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<p>Measuring carbon emissions at high temporal and spatial resolution covering all parts of the globe is key to understanding the sources and sinks of carbon. These measurements are critical for informing both climate modeling and policy decisions to mitigate climate change. Fragmented data sources and the requirement of significant programming knowledge to retrieve, clean, and analyze data from existing data sources pose a significant barrier for climate researchers. As understanding of climate science becomes crucial for fields beyond geophysical sciences, it is especially urgent to build tools that can enable researchers from diverse academic backgrounds to analyze carbon emission data from satellites. </p> <p>In this presentation, we will present a novel, user-friendly platform which has pre-built functions and analysis pipelines allowing scientists to perform common data analysis tasks without the need to write code. The underlying data lake combines NASA’s Orbiting Carbon Observatory (OCO-2 and OCO-3) data with other data sources (e.g. MODIS-based fire data) that facilitate a more accurate and complete understanding of the dynamics of the carbon cycle and the factors that influence it. </p> <p>We highlight how our approach integrating data discovery, access, and analysis of climate data can help democratize climate research and inform policymaking.</p> <p>Potential research questions that can be addressed using this approach include: </p> <p>(i) studying the impacts of fires on the global carbon cycle with MODIS fire products providing information on the location, intensity, and types of fires, </p> <p>(ii) studying the photosynthetic activity of plants and the carbon cycle assimilating OCO-2 SIF data. OCO2-SIF data measures the fluorescence emitted by plants as a result of photosynthesis, which can be used as an indicator of plant health and productivity and </p> <p>(iii) AI-assisted audit of industrial emissions incorporating publicly available data on critical CO2 emitting sectors e.g. power plants, steel mills, cement plants, atmospheric “spillover” from agricultural and forest fires, traffic emissions, demographic and economic variables, etc</p>

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