Download - Gridded Fields of Monthly Temperature and Precipitation for the Conterminous United States
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Gridded Fields of Monthly Temperature and
Precipitation for the ConterminousUnited States
Russell S. VoseChief, Product Development Branch
National Climatic Data Center
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FEMA Snow WorkshopEstes Park, CO, May 25-27, 2011
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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
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FEMA Snow WorkshopEstes Park, CO, May 25-27, 2011
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Not a New Idea
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FEMA Snow WorkshopEstes Park, CO, May 25-27, 2011
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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.
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FEMA Snow WorkshopEstes Park, CO, May 25-27, 2011
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12,061 Precipitation Stations
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FEMA Snow WorkshopEstes Park, CO, May 25-27, 2011
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Network Through Time
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FEMA Snow WorkshopEstes Park, CO, May 25-27, 2011
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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)
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FEMA Snow WorkshopEstes Park, CO, May 25-27, 2011
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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)
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FEMA Snow WorkshopEstes Park, CO, May 25-27, 2011
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Precipitation Averages
January
July
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FEMA Snow WorkshopEstes Park, CO, May 25-27, 2011
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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
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FEMA Snow WorkshopEstes Park, CO, May 25-27, 2011
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Creating Year/Month Grids
Final Grid = Base Period + Anomaly
Final Grid = Average Grid +
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FEMA Snow WorkshopEstes Park, CO, May 25-27, 2011
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Cross-Validation Errors (mm)
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FEMA Snow WorkshopEstes Park, CO, May 25-27, 2011
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Trends: 1980-2009
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FEMA Snow WorkshopEstes Park, CO, May 25-27, 2011
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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
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FEMA Snow WorkshopEstes Park, CO, May 25-27, 2011
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Other Gridded Products
And maybe even daily snow grids …
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FEMA Snow WorkshopEstes Park, CO, May 25-27, 2011
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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
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FEMA Snow WorkshopEstes Park, CO, May 25-27, 2011
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Historical Perspective• Relative to 1981-2010 Normals
– Daily frequencies and percentiles at stations– Grid using previously described techniques
• Relative to snow depth return levels– Pointwise extremal (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)