Dust Aerosol Retrieval Over the Oceans with the MODIS/VIIRS Dark Target algorithm. Part I: Dust Detection

Zhou, Yaping; Levy, Robert C.; Remer, L. A.; Mattoo, S.; Shi, Yingxi; Wang, C.

doi:10.1002/essoar.10502658.1

Cited by 3 publications

(3 citation statements)

References 81 publications

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“…To improve the retrieval of dust over ocean, we recently introduced a new nonspherical coarse mode into the over ocean retrieval and developed a logic tree to identify dust to direct the retrieval to this new coarse mode [150,151]. Before this modification, over the ocean, the DT algorithm was known to contain scattering-angle-dependent biases in its retrievals of AOD, Angstrom exponent (AE), and fine-mode fraction (FMF) for dust aerosols.…”

Section: Specific Lut With Nonspherical Coarse Mode For Dust Over Oceanmentioning

confidence: 99%

“…The modification uses a two-step strategy to improve the DT retrieval of dust over ocean. First, a combination of spectral tests using near-ultraviolet (UV) (deep blue), visible, and thermal infrared (TIR) wavelengths is applied to detect dusty pixels [150]. Second, a new dust model assumes a mixture of spheroid particles to represent global dust optical properties [151], and this new dust model is applied to only those pixels that are identified as dusty.…”

Section: Specific Lut With Nonspherical Coarse Mode For Dust Over Oceanmentioning

confidence: 99%

“…Years are divided into quarters. Black squares indicate important publications: (a)[15]; (b)[146]; (c)[20]; (d)[17]; (e)[147]; (f)[148]; (g)[22]; (h)[23]; (i)[21]; (j)[149]; (k)[24]; (l)[150] [151].…”

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confidence: 99%

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The Dark Target Algorithm for Observing the Global Aerosol System: Past, Present, and Future

et al. 2020

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The Dark Target aerosol algorithm was developed to exploit the information content available from the observations of Moderate-Resolution Imaging Spectroradiometers (MODIS), to better characterize the global aerosol system. The algorithm is based on measurements of the light scattered by aerosols toward a space-borne sensor against the backdrop of relatively dark Earth scenes, thus giving rise to the name “Dark Target”. Development required nearly a decade of research that included application of MODIS airborne simulators to provide test beds for proto-algorithms and analysis of existing data to form realistic assumptions to constrain surface reflectance and aerosol optical properties. This research in itself played a significant role in expanding our understanding of aerosol properties, even before Terra MODIS launch. Contributing to that understanding were the observations and retrievals of the growing Aerosol Robotic Network (AERONET) of sun-sky radiometers, which has walked hand-in-hand with MODIS and the development of other aerosol algorithms, providing validation of the satellite-retrieved products after launch. The MODIS Dark Target products prompted advances in Earth science and applications across subdisciplines such as climate, transport of aerosols, air quality, and data assimilation systems. Then, as the Terra and Aqua MODIS sensors aged, the challenge was to monitor the effects of calibration drifts on the aerosol products and to differentiate physical trends in the aerosol system from artefacts introduced by instrument characterization. Our intention is to continue to adapt and apply the well-vetted Dark Target algorithms to new instruments, including both polar-orbiting and geosynchronous sensors. The goal is to produce an uninterrupted time series of an aerosol climate data record that begins at the dawn of the 21st century and continues indefinitely into the future.

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Section: Specific Lut With Nonspherical Coarse Mode For Dust Over Oceanmentioning

confidence: 99%

Section: Specific Lut With Nonspherical Coarse Mode For Dust Over Oceanmentioning

confidence: 99%

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The Dark Target Algorithm for Observing the Global Aerosol System: Past, Present, and Future

et al. 2020

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Increasing aerosol optical depth spatial and temporal availability by merging datasets from geostationary and sun-synchronous satellites

Gupta,

Levy,

Mattoo

et al. 2024

Atmos. Meas. Tech.

