Articles | Volume 17, issue 10
https://doi.org/10.5194/amt-17-3279-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-3279-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Geostationary aerosol retrievals of extreme biomass burning plumes during the 2019–2020 Australian bushfires
Daniel J. V. Robbins
CORRESPONDING AUTHOR
School of Earth, Atmosphere and Environment, Monash University, Clayton, VIC 3800, Australia
ARC Centre of Excellence for Climate Extremes, Monash University, Melbourne, VIC 3800, Australia
Caroline A. Poulsen
School of Earth, Atmosphere and Environment, Monash University, Clayton, VIC 3800, Australia
Science and Innovation Group, Australian Bureau of Meteorology, Melbourne, VIC 3001, Australia
Steven T. Siems
School of Earth, Atmosphere and Environment, Monash University, Clayton, VIC 3800, Australia
ARC Centre of Excellence for Climate Extremes, Monash University, Melbourne, VIC 3800, Australia
ARC SRI Securing Antarctica's Environmental Future, Melbourne, VIC 3800, Australia
Simon R. Proud
STFC RAL Space and the National Centre for Earth Observation, Rutherford Appleton Laboratory, Didcot, OX11 0QX, UK
National Centre for Earth Observation, Space Park Leicester, Leicester, LE4 5SP, UK
Andrew T. Prata
School of Earth, Atmosphere and Environment, Monash University, Clayton, VIC 3800, Australia
ARC SRI Securing Antarctica's Environmental Future, Melbourne, VIC 3800, Australia
now at: Commonwealth Scientific and Industrial Research Organisation Environment, Research Way, Clayton, VIC 3168, Australia
Roy G. Grainger
National Centre for Earth Observation, University of Oxford, Clarendon Laboratory, Parks Road, Oxford OX1 3PU, UK
Adam C. Povey
National Centre for Earth Observation, University of Oxford, Clarendon Laboratory, Parks Road, Oxford OX1 3PU, UK
now at: School of Physics and Astronomy, University of Leicester, University Road, Leicester, LE1 7RH, UK
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Atmos. Chem. Phys., 25, 2631–2648, https://doi.org/10.5194/acp-25-2631-2025, https://doi.org/10.5194/acp-25-2631-2025, 2025
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Rui Song, Adam Povey, and Roy G. Grainger
Atmos. Meas. Tech., 17, 2521–2538, https://doi.org/10.5194/amt-17-2521-2024, https://doi.org/10.5194/amt-17-2521-2024, 2024
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The role of atmospheric aerosols on the Earth's climate and air quality is difficult to be determined quantitatively due to the drawback of available instruments. A widely used instrument to study the role is Optical Particle Counter (OPC). However, an assumption of particle refractive index is needed by OPCs to estimate particle size. This paper discusses SPARCLE 2: a new OPC that does not require such assumption. It was validated using standard particles and used to measure ambient air.
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Andrew T. Prata, Roy G. Grainger, Isabelle A. Taylor, Adam C. Povey, Simon R. Proud, and Caroline A. Poulsen
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Satellite observations are often used to track ash clouds and estimate their height, particle sizes and mass; however, satellite-based techniques are always associated with some uncertainty. We describe advances in a satellite-based technique that is used to estimate ash cloud properties for the June 2019 Raikoke (Russia) eruption. Our results are significant because ash warning centres increasingly require uncertainty information to correctly interpret,
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Daniel Robbins, Caroline Poulsen, Steven Siems, and Simon Proud
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A neural network (NN)-based cloud mask for a geostationary satellite instrument, AHI, is developed using collocated data and is better at not classifying thick aerosols as clouds versus the Japanese Meteorological Association and the Bureau of Meteorology masks, identifying 1.13 and 1.29 times as many non-cloud pixels than each mask, respectively. The improvement during the day likely comes from including the shortest wavelength bands from AHI in the NN mask, which the other masks do not use.
Maria-Elissavet Koukouli, Konstantinos Michailidis, Pascal Hedelt, Isabelle A. Taylor, Antje Inness, Lieven Clarisse, Dimitris Balis, Dmitry Efremenko, Diego Loyola, Roy G. Grainger, and Christian Retscher
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Luca Bugliaro, Dennis Piontek, Stephan Kox, Marius Schmidl, Bernhard Mayer, Richard Müller, Margarita Vázquez-Navarro, Daniel M. Peters, Roy G. Grainger, Josef Gasteiger, and Jayanta Kar
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The monitoring of ash dispersion in the atmosphere is an important task for satellite remote sensing since ash represents a threat to air traffic. We present an AI-based method that retrieves the spatial extension and properties of volcanic ash clouds with high temporal resolution during day and night by means of geostationary satellite measurements. This algorithm, trained on realistic observations simulated with a radiative transfer model, runs operationally at the German Weather Service.
Francisco Lang, Luis Ackermann, Yi Huang, Son C. H. Truong, Steven T. Siems, and Michael J. Manton
Atmos. Chem. Phys., 22, 2135–2152, https://doi.org/10.5194/acp-22-2135-2022, https://doi.org/10.5194/acp-22-2135-2022, 2022
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Marine low-level clouds cover vast areas of the Southern Ocean, and they are essential to the Earth system energy balance. We use 3 years of satellite observations to group low-level clouds by their spatial structure using a pattern-recognizing program. We studied two primary cloud type patterns, i.e. open and closed clouds. Open clouds are uniformly distributed over the storm track, while closed clouds are most predominant in the southeastern Indian Ocean. Closed clouds exhibit a daily cycle.
