Development and Evaluation of a Forward Snow Microwave Emission Model Kostas Andreadis1, Dennis Lettenmaier1 and Eric Wood2 1 Civil and Environmental Engineering, University of Washington 2 Civil and Environmental Engineering, Princeton University EGU General Assembly Vienna, 3 April 2006

Motivation Passive microwave remote sensing offers opportunity for observing snow properties over large areas Has many limitations (e.g. wet snow, saturation effects) Data assimilation provides framework for merging model and satellite-based information Direct assimilation of satellite SWE is problematic

Motivation (cont'd) Need for an accurate and robust snow microwave emission model Development of Land Surface Microwave Emission Model (LSMEM) Initial validation and sensitivity over data intensive sites (CLPX) Small-scale data assimilation experiment with a coupled hydrology-radiative transfer model

LSMEM Description Extension of warm season LSMEM (Gao et al. 2003) to include a snow microwave emission component Snow module based primarily on semi-empirical HUT model (Pulliainen et al. 1999) Assumes that scattering is mostly concentrated in the forward direction Extinction coefficient computed empirically Dielectric constants estimated from strong fluctuation theory

Dataset Description Cold Land Processes Experiment (CLPX) during winters 2002 & 2003 at several sites in Colorado, USA Multi-sensor, multi-scale measurements Ground-based radiometer (GBMR) and snowpits (Local Scale Observation Site) located within the 1x1 km Fraser Intensive Study Area GBMR footprint free of vegetation Satellite data (AMSR-E, SSM/I, MODIS etc) and aircraft data (PSR etc)

SNTHERM LSOS Validation Evaluate ability of SNTHERM to reproduce snow conditions over site of interest Validation data from snowpits over the LSOS (3 Feb-29 Mar 2003) Calibrate SNTHERM over entire period and use as benchmark simulation

Comparison with Ground-based Tb

Sensitivity and Error Estimation Sensitivity of Tb model prediction to various snow parameters x-axis values refer to difference from the nominal value y-axis values show difference between predicted and observed Tb Linear dependence might be an artifact of the model

Data Assimilation Experimental Design Benchmark simulation: calibrated SNTHERM over LSOS, with baseline forcing data (same with one used for GBMR validation) Prior simulation: uncalibrated SNTHERM without assimilation, with “wrong” forcings and initial state Filter simulation: uncalibrated SNTHERM with AMSRE assimilation from LSMEM, with “wrong” forcings and initial state “Wrong” forcings created from perturbing Precip, Tair, SW Rad, LW Rad and RH Ensemble Kalman Filter used as assimilation technique

Comparison with AMSR-E Data Scale discrepancy between model and observations Compare variability of percentiles 19 GHz (H) 37 GHz (H)

Assimilation Results State variables: snow depth, SWE and grain size EnKF allows for the representation of uncertainty in important parameters, i.e. grain size 19 GHz (V)

Assimilation Results Evaluate effects of assimilating different sets of microwave channels Very similar performance (RMSE: 0.139 and 0.142 respectively, versus 0.131 when using all channels) Data assimilation offers framework for evaluating and optimizing potential observation missions

Conclusions Data assimilation of microwave Tb shows potential for estimation of snow properties Limitations in snow microwave emission model Develop a more physically-based model, combining approaches from LSMEM, DMRT and MEMLS Develop multi-layer snow model component for the macroscale Variable Infiltration Capacity model Conduct further validation of LSMEM in different snow conditions as part of ongoing model inter-comparison

Questions?