Constraining Aerosol Phase Function Using Dual‐View Geostationary Satellites

Bian, Qijing; Kreidenweis, Sonia M.; Miller, Steven D.; Xu, Xiangde; Wang, Jun; Kahn, Ralph A.; Limbacher, James A.; Remer, L. A.; Levy, Robert C.

doi:10.1029/2021jd035209

Cited by 4 publications

(2 citation statements)

References 58 publications

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“…This includes the MODIS darktarget (DT) group (Gupta et al, 2019;Remer et al, 2020), the MAIAC group Li et al, 2019;Wang et al, 2022), the GRASP team (Li et al, 2020), and the NOAA aerosol team themselves (GOES AOD Algorithm Theoretical Basis Document [ATBD], 2018; Liu et al, 2018;Kondragunta et al, 2020;Zhang et al, 2020), among others. Other groups have sought to use observations of both GOES-R and GOES-S to constrain the scattering phase function of dust aerosols (e.g., Bian et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Most existing aerosol retrieval algorithms make use of a single sensor at a given time to determine aerosol loadings and properties. Bian et al (2021) used both GOES-16 and GOES-17 to constrain the phase functions of dust, but the algorithm they developed is not a fully-fledged aerosol retrieval algorithm. Govaerts et al (2010) used prior generations of geostationary imagers to retrieve daily aerosol optical depths and surface BRFs.…”

Section: Introductionmentioning

confidence: 99%

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MAGARA: A Multi-Angle Geostationary Aerosol Retrieval Algorithm

Limbacher¹,

Kahn²,

Friberg³

et al. 2023

Preprint

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MAGARA: A Multi-Angle Geostationary Aerosol Retrieval Algorithm

Limbacher¹,

Kahn²,

Friberg³

et al. 2023

Preprint

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Enhancing Our Vision of Aerosols: Progress in Scattering Phase Function Measurements

Bian,

Zhao

2024

Curr Pollution Rep

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Opinion: Aerosol remote sensing over the next 20 years

Remer,

Levy,

Martins

2024

Atmos. Chem. Phys.

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Abstract. More than 2 decades ago, aerosol remote sensing underwent a revolution with the launch of the Terra and Aqua satellites. Advancement continued via additional launches carrying new passive and active sensors. Capable of retrieving parameters characterizing aerosol loading, rudimentary particle properties and in some cases aerosol layer height, the satellite view of Earth's aerosol system came into focus. The modeling communities have made similar advances. Now the efforts have continued long enough that we can see developing trends in both the remote sensing and modeling communities, allowing us to speculate about the future and how the community will approach aerosol remote sensing 20 years from now. We anticipate technology that will replace today's standard multi-wavelength radiometers with hyperspectral and/or polarimetry, all viewing at multiple angles. These will be supported by advanced active sensors with the ability to measure profiles of aerosol extinction in addition to backscatter. The result will be greater insight into aerosol particle properties. Algorithms will move from being primarily physically based to include an increasing degree of machine-learning methods, but physically based techniques will not go extinct. However, the practice of applying algorithms to a single sensor will be in decline. Retrieval algorithms will encompass multiple sensors and all available ground measurements in a unifying framework, and these inverted products will be ingested directly into assimilation systems, becoming “cyborgs”: half observations, half model. In 20 years we will see a true democratization in space with nations large and small, private organizations, and commercial entities of all sizes launching space sensors. With this increasing number of data and aerosol products available, there will be a lot of bad data. User communities will organize to set standards, and the large national space agencies will lead the effort to maintain quality by deploying and maintaining validation ground networks and focused field experiments. Through it all, interest will remain high in the global aerosol system and how that system affects climate, clouds, precipitation and dynamics, air quality, the environment and public health, transport of pathogens and fertilization of ecosystems, and how these processes are adapting to a changing climate.

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Constraining Aerosol Phase Function Using Dual‐View Geostationary Satellites

Cited by 4 publications

References 58 publications

MAGARA: A Multi-Angle Geostationary Aerosol Retrieval Algorithm

MAGARA: A Multi-Angle Geostationary Aerosol Retrieval Algorithm

Enhancing Our Vision of Aerosols: Progress in Scattering Phase Function Measurements

Opinion: Aerosol remote sensing over the next 20 years

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