzdev, A., Skorokhod, A., Kirillova, N., Fedorova, E., and Kazakov, A.: Sentinel-5P TROPOMI data application and their comparison with OIAP spectroscopic datasets on CO and NO2 columns, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8820, https://doi.org/10.5194/egusphere-egu22-8820, 2022.

Methane (CH₄) is the second most important greenhouse gas, of which more than 60 % CH₄ is released through human activities. Satellite observations of CH₄ provide an efficient way to analyze its variations and emissions. The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel 5 Precursor (S5-P) satellite measures CH₄ at a high horizontal resolution of 7 × 7 km², showing the capability of identifying and quantifying the sources at a local to regional scale. The Middle East is one of the strong CH₄ hotspot regions in the world. However, it is difficult to estimate the emissions here because several sources are located near the coast or in places with complex topography, where the satellite observations are often of reduced quality. We use the WMF-DOAS XCH₄ v1.5 product, which has good spatial coverage over the ocean and mountains, to better estimate the emissions in the Middle East. 

The divergence method of Liu et al., (2021) has been proven to be a fast and efficient way to estimate CH₄ emissions from satellite observations. We have improved our method by comparing the fluxes in different directions for better background corrections over areas with complicated topographies. The performance of the updated algorithm was tested by comparing the estimated emissions from a 1-month WRF-CMAQ model simulation with its known emission inventory over the Middle East. The CH₄ emissions based on TROPOMI XCH₄ are then derived on a 0.25 grid for 2019 and 2020. With the WMF-DOAS product, sources from oil/gas platforms over the Persian Gulf and sources on the west coast of Turkmenistan become clearly visible in the emission maps. Sources in the mountain areas of Iran are also identified by our updated divergence method. The locations of fossil fuel related NO_X emissions usually overlap with CH₄ emissions as can be seen in the CAMS bottom-up inventory. Therefore, we have compared our CH₄ emission inventory with the emissions derived from TROPOMI observed NO₂, in order to gain more insight into the source of the emissions, especially concerning the oil/gas industry in the region.

How to cite: Liu, M., van der A, R., Eskes, H., Kang, H., Veefkind, P., Schneising, O., and Ding, J.: Quantification of local methane emissions over Middle East, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5741, https://doi.org/10.5194/egusphere-egu22-5741, 2022.