Development and Evaluation of a Forward Snow Microwave Emission Model

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

2. 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

3. 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

4. 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

5. 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)

6. 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

7. Comparison with Ground-based Tb

8. 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

9. 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

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

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

12. 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

13. 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

14. Questions?