Spatiotemporal Mapping of Temperature and Precipitation for the Development of a Multidecadal Climatic Dataset for Wisconsin

Results from the generation of a multidecadal gridded climatic dataset for 57 yr (1950-2006) of daily and monthly precipitation (P-Total), maximum temperature (T-max), and minimum temperature (T-min) are presented for the important agricultural and forest products state of Wisconsin. A total of 176 climate stations were used in the final gridded dataset that was constructed at 8-km (5.0') latitude-longitude resolution using an automated inverse distance weighting interpolation. Accuracy statistics for the interpolated data were based on a rigorous validation step using 104 first- and second-order climate observation stations withheld in the production of the gridded dataset. The mean absolute errors (MAE) for daily minimum and maximum temperatures averaged 1.51 degrees and 1.31 degrees C, respectively. Daily precipitation errors were also reasonable, ranging from -0.04 to 0.08 mm, on average, across all climate divisions in the state with an overall statewide MAE of 1.37 mm day (1). Correlation analysis suggested a high degree of explained variation for daily temperature (R-2 >= 0.97) and a moderate degree for daily precipitation (R-2 = 0.66), whereby the realism improved considerably for monthly precipitation accumulation totals (R-2 = 0.87). Precipitation had the best interpolation accuracy during the winter months, related to large-scale, synoptic weather systems, and accuracy was at a minimum in the wetter summer months when more precipitation originates from local-to-regional-scale convective forcing. Overall the grids showed coherent spatial patterns in temperature and precipitation that were expected for this region, such as the latitudinal gradient in temperature and longitudinal gradient in precipitation across the state. The grids will prove useful for a variety of regional-scale research and ecosystem modeling studies.