the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Geostationary aerosol retrievals of extreme biomass burning plumes during the 2019–20 Australian bushfires
Abstract. Extreme biomass burning (BB) events, such as those seen during the 2019–20 Australian bushfire season, are becoming more frequent and intense with climate change. Ground-based observations of these events can provide useful information on the macro- and micro-physical properties of the plumes, but these observations are sparse, especially in regions which are at risk of intense bushfire events. Satellite observations of extreme BB events provide a unique perspective, with the newest generation of geostationary imagers, such as the Advanced Himawari Imager (AHI), observing entire continents at moderate spatial and high temporal resolution. However, current passive satellite retrieval methods struggle to capture the high values of aerosol optical thickness (AOT) seen during these BB events. Accurate retrievals are necessary for global and regional studies of shortwave radiation, air quality modelling and numerical weather prediction. To address these issues, the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm has used AHI data to measure extreme BB plumes from the 2019–20 Australian bushfire season. The sensitivity of the retrieval to the assumed optical properties of BB plumes is explored by comparing retrieved AOT with AERONET L1.5 data over the AERONET site at Tumbarumba, New South Wales, between 1 December 2019 00:00 UTC to 3 January 2020 00:00 UTC. The study shows that for AOT values > 2, the sensitivity to assumed optical properties is substantial. The ORAC retrievals and AERONET data are compared against the JAXA Aerosol Retrieval Product (ARP), MODIS Deep Blue over land, MODIS MAIAC, SLSTR SYN and VIIRS Deep Blue products. The comparison shows the ORAC retrieval significantly improves coverage of optically thick plumes relative to the JAXA ARP, with approximately twice as many pixels retrieved and peak retrieved AOT values 1.4 higher than the JAXA ARP. The ORAC retrievals have accuracy scores between 0.742–0.744 compared to the values of 0.718–0.833 for the polar-orbiting satellite products, despite successfully retrieving approximately 28 times as many pixels over the study period as the most successful polar-orbiting satellite product. The AHI and MODIS satellite products are compared for three case studies covering a range of BB plumes over Australia. The results show good agreement between all products for plumes with AOT values ≤ 2. For extreme BB plumes, the ORAC retrieval finds values of AOT > 15, significantly higher than those seen in events classified as extreme by previous studies although with high uncertainty. A combination of hard limits in the retrieval algorithms and misclassification of BB plumes as cloud prevent the JAXA and MODIS products from returning AOT values significantly greater than 5.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
(20570 KB)
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(20570 KB) - Metadata XML
- BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-2179', Antti Lipponen, 08 Dec 2023
Extreme biomass burning events are becoming more and more common and intense as the climate changes. More intense fires may produce thicker smoke plumes. Therefore, it is important that the remote sensing instruments and retrieval algorithms produce reliable estimates of high aerosol optical thicknesses (AOTs). The Robbins et al. manuscript uses the 2019-2020 Australian bushfires as a case to further develop and evaluate the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm for AOT retrievals in cases of extreme biomass burning plumes using a geostationary satellite instrument. They also compare the performances of different satellite instruments and retrieval algorithms to retrieve extreme AOT values. Furthermore, due to their development work and comparison, their algorithm produces estimates of significantly higher AOTs than previous studies. In their comparison, they also find that the AOTs from large-scale fires are likely to have been systemically underestimated by the existing approaches.The development work, design of the experiments, and comparison