1. Wei Huang Class project presentation for ECE539
A Neural Network Approach to Estimate Snowfall Parameters from Passive Microwave Radiometer
Wei Huang
Class project presentation for ECE539

2. Overview
Knowing snowfall rate over the ground is an important part of hydrological circle.
Snowfall is closely related to our daily life.
Snowfall can emit thermal radiation in the microwave band, which can be observed by radiometer(microwave is parallel to light in the visible band ).
There are many factors that determine the accuracy of snowfall retrieval. The microwave response to snowfall is nonlinear in general.

3. Passive Microwave Response to Snowfall
Snow density and shape, which is subject to temperature, wind speed,etc
Surface type: sea surface(simple), land surface type(complex)
Cloud liquid water can also affect the observation.
Satellite based PM radiometer has a bigger observation range. Active radar observation can provide a more accurate estimation, but to a limited range

4. Method
Simulated database, which includes simulated brightness temperature at 89.0, 36.5GHz (V/H)and snowfall rate over the ground (mm/hr)
Satellite database ,which includes the brightness temperature at 89.0 and 36.5 GHz
Neural network(feed-forward back-prop network 4-4-1)
Comparison with available Radar data, which includes the radar derived snowfall rate

5. Results(Feature space)
Training set feature space:
89.0,36.6GHz, vertically and horizontally polarized.
Output: Snowfall rate.

6. ANN retrieval
Top row:
Left: Model output snowfall rate of Wakasabay.
Right: Trained ANN output snowfall rate.
(correlation: 0.98)
Bottom row:
Left: ANN output snowfall rate of Wakasakby
Right: Radar retrieved snowfall rate of Wakasabay. (correlation:0.95)

7. Conclusion
ANN works pretty well, when training data set can cover a wide range of features.