SI2-SSI: REAL-TIME LARGE-SCALE PARALLEL INTELLIGENT CO2 DATA ASSIMILATION SYSTEM

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Intellectual Merit: This proposal seeks to address this need by creating a state-of-the-art autonomous software platform for real-time integration of in-situ and satellite-based atmospheric CO2 measurements within a Data Assimilation (DA) system for producing estimates of global land and oceanic CO2 exchange at weekly to bi-weekly intervals. The proposed software infrastructure will be capable of autonomous processing of large volumes of data through a multi-stage pipeline, without the delays conventionally associated with such processing. Within the DA component, we will provide options for multiple DA algorithms for estimating global CO2 exchange. Users will, for the first time, have the capability to use these multiple methods as part of a single system for comparing estimates of CO2 exchange, and to obtain an improved understanding of the relative advantages of the various DA methods. As part of the analysis component of the software, we will build a carbon-climate surveillance system by drawing from a range of techniques in pattern recognition and high-dimensional statistical inference. This system will be able to detect and analyze localized variations in CO2 exchange within any user-specified spatio-temporal window. In addition, summaries of the CO2 exchange will be provided at annual and monthly temporal scales for continents and countries.Broader Impacts: This software can be used by researchers and governmental institutions for evaluating both the natural components of the carbon cycle and anthropogenic carbon emissions, as well as in the design of new satellites for improved monitoring of CO2. All data and software will be publicly available and open-source development platforms will be used whenever possible. The algorithm prototypes developed as part of this project will be used in undergraduate and graduate courses at the University of Michigan, and will be made available online for educators at other institutions. Finally, the project will train three graduate students, with a focus on developing their cross-disciplinary skills in the field of Earth science, statistics, computer science, and atmospheric science.