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PEcAn

PEcAn. The P redictive Ec osystem An alyzer. Motivation. Synthesize heterogeneous data Bridge gap between conceptual and computational models Summarize what we know, based on available data and mechanistic models

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PEcAn

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  1. PEcAn The Predictive Ecosystem Analyzer

  2. Motivation • Synthesize heterogeneous data • Bridge gap between conceptual and computational models • Summarize what we know, based on available data and mechanistic models • Identify sources of uncertainty -> prioritize data collection and model improvement • Make complex workflows accessible, reproducible, and extensible

  3. Design • Modular: • models can be coupled within PEcAn • PEcAn can be embedded into other workflows • High level functions • e.g. ‘run.meta.analysis’; ‘start.model.runs(model)’ • Web Interface • Remote execution of simulation models on HPC • Adoption of existing standards, libraries where possible • Virtual Machines easy to get up and running

  4. Modules • Analysis: • Meta-analysis • Data assimilation • Visualization • Priors • Uncertainty • more … • Utilities: • QAQC • Database • Logger • Settings • Models (min 2 functions each): • Ecosystem Demography v2 • BioCro • Sipnet • Dalec

  5. Site BETYdb: Informatics Backend • Citation Treatment Management Cultivar Traits, Yields, Ecosystem Services Species Covariates Variable Prior Functional Type

  6. Site BETYdb (part II): Model provenance • Citation Treatment Management Runs Ensembles Cultivar Workflows Posteriors Site Species Models Inputs Traits, Yields, Ecosystem Services Machines Variable Covariates Functional Type Variable Prior Functional Type

  7. PEcAn: Web Interface Configure Run Review Previous Runs Visualize, Export Results Analysis in R

  8. Future Directions • Model Intercomparisons • Integration into existing workflows • Automated ‘real-time’ data assimilation • Improved web-interface – enable end users to ask new questions

  9. More Information • Who: • David LeBauer, University of Illinois • Mike Dietze, Boston University • Rob Kooper, National Center for Supercomputing Applications • Shawn Serbin, Brookhaven National Laboratories • Where: • pecanproject.org • github.com/PecanProject • Funding: • Energy Biosciences Institute, NSF

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