Matthew W. Christensen, Andrew Gettelman, Jan Cermak, Guy Dagan, Michael Diamond, Alyson Douglas, Graham Feingold, Franziska Glassmeier, Tom Goren, Daniel P. Grosvenor, Edward Gryspeerdt, Ralph Kahn, Zhanqing Li, Po-Lun Ma, Florent Malavelle, Isabel L. McCoy, Daniel T. McCoy, Greg McFarquhar, Johannes Mülmenstädt, Sandip Pal, Anna Possner, Adam Povey, Johannes Quaas, Daniel Rosenfeld, Anja Schmidt, Roland Schrödner, Armin Sorooshian, Philip Stier, Velle Toll, Duncan Watson-Parris, Robert Wood, Mingxi Yang, and Tianle Yuan
Atmos. Chem. Phys., 22, 641–674, https://doi.org/10.5194/acp-22-641-2022, https://doi.org/10.5194/acp-22-641-2022, 2022
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Trace gases and aerosols (tiny airborne particles) are released from a variety of point sources around the globe. Examples include volcanoes, industrial chimneys, forest fires, and ship stacks. These sources provide opportunistic experiments with which to quantify the role of aerosols in modifying cloud properties. We review the current state of understanding on the influence of aerosol on climate built from the wide range of natural and anthropogenic laboratories investigated in recent decades.
Johannes de Leeuw, Anja Schmidt, Claire S. Witham, Nicolas Theys, Isabelle A. Taylor, Roy G. Grainger, Richard J. Pope, Jim Haywood, Martin Osborne, and Nina I. Kristiansen
Atmos. Chem. Phys., 21, 10851–10879, https://doi.org/10.5194/acp-21-10851-2021, https://doi.org/10.5194/acp-21-10851-2021, 2021
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Using the NAME dispersion model in combination with high-resolution SO2 satellite data from TROPOMI, we investigate the dispersion of volcanic SO2 from the 2019 Raikoke eruption. NAME accurately simulates the dispersion of SO2 during the first 2–3 weeks after the eruption and illustrates the potential of using high-resolution satellite data to identify potential limitations in dispersion models, which will ultimately help to improve efforts to forecast the dispersion of volcanic clouds.
Jane P. Mulcahy, Colin Johnson, Colin G. Jones, Adam C. Povey, Catherine E. Scott, Alistair Sellar, Steven T. Turnock, Matthew T. Woodhouse, Nathan Luke Abraham, Martin B. Andrews, Nicolas Bellouin, Jo Browse, Ken S. Carslaw, Mohit Dalvi, Gerd A. Folberth, Matthew Glover, Daniel P. Grosvenor, Catherine Hardacre, Richard Hill, Ben Johnson, Andy Jones, Zak Kipling, Graham Mann, James Mollard, Fiona M. O'Connor, Julien Palmiéri, Carly Reddington, Steven T. Rumbold, Mark Richardson, Nick A. J. Schutgens, Philip Stier, Marc Stringer, Yongming Tang, Jeremy Walton, Stephanie Woodward, and Andrew Yool
Geosci. Model Dev., 13, 6383–6423, https://doi.org/10.5194/gmd-13-6383-2020, https://doi.org/10.5194/gmd-13-6383-2020, 2020
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Aerosols are an important component of the Earth system. Here, we comprehensively document and evaluate the aerosol schemes as implemented in the physical and Earth system models, HadGEM3-GC3.1 and UKESM1. This study provides a useful characterisation of the aerosol climatology in both models, facilitating the understanding of the numerous aerosol–climate interaction studies that will be conducted for CMIP6 and beyond.
Nick Schutgens, Andrew M. Sayer, Andreas Heckel, Christina Hsu, Hiren Jethva, Gerrit de Leeuw, Peter J. T. Leonard, Robert C. Levy, Antti Lipponen, Alexei Lyapustin, Peter North, Thomas Popp, Caroline Poulsen, Virginia Sawyer, Larisa Sogacheva, Gareth Thomas, Omar Torres, Yujie Wang, Stefan Kinne, Michael Schulz, and Philip Stier
Atmos. Chem. Phys., 20, 12431–12457, https://doi.org/10.5194/acp-20-12431-2020, https://doi.org/10.5194/acp-20-12431-2020, 2020
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We intercompare 14 different datasets of satellite observations of aerosol. Such measurements are challenging but also provide the best opportunity to globally observe an atmospheric component strongly related to air pollution and climate change. Our study shows that most datasets perform similarly well on a global scale but that locally errors can be quite different. We develop a technique to estimate satellite errors everywhere, even in the absence of surface reference data.
