Princeton University. Assimilating AMSR Snow Brightness Temperatures into Forecasts of SWE in the Columbia River Basin: a Comparison of Two Methods Theodore J. Bohn 1 , Konstantinos M. Andreadis 1 , Dennis P. Lettenmaier 1 , Ding Liang 1 , Leung Tsang 1 , Matthias Drusch 2 , and Eric F. Wood 3
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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)
enKF / Experimental Design
Data Assimilation - Results
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
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.
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:
While this study is still in its preliminary stages, evidence so far suggests that:
Note: See the author for a list of references.
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.
Validation of SNTHERM at CLPX Snow Pits
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.
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.