92nd American Meteorological Society Annual Meeting (January 22-26, 2012)

Thursday, 26 January 2012: 1:30 PM
A Case Study Analyzing Climate Model Agricultural Decision Support
Room 357 (New Orleans Convention Center )
Archer L. Batcheller, Northrop Grumman Corporation, Bellevue, NE; and F. VanWijngaarden

The Global Earth Observation System of Systems (GEOSS) effort has identified nine areas in which society benefits from appropriate environmental information. We have targeted specific issues within these societal benefit areas by determining appropriate data sets needed and transforming these data into information useable by decision makers. Here we describe data exploitation within our service-oriented architecture, allowing us to ingest real-time or static data into a database with a spatial data engine. We then make appropriate manipulations to the data using domain knowledge relevant to the problem, and expose the data as services. A library of common widgets allows us to build custom portals that display and overlay the data for users to analyze. By using portals and a service-oriented architecture we can reuse services and widgets to rapidly assemble a view of geographic data, and assist decision-makers in applying and interpreting the latest scientific results. As a case study with our system, we have integrated data from Intergovernmental Panel on Climate Change (IPCC) climate models, crop yields, and environmental thresholds for crops to present a first level analysis of the impact of climate change on key crops grown in Mexico. By calibrating the model and performing sensitivity studies on the data, we gain an understanding of the impacts of select parameters on specified crops. Knowledge about changes in the regions that are favorable for crop growth is important for many stakeholders, ranging from individual farmers, to governments, to scientists working to create new seed varieties. Our work also highlights research opportunities in climate science by identifying the types and resolution of climate model variables required for agricultural impact analysis.

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