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A Neural Network Approach to Estimate Snowfall Parameters from Passive Microwave Radiometer

This project explores a neural network approach to estimate snowfall parameters from a passive microwave radiometer. The study compares the performance of the neural network model with radar-derived snowfall rates and demonstrates its effectiveness.

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A Neural Network Approach to Estimate Snowfall Parameters from Passive Microwave Radiometer

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  1. 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 2003.12.19 Wakasabay. Right: Trained ANN output snowfall rate. (correlation: 0.98) Bottom row: Left: ANN output snowfall rate of 2005.12.25. Wakasakby Right: Radar retrieved snowfall rate of 2005.12.25. Wakasabay. (correlation:0.95)

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

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