Cited articles
Abram, N. J., Henley, B. J., Gupta, A. S., Lippmann, T. J. R., Clarke, H., Dowdy, A. J., Sharples, J. J., Nolan, R. H., Zhang, T., Wooster, M. J., Wurtzel, J. B., Meissner, K. J., Pitman, A. J., Ukkola, A. M., Murphy, B. P., Tapper, N. J., and Boer, M. M.: Connections of climate change and variability to large and extreme forest fires in southeast Australia, Commun. Earth Environ., 2, 8, https://doi.org/10.1038/s43247-020-00065-8, 2021. a
Ackerman, A. S., Toon, O. B., Stevens, D. E., Heymsfield, A. J., Ramanathan, V., and Welton, E. J.: Reduction of Tropical Cloudiness by Soot, Science, 288, 1042–1047, https://doi.org/10.1126/science.288.5468.1042, 2000. a
AERONET: AERONET Aerosol Optical Depth Data Display Interface, Goddard Space Flight Center, Aerosol Robotic Network [data set], https://aeronet.gsfc.nasa.gov/cgi-bin/data_display_aod_v3, last access: 4 August 2023. a
Andreae, M. O.: Emission of trace gases and aerosols from biomass burning – an updated assessment, Atmos. Chem. Phys., 19, 8523–8546, https://doi.org/10.5194/acp-19-8523-2019, 2019. a
Arias, P. A., Bellouin, N., Coppola, E., Jones, R. G., Krinner, G., Marotzke, J., Naik, V., Palmer, M. D., Plattner, G.-K., Rogelj, J., Rojas, M., Sillmann, J., Storelvmo, T., Thorne, P. W., Trewin, B., Achuta Rao, K., Adhikary, B., Allan, R. P., Armour, K., Bala, G., Barimalala, R., Berger, S., Canadell, J. G., Cassou, C., Cherchi, A., Collins, W., Collins, W. D., Connors, S. L., Corti, S., Cruz, F., Dentener, F. J., Dereczynski, C., Di Luca, A., Diongue Niang, A., Doblas-Reyes, F. J., Dosio, A., Douville, H., Engelbrecht, F., Eyring, V., Fischer, E., Forster, P., Fox-Kemper, B., Fuglestvedt, J. S., Fyfe, J. C., Gillett, N. P., Goldfarb, L., Gorodetskaya, I., Gutierrez, J. M., Hamdi, R., Hawkins, E., Hewitt, H. T., Hope, P., Islam, A. S., Jones, C., Kaufman, D. S., Kopp, R. E., Kosaka, Y., Kossin, J., Krakovska, S., Lee, J.-Y., Li, J., Mauritsen, T., Maycock, T. K., Meinshausen, M., Min, S.-K., Monteiro, P. M. S., Ngo-Duc, T., Otto, F., Pinto, I., Pirani, A., Raghavan, K., Ranasinghe, R., Ruane, A. C., Ruiz, L., Sallée, J.-B., Samset, B. H., Sathyendranath, S., Seneviratne, S. I., Sörensson, A. A., Szopa, S., Takayabu, I., Treguier, A.-M., van den Hurk, B., Vautard, R., von Schuckmann, K., Zaehle, S., Zhang, X., and Zickfeld, K.: Technical Summary, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_TS.pdf (last access: 4 August 2023), 2021. a
Arriagada, N. B., Palmer, A. J., Bowman, D. M., Morgan, G. G., Jalaludin, B. B., and Johnston, F. H.: Unprecedented smoke-related health burden associated with the 2019–20 bushfires in eastern Australia, Med. J. Australia, 213, 282–283, https://doi.org/10.5694/mja2.50545, 2020. a
Attiya, A. A. and Jones, B. G.: Impact of Smoke Plumes Transport on Air Quality in Sydney during Extensive Bushfires (2019) in New South Wales, Australia Using Remote Sensing and Ground Data, Remote Sens.-Basel, 14, 5552, https://doi.org/10.3390/rs14215552, 2022. a
Bellouin, N., Boucher, O., Haywood, J., and Reddy, M. S.: Global estimate of aerosol direct radiative forcing from satellite measurements, Nature, 438, 1138–1141, https://doi.org/10.1038/nature04348, 2005. a
Bellouin, N., Quaas, J., Gryspeerdt, E., Kinne, S., Stier, P., Watson-Parris, D., Boucher, O., Carslaw, K. S., Christensen, M., Daniau, A.-L., Dufresne, J.-L., Feingold, G., Fiedler, S., Forster, P., Gettelman, A., Haywood, J. M., Lohmann, U., Malavelle, F., Mauritsen, T., McCoy, D. T., Myhre, G., Mülmenstädt, J., Neubauer, D., Possner, A., Rugenstein, M., Sato, Y., Schulz, M., Schwartz, S. E., Sourdeval, O., Storelvmo, T., Toll, V., Winker, D., and Stevens, B.: Bounding Global Aerosol Radiative Forcing of Climate Change, Rev. Geophys., 58, e2019RG000660, https://doi.org/10.1029/2019rg000660, 2020. a
Bessho, K., Date, K., Hayashi, M., Ikeda, A., Imai, T., Inoue, H., Kumagai, Y., Miyakawa, T., Murata, H., Ohno, T., Okuyama, A., Oyama, R., Sasaki, Y., Shimazu, Y., Shimoji, K., Sumida, Y., Suzuki, M., Taniguchi, H., Tsuchiyama, H., Uesawa, D., Yokota, H., and Yoshida, R.: An Introduction to Himawari-8/9- Japan's New-Generation Geostationary Meteorological Satellites, J. Meteorol. Soc. Jpn. Ser. II, 94, 151–183, https://doi.org/10.2151/jmsj.2016-009, 2016. a, b, c
Boer, M. M., de Dios, V. R., and Bradstock, R. A.: Unprecedented burn area of Australian mega forest fires, Nat. Clim. Change, 10, 171–172, https://doi.org/10.1038/s41558-020-0716-1, 2020. a
Bureau Of Meteorology: Bureau of Meteorology Satellite Low Level Data, NCI Australia [data set], https://doi.org/10.25914/6TV5-F523, 2022. a