of the results presented in Robbins et al. have been carried out carefully, and the authors' selections are well-justified and clear. The manuscript is very well written and easy to read. I only have some minor comments, which I have listed below.Minor comments:l.12 "L1.5" please write in full "level 1.5"l.15 "...MODIS MAIC SLSTR SYN..." comma missingl.45 "...can accurately monitor aerosol properties in the atmosphere directly above the site location.." To be precise, especially with larger solar zenith angles, the AERONET is also sampling some nearby station locations, not just the pointwise location. You may want to modify the text accordingly, to be precise.l.64 "..in recent years. such as..." extra .l.88 Please add the wavelength for the CALIOP AOTl.89 Please add the wavelength for the AOT listed on this linel.95 Please mention if the Zhuravleva et al. (2017) estimates were AERONET or satellite (which instrument/algorithm) estimatesl.130 Please confirm/clarify what exact AERONET data you use, the Direct Sun or Aerosol Inversions data? Now, it is not clear.l.158 Please consider changing SLSTR to Sentinel-3 Synergy as the Synergy data is (mostly) based on both SLSTR and OLCI instruments' data both flying on Sentinel-3 satellites. If you decide to change, also change SLSTR throughout the manuscript to "Sentinel-3 Synergy" or similar.l.161 Please clarify if you use the SYN surface reflectance and aerosol parameter (SY_2_SYN) or SYN AOD data (SY_2_AOD) product that are different Synergy products. If you used the SY_2_SYN data product, please justify why this data product, whose primary use is not aerosol information but surface reflectance, is used instead of the dedicated aerosol data product (SY_2_AOD).l.400 Please define "NN"Citation: https://doi.org/
10.5194/egusphere-2023-2179-RC1 -
AC2: 'Reply on RC1', Daniel Robbins, 16 Feb 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2179/egusphere-2023-2179-AC2-supplement.pdf
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AC2: 'Reply on RC1', Daniel Robbins, 16 Feb 2024
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RC2: 'Comment on egusphere-2023-2179', Marloes Penning de Vries, 22 Jan 2024
The manuscript presents the capabilities of a version of ORAC that was enhanced to more adequately retrieve aerosol optical thickness of extremely dense smoke plumes. The AOT of such plumes is usually underestimated by satellite algorithms due to the mis-classification of such observations as clouds and subsequent exclusion from analysis; or the values are capped to a certain maximum retrievable AOT by the retrieval algorithm. The ORAC algorithm shows good agreement with AERONET L1.5 data and performs better than the extensive and diverse set of satellite algorithms to which it was compared, both in terms of number of successful retrievals as in terms of retrieval quality (with reference to the single AERONET site near Canberra, AUS, as ground truth).
The study is well designed and executed, and the manuscript is well written. I do miss some discussion on two important aspects. First: the dependence of AOT retrieval on viewing angle. The effect of parallax on a plume of moderate altitude is mentioned, but the strongly elongated light path due to large viewing (and/or solar) angles is not discussed. This, I imagine, is a significant issue for regions at the edges of the geostationary field of view. Second: the authors state that at higher AOT the retrieval depends more on the assumed aerosol parameters (particle size distribution, refractive index) than for smaller AOT. The ORAC algorithm performs very well for the Tumbarumba station, but then it makes use of the aerosol parameters derived from Tumbarumba AERONET data. How does this translate to a global algorithm? The implication appears to be that extreme AOT values can only be retrieved from satellite if the aerosol parameters are accurately known for the region (or even fire event) in question. I'd like to read the authors' view on this point. Because although they stress that their algorithm is scientific and not operational, it may be assumed that they, or others, will continue the advancement of aerosol algorithms towards routine monitoring of even extreme biomass burning cases.
