# Data assimilation

Most research projects in this topic are currently focused on the use of sequential data assimilation methods to reduce model prediction uncertainty. Sequential data assimilation corrects model predictions when measurements are available. The correction is based on a statistical approach which determines optimal weights for the different pieces of information (model prediction, measurement data) which are included in the data assimilation procedure. The model uncertainty is characterized using a Monte Carlo approach with many different model runs. Each of the model runs gives different predictions because model parameters and model forcings differ between the many model runs. The different model parameters and model forcings are sampled from statistical distributions. Data assimilation also takes measurement uncertainty into account. For each of the grid cells and each of the many model runs optimal weights are determined for the model prediction on one hand and each of the measurements on the other hand. This procedure results in an update of the predicted states for each of the model runs at all grid cells. Together with states also parameters can be updated, with help of an augmented state vector approach.

Detailed information can by found here...

**Data assimilation with ParFlow-CLM**

This is one of several projects concerned with improving land surface model predictions through data assimilation approaches. Land surface models try to predict exchanges of water, energy and trace gases between the land surface and the lower atmosphere, as well as storage and transport of water and energy on the land surface. Land surface models are used for modeling the lower boundary condition of regional and global circulation models and play therefore an essential role both for weather forecasting and climate predictions. Predictions with land surface models are affected by several sources of uncertainty. These uncertainties can be reduced with help of measurement data. In an operational setting, measurement data can be assimilated with data assimilation methods to update states (for example soil moisture contents, soil temperature, leaf area index) and also model parameters (especially soil hydraulic and vegetation parameters).

This research project focuses on assimilating measurement data with ParFlow-CLM. ParFlow-CLM is a better land surface model than CLM. The ParFlow component calculates the subsurface water and energy fluxes and includes lateral exchange fluxes between grid cells and a realistic representation of groundwater. ParFlow also simulates subsurface flow and overland flow in a fully coupled manner.

We expect that data assimilation with ParFlow-CLM yields potentially better model predictions than CLM alone, as the model structure error is smaller. This should allow that data are more effective in correcting states and parameters of the simulation model, which is of especial relevance for a highly parameterized model like ParFlow. In order to test this and other questions ParFlow-CLM will be applied for the Rollesbroich site in the Rur catchment (TERENO), where exhaustive datasets are available for calibration and verification.

Contact

Questions on this project should be directed to HPSC TerrSys's Scientific Director.