Canadell, J. G., Meyer, C. P., Cook, G. D., Dowdy, A., Briggs, P. R., Knauer, J., Pepler, A., and Haverd, V.: Multi-decadal increase of forest burned area in Australia is linked to climate change, Nat. Commun., 12, 6921, https://doi.org/10.1038/s41467-021-27225-4, 2021. a
Chang, D. Y., Yoon, J., Lelieveld, J., Park, S. K., Yum, S. S., Kim, J., and Jeong, S.: Direct radiative forcing of biomass burning aerosols from the extensive Australian wildfires in 2019–2020, Environ. Res. Lett., 16, 044041, https://doi.org/10.1088/1748-9326/abecfe, 2021. a, b
Chen, J., Li, C., Ristovski, Z., Milic, A., Gu, Y., Islam, M. S., Wang, S., Hao, J., Zhang, H., He, C., Guo, H., Fu, H., Miljevic, B., Morawska, L., Thai, P., LAM, Y. F., Pereira, G., Ding, A., Huang, X., and Dumka, U. C.: A review of biomass burning: Emissions and impacts on air quality, health and climate in China, Sci. Total Environ., 579, 1000–1034, https://doi.org/10.1016/j.scitotenv.2016.11.025, 2017. a
Collins, L., Bradstock, R. A., Clarke, H., Clarke, M. F., Nolan, R. H., and Penman, T. D.: The 2019/2020 mega-fires exposed Australian ecosystems to an unprecedented extent of high-severity fire, Environ. Res. Lett., 16, 044029, https://doi.org/10.1088/1748-9326/abeb9e, 2021. a
Connolly, P. J., Vaughan, G., May, P. T., Chemel, C., Allen, G., Choularton, T. W., Gallagher, M. W., Bower, K. N., Crosier, J., and Dearden, C.: Can aerosols influence deep tropical convection? Aerosol indirect effects in the Hector island thunderstorm, Q. J. Roy. Meteor. Soc., 139, 2190–2208, https://doi.org/10.1002/qj.2083, 2012. a
Coppo, P., Ricciarelli, B., Brandani, F., Delderfield, J., Ferlet, M., Mutlow, C., Munro, G., Nightingale, T., Smith, D., Bianchi, S., Nicol, P., Kirschstein, S., Hennig, T., Engel, W., Frerick, J., and Nieke, J.: SLSTR: a high accuracy dual scan temperature radiometer for sea and land surface monitoring from space, J. Mod. Optic., 57, 1815–1830, https://doi.org/10.1080/09500340.2010.503010, 2010. a, b
Cox, C. and Munk, W.: Measurement of the Roughness of the Sea Surface from Photographs of the Sun's Glitter, J. Opt. Soc. Am., 44, 838–850, https://doi.org/10.1364/josa.44.000838, 1954. a
Cruz, M., Sullivan, A., Gould, J., Sims, N., Bannister, A., Hollis, J., and Hurley, R.: Anatomy of a catastrophic wildfire: The Black Saturday Kilmore East fire in Victoria, Australia, Forest Ecol. Manag., 284, 269–285, https://doi.org/10.1016/j.foreco.2012.02.035, 2012. a
Dickman, C. R.: Ecological consequences of Australia's “Black Summer” bushfires: Managing for recovery, Integr. Environ. Asses., 17, 1162–1167, https://doi.org/10.1002/ieam.4496, 2021. a
Eck, T. F., Holben, B. N., Giles, D. M., Slutsker, I., Sinyuk, A., Schafer, J. S., Smirnov, A., Sorokin, M., Reid, J. S., Sayer, A. M., Hsu, N. C., Shi, Y. R., Levy, R. C., Lyapustin, A., Rahman, M. A., Liew, S.-C., Cortijo, S. V. S., Li, T., Kalbermatter, D., Keong, K. L., Yuggotomo, M. E., Aditya, F., Mohamad, M., Mahmud, M., Chong, T. K., Lim, H.-S., Choon, Y. E., Deranadyan, G., Kusumaningtyas, S. D. A., and Aldrian, E.: AERONET Remotely Sensed Measurements and Retrievals of Biomass Burning Aerosol Optical Properties During the 2015 Indonesian Burning Season, J. Geophys. Res.-Atmos., 124, 4722–4740, https://doi.org/10.1029/2018jd030182, 2019. a, b
Engel-Cox, J. A., Holloman, C. H., Coutant, B. W., and Hoff, R. M.: Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality, Atmos. Environ., 38, 2495–2509, https://doi.org/10.1016/j.atmosenv.2004.01.039, 2004. a
Fernández-García, V., Beltrán-Marcos, D., Fernández-Guisuraga, J. M., Marcos, E., and Calvo, L.: Predicting potential wildfire severity across Southern Europe with global data sources, Sci. Total Environ., 829, 154729, https://doi.org/10.1016/j.scitotenv.2022.154729, 2022. a
Flemming, J., Benedetti, A., Inness, A., Engelen, R. J., Jones, L., Huijnen, V., Remy, S., Parrington, M., Suttie, M., Bozzo, A., Peuch, V.-H., Akritidis, D., and Katragkou, E.: The CAMS interim Reanalysis of Carbon Monoxide, Ozone and Aerosol for 2003–2015, Atmos. Chem. Phys., 17, 1945–1983, https://doi.org/10.5194/acp-17-1945-2017, 2017. a
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/jcli-d-16-0758.1, 2017. a
Giles, D. M., Sinyuk, A., Sorokin, M. G., Schafer, J. S., Smirnov, A., Slutsker, I., Eck, T. F., Holben, B. N., Lewis, J. R., Campbell, J. R., Welton, E. J., Korkin, S. V., and Lyapustin, A. I.: Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements, Atmos. Meas. Tech., 12, 169–209, https://doi.org/10.5194/amt-12-169-2019, 2019. a, b, c
González, R., Toledano, C., Román, R., Mateos, D., Asmi, E., Rodríguez, E., Lau, I. C., Ferrara, J., D'Elia, R., Antuña-Sánchez, J. C., Cachorro, V. E., Calle, A., and de Frutos, Á. M.: Characterization of Stratospheric Smoke Particles over the Antarctica by Remote Sensing Instruments, Remote Sens.-Basel, 12, 3769, https://doi.org/10.3390/rs12223769, 2020. a, b
