Methane Source Finder web application (user-interface and server)(NPO-51093-1)

We will combine our collective expertise in atmospheric chemistry, carbon cycle science, spectroscopy, data assimilation, machine learning software engineering, and science systems engineering to develop CEDAS to meet the following objectives: 1.Detect and characterize localized methane and CO2 anomalies that manifest in an intermittent and stochastic but with net high magnitude fluxes.2.Apply machine learning techniques to provide more autonomous and lower latency source detection, attribution and process understanding. 3.Automate and optimize methane and CO2 analysis workflows to reduce data product latency and improve on-demand services including quality control and validation. 4.Develop a regional-scale scale analysis framework that can assimilate surface and satellite atmospheric observations and high-resolution weather reanalysis products for any region globally. 5.Demonstrate a common data system that facilitates anomaly notification/response, data search, discovery, fusion and on-demand analytics.