Simulation and Assimilation of Passive Microwave Data Using a Snowpack Model Coupled to a Calibrated Radiative Transfer Model Over Northeastern Canada. Issue 7 (19th July 2018)

• Record Type: Journal Article
• Authors: Larue, F.; Royer, A.; De Sève, D.; Roy, A.; Picard, G.; Vionnet, V.; Cosme, E.
• Is Part Of: Water resources research. Volume 54:Issue 7(2018)
• Page Start: 4823
• Page End: 4848
• Publication Date: 2018-07-19
• Subjects: SWE estimates -- passive microwave -- snowpack model Crocus -- radiative transfer model DMRT‐ML -- data assimilation scheme -- Eastern Canada; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2017WR022132
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Over northern snowmelt‐dominated basins, the snow water equivalent (SWE) is of primary interest for hydrological forecasting. This paper evaluates first the performance of a detailed multilayer snowpack model (Crocus), driven by meteorological predictions generated by the Canadian Global Environmental Multiscale model, for hydrological applications. Simulations were compared to daily snow depth and SWE measurements over Québec, northeastern Canada (56–45°N), for 2012–2016, highlighting an overestimation of the annual maximum snow depth (35%) and of the annual maximum SWE (16%), which is not accurate enough for hydrological applications. To improve SWE simulations, a chain of models is implemented to simulate and to assimilate passive microwave satellite observations. The snowpack model is coupled to a microwave snow emission model (Dense Media Radiative Transfer‐Multilayers model, DMRT‐ML), and the comparison of simulated brightness temperatures ( T Bs ) with surface‐based T B measurements (at 11, 19 and 37 GHz) shows best results when the snow stickiness parameter is set to 0.17 in DMRT‐ML. The overall root‐mean‐square error (RMSE) obtained by the calibrated coupling reaches 27 K, significantly better than the RMSE obtained by considering nonsticky spheres in DMRT‐ML (43.0 K). The relevance of T B assimilation is tested with synthetic observations to evaluate the information content of each frequency for SWE estimates. The assimilation scheme is a Sequential Importance Resampling Particle filter using an ensemble of perturbed meteorological forcing data. The results show a SWE RMSE reduced by 82% with T B assimilation compared to without assimilation. Key Points: Crocus snowpack model is evaluated against unique sets of continuous snow depth and snow water equivalent measurements over Eastern Canada A snow passive microwave emission model is optimized to match radiometric observations by scaling the snow stickiness parameter to 0.17 The assimilation of synthetic passive microwave observations with a particle filter shows significant improvements in SWE estimates