The new‐generation polar‐orbiting operational environmental sensor, the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar‐orbiting Partnership (S‐NPP) satellite, provides critical daily global aerosol observations. As older satellite sensors age out, the VIIRS aerosol product will become the primary observational source for global assessments of aerosol emission and transport, aerosol meteorological and climatic effects, air quality monitoring, and public health. To prove their validity and to assess their maturity level, the VIIRS aerosol products were compared to the spatiotemporally matched Aerosol Robotic Network (AERONET) measurements. Over land, the VIIRS aerosol optical thickness (AOT) environmental data record (EDR) exhibits an overall global bias against AERONET of −0.0008 with root‐mean‐square error (RMSE) of the biases as 0.12. Over ocean, the mean bias of VIIRS AOT EDR is 0.02 with RMSE of the biases as 0.06. The mean bias of VIIRS Ocean Ångström Exponent (AE) EDR is 0.12 with RMSE of the biases as 0.57. The matchups between each product and its AERONET counterpart allow estimates of expected error in each case. Increased uncertainty in the VIIRS AOT and AE products is linked to specific regions, seasons, surface characteristics, and aerosol types, suggesting opportunity for future modifications as understanding of algorithm assumptions improves. Based on the assessment, the VIIRS AOT EDR over land reached Validated maturity beginning 23 January 2013; the AOT EDR and AE EDR over ocean reached Validated maturity beginning 2 May 2012, excluding the processing error period 15 October to 27 November 2012. These findings demonstrate the integrity and usefulness of the VIIRS aerosol products that will transition from S‐NPP to future polar‐orbiting environmental satellites in the decades to come and become the standard global aerosol data set as the previous generations' missions come to an end.

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The Visible/Infrared Imager Radiometer Suite (VIIRS) on board the Suomi National Polar‐orbiting Partnership (S‐NPP) satellite has been retrieving aerosol optical thickness (AOT), operationally and globally, over ocean and land since shortly after S‐NPP launch in 2011. However, the current operational VIIRS AOT retrieval algorithm over land has two limitations in its assumptions for land surfaces: (1) it only retrieves AOT over the dark surfaces and (2) it assumes that the global surface reflectance ratios between VIIRS bands are constants. In this work, we develop a surface reflectance ratio database over land with a spatial resolution 0.1° × 0.1° using 2 years of VIIRS top of atmosphere reflectances. We enhance the current operational VIIRS AOT retrieval algorithm by applying the surface reflectance ratio database in the algorithm. The enhanced algorithm is able to retrieve AOT over both dark and bright surfaces. Over bright surfaces, the VIIRS AOT retrievals from the enhanced algorithm have a correlation of 0.79, mean bias of −0.008, and standard deviation (STD) of error of 0.139 when compared against the ground‐based observations at the global AERONET (Aerosol Robotic Network) sites. Over dark surfaces, the VIIRS AOT retrievals using the surface reflectance ratio database improve the root‐mean‐square error from 0.150 to 0.123. The use of the surface reflectance ratio database also increases the data coverage of more than 20% over dark surfaces. The AOT retrievals over bright surfaces are comparable to MODIS Deep Blue AOT retrievals.

[1] Growing recognition of the importance of natural and anthropogenic aerosols in climate research led to numerous efforts to obtain information on aerosols based on model simulations, satellite remote sensing, and ground observations. This study describes an approach to combine information from independent sources that complement each other in their capabilities to achieve a global characterization of monthly mean clear-sky daytime aerosol optical depth. The following sources of information have been used: simulations from the Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model; retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the Terra satellite; and measurements from the Aerosol Robotic Network (AERONET). Leading empirical orthogonal functions (EOFs) are used to represent the significant variation signals from model and satellite results; the EOFs are fitted to the ground observations to propagate the AERONET information at a global scale. The methodology is implemented with a 2-year time record when collocated data from all three sources are available.

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False data injection (FDI) attacks, as a new class of cyberattacks, bring a severe threat to the security and reliable operation of the smart grid by damaging the state estimation of the power system. To address this issue, an extreme learning machine (ELM)-based one-class-one-network (OCON) framework is proposed for detecting the FDI attacks in this paper. Under this framework, to effectively detect bus-based FDI attacks and identify the bus node being attacked, the subnets of state identification layer in OCON adopt the ELM algorithm to accurately divide the false data and the normal data. After that, a global layer is employed to analyze whether the bus node associated with its corresponding subnet is attacked by false data utilizing the results from the state identification layer. Finally, in order to improve the resilience of the power system, a prediction recovery strategy is proposed to remedy the detected false data by exploiting the spatial correlation of power data. The proposed framework is tested on the IEEE 14 bus system using real load data from New York independent system operator. The simulation results demonstrate that the proposed framework not only accurately recognizes the multiple bus nodes under the FDI attacks but also efficiently recovers the data injected by false data. INDEX TERMS Smart grid, false data injection (FDI) attacks, extreme learning machine (ELM), one-class-one-network (OCON).

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