DAILY AIR-TEMPERATURE INTERPOLATED AT HIGH-SPATIAL-RESOLUTION OVER A LARGE MOUNTAINOUS REGION

Two methods are investigated for interpolating daily minimum and maxim um air temperatures (T-min and T-max) at a 1 km spatial resolution ove r a large mountainous region (830 000 km(2)) in the U.S. Pacific North west. The methods were selected because of their ability to (1) accoun t for the effect of elevation on temperature and (2) efficiently handl e large volumes of data. The first method, the neutral stability algor ithm (NSA), used the hydrostatic and potential temperature equations t o convert measured temperatures and elevations to sea-level potential temperatures. The potential temperatures were spatially interpolated u sing an inverse-squared-distance algorithm and then mapped to the elev ation surface of a digital elevation model (DEM). The second method, l inear lapse rate adjustment (LLRA), involved the same basic procedure as the NSA, but used a constant linear lapse rate instead of the poten tial temperature equation. Cross-validation analyses were performed us ing the NSA and LLRA methods to interpolate T-min and T-max each day f or the 1990 water year, and the methods were evaluated based on mean a nnual interpolation error (IE). The NSA method showed considerable bia s for sites associated with vertical extrapolation. A correction based on climate station/grid cell elevation differences was developed and found to successfully remove the bias. The LLRA method was tested usin g 3 lapse rates, none of which produced a serious extrapolation bias. The bias-adjusted NSA and the 3 LLRA methods produced almost identical levels of accuracy (mean absolute errors between 1.2 and 1.3 degrees C), and produced very similar temperature surfaces based on image diff erence statistics. In terms of accuracy, speed, and ease of implementa tion, LLRA was chosen as the best of the methods tested.