A neural netwotk based approach for multi-spectral snowfall detection and estimation

The principal intent of this research is to: (a) investigate the potential of passive microwave data from AMSU in detecting snowfall events and in measuring their intensity, and (b) evaluate the effect of both land cover and atmospheric conditions on the retrieval accuracy. Additional information such as cloud cover and air temperature were added to the process to reduce misidentified snowfall pixels. Two products retrieved from AMSU brightness temperatures were used to estimate surface temperature and to map the snow cover extent. In this project, a neural-network-based model has been developed and has shown a great potential in detecting and estimating the intensity of snowfall events. This algorithm has been applied for different snow storms occurred between 2002 and 2003 in three different locations in the North-East of United States. These locations were selected because of the high amount of snowfall every year. Only pixels with cloud cover and falling within a specific range of temperature are presented to the snowfall detection model. Surface temperature collected from ground station-based observations archived by the National Climatic Data Center (NCDC) were used for this test. Different heavy storm events and non-snowfall observations that occurred at the same time as AMSU acquisition were selected. Hourly snow accumulation data collected by the NCDC were used as truth data to train and validate the model. To reduce the risk of erroneous identification of snowfall pixels, only storms lasting more than three hours were selected. Such criteria will undoubtedly increase the level of confidence that snowfall coincides with AMSU acquisition time. The neural network based snowfall product was compared with the snowfall detection algorithm over land developed in 2003 by Kongoli et al [1]. The preliminary results indicate that the neural-network-based model provides a significant improvement in snowfall detection accuracy over existing satellite-based methods.