Citation: https://doi.org/10.5194/egusphere-2023-2179-RC2 -
AC1: 'Reply on RC2', Daniel Robbins, 16 Feb 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2179/egusphere-2023-2179-AC1-supplement.pdf
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AC1: 'Reply on RC2', Daniel Robbins, 16 Feb 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-2179', Antti Lipponen, 08 Dec 2023
Extreme biomass burning events are becoming more and more common and intense as the climate changes. More intense fires may produce thicker smoke plumes. Therefore, it is important that the remote sensing instruments and retrieval algorithms produce reliable estimates of high aerosol optical thicknesses (AOTs). The Robbins et al. manuscript uses the 2019-2020 Australian bushfires as a case to further develop and evaluate the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm for AOT retrievals in cases of extreme biomass burning plumes using a geostationary satellite instrument. They also compare the performances of different satellite instruments and retrieval algorithms to retrieve extreme AOT values. Furthermore, due to their development work and comparison, their algorithm produces estimates of significantly higher AOTs than previous studies. In their comparison, they also find that the AOTs from large-scale fires are likely to have been systemically underestimated by the existing approaches.The development work, design of the experiments, and comparison of the results presented in Robbins et al. have been carried out carefully, and the authors' selections are well-justified and clear. The manuscript is very well written and easy to read. I only have some minor comments, which I have listed below.Minor comments:l.12 "L1.5" please write in full "level 1.5"l.15 "...MODIS MAIC SLSTR SYN..." comma missingl.45 "...can accurately monitor aerosol properties in the atmosphere directly above the site location.." To be precise, especially with larger solar zenith angles, the AERONET is also sampling some nearby station locations, not just the pointwise location. You may want to modify the text accordingly, to be precise.l.64 "..in recent years. such as..." extra .l.88 Please add the wavelength for the CALIOP AOTl.89 Please add the wavelength for the AOT listed on this linel.95 Please mention if the Zhuravleva et al. (2017) estimates were AERONET or satellite (which instrument/algorithm) estimatesl.130 Please confirm/clarify what exact AERONET data you use, the Direct Sun or Aerosol Inversions data? Now, it is not clear.l.158 Please consider changing SLSTR to Sentinel-3 Synergy as the Synergy data is (mostly) based on both SLSTR and OLCI instruments' data both flying on Sentinel-3 satellites. If you decide to change, also change SLSTR throughout the manuscript to "Sentinel-3 Synergy" or similar.l.161 Please clarify if you use the SYN surface reflectance and aerosol parameter (SY_2_SYN) or SYN AOD data (SY_2_AOD) product that are different Synergy products. If you used the SY_2_SYN data product, please justify why this data product, whose primary use is not aerosol information but surface reflectance, is used instead of the dedicated aerosol data product (SY_2_AOD).l.400 Please define "NN"Citation: https://doi.org/
10.5194/egusphere-2023-2179-RC1 -
AC2: 'Reply on RC1', Daniel Robbins, 16 Feb 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2179/egusphere-2023-2179-AC2-supplement.pdf
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AC2: 'Reply on RC1', Daniel Robbins, 16 Feb 2024
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RC2: 'Comment on egusphere-2023-2179', Marloes Penning de Vries, 22 Jan 2024
The manuscript presents the capabilities of a version of ORAC that was enhanced to more adequately retrieve aerosol optical thickness of extremely dense smoke plumes. The AOT of such plumes is usually underestimated by satellite algorithms due to the mis-classification of such observations as clouds and subsequent exclusion from analysis; or the values are capped to a certain maximum retrievable AOT by the retrieval algorithm. The ORAC algorithm shows good agreement with AERONET L1.5 data and performs better than the extensive and diverse set of satellite algorithms to which it was compared, both in terms of number of successful retrievals as in terms of retrieval quality (with reference to the single AERONET site near Canberra, AUS, as ground truth).
The study is well designed and executed, and the manuscript is well written. I do miss some discussion on two important aspects. First: the dependence of AOT retrieval on viewing angle. The effect of parallax on a plume of moderate altitude is mentioned, but the strongly elongated light path due to large viewing (and/or solar) angles is not discussed. This, I imagine, is a significant issue for regions at the edges of the geostationary field of view. Second: the authors state that at higher AOT the retrieval depends more on the assumed aerosol parameters (particle size distribution, refractive index) than for smaller AOT. The ORAC algorithm performs very well for the Tumbarumba station, but then it makes use of the aerosol parameters derived from Tumbarumba AERONET data. How does this translate to a global algorithm? The implication appears to be that extreme AOT values can only be retrieved from satellite if the aerosol parameters are accurately known for the region (or even fire event) in question. I'd like to read the authors' view on this point. Because although they stress that their algorithm is scientific and not operational, it may be assumed that they, or others, will continue the advancement of aerosol algorithms towards routine monitoring of even extreme biomass burning cases.
Citation: https://doi.org/10.5194/egusphere-2023-2179-RC2 -
AC1: 'Reply on RC2', Daniel Robbins, 16 Feb 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2179/egusphere-2023-2179-AC1-supplement.pdf
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AC1: 'Reply on RC2', Daniel Robbins, 16 Feb 2024
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Daniel Jamie Victor Robbins
Caroline Poulsen
Steven Siems
Simon Proud
Andrew Prata
Roy Grainger
Adam Povey
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(20570 KB) - Metadata XML