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Gridded Fields of Monthly Temperature and Precipitation for the Conterminous United States

Gridded Fields of Monthly Temperature and Precipitation for the Conterminous United States. Russell S. Vose Chief, Product Development Branch National Climatic Data Center. Objective. Create monthly 5 km gridded fields Temperature (maximum, minimum, average) Precipitation

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Gridded Fields of Monthly Temperature and Precipitation for the Conterminous United States

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  1. Gridded Fields of Monthly Temperature and Precipitation for theConterminous United States • Russell S. Vose • Chief, Product Development Branch • National Climatic Data Center

  2. Objective • Create monthly 5 km gridded fields • Temperature (maximum, minimum, average) • Precipitation • Focus on two periods • 1895-present (every single month) • Rapid near-real-time updates • Use published methods • Bias adjustments • Physiographically sensitive interpolation • Fully automated

  3. Not a New Idea

  4. Two Important Points • The emphasis is on creating a gridded product that can be used to compute robust averages over areas (e.g., counties). • The point-based estimates should be good in most places, but point accuracy was somewhat a secondary consideration.

  5. 12,061 Precipitation Stations

  6. Network Through Time

  7. Approach • Climatologically aided interpolation • Create a base-period grid of “average” conditions using sophisticated methods • Use the base-period grid as the first guess for gridding each year and month • Primary advantages • Grid for each year and month contains information from all stations (vs. just those available at that time) • Therefore, less sensitive to network variability (think 1895)

  8. Base-Period Climatology • Thin-plate smoothing splines • More general version of multiple linear regression • Smoothed non-parametric model vs. traditional regression • Smoothness determined from the data • ANUSPLIN used here • ANU = Australian National University • Smoothing by minimizing generalized cross validation • Spatially varying relationship between dependent and independent variables (latitude, longitude, elevation, inversion height, slope, aspect)

  9. Precipitation Averages January July

  10. Year/Month Grids • Three steps • Computation of year/month anomalies for each station • Gridding of year/month anomalies • Adding year/month anomaly grids to base-period grids • SPHEREMAP used here • Inverse distance interpolation (distance/directional weights) • Temperature anomaly = observation minus average • Precipitation anomaly = observation divided by average

  11. Creating Year/Month Grids Final Grid = Base Period + Anomaly Final Grid = Average Grid +

  12. Cross-Validation Errors (mm)

  13. Trends: 1980-2009

  14. Operational Issues • Update schedule • Updates start when < 9 days are missing in the month • E.g., will produce initial map of March on the 23rd • Revise daily thereafter until no new data • Availability • Running as an experimental product since January 2010 • Contact me if you want them • Full release when paper accepted for publication

  15. Other Gridded Products And maybe even daily snow grids …

  16. Daily Snow Depth: Real-Time • Maximize the station network • GHCN-Daily (COOP, CoCoRaHS) + SNOTEL • Eliminate the bogosities • GHCN-Daily QA, account for obs. time, missing values • Interpolate to a high-resolution grid • Elevation, slope, aspect, satellite-based snow extent • Generate gridded error fields • Cross-validation, Bayesian standard errors • Live with it in the West • Accuracy limited by coarse-resolution networks

  17. Historical Perspective • Relative to 1981-2010 Normals • Daily frequencies and percentiles at stations • Grid using previously described techniques • Relative to snow depth return levels • Pointwiseextremal (GEV) distributions at stations (based on annual maximum snow depth), then grid • Or direct estimation of a spatially smooth GEV distribution derived from all stations (Blanchet and Lehning, 2010)

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