Keywords
informatics, data integration, metadata
Location
Session A1: Leveraging Cyberinfrastructure to Advance Scientific Productivity and Reproducibility in the Water Sciences
Start Date
16-6-2014 3:40 PM
End Date
16-6-2014 5:20 PM
Abstract
A central challenge to environmental forecasting in hydrological and land surface modeling is how to integrate multiple data sources over a wide range of spatial scales. Furthermore how can this complex task be achieved in the most productive and reproducible way with a robust informatics architecture? At the National Ecological Observatory Network (NEON) we are collecting a variety of biophysical and biogeochemical measurements which can be used with models to perform temporal forecasting on decadal timescales. To take advantage of this data we are developing a data assimilation framework. Using this framework NEON data can be combined with the Community Land Model, which features a fully coupled carbon and nitrogen cycle (CLM-CN). Our goal is to produce optimal solutions for model states, fluxes and parameter values, with their associated uncertainties, at regional to continental scales. Here we describe our initial trials of programmatically integrating NEON data streams with the CLM using the Data Assimilation Research Testbed (DART), a community tool for ensemble data assimilation (DA). We will provide an overview of the NEON informatics architecture, the workflow we employ, and outline how our emphasis on metadata and semantic infrastructure from the NEON project will enable others to use these data within their own data assimilation frameworks.
Included in
Civil Engineering Commons, Data Storage Systems Commons, Environmental Engineering Commons, Other Civil and Environmental Engineering Commons
Integrating NEON data with existing models: An example with the Community Land Model
Session A1: Leveraging Cyberinfrastructure to Advance Scientific Productivity and Reproducibility in the Water Sciences
A central challenge to environmental forecasting in hydrological and land surface modeling is how to integrate multiple data sources over a wide range of spatial scales. Furthermore how can this complex task be achieved in the most productive and reproducible way with a robust informatics architecture? At the National Ecological Observatory Network (NEON) we are collecting a variety of biophysical and biogeochemical measurements which can be used with models to perform temporal forecasting on decadal timescales. To take advantage of this data we are developing a data assimilation framework. Using this framework NEON data can be combined with the Community Land Model, which features a fully coupled carbon and nitrogen cycle (CLM-CN). Our goal is to produce optimal solutions for model states, fluxes and parameter values, with their associated uncertainties, at regional to continental scales. Here we describe our initial trials of programmatically integrating NEON data streams with the CLM using the Data Assimilation Research Testbed (DART), a community tool for ensemble data assimilation (DA). We will provide an overview of the NEON informatics architecture, the workflow we employ, and outline how our emphasis on metadata and semantic infrastructure from the NEON project will enable others to use these data within their own data assimilation frameworks.
