Correcting snowfall from gauge observations using passive microwave brightness temperature data and data assimilation

In this paper a basic study is presented to develop a method to correct solid precipitation data front gauge observations using the passive microwave brightness temperature data and data assimilation. Observation and modeling results are indicating a high sensitivity of the 89 GHz channel to fresh snow on the ground. The change in the brightness temperature due to snow is used to correct observed solid precipitation data. The old brightness temperature data, the adjusted precipitation and the density of the fresh snow are input parameter for a radiative transfer model. The result is compared with the new, brightness temperature and by iteration the precipitation data is adjusted until the modeled value agrees well with the new brightness temperature. Good results have been achieved using snow pit data front Sapporo and modeled brightness temperatures. The results of a sensitivity study indicate that the approach is especially sensitive to detect larger amount of new snow on the ground, on the other nand the error is large for low precipitation events. Furthermore the results indicate that the sensitivity is too low to assimilate the new, snow density.