Feasibility of Snowpack Characterization Using Remote Sensing and Advanced Data Assimilation Techniques
An ensemble-based radiometric data assimilation framework was developed to test the feasibility of snow water equivalent (SWE) estimation. Season-long synthetic experiments were run for conditions at Mammoth Mountain were passive microwave observations at SSM/I and AMSR-E frequencies and synthetic broadband albedo observations were 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 allows 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 were 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 was explored. Results from the assimilation were compared to those from a pure modeling approach and from a remote sensing inversion approach, and the effects of measurement error and ensemble size were investigated.