Spatiotemporal Heterogeneity of Aerosol and Cloud Properties Over the Southeast Atlantic: An Observational Analysis

Chang, Ian; Gao, Lan; Burton, S. P.; Chen, Hong; Diamond, Michael; Ferrare, R. A.; Flynn, Connor; Kacenelenbogen, M. S.; LeBlanc, Samuel; Meyer, Kerry; Pistone, Kristina; Schmidt, S.; Segal-Rozenhaimer, Michal; Shinozuka, Y.; Wood, Robert; Zuidema, Paquita; Redemann, J.; Christopher, Sundar A.

doi:10.1029/2020gl091469

Cited by 16 publications

(9 citation statements)

References 82 publications

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“…Machado et al (2020) [47] identified a mixture of forest vegetation, savannah, and urban constructions in the limits of analysis of this site. In addition, the classic method used here to compare AERONET measurements with MODIS products uses the averages of AERONET measurements in the 30 min interval of the satellite passage, which can also lead to inaccuracies related to the simultaneous measurement [48].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of MODIS Dark Target AOD Product with 3 and 10 km Resolution in Amazonia

et al. 2022

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The techniques and analyses employed by remote sensing provide key information about atmospheric particle properties at regional and global scales. However, limitations in optical spectral models used to represent the different types of aerosols in the atmosphere and their effects (direct and indirect) are still one of the major causes of sources of uncertainties and substantial impacts in climate prediction. There are no studies yet in South America, especially in the Amazon Basin, that have evaluated the advantages, disadvantages, inconsistencies, applicability, and suitability of the MODIS sensor (Moderate Resolution Imaging Spectroradiometer) destined for monitoring the ambient aerosol optical thickness over rivers and continents. In this study, the results of the DT (Dark Target) algorithm for products with 3 km and 10 km resolutions were systematically evaluated for six sites in the Amazon rainforest. The comparisons between the products were carried out with the AERONET (Aerosol Robotic Network) measurements, which were used as reference. Statistical parameters between AERONET vs. MODIS were also evaluated based on biomass burning records in the site regions. Here, the DT 10 km product showed satisfactory performance for the Amazon region, with observations between the expected error (EE) limits above 66%, in addition to R > 0.8 and RMSE < 0.3. However, the regional analysis for the two sites in the central and southern regions of the Amazon basin did not have the same performance, where the results showed an EE of 24 and 47%, respectively. The DT 3 km product did not perform well in any site, with an EE below 50%. Both products overestimated the AOD, but the 3 km product overestimated it approximately four times more due to its algorithm setup. Thus, we recommend the 10 km product for general analysis in Amazonia. Regional biomass burning records showed a direct relationship with the AERONET vs. MODIS DT with overestimation of both products. All variations between products and sites were justified based on the difficulty of retrieving surface reflectance and the model selected for local aerosols. Improvements in the optical spectral model currently implemented in the algorithms, with more realistic representations of the main types of the aerosol present in the Amazon Basin, may contribute to better performance among the evaluated products.

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Section: Discussionmentioning

confidence: 99%

Evaluation of MODIS Dark Target AOD Product with 3 and 10 km Resolution in Amazonia

et al. 2022

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“…The AHI cloud data set includes cloud effective radius (Re), COT, and cloud top temperature (CTT), which have a spatial and temporal resolution of 5 km and 10 min, respectively (Letu et al., 2016). To make the comparison consistent between observations and model simulations, the cloud data set was processed to be spatially corresponding to the surface sites and temporally consistent with the observation duration to avoid bias from spatiotemporal variability (Chang et al., 2021) (see more details in Supporting Information ). We also synchronized the time resolution of this data set to 1 hr.…”

Section: Methodsmentioning

confidence: 99%

Revealing Bias of Cloud Radiative Effect in WRF Simulation: Bias Quantification and Source Attribution

et al. 2022

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The solar irradiance is an important parameter for the entire earth climate system since it governs the physical processes at the land surface (i.e., glacier melting, potential evapotranspiration, and diurnal cycling development of the planetary boundary layer (PBL)), and impacts on water and energy balance in the earth atmosphere (Ramanathan et al., 2001;Rosenfeld, 2006aRosenfeld, , 2006b. One objective of the studies focusing on man-made climate change is to quantify the variation of surface solar irradiance directly and indirectly caused by air pollutants from anthropogenic emissions (Jing & Suzuki, 2018;Powers et al., 2017). Additionally, solar irradiance forecast plays a key role in accelerating the ongoing global transition from conventional energy to renewable energy (Jimenez et al., 2016). Therefore, representation of physical processes controlling solar irradiance in the atmospheric models is important to estimate the radiation budget of the earth system, assess climate change, and accelerate the common application of green energy.The accuracy of simulating solar irradiance damping by aerosols, clouds, and light-absorbing gases during the transport from the top of the atmosphere to the earth's surface is usually unsatisfactory (Feng & Wang, 2019). The uncertainty of the solar irradiance simulation in mesoscale and global models is believed to intimately link to cloud radiative effect (CRE) since clouds usually show much higher solar irradiance extinct efficiency and sharper evolution than the gases and aerosols (Paquin-Ricard et al., 2010;Ruiz-Arias et al., 2016). Simulation of cloud-solar irradiance effect consists of two parts: (a) simulating cloud properties and (b) quantifying the solar irradiance extinction in the cloudy atmosphere. Therefore, solar irradiance forecast is determined by three

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“…Secondly, instead of providing the fixed temporal resolution, we offer an optional range of time for users to choose so that users are allowed to study any atmospheric events ranging from days to years by knowing the particular start date and end date of the events [18,19]. In turn, as the temporal resolution is defined in a flexible way to cover different time ranges, it cannot generate L3 outputs with regular daily, 8-day and monthly temporal resolutions within the defined range of time.…”

Section: General Procedures Of the Flexible Aggregationmentioning

confidence: 99%

Efficient and Flexible Aggregation and Distribution of MODIS Atmospheric Products Based on Climate Analytics as a Service Framework

et al. 2021

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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.

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Spatiotemporal Heterogeneity of Aerosol and Cloud Properties Over the Southeast Atlantic: An Observational Analysis

Cited by 16 publications

References 82 publications

Evaluation of MODIS Dark Target AOD Product with 3 and 10 km Resolution in Amazonia

Evaluation of MODIS Dark Target AOD Product with 3 and 10 km Resolution in Amazonia

Revealing Bias of Cloud Radiative Effect in WRF Simulation: Bias Quantification and Source Attribution

Efficient and Flexible Aggregation and Distribution of MODIS Atmospheric Products Based on Climate Analytics as a Service Framework

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