Improving operational streamflow via remotely sensed microwave radiance data
In this study, SWE, modeled with a distributed version of the National Weather Service's (NWS) SNOW-17 model, and model parameters are updated with remotely sensed brightness temperature (TB). This study consists of both a synthetic experiment, to validate the technique, and a real experiment to test the effectiveness of the method presented. The TB used in this study is produced by the AMSR-E instrument flown on the NASA Aqua satellite. In order to assimilate TB, the SNOW-17 model is coupled with the Microwave Emission Model for Layered Snowpack as an observational operator.
The simulated snowmelt from the SNOW-17 model is run through the Sacramento Soil Moisture Accounting model (SAC-SMA) to determine the effectiveness for operational streamflow forecasting. To test the usefulness of the assimilation three scenarios will be compared: no data assimilation, data assimilation just for state updating, and data assimilation with dual state-parameter updating.