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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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Abstract. We trained two Random Forest (RF) machine learning models for cloud mask andcloud thermodynamic-phase detection using spectral observations from Visible InfraredImaging Radiometer Suite (VIIRS)on board Suomi National Polar-orbiting Partnership (SNPP). Observations from Cloud-Aerosol Lidarwith Orthogonal Polarization (CALIOP) were carefully selected toprovide reference labels. The two RF models were trained for all-day anddaytime-only conditions using a 4-year collocated VIIRS and CALIOP dataset from2013 to 2016. Due to the orbit difference, the collocated CALIOP and SNPPVIIRS training samples cover a broad-viewing zenith angle range, which is agreat benefit to overall model performance. The all-day model uses three VIIRSinfrared (IR) bands (8.6, 11, and 12 µm), and the daytime model uses fiveNear-IR (NIR) and Shortwave-IR (SWIR) bands (0.86, 1.24, 1.38, 1.64, and 2.25 µm) together with the three IR bands to detect clear, liquid water, and icecloud pixels. Up to seven surface types, i.e., ocean water, forest, cropland,grassland, snow and ice, barren desert, and shrubland, were consideredseparately to enhance performance for both models. Detection of cloudypixels and thermodynamic phase with the two RF models was compared againstcollocated CALIOP products from 2017. It is shown that, when using a conservativescreening process that excludes the most challenging cloudy pixels forpassive remote sensing, the two RF models have high accuracy rates incomparison to the CALIOP reference for both cloud detection andthermodynamic phase. Other existing SNPP VIIRS and Aqua MODIS cloud mask andphase products are also evaluated, with results showing that the two RFmodels and the MODIS MYD06 optical property phase product are the top threealgorithms with respect to lidar observations during the daytime. During thenighttime, the RF all-day model works best for both cloud detection andphase, particularly for pixels over snow and ice surfaces. The present RFmodels can be extended to other similar passive instruments if trainingsamples can be collected from CALIOP or other lidars. However, the qualityof reference labels and potential sampling issues that may impact modelperformance would need further attention.