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1. Theoretical Uncertainty Analysis of Satellite Retrieved Aerosol Optical Depth Associated with Surface Albedo and Aerosol Optical Properties

   https://doi.org/10.3390/rs13030344  

   Huang, Jingting; Arnott, William Patrick; Barnard, James C.; Holmes, Heather A. (February 2021, Remote Sensing)

   null (Ed.)

   Deriving aerosol optical depth (AOD) from space-borne observations is still challenging due to uncertainties associated with sensor calibration drift, cloud screening, aerosol type classification, and surface reflectance characterization. As an initial step to understanding the physical processes impacting these uncertainties in satellite AOD retrievals, this study outlines a theoretical approach to estimate biases in the satellite aerosol retrieval algorithm affected by surface albedo and prescribed aerosol optical properties using a simplified radiative transfer model with a traditional error propagation approach. We expand the critical surface reflectance concept to obtain the critical surface albedo (CSA), critical single scattering albedo (CSSA), and critical asymmetry parameter (CAP). The top-of-atmosphere (TOA) reflectance is not sensitive to significant variability in aerosol loading (AOD) at the critical value; thus, the AOD cannot be determined. Results show that 5% bias in surface albedo (A), single scattering albedo (SSA), or asymmetry parameter (g) lead to large retrieved AOD errors, especially high under conditions when A, SSA, or g are close to their critical values. The results can be useful for future research related to improvements of satellite aerosol retrieval algorithms and provide a preliminary framework to analytically quantify AOD uncertainties from satellite retrievals. 

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2. A novel retrieval of global dust optical depth and effective diameter based on MODIS thermal infrared observations

   https://doi.org/10.1016/j.rse.2025.115083  

   Zheng, Jianyu; Yu, Hongbin; Zhou, Yaping; Shi, Yingxi; Zhang, Zhibo; Di_Biagio, Claudia; Formenti, Paola; Smirnov, Alexander (October 2025, Remote Sensing of Environment)

   Airborne mineral dust significantly influences Earth’s climate through perturbing Earth’s radiation budget, modulating cloud formation and microphysical properties, and fertilizing the biosphere. Recent field campaigns have revealed substantially more coarse-mode dust particles in the atmosphere than previously recognized, yet current satellite retrievals and climate models inadequately represent these particles. This study presents a novel retrieval algorithm for dust aerosol optical depth at 10 μm (AOD10μm) and effective diameter (Deff) using Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared (TIR) observations over global land and ocean. Building upon the previous synergistic approach for MODIS and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), we improve the retrieval from CALIOP-track-limited coverage to full-swath MODIS observations at 10-km resolution over both ocean and land surfaces. The retrieval improvements include: (1) application of climatological CALIOP dust vertical profiles (2007–2017) to constrain dust vertical distribution for off-CALIOP-track pixels; (2) the improved optimization method to effectively handle nonmonotonic cost functions arising from temperature inversions within the Saharan Air Layer; and (3) extension to land surfaces through incorporation of MODIS-retrieved surface emissivity and ERA5 reanalysis data. Validation against coarse-mode AOD from global AERONET (N = 4703) and MAN (N = 1673) observations yields R = 0.82 and 0.85 for AOD10μm, with retrieval uncertainty characterized as ε = 15 % × AOD + 0.04. The retrieved Deff demonstrates excellent agreement with in-situ measurements collected from 24 field campaigns around the globe (R = 0.84, MBE = 0.23 μm, RMSE = 0.73 μm), capturing the particle size variation from near-source regions (Deff = 7–8 μm) to long-range transport (Deff = 3–5 μm). Case studies of dust events over the Namibian coast and trans-Atlantic corridor demonstrate the retrieval’s capability to resolve episodic dust properties and size-dependent deposition during transport. This improved retrieval addresses the critical observational gap for coarse and super-coarse dust particles (D > 5 μm), providing essential constraints for dust life cycle studies and climate model evaluation. 

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3. Reconstructing aerosol optical depth using spatiotemporal Long Short-Term Memory convolutional autoencoder

   https://doi.org/10.1038/s41597-023-02696-w  

   Liang, Lu; Daniels, Jacob; Biancardi, Michael; Zhou, Yuye (December 2023, Scientific Data)

   Abstract Aerosol Optical Depth (AOD) is a crucial atmospheric parameter in comprehending climate change, air quality, and its impacts on human health. Satellites offer exceptional spatiotemporal AOD data continuity. However, data quality is influenced by various atmospheric, landscape, and instrumental factors, resulting in data gaps. This study presents a new solution to this challenge by providing a long-term, gapless satellite-derived AOD dataset for Texas from 2010 to 2022, utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-angle Implementation of Atmospheric Correction (MAIAC) products. Missing AOD data were reconstructed using a spatiotemporal Long Short-Term Memory (LSTM) convolutional autoencoder. Evaluation against an independent test dataset demonstrated the model’s effectiveness, with an average Root Mean Square Error (RMSE) of 0.017 and an R²value of 0.941. Validation against the ground-based AERONET dataset indicated satisfactory agreement, with RMSE values ranging from 0.052 to 0.067. The reconstructed AOD data are available at daily, monthly, quarterly, and yearly scales, providing a valuable resource to advance understanding of the Earth’s atmosphere and support decision-making concerning air quality and public health. 