Goodman, S. J.: GOES-R Series Introduction, in: The GOES-R Series, Elsevier, 1–3, https://doi.org/10.1016/b978-0-12-814327-8.00001-9, 2020. a
Gott, B.: Aboriginal fire management in south-eastern Australia: aims and frequency, J. Biogeogr., 32, 1203–1208, https://doi.org/10.1111/j.1365-2699.2004.01233.x, 2005. a
Graham, A. M., Pringle, K. J., Pope, R. J., Arnold, S. R., Conibear, L. A., Burns, H., Rigby, R., Borchers-Arriagada, N., Butt, E. W., Kiely, L., Reddington, C., Spracklen, D. V., Woodhouse, M. T., Knote, C., and McQuaid, J. B.: Impact of the 2019/2020 Australian Megafires on Air Quality and Health, GeoHealth, 5, e2021GH000454, https://doi.org/10.1029/2021gh000454, 2021. a, b
Hanssen, A. and Kuipers, W.: On the Relationship Between the Frequency of Rain and Various Meteorological Parameters (with Reference to the Problem Of Objective Forecasting), Koninkl. Nederlands Meterologisch Institut, Mededelingen en Verhandelingen, Staatsdrukkerij- en Uitgeverijbedrijf, https://books.google.com.au/books?id=nTZ8OgAACAAJ (last access: 4 August 2023), 1965. a, b
Haywood, J. and Boucher, O.: Estimates of the direct and indirect radiative forcing due to tropospheric aerosols: A review, Rev. Geophys., 38, 513–543, https://doi.org/10.1029/1999rg000078, 2000. a
Henocq, C., North, P., Heckel, A., Ferron, S., Lamquin, N., Dransfeld, S., Bourg, L., TOTE, C., and Ramon, D.: OLCI/SLSTR SYN L2 Algorithm and Products Overview, in: IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018, IEEE, https://doi.org/10.1109/igarss.2018.8517420, 2018. a
Hirsch, E. and Koren, I.: Record-breaking aerosol levels explained by smoke injection into the stratosphere, Science, 371, 1269–1274, https://doi.org/10.1126/science.abe1415, 2021. a
Holben, B., Eck, T., Slutsker, I., Tanré, D., Buis, J., Setzer, A., Vermote, E., Reagan, J., Kaufman, Y., Nakajima, T., Lavenu, F., Jankowiak, I., and Smirnov, A.: AERONET – A Federated Instrument Network and Data Archive for Aerosol Characterization, Remote Sens. Environ., 66, 1–16, https://doi.org/10.1016/s0034-4257(98)00031-5, 1998. a, b, c
Holmlund, K., Grandell, J., Schmetz, J., Stuhlmann, R., Bojkov, B., Munro, R., Lekouara, M., Coppens, D., Viticchie, B., August, T., Theodore, B., Watts, P., Dobber, M., Fowler, G., Bojinski, S., Schmid, A., Salonen, K., Tjemkes, S., Aminou, D., and Blythe, P.: Meteosat Third Generation (MTG): Continuation and Innovation of Observations from Geostationary Orbit, B. Am. Meteorol. Soc., 102, E990–E1015, https://doi.org/10.1175/bams-d-19-0304.1, 2021. a
Hsu, N. C., Lee, J., Sayer, A. M., Kim, W., Bettenhausen, C., and Tsay, S.-C.: VIIRS Deep Blue Aerosol Products Over Land: Extending the EOS Long-Term Aerosol Data Records, J. Geophys. Res.-Atmos., 124, 4026–4053, https://doi.org/10.1029/2018jd029688, 2019. a, b
Huang, X. and Ding, A.: Aerosol as a critical factor causing forecast biases of air temperature in global numerical weather prediction models, Sci. Bull., 66, 1917–1924, https://doi.org/10.1016/j.scib.2021.05.009, 2021. a, b
Hutchinson, M., Stein, J., Stein, J., Anderson, H., and Tickle, P.: GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008, Geoscience Australia, http://pid.geoscience.gov.au/dataset/ga/66006 (last access: 27 March 2023), 2008. a
Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019, 2019. a
Isaza, A., Kay, M., Evans, J. P., Prasad, A., and Bremner, S.: Air quality impacts on rooftop photovoltaic energy production during the 2019–2020 Australian bushfires season, Sol. Energy, 257, 240–248, https://doi.org/10.1016/j.solener.2023.04.014, 2023. a
JAXA: JAXA Himawari Monitor P-Tree, Japan Aerospace Exploration Agency [data set], https://www.eorc.jaxa.jp/ptree/, last access: 4 August 2023. a
Juliano, T. W., Jiménez, P. A., Kosović, B., Eidhammer, T., Thompson, G., Berg, L. K., Fast, J., Motley, A., and Polidori, A.: Smoke from 2020 United States wildfires responsible for substantial solar energy forecast errors, Environ. Res. Lett., 17, 034010, https://doi.org/10.1088/1748-9326/ac5143, 2022. a
Justice, C., Townshend, J., Vermote, E., Masuoka, E., Wolfe, R., Saleous, N., Roy, D., and Morisette, J.: An overview of MODIS Land data processing and product status, Remote Sens. Environ., 83, 3–15, https://doi.org/10.1016/s0034-4257(02)00084-6, 2002. a
Knipling, E. B.: Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation, Remote Sens. Environ.,s 1, 155–159, https://doi.org/10.1016/s0034-4257(70)80021-9, 1970. a
