Meteorology at the land surface affects many processes in the terrestrial biogeochemical system. Measurements of near-surface meteorological conditions are made at many locations, but we are often faced with having to perform ecosystem process simulations in areas where no meteorological measurements have been taken. In some cases it is possible to install new instrumentation for a particular study, but there are many situations where this is not a feasible solution. These problems are particularly important for simulations over large regions, where the number of simulation points is likely to be far greater than the number of observation stations. Daymet is a model that generates daily surfaces of temperature, precipitation, humidity, and radiation over large regions of complex terrain. The required model inputs include digital elevation data and observations of maximum temperature, minimum temperature, and precipitation from ground-based meteorological stations. The Daymet method is based on the spatial convolution of a truncated Gaussian weighting filter with the set of station locations. Sensitivity to the typical heterogeneous distribution of stations in complex terrain is accomplished with an iterative station density algorithm. Spatially and temporally explicit empirical analyses of the relationships of temperature and precipitation to elevation are performed. A daily precipitation occurrence algorithm is introduced, as a precursor to the prediction of daily precipitation amount. Surfaces of humidity (vapor pressure deficit) are generated as a function of the predicted daily minimum temperature and the predicted daily average daylight temperature. Daily surfaces of incident solar radiation are generated as a function of Sun-slope geometry and interpolated diurnal temperature range.