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4. Assessment of smoke plume height products derived from multisource satellite observations using lidar-derived height metrics for wildfires in the western US

   https://doi.org/10.5194/acp-24-3673-2024  

   Huang, Jingting; Loría-Salazar, S Marcela; Deng, Min; Lee, Jaehwa; Holmes, Heather A (March 2024, Atmospheric Chemistry and Physics)

   Abstract. As wildfires intensify and fire seasons lengthen across the western US, the development of models that can predict smoke plume concentrations and track wildfire-induced air pollution exposures has become critical. Wildfire smoke plume height is a key indicator of the vertical placement of plume mass emitted from wildfire-related aerosol sources in climate and air quality models. With advancements in Earth observation (EO) satellites, spaceborne products for aerosol layer height or plume injection height have recently emerged with increased global-scale spatiotemporal resolution. However, to evaluate column radiative effects and refine satellite algorithms, vertical profiles of regionally representative aerosol properties from wildfires need to be measured directly. In this study, we conducted the first comprehensive evaluation of four passive satellite remote-sensing techniques specifically designed for retrieving plume height. We compared these satellite products with the airborne Wyoming Cloud Lidar (WCL) measurements during the 2018 Biomass Burning Flux Measurements of Trace Gases and Aerosols (BB-FLUX) field campaign in the western US. Two definitions, namely, “plume top” and “extinction-weighted mean plume height”, were used to derive the representative heights of wildfire smoke plumes, based on the WCL-derived vertical aerosol extinction coefficient profiles. Using these two definitions, we performed a comparative analysis of multisource satellite-derived plume height products for wildfire smoke. We provide a discussion related to which satellite product is most appropriate for determining plume height characteristics near a fire event or estimating downwind plume rise equivalent height, under multiple aerosol loadings. Our findings highlight the importance of understanding the sensitivity of different passive remote-sensing techniques on space-based wildfire smoke plume height observations, in order to resolve ambiguity surrounding the concept of “effective smoke plume height”. As additional aerosol-observing satellites are planned in the coming years, our results will inform future remote-sensing missions and EO satellite algorithm development. This bridges the gap between satellite observations and plume rise modeling to further investigate the vertical distribution of wildfire smoke aerosols. 

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5. Observationally constrained representation of brown carbon emissions from wildfires in a chemical transport model

   https://doi.org/10.1039/D1EA00059D  

   Neyestani, Soroush E.; Saleh, Rawad (March 2022, Environmental Science: Atmospheres)

   The month of August 2015 featured extensive wildfires in the Northwestern U.S. and no significant fires in Alaska and Canada. With the majority of carbonaceous aerosols (CA), including black carbon (BC) and brown carbon (BrC), over the U.S. dominated by emissions from Northwestern wildfires, this month presented a unique opportunity for testing wildfire BrC representation in the Weather Research and Forecasting model with chemistry (WRF-Chem). We performed parallel simulations that (1) did not account for BrC absorption, (2) accounted for BrC absorption, and (3) accounted for BrC absorption as well as its decay due to photobleaching. We used a comprehensive set of extensive and pseudo-intensive optical properties, namely the aerosol optical depth (AOD), aerosol absorption optical depth (AAOD), absorption Ångström exponent (AAE), and single scattering albedo (SSA) to constrain the model output against observations from the Aerosol Robotic Network (AERONET). We found that accounting for BrC absorption and photobleaching resulted in the best agreement with observations in terms of aerosol absorption (AAOD and AAE). However, the model severely underestimated AOD and SSA compared to observations. We attributed this discrepancy to missing scattering due to missing secondary organic aerosol (SOA) formation from wildfire emissions in the model. To test this hypothesis, we applied a zeroth-order representation of wildfire SOA, which significantly improved the AOD and SSA model-observation comparison. Our findings indicate that BrC absorption, the decay of its absorption due to photobleaching, as well as SOA formation should be accounted for in chemical transport models in order to accurately represent CA emissions from wildfires. 

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