Smart Interpolation of Annually Averaged Air Temperature in the United States

Abstract

Two ?smart? interpolation procedures are presented and assessed with respect to their ability to estimate annual-average air temperatures at unsampled points in space from available station averages. Smart approaches examined here improve upon commonly used procedures in that they incorporate spatially high-resolution digital elevation information, an average environmental lapse rate, and/or another higher-resolution longer-term average temperature field. Two other straightforward or commonly used interpolation methods also are presented and evaluated as benchmarks to which the smart interpolators can be compared. Interpolation from a spatially high-resolution, long-term-average air temperature climatology serves as a first approximation, while ?traditional? interpolation (from a single realization of annual average air temperature on a single station network) is the other benchmark. Traditional interpolation continues to be the most commonly used interpolation approach within many of the atmospheric and environmental sciences. Smart approaches are significantly more accurate than either traditional methods or estimates spatially interpolated from a high-resolution climatology alone. A smart interpolation method that makes combined use of a digital elevation model (DEM) and traditional interpolation was nearly 24% more accurate than traditional interpolation by itself. Average error associated with this DEM-assisted interpolation algorithm, for interpolating yearly average air temperatures in the United States, was 0.44°C. The other smart method that was evaluated combines DEM information with a high-resolution average air temperature field. It was even more accurate, as expressed in an overall average interpolation error of only 0.38°C per year, which makes it some 34% more accurate than traditional interpolation. It is likely that the performance of smart interpolation, relative to traditional interpolation, will be even better when used with relatively sparse station networks.