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Abstract. This comprehensive study analyzed aerosol products from six low-Earth orbit (LEO) and geostationary Earth orbit (GEO) sensors. LEO sensors like the MODerate resolution Imaging Spectroradiometer (MODIS) and VIsible InfraRed Suite (VIIRS) provide one to two daily global measurements, while GEO sensors (Advanced Himawari Imager: AHI, Advanced Baseline Imager: ABI) offer high-frequency data (∼ 10 min) over specific regions. The combination of LEO and GEO capabilities offers expanded coverage of the global aerosol system if aerosol retrievals are applied consistently across all sensors and packaged in an easy-to-use product. The Dark Target aerosol retrieval algorithm was applied to the six sensors, and the resulting Level 2 aerosol optical depth (AOD) products were gridded and merged into a Level 3 quarter-degree latitude–longitude grid with a 30 min temporal resolution, providing the necessary consistency and packaging. Validation of this packaged Level 3 AOD product against Aerosol Robotics NETwork (AERONET) measurements across global locations showcased the merged product's robustness with a correlation coefficient of 0.83, revealing a global mean bias of approximately ±0.05, with 65.5 % of retrievals falling within an expected uncertainty range, underlining the reliability of the dataset. The new gridded Level 3 dataset significantly improved daily global coverage to nearly 45 %, overcoming the limitations of individual sensors, which typically range from 12 % to 25 %. Furthermore, this merged dataset approximates the diurnal cycle of AOD observed by AERONET, thus offering insights into diurnal signatures retrieved elsewhere. The resulting dataset's high spatiotemporal resolution and improved global coverage, especially in regions covered by GEO sensors (Americas and Asia), make it a valuable tool for diverse applications. Tracking aerosol transport from phenomena like wildfires and dust storms is gaining precision, enabling enhanced air quality forecasting and hindcasting. Additionally, the study positions the merged dataset as a significant asset for evaluating and intercomparing regional or global model simulations, which was previously unattainable in such a gridded format. The dataset and fusion framework layout in this study have the potential to include data from recently (future) launched other GEO (FCI, AMI) and LEO (PACE, VIIRS-JPSS) sensors.

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Large synthesis of in situ field measurements of the size distribution of mineral dust aerosols across their life cycles

Formenti,

Di Biagio

2024

Earth Syst. Sci. Data

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Abstract. Mineral dust aerosol is important in the Earth system, and the correct representation of its size distribution is fundamental for shaping the current state and evolution of the climate. Despite many observational dust size data that are available in the literature, using this body of information to properly guide the development and validation of climate models and remote sensing retrievals remains challenging. In this study we collect, evaluate, harmonize, and synthesize 58 size distribution data from the past 50 years of in situ field observations with the aim of providing a consistent dataset to the community for use in constraining the representation of dust size across its life cycle. Four levels (LEVs) of data treatment are defined, going from original data (LEV0), data interpolated and normalized on a standardized diameter grid (LEV1), and data in which original particle diameters are converted to a common geometrical definition under both spherical (LEV2a) and aspherical (LEV2b) assumptions. Size distributions are classified as emission or source (SOURCE, <1 d from emission; number of datasets in this category N=12), mid-range transport (MRT, 1–4 d of transport; N=36), and long-range transport (LRT, >4 d of transport; N=10). The harmonized dataset shows consistent features suggesting the conservation of airborne particles with time and a decrease in the main coarse-mode diameter from a value on the order of 10 µm (in volume) for SOURCE dust to a value on the order of 1–2 µm for LRT conditions. An additional mode becomes evident below 0.4 µm for MRT and LRT dust. Data for the three levels (LEV1, LEV2a, and LEV2b) and the three categories (SOURCE, MRT, and LRT), together with statistical metrics (mean, median, 25th and 75th percentiles, and standard deviation), are available as follows: SOURCE (https://doi.org/10.57932/58dbe908-9394-4504-9099-74a3e77140e9, Formenti and Di Biagio, 2023a); MRT (https://doi.org/10.57932/31f2adf7-74fb-48e8-a3ef-059f663c47f1, Formenti and Di Biagio, 2023b); LRT (https://doi.org/10.57932/17dc781c-3e9d-4908-85b5-5c99e68e8f79, Formenti and Di Biagio, 2023c).

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Dust Aerosol Retrieval Over the Oceans with the MODIS/VIIRS Dark Target algorithm. Part I: Dust Detection

Cited by 3 publications

References 81 publications

The Dark Target Algorithm for Observing the Global Aerosol System: Past, Present, and Future

The Dark Target Algorithm for Observing the Global Aerosol System: Past, Present, and Future

Increasing aerosol optical depth spatial and temporal availability by merging datasets from geostationary and sun-synchronous satellites

Large synthesis of in situ field measurements of the size distribution of mineral dust aerosols across their life cycles

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