1. Multi-sensor and multi-scale data assimilation of remotely sensed snow observations Konstantinos Andreadis 1, Dennis Lettenmaier 1, and Dennis McLaughlin 2 1. Department of Civil and Environmental Engineering, Box 352700, University of Washington, Seattle, WA 98195 2. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139 Catchment-scale Hydrological Modeling & Data Assimilation International Workshop, 9-11 January 2008, Melbourne, Australia ABSTRACT A synthetic twin experiment is used to evaluate a data assimilation system that would ingest remotely sensed observations from passive microwave and visible wavelength sensors (snow water equivalent and snow cover extent derived products, respectively) with the objective of estimating snow water equivalent. Two data assimilation techniques are used, the Ensemble Kalman filter and the Ensemble Multiscale Kalman filter. One of the challenges inherent in such a data assimilation system is the discrepancy in spatial scales between the different types of snow-related observations. This study makes a first assessment of the feasibility of a system that would assimilate observations from multiple sensors and at different spatial scales for snow water equivalent estimation. Motivation 1 Importance of snow to the hydrologic cycle through its effects on water storage and land surface energy balance Strategies for large scale observation of snow properties has focused on remote sensing Visible wavelength sensors  Snow Cover Extent observations  No information on water storage and cloud cover limitations Passive microwave wavelength sensors  Brightness temperature a function of snow properties  Snow water equivalent observations  Problems with presence of wet snow, signal saturation and snow metamorphism Additional information from hydrology models  Forced with meteorological data and represent the effects of soils, topography and vegetation  Uncertainties in forcing data and model parameters Objective of study is to evaluate and compare data assimilation techniques using multi-scale remotely sensed observations of snow cover and water equivalent 2 Assimilation Techniques Experimental Design Elevation (m)‏ Forest Cover (%)‏ Study domain is part of the upper Colorado River basin Covers parts of Wyoming, Utah and Colorado Relatively high elevation (average 2,300 m)‏ Denser forest cover in SE, S and NW parts of the basin Summary & Future Research 6 Multiscale tree provides a physically consistent framework for assimilation of multi-sensor observations Similar performance between techniques, probably because of the selected tree topology (in order to have MODIS at finest scale and AMSR-E at one scale above)‏ Increasing model spatial resolution (therefore increasing problem dimensionality), will lead to larger finest scale state vectors and hypothetically larger differences between the EnKF and the MSEnKF Perform similar experiment but assimilating passive and active microwave brightness temperatures, and using a forward radiative transfer model (e.g. DMRT)‏ Andreadis, K., P. Storck, and D. Lettenmaier (2008): Modeling snow accumulation and ablation in forested environments, submitted to Water Resources Research. Evensen, G. (2004): Sampling strategies and square root analysis schemes for the EnKF, Ocean Dynamics, 54, 539-560. Zhou, Y., D. McLaughlin, and D. Entekhabi (2007): An Ensemble multiscale filter for large nonlinear data assimilation problems, submitted to Monthly Weather Review. 5 Assimilation Results (Spatial)‏ 5 Assimilation Results (Temporal)‏ Two techniques are evaluated in this preliminary test, both based on the Ensemble Kalman filter (EnKF)‏ Model error covariance represented through an ensemble of model states Update occurs sequentially every time an observation is available First technique: square root impleme- ntation of EnKF (Evensen, 2004)‏ Second technique: Multiscale EnKF (MSEnKF, Zhou et al. 2007)‏ Covariances are represented through a multiscale tree that relates states through local parent-child relationships States assigned to finest scale nodes, while measurements are assigned according to their spatial support Initial State Forecast Analysis Member 1 Member 2 Observation Time t1t1 t2t2 t3t3 Root (M=0)‏..... M=1 M=2..... State Vector M=4 [AMSR-E] M=5 [MODIS] Identical twin synthetic experiment Snow properties are simulated with the Variable Infiltration Capacity (VIC) model (Andreadis et al., 2008)‏ Truth: model simulation with nominal forcings (precipitation and air temperature)‏ Open-loop: corrupt nominal forcings with errors, generate an ensemble about those, and simulate snow properties with that ensemble Filter: model simulation with open-loop ensemble of forcings, and assimilation of synthetic observations (both EnKF and MSEnKF)‏ Observations: synthetically generated by adding errors to true fields of snow water equivalent (SWE) and cover extent (SCE)‏ Spatial resolutions emulating MODIS aggregated to model resolution (~10 km) and AMSR-E (25 km)‏ Errors being N(0,20 mm) for SWE and N(0,0.1) for SCE 3 Creating the tree topology SCE observations on finest scale and SWE observations at scale immediately above, dictating tree levels to be 6 since finest scale is ~10 km Three criteria used to automatically assign states to neighboring nodes: distance, elevation, and forest cover The algorithm moves from coarser scales down the tree, assigning blocks (no need to be rectangular) of model pixels to nodes based on a distance threshold first, and then elevation and forest cover as scale becomes finer Tree topology represents the spatial structure of physiographic controls on snow accumulation and ablation processes Basin Snow Cover Extent Basin Snow Water Equivalent Time series of basin-averaged SWE (left plot) and SCE (right plot)‏ Study period: 1 Sep 2003 – 31 May 2004 MSEnKF and EnKF simulations similar improvement over Open-loop Spatial maps of different SWE simulations for selected dates Open-loop forcings created by perturbing precipitation and temperature with lognormal and gaussian multiplicative errors respectively and generating an ensemble about those perturbed values TruthOpen-loopMSEnKF EnKFObserved 1 Dec 2003 15 Jan 2004 10 Mar 2004