Microwave Snow Emission Intercomparison

1. Rafal Wójcik1, Eric Wood1, Konstantinos M. Andreadis2, Dennis P. Lettenmaier2 1Dept. of Civil and Environmental Engineering, Princeton University 2Dept. of Civil and Environmental Engineering, University of Washington Univeristy of Washington Microwave Snow Emission Intercomparison

2. Forward Emission Models for Snow Microwave Tb Retrival DMTR, LSMEM, MEMLS Models Testing and Validation NASA's CLPX experiment Sensitivity analyses, emission models vs CLPX and AMSR-E data Estimation of snow/water operators Bayesian data assimilation framework Bayesian model averaging Conclusions, Challenges, and Future Work Outline Univeristy of Washington

3. Research Questions Univeristy of Washington • Can a forward model of surface microwave emission be developed that is capable of providing realistic brightness temperatures for snow covered areas, and can the inputs for the forward model be provided by operational observations and/or physical snow model output? • Is the modeled/predicted Tb sufficiently accurate and useful for assimilation into operational NWP?

4. Forward emission models for snow Tb retrival Univeristy of Washington All Seasons LSMEM (Drusch et al, 2004) • Calculates microwave emission from a surface partially covered with vegetation and/or snow • Snow component based on the semi-empirical HUT emission model • Treats snowpack as a single homogeneous layer • Dielectric constants of ice and snow calculated from different optional models • Inputs include snow depth, density, temperature, grain size and ground temperature DMRT (Tseng et al, 2000) • Calculates Tb from a densely packed medium • A quasi-crystalline approximation is used to calculate absorption characteristics with particles allowed to form clusters • The distorted Born approximation is used to calculate the scattering coefficients • Inputs include snow depth, snow temperature, fractional volume and grain size MEMLS (Metzler, 1998) • Calculates Tb from a multi-layer snow medium • The absorption coefficient is derived from snow density, frequency and temperature • The scattering depens on snow density, frequency and correlation length • Inputs include snow depth, temperature,density, ground temperature and correlation length • So far successfully validated only for dry snow conditions

5. Models Validation and Testing Validation with CLPX Observations • Ground-Based Microwave Radiometer (GBMR) • Dense Snow Pit measurements • 12-13 Dec 2002 & 19-24 Mar 2003 • Snow on bare ground (no vegetation) • Assume snow measurements representative of entire LSOS (100 x 100 m) Univeristy of Washington

6. Models Validation and Testing Univeristy of Washington • Microwave emission from full snow coverage at 65° • AS-LSMEM was run for different grain sizes to capture observed stratigraphy • Strong dependence of results with assumed grain size Full snow cover Partial snow cover

7. Models Validation and Testing Univeristy of Washington Can the inputs for a forward model be provided by physical snow model output to obtain realistic Tb estimates? • force AS-LSMEM, DRTM, MEMLS with outputs from SNTHERM • compare estimated Tb with GBMR and AMSR-E measurements at 18.7 and 36.7 (h/v) Ghz

8. Models Validation and Testing Univeristy of Washington SNTHERM at CLPX Snow Pits, Feb-March 2003 Date Date

9. Models Validation against GBMR data Univeristy of Washington

10. Models’ Validation against AMSR-E data Univeristy of Washington

11. Estimation of snow/water operators Output Operators LSMEM, DMRT, MEMLS, CRTM Ensemble of Hydrologic Models SNTHERM, Noah, VIC etc. <Propagation Step> Initial State Ensemble Probabilistic Snow/water Operator Forcing Data Multi-sensor measurements AMSR-E, MODIS, GBMR etc. Assimilation Algorithm <Update Step> Univeristy of Washington Data assimilation BMA

12. Estimation of snow/water operators Prior state, xk|k-1 Bayesian Filtering: Posterior state, xk|k Observational operator: Measure of uncertainty in observation given state (or output) (by Gaussian mixture) (1) Integration (construct the filter): Monte Carlo (GEnKF, PF) (2) Model the uncertainty in state give the observation (Observational operator): non-additive, non-Gaussian

13. Estimation of snow/water operators Univeristy of Washington An example of snow/water operator for 1 predicted quantity | 1 model :

14. Estimation of snow/water operators Univeristy of Washington Bayesian model averaging (BMA) for 1 predicted quantity | many models Assumption: Models are realizations of univariate random variable

15. Estimation of snow/water operators Univeristy of Washington Bayesian model averaging (BMA) for 1 predicted quantity | many models

16. Estimation of snow/water operators Univeristy of Washington Bayesian model averaging (BMA) for 1 predicted quantity | many models

17. Conclusions, challenges and future work • Great potential is seen on the estimation of snow Tb by forward emission models • Tb could be used to fine tune snow modules in land surface models • at variety of spatial and temporal scales ---> improved water budget estimates • Data assimilation offers the framework to: - optimally combine forward emission models and remote sensing observations of snow Tb - account for the limitations of both by uncertainty description • Intercomparison with existing emission models including CRTM • Use of CLPX airborne data in the intercomparison study • Implementation of emission models in LIS and CRTM • Data assimilation of multi-model snow Tb estimates conditioned on observations Univeristy of Washington Potentials Challenges and Future Work

18. The End Thank you. Univeristy of Washington