1 / 7

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.

deweyk
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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.

More Related