ABSTRACT

1. PrincetonUniversity Assimilating AMSR Snow Brightness Temperatures into Forecasts of SWE in the Columbia River Basin: a Comparison of Two Methods Theodore J. Bohn1, Konstantinos M. Andreadis1, Dennis P. Lettenmaier1, Ding Liang1, Leung Tsang1, Matthias Drusch2, and Eric F. Wood3 1Department of Civil and Environmental Engineering, Box 352700, University of Washington, Seattle, WA 98195 2European Centre for Medium-Range Weather Forecasts 3Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 5th International GEWEX Conference (June 20-24 2005) 3 4 enKF / Experimental Design Data Assimilation - Results ABSTRACT Snow is a major component of the hydrological cycle. Many important natural phenomena, such as the behavior of climate and the availability of water resources, show a strong relationship with snow water equivalent (SWE)and snow extent, especially in mountainous regions like the Columbia River basin. While hydrological models predict these quantities, model biases and the uncertainties of input data can lead to large errors in results. Meanwhile, remote sensing observations, such as passive microwave brightness temperatures, provide accurate estimates of snow characteristics, but do not measure SWE directly and have lower temporal resolution than hydrological models. Data assimilation can combine the strengths of both types of estimation by periodically updating model forecasts with remote sensing observations. However, since satellite observations measure snow brightness temperatures and hydrological models predict SWE, we must convert these estimates to a common form before assimilation. One way to do this is to convert predicted SWE into brightness temperatures via a radiative transfer model. Here we compare the performance of two radiative transfer models, Land Surface Microwave Emission Model1 (LSMEM) and the Dense Media Radiative Transfer2 (DMRT) model, in assimilating remotely-sensed observations of snow into SWE predicted by the SNTHERM.893 hydrological model in the Columbia River basin. 3US Army Corps of Engineers, Cold Regions Research & Engineering Laboratory • Ensemble Kalman Filter (enKF) • Data assimilation provides the framework to optimally merge information from both models and observations, and account for the uncertainties in both • Ensemble Kalman filtering is a data assimilation technique that has been applied with increasing frequency in hydrology Simulated snow depth, before (prior) and after (enKF) assimilation, in the Stanley Basin (43.60 N, 114.67 W). Assimilation begins on Jan 1, 2003. Assimilation lowers the predicted snow depth. 1 • Experimental Design • SNTHERM (uncalibrated) is used to simulate the snow pack in a point location in Columbia Basin (Stanley basin, ID, 43.60 N, 114.67 W) • Meteorological inputs come from 3-hourly disaggregated LDAS 1/8-degree forcings • Ensembles are generated by perturbing precipitation and air temperature, with log-normally and normally distributed errors respectively • AMSR-E brightness temperatures at 18.7 and 36.5 GHz (both H and V polarizations) are assimilated into two coupled model systems: SNTHERM coupled with LSMEM and SNTHERM coupled with DMRT • Non-linear operator resolved by state augmentation • Standard deviations of 10 K assumed for both 18.7 and 36.5 GHz • Total snow pack depth is updated every 2 days • Resulting snow characteristics compared to independent observations at SNOTEL site (43.60 N, 114.67 W) Validation Site: CLPX LSOS Simulated (SNTHERM/LSMEM before and after assimilation) and observed (AMSR-E) high-altitude brightness temperatures over the Stanley Basin (43.60 N, 114.67 W), for 18.7 GHz h/v, and 36.5 GHz h/v (a-d, respectively). Assimilation begins on Jan 1, 2003. Note that a more sophisticated estimate of the variance of the AMSR-E measurements could potentially bring predicted brightness temperatures closer to observations. Simulated (SNTHERM before and after assimilation) and observed (SNOTEL) snow water equivalent in the Stanley Basin (43.60 N, 114.67 W). Assimilation begins on Jan 1, 2003. Assimilation lowers the predicted snow water equivalent, bringing it closer to the observations. Note that a more sophisticated estimate of the variance of the AMSR-E measurements could potentially bring predicted SWE even closer to observations. Observations taken from the Local Scale Observation Site (LSOS) of the Cold Land Process Experiment (CLPX) in Fraser Park, CO: • Meteorological station: precipitation, air temperature, wind speed, solar and long-wave radiation • Snow pit: depth, density, temperature, grain size • Ground-based radiometer (GBMR): brightness temperatures at 18.7GHz and 36.5GHz, H & V polarizations 2 Model Validation CONCLUDING REMARKS While this study is still in its preliminary stages, evidence so far suggests that: • Assimilation of passive microwave brightness temperatures into a hydrological model via a radiative transfer model such as LSMEM or DMRT can improve estimates of snow pack properties such as snow depth and snow water equivalent, both at sites where observations exist and in areas where observations are sparse. • Care must be taken when comparing predicted snow properties to those observed at SNOTEL sites; SNOTEL observations are point measurements, while AMSR-E measurements and our input meteorological forcings are areal averages. AMSR-E brightness temperatures are influenced by heterogeneous land cover and fluctuations of moisture content in the intervening atmosphere, while point measurements on the ground are not influenced by these. Future Work: • Data assimilation with the DMRT model • More sophisticated estimation of observation errors • Examination of multiple sites around the Columbia Basin, including sites with extensive forest cover • Substitution of the VIC (Variable Infiltration Capacity) large-scale hydrological model for SNTHERM, to enable comparison of predicted and observed stream flow Note: See the author for a list of references. • LSMEM • 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 • Calculates brightness temperature 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 Simulated (LSMEM and DMRT) and observed (GBMR) near-surface brightness temperatures at LSOS, based on snow properties predicted by SNTHERM, for 18.7 GHz h/v, and 36.5 GHz h/v (a-d, respectively). Both models exhibit diurnal variations, more pronounced in LSMEM. SNTHERM.89 • Multi-layer snow model • Solves energy and mass balance equations • Accounts for densification, metamorphosis, freeze/melt, liquid water percolation • Inputs: precipitation, air temperature, wind speed, solar and long-wave radiation • Outputs: snow depth, vertical profiles of density, temperature, grain size A B C D Validation of SNTHERM at CLPX Snow Pits A B A Simulated (LSMEM) and observed (AMSR-E) high-altitude brightness temperatures at LSOS, based on snow properties predicted by SNTHERM, for 18.7 GHz h/v, and 36.5 GHz h/v (a-d, respectively). The abrupt dip on 3/20 coincides with a large deposition and melting event. A B D B C C D Simulated and observed a) snow depth, b) bulk density, c) temperature, and d) grain size at CLPX LSOS Snow Pits, Feb-March 2003. Simulated and observed near-surface brightness temperatures at LSOS, for 18.7h, 18.7v, 36.5h, 36.5v channels: a) DMRT, b) LSMEM. Simulations were based on observed snow depth, temperature, and grain size. NOTE: Vertical scale varies among the plots; DMRT is closer to obs at 36.5 GHz than LSMEM is.