Robust neural network system design for detecting and estimating snowfall from the Advanced Microwave Sounding Unit

The principal intent of this research is to: (a) investigate the potential of passive microwave data from Advanced Microwave Sounding Unit (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. A neural-network-based model has been developed and has shown a great potential in detecting snowfall events and classifying their intensity into light, moderate or heavy. This algorithm has been applied for different snow storms which occurred in four winter seasons in the Northeastern United States. Additional information such as cloud cover and air temperature were added to the process to reduce misidentified snowfall pixels. 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 and 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. The preliminary results indicate that the neural-network-based model provides a significant improvement in snowfall detection accuracy over existing satellite-based methods.