Open AccessFeasibility Test of Multifrequency Radiometric Data Assimilation to Estimate Snow Water Equivalent
Author(s) -
M. T. Durand,
S. A. Margulis
Publication year - 2006
Publication title -
journal of hydrometeorology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.733
H-Index - 123
eISSN - 1525-755X
pISSN - 1525-7541
DOI - 10.1175/jhm502.1
Subject(s) - snowpack , snow , environmental science , remote sensing , data assimilation , radiometer , atmospheric radiative transfer codes , special sensor microwave/imager , radiometry , radiative transfer , ensemble kalman filter , meteorology , microwave , geology , brightness temperature , kalman filter , computer science , geography , telecommunications , physics , quantum mechanics , artificial intelligence , extended kalman filter
A season-long, point-scale radiometric data assimilation experiment is performed in order to test the feasibility of snow water equivalent (SWE) estimation. Synthetic passive microwave observations at Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) frequencies and synthetic broadband albedo observations are assimilated simultaneously in order to update snowpack states in a land surface model using the ensemble Kalman filter (EnKF). The effects of vegetation and atmosphere are included in the radiative transfer model (RTM). The land surface model (LSM) was given biased precipitation to represent typical errors introduced in modeling, yet was still able to recover the true value of SWE with a seasonally integrated rmse of only 2.95 cm, despite a snow depth of around 3 m and the presence of liquid water in the snowpack. This ensemble approach is ideal for investigating the complex theoretical relationships between the snowpack properties and the observations, and exploring the implications of these relationships for the inversion of remote sensing measurements for estimating snowpack properties. The contributions of each channel to recovering the true SWE are computed, and it was found that the low-frequency 10.67-GHz AMSR-E channels contain information even for very deep snow. The effect of vegetation thickness on assimilation results is explored. Results from the assimilation are compared to those from a pure modeling approach and from a remote sensing inversion approach, and the effects of measurement error and ensemble size are investigated.