Kochanski, A. K., Mallia, D. V., Fearon, M. G., Mandel, J., Souri, A. H., and Brown, T.: Modeling Wildfire Smoke Feedback Mechanisms Using a Coupled Fire-Atmosphere Model With a Radiatively Active Aerosol Scheme, J. Geophys. Res.-Atmos., 124, 9099–9116, https://doi.org/10.1029/2019jd030558, 2019. a
Li, F., Zhang, X., and Kondragunta, S.: Highly anomalous fire emissions from the 2019–2020 Australian bushfires, Environmental Research Communications, 3, 105005, https://doi.org/10.1088/2515-7620/ac2e6f, 2021a. a
Lisok, J., Rozwadowska, A., Pedersen, J. G., Markowicz, K. M., Ritter, C., Kaminski, J. W., Struzewska, J., Mazzola, M., Udisti, R., Becagli, S., and Gorecka, I.: Radiative impact of an extreme Arctic biomass-burning event, Atmos. Chem. Phys., 18, 8829–8848, https://doi.org/10.5194/acp-18-8829-2018, 2018. a
Liu, L., Cheng, Y., Wang, S., Wei, C., Pöhlker, M. L., Pöhlker, C., Artaxo, P., Shrivastava, M., Andreae, M. O., Pöschl, U., and Su, H.: Impact of biomass burning aerosols on radiation, clouds, and precipitation over the Amazon: relative importance of aerosol–cloud and aerosol–radiation interactions, Atmos. Chem. Phys., 20, 13283–13301, https://doi.org/10.5194/acp-20-13283-2020, 2020. a
Lyapustin, A., Wang, Y., Korkin, S., and Huang, D.: MODIS Collection 6 MAIAC algorithm, Atmos. Meas. Tech., 11, 5741–5765, https://doi.org/10.5194/amt-11-5741-2018, 2018. a, b, c
Markowicz, K., Chilinski, M., Lisok, J., Zawadzka, O., Stachlewska, I., Janicka, L., Rozwadowska, A., Makuch, P., Pakszys, P., Zielinski, T., Petelski, T., Posyniak, M., Pietruczuk, A., Szkop, A., and Westphal, D.: Study of aerosol optical properties during long-range transport of biomass burning from Canada to Central Europe in July 2013, J. Aerosol Sci., 101, 156–173, https://doi.org/10.1016/j.jaerosci.2016.08.006, 2016. a, b, c, d, e, f
Matus, A. V., L′Ecuyer, T. S., and Henderson, D. S.: New Estimates of Aerosol Direct Radiative Effects and Forcing From A-Train Satellite Observations, Geophys. Res. Lett., 46, 8338–8346, https://doi.org/10.1029/2019gl083656, 2019. a
McGarragh, G. R., Poulsen, C. A., Thomas, G. E., Povey, A. C., Sus, O., Stapelberg, S., Schlundt, C., Proud, S., Christensen, M. W., Stengel, M., Hollmann, R., and Grainger, R. G.: The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approach, Atmos. Meas. Tech., 11, 3397–3431, https://doi.org/10.5194/amt-11-3397-2018, 2018. a, b, c, d, e
Miller, S. D., Schmit, T. L., Seaman, C. J., Lindsey, D. T., Gunshor, M. M., Kohrs, R. A., Sumida, Y., and Hillger, D.: A Sight for Sore Eyes: The Return of True Color to Geostationary Satellites, B. Am. Meteorol. Soc., 97, 1803–1816, https://doi.org/10.1175/bams-d-15-00154.1, 2016. a
Morgan, G. W., Tolhurst, K. G., Poynter, M. W., Cooper, N., McGuffog, T., Ryan, R., Wouters, M. A., Stephens, N., Black, P., Sheehan, D., Leeson, P., Whight, S., and Davey, S. M.: Prescribed burning in south-eastern Australia: history and future directions, Aust. Forestry, 83, 4–28, https://doi.org/10.1080/00049158.2020.1739883, 2020. a
Mukai, S., Sano, I., and Nakata, M.: Improved Algorithms for Remote Sensing-Based Aerosol Retrieval during Extreme Biomass Burning Events, Atmosphere, 12, 403, https://doi.org/10.3390/atmos12030403, 2021. a, b, c
Nakajima, T. and King, M. D.: Determination of the Optical Thickness and Effective Particle Radius of Clouds from Reflected Solar Radiation Measurements. Part I: Theory, J. Atmos. Sci., 47, 1878–1893, https://doi.org/10.1175/1520-0469(1990)047<1878:dotota>2.0.co;2, 1990. a
NASA: Earthdata Search, NASA [data set], https://search.earthdata.nasa.gov/search, last access: 4 August 2023. a
Omar, A. H.: Development of global aerosol models using cluster analysis of Aerosol Robotic Network (AERONET) measurements, J. Geophys. Res., 110, D10S14, https://doi.org/10.1029/2004jd004874, 2005. a
Omar, A. H., Winker, D. M., Vaughan, M. A., Hu, Y., Trepte, C. R., Ferrare, R. A., Lee, K.-P., Hostetler, C. A., Kittaka, C., Rogers, R. R., Kuehn, R. E., and Liu, Z.: The CALIPSO Automated Aerosol Classification and Lidar Ratio Selection Algorithm, J. Atmos. Ocean. Tech., 26, 1994–2014, https://doi.org/10.1175/2009jtecha1231.1, 2009. a
Pan, X., Ichoku, C., Chin, M., Bian, H., Darmenov, A., Colarco, P., Ellison, L., Kucsera, T., da Silva, A., Wang, J., Oda, T., and Cui, G.: Six global biomass burning emission datasets: intercomparison and application in one global aerosol model, Atmos. Chem. Phys., 20, 969–994, https://doi.org/10.5194/acp-20-969-2020, 2020. a
Papanikolaou, C.-A., Kokkalis, P., Soupiona, O., Solomos, S., Papayannis, A., Mylonaki, M., Anagnou, D., Foskinis, R., and Gidarakou, M.: Australian Bushfires (2019–2020): Aerosol Optical Properties and Radiative Forcing, Atmosphere, 13, 867, https://doi.org/10.3390/atmos13060867, 2022. a, b
