Multiscale assimilation of Advanced Microwave Scanning Radiometer–EOS snow water equivalent and Moderate Resolution Imaging Spectroradiometer snow cover fraction observations in northern Colorado

Abstract

[1] Eight years (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) of Advanced Microwave Scanning Radiometer-EOS (AMSR-E) snow water equivalent (SWE) retrievals and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) observations are assimilated separately or jointly into the Noah land surface model over a domain in Northern Colorado. A multiscale ensemble Kalman filter (EnKF) is used, supplemented with a rule-based update. The satellite data are either left unscaled or are scaled for anomaly assimilation. The results are validated against in situ observations at 14 high-elevation Snowpack Telemetry (SNOTEL) sites with typically deep snow and at 4 lower-elevation Cooperative Observer Program (COOP) sites. Assimilation of coarse-scale AMSR-E SWE and fine-scale MODIS SCF observations both result in realistic spatial SWE patterns. At COOP sites with shallow snowpacks, AMSR-E SWE and MODIS SCF data assimilation are beneficial separately, and joint SWE and SCF assimilation yields significantly improved root-mean-square error and correlation values for scaled and unscaled data assimilation. In areas of deep snow where the SNOTEL sites are located, however, AMSR-E retrievals are typically biased low and assimilation without prior scaling leads to degraded SWE estimates. Anomaly SWE assimilation could not improve the interannual SWE variations in the assimilation results because the AMSR-E retrievals lack realistic interannual variability in deep snowpacks. SCF assimilation has only a marginal impact at the SNOTEL locations because these sites experience extended periods of near-complete snow cover. Across all sites, SCF assimilation improves the timing of the onset of the snow season but without a net improvement of SWE amounts.

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

“…However, GRACE TWS retrievals feature a very coarse resolution (around 100 km) so that they would only be useful in conjunction with fSCA retrievals for very large scale applications. On the other hand, the use of higher-resolution PM SWE retrievals (order 25 km) in the assimilation has shown particular promise (e.g., De Lannoy et al, 2012;Li et al, 2017). At the same time, PM SWE retrievals are not accurate in complex topography and forested areas, nor for wet and deep snowpacks (Foster et al, 2005), which might limit the applicability of such multisensor assimilation approaches.…”

“…De Lannoy et al (2010 used the EnKF in a twin experiment to assimilate synthetic PM SWE retrievals and greatly outperformed the OL. This was extended to a real multisensor experiment by jointly assimilating PM SWE and MODIS fSCA retrievals (De Lannoy et al, 2012). Li et al (2017) used the ES to assimilate PM SWE retrievals and estimate the SWE distribution, markedly outperforming the OL.…”

Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions

“…Several authors have already shown the added value of DA in snow-dominated watersheds to improve the estimation of the state of the watershed (De Lannoy et al, 2012;Dechant and Moradkhani, 2011;Nagler et al, 2008;Slater and Clark, 2006;Andreadis and Lettenmaier, 2006). Some studies have also integrated DA in ensemble forecast systems for relatively short-term (up to 5-10 days) hydrologic forecasts (Abaza et al, 2014(Abaza et al, , 2015He et al, 2012), but studies focusing on longer forecast periods are scarce even though the need exists for water resource managers.…”

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.…”