Plume detection and emission estimate for biomass burning plumes from TROPOMI carbon monoxide observations using APE v1.1

Goudar, Manu; Anema, Juliëtte C. S.; Kumar, Rajesh; Borsdorff, Tobias; Landgraf, Jochen

doi:10.5194/gmd-16-4835-2023

Cited by 3 publications

(2 citation statements)

References 53 publications

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“…Automated detection of TROPOMI-based (fire) emission plumes has only started to be developed in recent years (Kurchuba et al, 2021;Finch et al, 2022;Goudar et al, 2023;Schuit et al, 2023), especially thanks to the recent advance of data-intensive artificial intelligence analysis techniques, but has the potential to further advance satellite-data-based estimates of fire emissions.…”

Section: Discussionmentioning

confidence: 99%

Assessment of satellite observation-based wildfire emissions inventories using TROPOMI data and IFS-COMPO model simulations

de Laat,

Huijnen,

Andela

et al. 2024

Preprint

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Abstract. Fires are a key component of the global carbon cycle and humans are changing their characteristics. Fire emission monitoring is important to keep track of those changes and TROPOMI satellite observations of tropospheric nitrogen dioxide, carbon monoxide and the absorbing aerosol index can be used to quantify and verify the accuracy and precision of global wildfire emission estimates on a daily basis. Here we use TROPOMI observations to evaluate a new fire emission database based on Global Fire Atlas input for the Sense4Fire project (GFA-S4F) and from the Copernicus Atmosphere Monitoring (CAMS) Global Fire Assimilation System (GFAS) for a number of test regions worldwide representative of the most important wildfire type environments. The main focus is on Amazon and Cerrado biomes (tropical rain forests and deforestation) during August–September 2020, but analyses are also made for a region in sub-Saharan Africa (savannah) as well as two regions in Siberia (steppe and boreal forests/tundra). GFA-S4F and GFAS fire emissions are used as input for global atmospheric composition model simulations based on IFS-COMPO, i.e. an extension of ECMWF’s Integrated Forecasting System (IFS) for simulating atmospheric composition. Comparing the model output with the TROPOMI observations then provides an indirect check on the realism of these emission estimates. Furthermore, for tropospheric nitrogen dioxide the IFS-COMPO model simulations are also used to estimate the model sensitivity of tropospheric nitrogen dioxide columns with respect to fire emission changes. This local relationship is used to optimize the fire NOx emissions directly using the Sentinel-5p nitrogen dioxide observations. The results reveal that for small fires emission nitrogen dioxide estimates are realistic on average albeit with a large spread, i.e. for individual fires emissions can be significantly under or overestimated regardless of emission database. However, for large fires nitrogen dioxide emissions are systematically and largely overestimated in all four regions. The overestimation can be an order of magnitude or even more. For area total nitrogen dioxide emissions this “large fire bias” is of minor importance, i.e. total nitrogen dioxide emissions are dominated by small fires. The GFA-S4F emission estimates were characterized by a larger positive bias for large fire NO2 emission cases compared to GFAS. The source of this bias is not well understood. With optimized NO2 emissions by direct adjustment of emission using TROPOMI nitrogen dioxide observations the large positive bias can efficiently be resolved. Combined with an update of soil NOx emissions – causing too low background NOx levels – a fairly good agreement between IFS-COMPO and TROPOMI was reached. Carbon monoxide was generally underestimated using GFAS emission (~50 % on average for the selected regions). Updating carbon monoxide emissions over the Amazon region by incorporating more Sentinel satellite data (GFA-S4F) did reduce this fire CO bias significantly (to ~25 % on average). Overall, the results show that TROPOMI data allows for systematically identifying uncertainties and errors in satellite-data based fire emissions. The results also suggest that the use of dynamic emission factors may further improve satellite based global emissions inventories. In addition, the results also highlight that the use of TROPOMI data could be much more detailed and refined towards assessing individual fires on a daily basis for better understanding fire dynamics and to improve and diversify fire emission factors.

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Section: Discussionmentioning

confidence: 99%

Assessment of satellite observation-based wildfire emissions inventories using TROPOMI data and IFS-COMPO model simulations

de Laat,

Huijnen,

Andela

et al. 2024

Preprint

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“…More particularly, we compared the average value for the months of January and June 2021 at approximately 10:00 UTC for the nested domain of the simulation after regridding the WRF outputs to the TROPOMI observations and applying the same averaging kernel to the vertical levels. A 270 Gaussian filter has also been applied in order to avoid some of the background noise Goudar et al, 2023).…”

Section: Comparison Of Satellite Tropomi Observations and Wrf-chem Si...mentioning

confidence: 99%

From CO₂ emissions to atmospheric NO₂ mixing ratios: simulating chemical processes and their impacts on TROPOMI retrievals over the Middle East

