gridded fields of monthly temperature and precipitation for the conterminous united states

Gridded Fields of Monthly Temperature and

Precipitation for the ConterminousUnited States

Russell S. VoseChief, Product Development Branch

National Climatic Data Center

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

FEMA Snow WorkshopEstes Park, CO, May 25-27, 2011

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Not a New Idea

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.

FEMA Snow WorkshopEstes Park, CO, May 25-27, 2011

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12,061 Precipitation Stations

FEMA Snow WorkshopEstes Park, CO, May 25-27, 2011

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Network Through Time

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)

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)

FEMA Snow WorkshopEstes Park, CO, May 25-27, 2011

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Precipitation Averages

January

July

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

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 +

FEMA Snow WorkshopEstes Park, CO, May 25-27, 2011

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Cross-Validation Errors (mm)

FEMA Snow WorkshopEstes Park, CO, May 25-27, 2011

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Trends: 1980-2009

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

FEMA Snow WorkshopEstes Park, CO, May 25-27, 2011

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Other Gridded Products

And maybe even daily snow grids …

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

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)