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Peter Thornton, ORNL (PI) Bob Cook, ORNL (Co-I) Russ Vose, NCDC (Co-I) Michele Thornton, IIA

Peter Thornton, ORNL (PI) Bob Cook, ORNL (Co-I) Russ Vose, NCDC (Co-I) Michele Thornton, IIA Ben Mayer, ORNL 1 April 2013, NASA Terrestrial Ecology PI Meeting, La Jolla, CA. High-resolution surface weather data, with uncertainty quantification, for terrestrial ecosystem process models.

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Peter Thornton, ORNL (PI) Bob Cook, ORNL (Co-I) Russ Vose, NCDC (Co-I) Michele Thornton, IIA

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  1. Peter Thornton, ORNL (PI) Bob Cook, ORNL (Co-I) Russ Vose, NCDC (Co-I) Michele Thornton, IIA Ben Mayer, ORNL 1 April 2013, NASA Terrestrial Ecology PI Meeting, La Jolla, CA High-resolution surface weather data, with uncertainty quantification, for terrestrial ecosystem process models

  2. Motivation • Many terrestrial ecosystem process models have in common a requirement for high-quality surface weather data • Models moving toward higher spatial resolution • Current data sources at the global scale are coarse resolution, have known biases, and do not include uncertainty estimates

  3. Background • Daymet method can meet many of the needs of current models • Mainly applied over conterminous U.S., and more recently Mexico and southern Canada • 15-year history of serving a broad community through on-line data distribution

  4. Example application: North America

  5. (2002)

  6. Uncertainty quantification

  7. New project objectives • Apply operational Daymet algorithm to existing global dataset (GHCND) • Extend input observational dataset through collaboration with partners in Africa, Asia, and other regions • Improve algorithms for application in data-sparse regions • Add longwave radiation and generate sub-daily outputs

  8. Example application to new region

  9. Sub-daily outputs: precipitation Using NEXRAD (in U.S.) to improve estimates of precipitation occurrence and sub-daily co-distributions of occurrence and intensity.

  10. Countries/time spans with suitable station density for Task 1

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