Cheliotis,

Lauvaux,

Lian

et al. 2023

Preprint

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Abstract. As many large metropolitan areas have pledged for a rapid decrease of their greenhouse gas emissions through ambitious climate mitigation policies, the need for rapid and robust quantification methods became more pressing. At the global scale, the scarcity of satellite carbon dioxide (CO2) observations remains the major roadblock to producing independent city-scale CO2 emissions estimates from atmospheric data, except for a handful of cities benefiting from a dense network of ground-based CO2 sensors. In this study, we quantify the potential of assimilating indirect measurements from spaceborne sensors (here nitrogen dioxide – NO2) to constrain fossil fuel CO2 emissions, relying on the co-emission of these two species during combustion. We developed a modeling framework using a NOx-aerosol chemistry transport model (WRF-Chem) and performed simulations of NO2 and CO2 over the Middle-East, an area known for its large cities, its frequent clear sky conditions and a fairly constant albedo from its desertic land. We first demonstrate the importance of production/destruction processes impacting NO2 lifetime at short and long distances from the source, suggesting that simplified approaches may be impacted by large errors. In comparison to TROPOMI satellite observations, the simulated NO2 plumes from emissions inventories (EDGAR) revealed large misattribution of NO2 emissions at fine scales, hence an uncertain disaggregation of national emissions to single point sources in the industrial and energy sectors in the EDGAR inventory. We further studied the relationship between NO2 and CO2 during summer and winter seasons by simulating the enhancement ratios (δNO2:δXCO2) in plumes produced by cities and point sources We found that the enhancement ratios are consistent with the observed ratios derived from the ESA TROPOMI (NO2) and NASA OCO-3 missions (XCO2). We conclude here that the spatial misattribution of NO2 emissions parallels the misattribution of CO2 emissions, and that improved NO2 inventories could therefore be used to improve the monitoring of CO2 emissions at sub-national scales in current global inventories when a sufficiently-large amount of NO2 satellite measurements are available.

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Biomass burning CO emissions: exploring insights through TROPOMI-derived emissions and emission coefficients

Griffin,

Chen,

Anderson

et al. 2024

Atmos. Chem. Phys.

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Abstract. Emissions from biomass burning are a significant source of air pollution, which can adversely impact air quality and ecosystems thousands of kilometres downwind. These emissions can be estimated by a bottom-up approach that relies on fuel consumed and standardized emission factors. Emissions are also commonly derived with a top-down approach, using satellite-observed fire radiative power (FRP) as a proxy for fuel consumption. Biomass burning emissions can also be estimated directly from satellite trace gas observations, including carbon monoxide (CO). Here, we explore the potential of satellite-derived CO emission rates from biomass burning and provide new insights into the understanding of satellite-derived fire CO emissions globally, with respect to differences in regions and vegetation type. Specifically, we use the TROPOMI (Tropospheric Monitoring Instrument) high-spatial-resolution satellite datasets to derive burning CO emissions directly for individual fires between 2019 and 2021 globally. Using synthetic data (with known emissions), we show that the direct emission estimate methodology has a 34 % uncertainty for deriving CO emissions (and a total uncertainty of 44 % including wind and CO column uncertainty). From the TROPOMI-derived CO emissions, we derive biome-specific emission coefficients (emissions relative to FRP) by combining the direct emission estimates and the satellite-observed FRP from the Moderate Resolution Imaging Spectrometer (MODIS). These emission coefficients are used to establish annual top-down CO emission inventories from biomass burning, showing that Southern Hemisphere Africa has the highest CO biomass burning emissions (over 25 % of global total of 300–390 Mt(CO) yr−1 between 2003–2021), and almost 25 % of global CO biomass burning emissions are from broadleaved evergreen tree fires. A comprehensive comparison between direct estimates, top-down and bottom-up approaches, provides insight into the strengths and weaknesses of each method: FINN2.5 has higher CO emissions, by a factor between 2 and 5, than all other inventories assessed in this study. Trends over the past 2 decades are examined for different regions around the globe, showing that global CO biomass burning emissions have, on the whole, decreased (by 5.1 to 8.7 Mt(CO) yr−1), where some regions experience increased and others decreased emissions.

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Plume detection and emission estimate for biomass burning plumes from TROPOMI carbon monoxide observations using APE v1.1

Cited by 3 publications

References 53 publications

Assessment of satellite observation-based wildfire emissions inventories using TROPOMI data and IFS-COMPO model simulations

Assessment of satellite observation-based wildfire emissions inventories using TROPOMI data and IFS-COMPO model simulations

From CO₂ emissions to atmospheric NO₂ mixing ratios: simulating chemical processes and their impacts on TROPOMI retrievals over the Middle East

Biomass burning CO emissions: exploring insights through TROPOMI-derived emissions and emission coefficients

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Plume detection and emission estimate for biomass burning plumes from TROPOMI carbon monoxide observations using APE v1.1

Cited by 3 publications

References 53 publications

Assessment of satellite observation-based wildfire emissions inventories using TROPOMI data and IFS-COMPO model simulations

Assessment of satellite observation-based wildfire emissions inventories using TROPOMI data and IFS-COMPO model simulations

From CO2 emissions to atmospheric NO2 mixing ratios: simulating chemical processes and their impacts on TROPOMI retrievals over the Middle East

Biomass burning CO emissions: exploring insights through TROPOMI-derived emissions and emission coefficients

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Resources

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From CO₂ emissions to atmospheric NO₂ mixing ratios: simulating chemical processes and their impacts on TROPOMI retrievals over the Middle East