An ensemble-based snow data assimilation framework with applications to permafrost modeling

• Main Authors: Aalstad, K., Westermann, S., Boike, Julia, Bertino, L., Aas, K. S.
• Format: Conference Object
• Language: unknown
• Published: 2016
• Subjects: permafrost; Svalbard
• Online Access: https://epic.awi.de/id/eprint/43316/; https://media.gfz-potsdam.de/bib/ICOP/ICOP_2016_Book_of_Abstracts.pdf; https://hdl.handle.net/10013/epic.49777

conductivity, is a key control on the thermal state of near surface permafrost. At the same time, accurately estimating the seasonal snow cycle at the kilometre scale is a considerable hydrometeorological challenge. Consequently, snow represents a major source of uncertainty in permafrost models. To constrain this snow induced uncertainty we propose a new ensemblebased snow data assimilation framework (ESDA) for fine scale snow state estimation that fuses a simple subgrid snow model and fine scale satellite-based surface albedo retrievals using the ensemble Kalman filter (EnKF; reviewed in Evensen [2009]). The potential of ESDA is demonstrated for the Bayelva catchment near Ny Åelsund (Svalbard, Norway) where independent ground-based observations of snow cover and the near surface ground thermal state were available to perform validation. On the modeling side of ESDA we adopt the subgrid snow distribution model (SSNOWD; see Liston [2004]) to estimate the snow water equivalent depth distribution, snow cover fraction and surface albedo at the grid scale (1 km). These model runs are forced by melt and net precipitation rates based on the energy and water balance derived from the meteorological fields provided by a (3 km resolution) Weather Research and Forecasting (WRF) model run. For observations our system makes combined use of two relatively new high level products: frequently available coarser scale (500 m) albedo retrievals from MODIS (MCD43A version 6) and intermittently available finer scale (30 m) albedos derived from Landsat8 surface reflectance retrievals. In the last step of the framework we apply the EnKF; a robust sequential data assimilation method that yields the optimal estimate of a system state based on the combined information from model results and observations, both of which are uncertain, provided a set of assumptions hold (see Evensen [2009]). The EnKF has been successfully implemented for a range of applications in numerous fields including oceanography, meteorology, hydrology, mining and ...