Petrenko, M., Kahn, R., Chin, M., and Limbacher, J.: Refined Use of Satellite Aerosol Optical Depth Snapshots to Constrain Biomass Burning Emissions in the GOCART Model, J. Geophys. Res.-Atmos., 122, 10,983–11,004, https://doi.org/10.1002/2017jd026693, 2017. a, b, c
Poulsen, C. A., Siddans, R., Thomas, G. E., Sayer, A. M., Grainger, R. G., Campmany, E., Dean, S. M., Arnold, C., and Watts, P. D.: Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR, Atmos. Meas. Tech., 5, 1889–1910, https://doi.org/10.5194/amt-5-1889-2012, 2012. a, b, c, d
Povey, A., McGarragh, G., Proud, S., Poulsen, C., Thomas, G., Philipp, D., Prata, A., and Stengel, M.: ORAC-CC/orac: ORAC Code for DOI (v09-beta), Zenodo [code], https://doi.org/10.5281/zenodo.11217795, 2024. a
Prata, A. T., Grainger, R. G., Taylor, I. A., Povey, A. C., Proud, S. R., and Poulsen, C. A.: Uncertainty-bounded estimates of ash cloud properties using the ORAC algorithm: application to the 2019 Raikoke eruption, Atmos. Meas. Tech., 15, 5985–6010, https://doi.org/10.5194/amt-15-5985-2022, 2022. a, b
Reisen, F., Meyer, C. M., and Keywood, M. D.: Impact of biomass burning sources on seasonal aerosol air quality, Atmos. Environ., 67, 437–447, https://doi.org/10.1016/j.atmosenv.2012.11.004, 2013. a
Ribeiro, L. M., Viegas, D. X., Almeida, M., McGee, T. K., Pereira, M. G., Parente, J., Xanthopoulos, G., Leone, V., Delogu, G. M., and Hardin, H.: Extreme wildfires and disasters around the world, in: Extreme Wildfire Events and Disasters, Elsevier, 31–51, https://doi.org/10.1016/b978-0-12-815721-3.00002-3, 2020. a
Rodgers, C. D.: Inverse methods for atmospheric sounding: theory and practice, vol. 2, World Scientific, ISBN 9789814498685, 2000. a
Sayer, A. M., Thomas, G. E., and Grainger, R. G.: A sea surface reflectance model for (A)ATSR, and application to aerosol retrievals, Atmos. Meas. Tech., 3, 813–838, https://doi.org/10.5194/amt-3-813-2010, 2010. a
Sayer, A. M., Hsu, N. C., Lee, J., Bettenhausen, C., Kim, W. V., and Smirnov, A.: Satellite Ocean Aerosol Retrieval (SOAR) Algorithm Extension to S-NPP VIIRS as Part of the “Deep Blue” Aerosol Project, J. Geophys. Res.-Atmos., 123, 380–400, https://doi.org/10.1002/2017jd027412, 2018. a, b
Sayer, A. M., Hsu, N. C., Lee, J., Kim, W. V., and Dutcher, S. T.: Validation, Stability, and Consistency of MODIS Collection 6.1 and VIIRS Version 1 Deep Blue Aerosol Data Over Land, J. Geophys. Res.-Atmos., 124, 4658–4688, https://doi.org/10.1029/2018jd029598, 2019. a, b
Schmit, T. J., Griffith, P., Gunshor, M. M., Daniels, J. M., Goodman, S. J., and Lebair, W. J.: A Closer Look at the ABI on the GOES-R Series, B. Am. Meteorol. Soc., 98, 681–698, https://doi.org/10.1175/bams-d-15-00230.1, 2017. a
Schutgens, N., Sayer, A. M., Heckel, A., Hsu, C., Jethva, H., de Leeuw, G., Leonard, P. J. T., Levy, R. C., Lipponen, A., Lyapustin, A., North, P., Popp, T., Poulsen, C., Sawyer, V., Sogacheva, L., Thomas, G., Torres, O., Wang, Y., Kinne, S., Schulz, M., and Stier, P.: An AeroCom–AeroSat study: intercomparison of satellite AOD datasets for aerosol model evaluation, Atmos. Chem. Phys., 20, 12431–12457, https://doi.org/10.5194/acp-20-12431-2020, 2020. a
Sellitto, P., Belhadji, R., Kloss, C., and Legras, B.: Radiative impacts of the Australian bushfires 2019–2020 – Part 1: Large-scale radiative forcing, Atmos. Chem. Phys., 22, 9299–9311, https://doi.org/10.5194/acp-22-9299-2022, 2022. a
Sinyuk, A., Holben, B. N., Eck, T. F., Giles, D. M., Slutsker, I., Korkin, S., Schafer, J. S., Smirnov, A., Sorokin, M., and Lyapustin, A.: The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2, Atmos. Meas. Tech., 13, 3375–3411, https://doi.org/10.5194/amt-13-3375-2020, 2020. a
Sorek-Hamer, M., Chatfield, R., and Liu, Y.: Review: Strategies for using satellite-based products in modeling PM2.5 and short-term pollution episodes, Environ. Int., 144, 106057, https://doi.org/10.1016/j.envint.2020.106057, 2020. a
Stamnes, K., Tsay, S.-C., Wiscombe, W., and Jayaweera, K.: Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media, Appl. Optics, 27, 2502, https://doi.org/10.1364/ao.27.002502, 1988. a
Sutherland, R. A. and Khanna, R. K.: Optical Properties of Organic-based Aerosols Produced by Burning Vegetation, Aerosol Sci. Tech., 14, 331–342, https://doi.org/10.1080/02786829108959495, 1991. a, b, c
Thomas, G. E., Carboni, E., Sayer, A. M., Poulsen, C. A., Siddans, R., and Grainger, R. G.: Oxford-RAL Aerosol and Cloud (ORAC): aerosol retrievals from satellite radiometers, in: Satellite Aerosol Remote Sensing over Land, Springer Berlin Heidelberg, 193–225, https://doi.org/10.1007/978-3-540-69397-0_7, 2009. a, b, c, d
