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Abstract Super‐coarse dust particles (diameters >10 μm) are evidenced to be more abundant in the atmosphere than model estimates and contribute significantly to the dust climate impacts. Since super‐coarse dust accounts for less dust extinction in the visible‐to‐near‐infrared (VIS‐NIR) than in the thermal infrared (TIR) spectral regime, they are suspected to be underestimated by remote sensing instruments operates only in VIS‐NIR, including Aerosol Robotic Networks (AERONET), a widely used data set for dust model validation. In this study, we perform a radiative closure assessment using the AERONET‐retrieved size distribution in comparison with the collocated Atmospheric Infrared Sounder (AIRS) TIR observations with comprehensive uncertainty analysis. The consistently warm bias in the comparisons suggests a potential underestimation of super‐coarse dust in the AERONET retrievals due to the limited VIS‐NIR sensitivity. An extra super‐coarse mode included in the AERONET‐retrieved size distribution helps improve the TIR closure without deteriorating the retrieval accuracy in the VIS‐NIR. 

Abstract. In this study, we developed a novel algorithm based on the collocatedModerate Resolution Imaging Spectroradiometer (MODIS) thermal infrared (TIR)observations and dust vertical profiles from the Cloud–Aerosol Lidar withOrthogonal Polarization (CALIOP) to simultaneously retrieve dust aerosoloptical depth at 10 µm (DAOD10 µm) and the coarse-mode dusteffective diameter (Deff) over global oceans. The accuracy of theDeff retrieval is assessed by comparing the dust lognormal volumeparticle size distribution (PSD) corresponding to retrieved Deff withthe in situ-measured dust PSDs from the AERosol Properties – Dust(AER-D), Saharan Mineral Dust Experiment (SAMUM-2), and Saharan Aerosol Long-Range Transport and Aerosol–Cloud-InteractionExperiment (SALTRACE) fieldcampaigns through case studies. The new DAOD10 µm retrievals wereevaluated first through comparisons with the collocated DAOD10.6 µmretrieved from the combined Imaging Infrared Radiometer (IIR) and CALIOPobservations from our previous study (Zheng et al., 2022). The pixel-to-pixelcomparison of the two DAOD retrievals indicates a good agreement(R∼0.7) and a significant reduction in (∼50 %) retrieval uncertainties largely thanks to the better constraint ondust size. In a climatological comparison, the seasonal and regional(2∘×5∘) mean DAOD10 µm retrievals basedon our combined MODIS and CALIOP method are in good agreement with the twoindependent Infrared Atmospheric Sounding Interferometer (IASI) productsover three dust transport regions (i.e., North Atlantic (NA; R=0.9),Indian Ocean (IO; R=0.8) and North Pacific (NP; R=0.7)). Using the new retrievals from 2013 to 2017, we performed a climatologicalanalysis of coarse-mode dust Deff over global oceans. We found thatdust Deff over IO and NP is up to 20 % smaller than that over NA.Over NA in summer, we found a ∼50 % reduction in the numberof retrievals with Deff>5 µm from 15 to35∘ W and a stable trend of Deff average at 4.4 µm from35∘ W throughout the Caribbean Sea (90∘ W). Over NP inspring, only ∼5 % of retrieved pixels with Deff>5 µm are found from 150 to 180∘ E, whilethe mean Deff remains stable at 4.0 µm throughout eastern NP. To the best of our knowledge, this study is the first to retrieve both DAOD andcoarse-mode dust particle size over global oceans for multiple years. Thisretrieval dataset provides insightful information for evaluating dustlongwave radiative effects and coarse-mode dust particle size in models. 

MODIS (Moderate Resolution Imaging Spectroradiometer) is a key instrument onboard NASA’s Terra (launched in 1999) and Aqua (launched in 2002) satellite missions as part of the more extensive Earth Observation System (EOS). By measuring the reflection and emission by the Earth-Atmosphere system in 36 spectral bands from the visible to thermal infrared with near-daily global coverage and high-spatial-resolution (250 m ~ 1 km at nadir), MODIS is playing a vital role in developing validated, global, interactive Earth system models. MODIS products are processed into three levels, i.e., Level-1 (L1), Level-2 (L2) and Level-3 (L3). To shift the current static and “one-size-fits-all” data provision method of MODIS products, in this paper, we propose a service-oriented flexible and efficient MODIS aggregation framework. Using this framework, users only need to get aggregated MODIS L3 data based on their unique requirements and the aggregation can run in parallel to achieve a speedup. The experiments show that our aggregation results are almost identical to the current MODIS L3 products and our parallel execution with 8 computing nodes can work 88.63 times faster than a serial code execution on a single node. 

With the advances of satellite remote sensing techniques, we are receiving huge amount of satellite observation data for the Earth. While the data greatly helps Earth scientists on their research, conduct- ing data processing and analytics from the data is getting more and more time consuming and complicated. One common data processing task is to aggregate satellite observation data from original pixel level to latitude-longitude grid level to easily obtain global information and work with global climate models. This paper focuses on how to best aggregate NASA MODIS satellite data products from pixel level to grid level in a distributed environment and provision the aggregation capa- bility as a service for Earth scientists to use easily. We propose three different approaches of parallel data aggregation and employ three par- allel platforms (Spark, Dask and MPI) to implement the approaches. We run extensive experiments based on these parallel approaches and platforms on a local cluster to benchmark their differences in execution performance and discuss key factors to achieve good speedup. We also study how to make the provisioned service adaptable to different service libraries and protocols via a unified framework.