the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
MAGARA: A Multi-Angle Geostationary Aerosol Retrieval Algorithm
James A. Limbacher
Ralph A. Kahn
Mariel D. Friberg
Jaehwa Lee
Tyler Summers
Hai Zhang
Abstract. For over 40 years, the Geostationary Operational Environmental Satellite (GOES) system has provided frequent snapshots of the Western Hemisphere, with its data used for a variety of tasks ranging from weather forecasting to wildfire detection. Located on the GOES-16, GOES-17, and GOES-18 platforms, the Advanced Baseline Imager (ABI) is the first GOES-series imager that meets the precision requirements (e.g., ≥ 10 bits per datum) for high-quality, aerosol-related research. Here, we present a pixel-level (up to 1 km) Multi-Angle Geostationary Aerosol Retrieval Algorithm (MAGARA) that leverages the ABI instruments on the GOES-16 and GOES-17 platforms, as well as the differences in autocorrelation time-scales between surface reflectance, aerosol type, and aerosol loading. MAGARA retrieves pixel-level aerosol loading and fine-mode fraction at up to the cadence of the measurements (10 minutes), fine-and-coarse mode aerosol particle properties at a daily cadence, and surface properties under a framework that combines the unique information content in multi-angle radiances (e.g., sensitivity to aerosol type from multiple scattering-angle observations) with the robust surface characterization inherent to temporally tiled algorithms such as the MAIAC method.
We present three case studies, tiling radiances for several days over the Desert Southwest (2 cases) and the Pacific Northwest (1 case). We observed/retrieved smoke from the following major fires: the Camp Fire (November 5th–12th, 2018), the Williams Flats Fire (July 29th–August 8th, 2019), and the Kincade Fire (October 23rd–November 1st, 2019). Because GOES-17 was not making observations during the Camp Fire, we present this as a unique case demonstrating the efficacy of the multi-angle algorithm using only a single ABI sensor. We compare MAGARA retrievals of fine-mode (FM) AOD, coarse-mode (CM) AOD, and single-scattering albedo (SSA) with coincident AErosol RObotic NETwork (AERONET) spectral deconvolution algorithm (SDA) and inversion retrievals for the same period. We also compare MAGARA results against bias-corrected NOAA GOES-16 and GOES-17 retrieved 550 nm AOD.
For the 8,443 coincidences of MAGARA and the NOAA bias-corrected product with AERONET, MAGARA (NOAA bias-corrected product) 550 nm AOD error statistics are as follows: median-absolute error (MAE) = 0.016 (0.021), root-mean-squared error (RMSE) = 0.040 (0.049), and linear correlation coefficient (r) = 0.785 (0.666). At pixel-level resolution, the disparity between MAGARA and the NOAA bias-corrected product increases substantially, with MAGARA suffering less degradation in the results, likely due to lower pixel-to-pixel noise.
We report the following over-land MAGARA 500 nm fine-mode fraction error statistics for the 384 MAGARA/AERONET coincidences with MAGARA 500 nm AOD > 0.3: MAE = 0.031, RMSE = 0.100, and r = 0.902. Combined with the presented figures of daily averaged retrieved aerosol particle properties, this suggests that MAGARA has good sensitivity to fine-mode fraction over land, especially for smoky regions.
We also compare retrievals of MAGARA spectral single-scattering albedo with AERONET. Results suggest that a 1-parameter bias correction can substantially reduce MAGARA errors at high AOD. For the MAGARA retrieved spectral AOD > 0.5 (n = 116), this bias correction reduces MAE by 65 % (0.028 → 0.010), RMSE by 50 % (0.030 → 0.015), and improves correlation by 0.03 (0.84 → 0.87).
MAGARA performs best in regions where surface reflectance varies over long-time scales with minimal clouds. This represents a large portion of the western half of the US, much of North-Central Africa and the Middle East, some of Central Asia, and much of Australia. For these regions, aerosol type and aerosol loading on time scales as short as 10 minutes could allow for novel research into aerosol-cloud interactions, improvements to air-quality modeling and forecasting, and tighter constraints on direct aerosol radiative forcing.
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James A. Limbacher et al.
Status: closed
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RC1: 'Comment on amt-2023-146', Anonymous Referee #1, 21 Sep 2023
This paper proposes a new algorithm to retrieve aerosol optical depth, fine mode fraction, surface properties as well as fine-and-coarse aerosol particle properties. The algorithm has been applied to three cases and evaluated with AErosol Robotic NETwork (AERONET). The results proves that the algorithm works well in retrieving in aerosol properties including AOD, single scattering albedo and fine mode fraction. The paper is well written. I would suggest to accept the paper after minor revisions. Specific comments are give below.
1) The modules are looped through fixed numbers of times instead of optimal numbers of times. Does it serve for parallel programming? Could you explain how you determine the certain numbers the different modules are looped? Have you done any experiments to prove that there is really a need to have so many loops?I would like to know how long it takes to process a 50-by-50-pixel region?
2) In "Aerosol/Surface retrieval iteration" section of Fig. 3, "If iterInd==4" should be "If iterInd==5".
3) In Fig. 5, the aerosol loading over water in some area (eg. upper left area on 24, 25, 27 October) should be large, while the retrieved AOD values are small. Could you give some explanation?
4) Please add unit on x axis in Fig. 8.