Torres, O., Jethva, H., Ahn, C., Jaross, G., and Loyola, D. G.: TROPOMI aerosol products: evaluation and observations of synoptic-scale carbonaceous aerosol plumes during 2018–2020, Atmos. Meas. Tech., 13, 6789–6806, https://doi.org/10.5194/amt-13-6789-2020, 2020. a, b
van Donkelaar, A., Martin, R. V., Levy, R. C., da Silva, A. M., Krzyzanowski, M., Chubarova, N. E., Semutnikova, E., and Cohen, A. J.: Satellite-based estimates of ground-level fine particulate matter during extreme events: A case study of the Moscow fires in 2010, Atmos. Environ., 45, 6225–6232, https://doi.org/10.1016/j.atmosenv.2011.07.068, 2011. a, b, c
van Oldenborgh, G. J., Krikken, F., Lewis, S., Leach, N. J., Lehner, F., Saunders, K. R., van Weele, M., Haustein, K., Li, S., Wallom, D., Sparrow, S., Arrighi, J., Singh, R. K., van Aalst, M. K., Philip, S. Y., Vautard, R., and Otto, F. E. L.: Attribution of the Australian bushfire risk to anthropogenic climate change, Nat. Hazards Earth Syst. Sci., 21, 941–960, https://doi.org/10.5194/nhess-21-941-2021, 2021. a
Vogel, A., Alessa, G., Scheele, R., Weber, L., Dubovik, O., North, P., and Fiedler, S.: Uncertainty in Aerosol Optical Depth From Modern Aerosol-Climate Models, Reanalyses, and Satellite Products, J. Geophys. Res.-Atmos., 127, e2021JD035483, https://doi.org/10.1029/2021jd035483, 2022. a
Walter, C. M., Schneider-Futschik, E. K., Knibbs, L. D., and Irving, L. B.: Health impacts of bushfire smoke exposure in Australia, Respirology, 25, 495–501, https://doi.org/10.1111/resp.13798, 2020. a
Wei, X., Chang, N.-B., Bai, K., and Gao, W.: Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives, Crit. Rev. Env. Sci. Tec., 50, 1640–1725, https://doi.org/10.1080/10643389.2019.1665944, 2019. a
Wen, B., Wu, Y., Xu, R., Guo, Y., and Li, S.: Excess emergency department visits for cardiovascular and respiratory diseases during the 2019–20 bushfire period in Australia: A two-stage interrupted time-series analysis, Sci. Total Environ., 809, 152226, https://doi.org/10.1016/j.scitotenv.2021.152226, 2022. a
Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z., Hunt, W. H., and Young, S. A.: Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms, J. Atmos. Ocean. Tech., 26, 2310–2323, https://doi.org/10.1175/2009jtecha1281.1, 2009. a
Yang, X., Zhao, C., Yang, Y., and Fan, H.: Long-term multi-source data analysis about the characteristics of aerosol optical properties and types over Australia, Atmos. Chem. Phys., 21, 3803–3825, https://doi.org/10.5194/acp-21-3803-2021, 2021. a
Ye, X., Deshler, M., Lyapustin, A., Wang, Y., Kondragunta, S., and Saide, P.: Assessment of Satellite AOD during the 2020 Wildfire Season in the Western U. S., Remote Sens.-Basel, 14, 6113, https://doi.org/10.3390/rs14236113, 2022. a
Yoshida, M., Kikuchi, M., Nagao, T. M., Murakami, H., Nomaki, T., and Higurashi, A.: Common Retrieval of Aerosol Properties for Imaging Satellite Sensors, J. Meteorol. Soc. Jpn. Ser. II, 96B, 193–209, https://doi.org/10.2151/jmsj.2018-039, 2018. a, b, c
Zhang, W., Xu, H., and Zheng, F.: Aerosol Optical Depth Retrieval over East Asia Using Himawari-8/AHI Data, Remote Sens.-Basel, 10, 137, https://doi.org/10.3390/rs10010137, 2018. a
Zhong, Q., Schutgens, N., van der Werf, G., van Noije, T., Tsigaridis, K., Bauer, S. E., Mielonen, T., Kirkevåg, A., Seland, Ø., Kokkola, H., Checa-Garcia, R., Neubauer, D., Kipling, Z., Matsui, H., Ginoux, P., Takemura, T., Le Sager, P., Rémy, S., Bian, H., Chin, M., Zhang, K., Zhu, J., Tsyro, S. G., Curci, G., Protonotariou, A., Johnson, B., Penner, J. E., Bellouin, N., Skeie, R. B., and Myhre, G.: Satellite-based evaluation of AeroCom model bias in biomass burning regions, Atmos. Chem. Phys., 22, 11009–11032, https://doi.org/10.5194/acp-22-11009-2022, 2022. a
Zhuravleva, T. B., Kabanov, D. M., Nasrtdinov, I. M., Russkova, T. V., Sakerin, S. M., Smirnov, A., and Holben, B. N.: Radiative characteristics of aerosol during extreme fire event over Siberia in summer 2012, Atmos. Meas. Tech., 10, 179–198, https://doi.org/10.5194/amt-10-179-2017, 2017. a, b, c, d, e
Short summary
Extreme wildfire events are becoming more common with climate change. The smoke plumes associated with these wildfires are not captured by current operational satellite products due to their high optical thickness. We have developed a novel aerosol retrieval for the Advanced Himawari Imager to study these plumes. We find very high values of optical thickness not observed in other operational satellite products, suggesting these plumes have been missed in previous studies.
Extreme wildfire events are becoming more common with climate change. The smoke plumes...