Comparison of Snow Water Equivalent Retrieved from SSM/I Passive Microwave Data Using Artificial Neural Network, Projection Pursuit, and Nonlinear Regressions
Title | Comparison of Snow Water Equivalent Retrieved from SSM/I Passive Microwave Data Using Artificial Neural Network, Projection Pursuit, and Nonlinear Regressions |
Publication Type | Conference Proceedings |
Year of Conference | 2009 |
Authors | Gan, T. Y., Kalinga O., and Singh P. R. |
Conference Name | 77th Annual Western Snow Conference |
Series Title | Proceedings of the 77th Annual Western Snow Conference |
Date Published | April 2009 |
Publisher | Western Snow Conference |
Conference Location | Canmore, AB |
Keywords | Snow water equivalent, SSMI, passive microwave, neural network, Red River Basin |
Abstract | The snow water equivalent (SWE) for the Red River basin of North Dakota and Minnesota was retrieved from data acquired by passive microwave SSM/I (Special Sensor Microwave Imager) sensors mounted on the US Defense Meteorological Satellite Program (DMSP) satellites, by an artificial neural network called Modified Counter Propagation Network (MCPN), a Projection Pursuit Regression (PPR) and a nonlinear regression. The airborne gamma-ray measurements of SWE for 1989 and 1997 were used as observed SWE, and SSM/I data of 19 and 37 GHz frequencies, in both horizontal and vertical polarization, were used for the calibration (1989 data from DMSP-F8) and validation (1997 data from DMSP-F10 and F13 of both ascending and descending overpass times were combined) of the models. The SSM/I data were screened for the presence of wet snow, large water bodies like lakes and rivers, and depth-hoar. The MCPN model produced encouraging results in both calibration and validation stages (R2 was about 0.9 for both calibration (C) and validation (V)), better than PPR (R2 was 0.86 for C and 0.62 for V), which in turn was better than the multivariate nonlinear regression at the calibration stage (R2 was 0.78 for C and 0.71 for V). |
URL | sites/westernsnowconference.org/PDFs/2009Gan2.pdf |