06 Dec 2022
06 Dec 2022
Status: this preprint is open for discussion.

Plume detection and estimate emissions for biomass burning plumes from TROPOMI Carbon monoxide observations using APE v1.0

Manu Goudar1, Juliëtte Anema2, Rajesh Kumar3, Tobias Borsdorff1, and Jochen Landgraf1 Manu Goudar et al.
  • 1SRON Netherlands Institute for Space Research, Leiden, The Netherlands
  • 2Royal Netherlands Meteorological Institute (KNMI)
  • 3National Center for Atmospheric Research (NCAR)

Abstract. The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor (S-5P) satellite, launched in 2017, measures the total column concentration of the trace gas Carbon Monoxide (CO) daily on a global scale and at a high spatial resolution of 7 x 7 km2, improved to 5.5 x 7 km2 in August 2019. The TROPOMI observations show plumes of CO due to localized CO emissions from industrial sources and biomass burning. In this paper, we quantify these CO emissions for biomass burning by an automated algorithm, APE, to detect plumes and quantify the CO emission rate using cross-sectional flux method. Furthermore, the influence of a constant and a varying plume height in downwind direction on emissions is investigated and algorithm uncertainties are quantified. The VIIRS active fire data in conjunction with the TROPOMI CO datasets is used to identify fires and the fire locations. Then, an automated plume detection algorithm using traditional image processing algorithms is developed and utilized to identify plumes. For these plumes, the emission rate is estimated by the cross-section flux method at three different plume heights. The first two are constant plume heights at a 100 m and an IS4FIRES injection height from Global Fire Assimilation System. And the last one is a varying plume height in downwind direction. A 3D Lagrangian model is used to simulate tracer particles where the source locations for the simulation are based on the VIIRS fire counts and IS4FIRES injection height. 3D velocities at 137 model levels (ERA5) are utilized to simulate tracer particles. We demonstrate the quality and validity of our automated approach by investigating biomass burning events and their emissions for Australia on Oct 2019 and the US on Sept 2020. A total of 110 and 31 individual fire plumes in Australia and the US, respectively were detected and their emissions estimated. The emissions were severely under-predicted and negative for 11 cases when based on constant plume height of 100 m compared to emissions based on varying plume height. Furthermore, the effect of the changing plume height in downwind direction on the emission estimate compared to emissions from constant IS4FIRES plume height was minor as 124 cases are found to have emission variation less than 10 %. However, we were able to identify several cases where the flux estimates become more reliable with varying plume height. Thus, the varying plume height in downwind direction is considered for the automated algorithm. The cross-section flux method is found to have an uncertainty of 38 % in one of the idealized cases. However, overall uncertainty of the algorithm is difficult to quantify as conditions for each fire are unique.

Manu Goudar et al.

Status: open (until 31 Jan 2023)

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Manu Goudar et al.

Manu Goudar et al.


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Short summary
A framework was developed to automatically detect plumes and compute estimate emissions from cross-sectional flux method (CFM) for biomass burning events in TROPOMI CO datasets using VIIRS Active fire data. The emissions were more reliable when changing plume height in downwind direction was used compared to constant injection height. The CFM was found to have large uncertainty even when the meteorological conditions were accurate. Thus highlighting a need for better inversion models for fires.