Serbin, S.P. and C.J. Kucharik.  Spatio-temporal mapping of daily temperature and precipitation for the development of a multi-decadal climatic dataset for Wisconsin.  Submitted to Agricultural and Forest Meteorology.  Feb 2008.

Abstract
Here we present results from the generation of a multi-decadal climatic data set for 57 years (1950 – 2006) of daily to monthly precipitation (PTotal), maximum temperature (Tmax), and minimum temperature (Tmin) for the important agricultural and forest products state of Wisconsin using observations from up to 176 climate stations.  The data set was constructed at 8 km (5.0’) latitude-longitude resolution using an automated Inverse Distance Weighting (IDW) interpolation scheme.  We performed a rigorous test of the predictive accuracy of the IDW gridded surfaces using 104 stations withheld in the production of the climate grids in a post-gridding validation step.  The mean bias errors appear reasonable, ranging from -0.75 to 0.96 °C for temperature and -0.04 to 0.08 mm for precipitation, on average, across all climate divisions.  Our results suggest a high degree of explained variation for daily temperature (R2 &Mac179; 0.97) and a moderate degree for daily precipitation (R2 = 0.66), whereby the realism improves considerably for monthly precipitation accumulation totals (R2=0.87). We also observed a small seasonal variation in accuracy of the climate grids, with decreasing predictive capability as precipitation totals increased during the wetter summer months, when more precipitation originates from convective forcing.  The grids show clear and coherent spatial patterns in temperature and precipitation that are to be expected for this region.  For example, latitudinal gradients in temperature and precipitation are observed across the state, with decreasing temperature towards the north and increasing accumulation of precipitation toward the Northwest in the summer.  The grids will be useful for a variety of regional scale research and ecosystem process modeling studies analyzing climate change and impacts of previous climate variability and change on natural resources.