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Development of a 103-Year High-Resolution Climate Data Set for the Conterminous United States

Development of a 103-Year High-Resolution Climate Data Set for the Conterminous United States. Wayne Gibson 1 , Christopher Daly 1 , Tim Kittel 2 , Doug Nychka 2 , Craig Johns 2 , Nan Rosenbloom 2 , Alan McNab 3 , and George Taylor 1.

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Development of a 103-Year High-Resolution Climate Data Set for the Conterminous United States

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  1. Development of a 103-Year High-Resolution Climate Data Set for the Conterminous United States Wayne Gibson1, Christopher Daly1, Tim Kittel2, Doug Nychka2, Craig Johns2, Nan Rosenbloom2, Alan McNab3, and George Taylor1 1 Spatial Climate Analysis Service,Oregon State University, Corvallis, OR 97331, USA 2 National Center for Atmospheric Research, Boulder, CO 80307, USA 3 National Climatic Data Center, Asheville, NC 29901, USA

  2. Introduction • Why is the data set so useful? • unique • complete in space and time for long time period (US, 103 years) • high resolution (4km) • spatial QC of the station data prior to modeling • many applications need this type of data • Methodology used to create grids • Statistical infilling of incomplete station data (NCAR) • PRISM model used to spatially map the station data • PRISM products • Official USDA 1961-1990 normals for the US • New NCDC Climate Atlas of the US (48 parameters) • Canada, China, European Alps, Pacific Islands, Puerto Rico

  3. Project Overview • Main objective • To create serially complete, high quality, topographically sensitive, high resolution grids for the conterminous United States (ppt, Tmin, and Tmax) • To create a serially complete infilled station data set • Progression • Year 1: Preliminary precipitation grids created for 1948-1993 • Year 2: Development of a semi-automated Quality Control (QC) system (ASSAY QC, based on PRISM) • Year 3: Development of a more robust methodology for station data infilling (National Center for Atmospheric Research) • Year 4: Creation of final grids for the time period 1895-1997

  4. Collection of Station Data • HCN: Historical Climate Network (1895-1997) • COOP: National Weather Service Cooperative Network (1895-1997) • MCC: COOP data from the Midwestern Climate Center (1895-1947) • SNOTEL: SNOwpack TELemetry Network, National Resource Conservation Service (1978-1997) • AG: Agricultural climate data (1961-1993) • MISC: Miscellaneous data (storage gauges, snow course) Inconsistencies between Station Data Networks

  5. Observation Networks over Time

  6. Observation Networks vs Elevation

  7. Data QC: Station Metadata Checks • Elevation • Using Geographical Information Systems (GIS) • Latitude, longitude, and elevation: • Analyzed each stations metadata for changes • Total of 100 metadata errors • Horizontal position errors <= 2 degrees • Elevation errors <= 1200 m

  8. Data QC: PRISM based QC system – ASSAY QC • What is Bad Data • Data having transcription errors • We are not attempting to identify errors such as gauge under catch, observation methods, or instrumentation changes. • ASSAY QC – automated method • Jackknifed prediction • Compare predicted to observed value • Tag large differences as “candidate” outliers. • Process of evaluating detection of outliers with actual station data (monthly and daily observations) • Post Processing • Additional Check to “Candidates” Applied Based on Closest/Highest Weighted Station: • Distance • Elevation • Precipitation Amount • Large Outliers in the Observations • Manual Checks • List of “Bad” Observation. Mark as Missing.

  9. QC Results - Precipitation • 2371 monthly data errors out of 6,345,675 station-months for a detection rate of 0.0374% • This are about 2 errors per monthly grid, not insignificant • Also keep in mind that there is a propagation of errors in space. (50km radius or greater)

  10. Example Outlier

  11. Issues • Inconsistencies among Observation Networks • SNOTEL vs COOP (ppt) • HCN vs COOP (adjusted vs raw) • Station data infilling errors • Climatologically aided interpolation (climate as predictor) • ASSAY QC improvements • Easily detects outliers • Run iteratively • Independent evaluation • Long term runoff

  12. Fullerton: Location to Other Stations

  13. HCN vs COOP: Inconsistencies

  14. HCN vs COOP: Inconsistencies

  15. Summary • Important data set. High quality, high resolution, and long duration. • Can be used to support a variety of research topics in many disciplines. • Precipitation, Tmin, and Tmax • Summer 2002 • ftp://ftp.ncdc.noaa.gov/pub/data/prism100 http://www.ocs.oregonstate.edu/prism

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