Citation: https://doi.org/10.5194/amt-2023-146-RC1 - AC1: 'Reply on RC1', James Limbacher, 29 Sep 2023
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RC2: 'Comment on amt-2023-146', Anonymous Referee #2, 22 Sep 2023
General comments:
This paper describes a pixel-level (up to 1 km) Multi-Angle Geostationary Aerosol Retrieval Algorithm that retrieves pixel-level aerosol loading and fine-mode fraction at up to the cadence of the measurements (10 minutes), fine-and-coarse mode aerosol particle properties at a daily cadence. Several case studies over the Desert Southwest, Pacific Northwest, and fire occurred regions are presented. The fine-mode AOD, coarse-mode AOD, and single-scattering albedo (SSA) of MAGARA are compared to the AERONET and NOAA GOES-16 and GOES-17 products, which shows acceptable agreements. Aerosol type and loading of MAGARA at temporal resolution of 10 minutes are helpful for the new insights of aerosol-cloud interactions, improvements of air-quality modeling and forecasting, and additional constraints on direct aerosol radiative forcing. Therefore, the efforts on retrieval of detailed aerosol optical properties with high temporal resolution in this study are commendable and the work is meaningful. However, I have some comments on the current manuscript.
Major comments:
- The abstract is too long and it needs to be further summarized.
- Could you provide some comparisons of MAGARA products to geostationary Himawari-8/AHI products, at least for aerosol optical depth and angstrom exponent?
- Did you run the MAGARA algorithm with some artificial data? How about the uncertainty of MAGARA retrievals? Could you provide some quantitative assessment?
- Could you provide the aerosol component retrievals in the MAGARA algorithm? I did not see any results about the component retrievals except fine and coarse mode AOD, FMF, and SSA. In my opinion, the Table 1 describes the climatology of aerosol types, not aerosol component. So, if yes, I strongly recommend using “aerosol types” to replace “aerosol component” throughout the texts including the texts in the figures (Figure 2)
Minor comments:
- The texts in the maps are too small. Please improve it.
- I think two digits are enough for the statistics.
- Please provide the full name of the abbreviation when mentioned at the first time. For example, MAIAC in line 23, AOD in line 28, GRASP in line 101
Citation: https://doi.org/10.5194/amt-2023-146-RC2 - AC2: 'Reply on RC2', James Limbacher, 29 Sep 2023
Status: closed
-
RC1: 'Comment on amt-2023-146', Anonymous Referee #1, 21 Sep 2023
This paper proposes a new algorithm to retrieve aerosol optical depth, fine mode fraction, surface properties as well as fine-and-coarse aerosol particle properties. The algorithm has been applied to three cases and evaluated with AErosol Robotic NETwork (AERONET). The results proves that the algorithm works well in retrieving in aerosol properties including AOD, single scattering albedo and fine mode fraction. The paper is well written. I would suggest to accept the paper after minor revisions. Specific comments are give below.
1) The modules are looped through fixed numbers of times instead of optimal numbers of times. Does it serve for parallel programming? Could you explain how you determine the certain numbers the different modules are looped? Have you done any experiments to prove that there is really a need to have so many loops?I would like to know how long it takes to process a 50-by-50-pixel region?
2) In "Aerosol/Surface retrieval iteration" section of Fig. 3, "If iterInd==4" should be "If iterInd==5".
3) In Fig. 5, the aerosol loading over water in some area (eg. upper left area on 24, 25, 27 October) should be large, while the retrieved AOD values are small. Could you give some explanation?
4) Please add unit on x axis in Fig. 8.
Citation: https://doi.org/10.5194/amt-2023-146-RC1 - AC1: 'Reply on RC1', James Limbacher, 29 Sep 2023
-
RC2: 'Comment on amt-2023-146', Anonymous Referee #2, 22 Sep 2023
General comments:
This paper describes a pixel-level (up to 1 km) Multi-Angle Geostationary Aerosol Retrieval Algorithm that retrieves pixel-level aerosol loading and fine-mode fraction at up to the cadence of the measurements (10 minutes), fine-and-coarse mode aerosol particle properties at a daily cadence. Several case studies over the Desert Southwest, Pacific Northwest, and fire occurred regions are presented. The fine-mode AOD, coarse-mode AOD, and single-scattering albedo (SSA) of MAGARA are compared to the AERONET and NOAA GOES-16 and GOES-17 products, which shows acceptable agreements. Aerosol type and loading of MAGARA at temporal resolution of 10 minutes are helpful for the new insights of aerosol-cloud interactions, improvements of air-quality modeling and forecasting, and additional constraints on direct aerosol radiative forcing. Therefore, the efforts on retrieval of detailed aerosol optical properties with high temporal resolution in this study are commendable and the work is meaningful. However, I have some comments on the current manuscript.
Major comments:
- The abstract is too long and it needs to be further summarized.
- Could you provide some comparisons of MAGARA products to geostationary Himawari-8/AHI products, at least for aerosol optical depth and angstrom exponent?
- Did you run the MAGARA algorithm with some artificial data? How about the uncertainty of MAGARA retrievals? Could you provide some quantitative assessment?
- Could you provide the aerosol component retrievals in the MAGARA algorithm? I did not see any results about the component retrievals except fine and coarse mode AOD, FMF, and SSA. In my opinion, the Table 1 describes the climatology of aerosol types, not aerosol component. So, if yes, I strongly recommend using “aerosol types” to replace “aerosol component” throughout the texts including the texts in the figures (Figure 2)
Minor comments:
- The texts in the maps are too small. Please improve it.
- I think two digits are enough for the statistics.
- Please provide the full name of the abbreviation when mentioned at the first time. For example, MAIAC in line 23, AOD in line 28, GRASP in line 101
Citation: https://doi.org/10.5194/amt-2023-146-RC2 - AC2: 'Reply on RC2', James Limbacher, 29 Sep 2023
James A. Limbacher et al.
James A. Limbacher et al.
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