AGU Fall Meeting 2008 Multi-scale assimilation of remotely sensed snow observations for hydrologic estimation Kostas Andreadis, and Dennis Lettenmaier Civil and Environmental Engineering, University of Washington

AGU Fall Meeting 2008 Motivation Long-term global passive microwave and visible wavelength dataset In-situ measurements unable to capture large-scale variability Number of issues with observations and models (errors and spatial scaling) Data assimilation not “black box” Two-fold goal of examining a novel data assimilation technique and evaluating remotely sensed snow observations in such a system

AGU Fall Meeting 2008 Data assimilation techniques Initial State Forecast Analysis Observation Time t1t2t3 Ensemble Kalman filter Error covariance computed from ensemble of states Multiscale Ensemble Kalman filter Approximates model covariances by tree structure Represents large-scale covariance through local relationships between child-parent nodes Consistent spatial localization Similar updating to EnKF

AGU Fall Meeting 2008 Upper Colorado river basin Synthetic twin experiment (10/2001 to 4/2002) Nominal precipitation/air temperature used to generate true SWE and SCE Synthetic satellite observations (visible and microwave) generated from truth Resampled P/T from climatology used to represent model uncertainties EnKF and EnMKF assimilation using resampled forcings Experimental design

AGU Fall Meeting 2008 Model descriptions Variable Infiltration Capacity snow hydrology model Subgrid variability in topography and land cover Predicted SWE and SCE Dense Media Radiative Transfer passive microwave emission model Predicted T B a function of depth, grain size, density, temperature Tsang et al. (2000)

AGU Fall Meeting 2008 Constructing the tree... Tree must be constructed based on physical constraints (e.g. physiography) Structure could be dynamic or static Start at coarsest scale (root node), with branches being populated according to: Distance Elevation Forest cover Zhou et al. (2008)

AGU Fall Meeting 2008 Assimilation of T B – SWE maps SWE differences (in mm) from truth at update times for three simulations TruthTruth-OpenloopTruth-EnKFTruth-EnMKF 29 Dec Feb 2002

AGU Fall Meeting 2008 The forested pixel problem... Forest cover can “mask” microwave emission Difficult to extract SWE information because T B innovations are small SWE Correlation of forested pixels with closest non-forested pixel T B innovation (K) of forested pixels (>10%) SWE update (mm) of forested pixels (>10%)

AGU Fall Meeting 2008 Assimilation of SCE/T B – SWE maps SWE differences from truth at update times for three simulations TruthTruth-OpenloopTruth-EnKFTruth-EnMKF 29 Dec Feb 2002

AGU Fall Meeting 2008 Assimilation of SCE – SWE maps SWE differences (in mm) from truth at update times for three simulations TruthTruth-OpenloopTruth-EnKFTruth-EnMKF 29 Dec Feb 2002

AGU Fall Meeting 2008 Assimilation of T B – T B maps 36.5 GHz (Vertical Pol.) T B (in K) at update times for four simulations TruthOpenloopEnKFEnMKF 29 Dec Feb 2002

AGU Fall Meeting 2008 SWE Time series RMSEs of 12.6, 10.3 mm for EnKF, EnMKF respectively versus 35.1 mm for Open-loop Truth Open-loop EnKF EnMKF

AGU Fall Meeting 2008 Conclusions Novel data assimilation technique Small differences between EnKF and EnMKF (perhaps due to problem scale) Satellite retrievals are problematic (e.g. forest cover), but assimilation seems to overcome some of those problems when combined Other types of measurements (active microwave, melt state) Improved forward models (e.g. multi-layer snow and microwave emission models)