Multisensor snow data assimilation at the continental scale: The value of Gravity Recovery and Climate Experiment terrestrial water storage information

Abstract

, toward the goal of better estimation of snowpack (in particular, snow water equivalent and snow depth) via incorporating both Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) information into the Community Land Model. The different properties associated with the SCF and TWS observations are accommodated through a unified approach using the ensemble Kalman filter and smoother. Results show that this multisensor approach can provide significant improvements over a MODIS-only approach, for example, in the Saint Lawrence, Fraser, Mackenzie, Churchill & Nelson, and Yukon river basins and the southwestern rim of Hudson Bay. At middle latitudes, for example, the North Central and Missouri river basins, the inclusion of GRACE information preserves the advantages (compared with the open loop) shown in the MODIS-only run. However, in some highlatitude areas and given months the open loop run shows a comparable or even better performance, implying considerable room for refinements of the multisensor algorithm. In addition, ensemble-based metrics are calculated and interpreted domainwide. They indicate the potential importance of accurate representation of snow water equivalent autocovariance in assimilating TWS observations and the regional and/or seasonal dependence of GRACE's capability to reduce ensemble variance. These analyses contribute to clarifying the effects of GRACE's special features (e.g., a vertical integral of different land water changes, coarse spatial and temporal resolution) in the snow data assimilation system.

Data assimilation of GRACE terrestrial water storage estimates into a regional hydrological model of the Rhine River basin

“…Houborg et al (2012) and Li et al (2012) applied a similar strategy to improve the drought indicator over North America and Europe, respectively. Su et al (2010) and Forman et al (2012) extended the work of Zaitchik et al (2008) to improve the estimated snow water equivalent over North America and northwestern Canada, respectively. All results from earlier studies reported that assimilating GRACE improved, or at least did not degrade, the hydrology model's performance.…”

“…Several earlier studies have employed data assimilation to combine the strengths of hydrological modelling and GRACE observations and to mitigate their respective weaknesses (Zaitchik et al, 2008;Su et al, 2010;Houborg et al, 2012;Li et al, 2012;Forman et al, 2012). In data assimilation, the model states are constrained by observations, taking into account the estimated uncertainties for both the model states and the observations (Evensen, 2003;Reichle, 2008).…”

Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites

“…They found a significant reduction in uncertainty when retrievals were assimilated simultaneously as opposed to sequentially. At the continental scale, a multisensor assimilation of both GRACE TWS and MODIS fSCA using the ES and EnKF for TWS and fSCA, respectively, yielded significant improvements relative to the OL (Su et al, 2010). De Lannoy et al (2010 used the EnKF in a twin experiment to assimilate synthetic PM SWE retrievals and greatly outperformed the OL.…”

A Particle Filter scheme for multivariate data assimilation into a point-scale snowpack model in Alpine environment

“…Unlike the KF and EKF schemes, this method does not require a model linearization since the error estimates are evaluated from an ensemble of possible model realizations using the Monte Carlo approach (Evensen, 2003). In the recent past, an increasing number of 15 studies on snow hydrology have contributed to confirm the EnKF as a well-performing technique enabling to enhance the accuracy of hydrological simulations by consistently updating model predictions through the assimilation of snow-related observations (Andreadis and Lettenmaier, 2005;Durand and Margulis, 2006;Clark et al, 2006;Slater and Clark, 2006;Su et al, 2008;Durand and Margulis, 2008;Su et al, 2010;De Lannoy et al, 2012;Magnusson et al, 2014;Griessinger et al, handling systems nonlinearities, PF schemes are currently garnering a growing attention for snow modelling applications. Leisenring and Moradkhani (2011) compared the performances of common sequential EnKF-based DA methods and PF variants at assimilating synthetic SWE measurements to improve its seasonal predictions and to estimate some sensitive parameters in a small-scale snowpack model.…”