Assimilation of surface reflectance in snow simulations: Impact on bulk snow variables

Overview & Synopsis

Data assimilation of snow observations significantly improves the accuracy of snow cover simulations. However, remotely-sensed snowpack observations made in areas of complex topography are typically subject to large error and biases, creating a challenge for data assimilation. To improve the reliability of ensemble snowpack simulations, this study investigated the appropriate conditions for assimilating MODIS-like synthetic surface reflectances. We used a simulation system that included the Particle Filter data assimilation technique. More than 270 ensemble simulations involving assimilation of synthetic observations were conducted in a twin experiment procedure for three snow seasons. These tests were aimed at establishing the spectral combination of MODIS-like reflectances that convey the more information in the assimilation system, rendering the most reliable snowpack simulation, and determining the maximum observation errors that the assimilation system could tolerate. The assimilation of the first seven MODIS-like bands, covering visible and near-infrared wavelengths, provided the best scores compared with any other band combination, and thus are highly recommended for use when possible. The simulation system tolerated a maximum deviation from ground truth of 5% without loss of performance. However, the assimilation of the first seven bands of true MODIS surface of reflectance fails on improving simulation results in rouged mountain areas.