AbstractPassive microwave brightness temperatures of snowpacks depend not only on the snow depth, but also on the internal snowpack properties, particularly the grain size, which changes through the winter. Algorithms that assume a constant grain size can yield erroneous estimates of snow depth or water equivalent. For snowpacks that are subject to temperatures well below freezing, the bulk temperature gradient through the snowpack controls the metamorphosis of the snow grains. This study used National Weather Service (NWS) station measurements of snow depth and air temperature from the Northern US Great Plains to determine temporal and spatial variability of the snow depth and bulk snowpack temperature gradient. This region is well suited for this study because it consists primarily of open farmland or prairie, has little relief, is subject to very cold temperatures, and has more than 280 reporting stations. A geostatistical technique called Kriging was used to grid the randomly spaced snow depth measurements. The resulting snow depth maps were then compared with the passive microwave observations from the Special Sensor Microwave Imager (SSM/I). Two snow seasons were examined: 1988–89, a typical snow year, and 1996–97, a record year for snow that was responsible for extensive flooding in the Red River Basin. Inspection of the time series of snow depth and microwave spectral gradient (the difference between the 19 and 37 GHz bands) showed that while the snowpack was constant, the spectral gradient continued to increase. However, there was a strong correlation (0·6 < R2 < 0·9) between the spectral gradient and the cumulative bulk temperature gradient through the snowpack (TGI). Hence, TGI is an index of grain size metamorphism that has occurred within the snowpack. TGI time series from 21 representative sites across the region and the corresponding SSM/I observations were used to develop an algorithm for snow depth that requires daily air temperatures. Copyright © 2002 John Wiley & Sons, Ltd.