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National models of the electric sector typically consider a handful of generator operating periods per year, while pollutant fate and transport models have an hourly resolution. We bridge that scale gap by introducing a novel fundamental-based temporal downscaling method (TDM) for translating national or regional energy scenarios to hourly emissions. Optimization-based generator dispatch is used to account for variations in emissions stemming from weather-sensitive power demands and wind and solar generation. The TDM is demonstrated by downscaling emissions from the electricity market module in the National Energy Model System. As a case study, we implement the TDM in the Virginia−Carolinas region and compare its results with traditional statistical downscaling used in the Sparse Matrix Operator Kernel Emissions (SMOKE) processing model. We find that the TDM emission profiles respond to weather and that nitrogen oxide emissions are positively correlated with conditions conducive to ozone formation. In contrast, SMOKE emission time series, which are rooted in historical operating patterns, exhibit insensitivity to weather conditions and potential biases, particularly with high renewable penetration and climate change. Relying on SMOKE profiles can also obscure variations in emission patterns across different policy scenarios, potentially downplaying their impacts on power system operations and emissions. 

The classification of frequently occurring terrestrial-origin transient signals, called glitches, in the time series data from gravitational wave detectors is important for mitigating their adverse effects on searches for rare and valuable astrophysical signals. While formally a time series classification problem, recent successes in glitch classification have all come from using their time-frequency image representations. Using transfer learning with the VGG16 deep convolutional neural network for image classification, we compare the efficacy of different types of image representations for classifying simulated glitches. We find the novel result that training the network with 2D plots of the noisy glitch time series provides better classification accuracy than their time-frequency images.