ENVIRONMENT AND ENERGY

Clouds and precipitation are the major source of uncertainty in numerical predictions of weather and climate. A common analysis of polarimetric radar observations and synthetic radar data from numerical simulations provides new methods to evaluate models. Using the Terrestrial Systems Modeling Platform, researchers conducted ensemble simulations for multiple summertime storms over north-western Germany. The simulated cloud processes were compared in the radar space using a forward operator with the measurements from X-band polarimetric radars. In addition, sensitivity studies were conducted using different background aerosol states and land cover types in the model to better understand land-aerosol-cloud-precipitation interactions.

A multi-institutional team of researchers is developing a data assimilation framework for coupled atmosphere-land-surface-groundwater models. These coupled models, which potentially allow a more accurate description of the coupled terrestrial water and energy fluxes, in particular fluxes across compartments, are affected by large uncertainties related to uncertain input parameters, initial conditions and boundary conditions. Data assimilation can alleviate these limitations and this project is focused in particular on the value of coupled data assimilation which means that observations in one compartment (e.g., subsurface) are used to update states, and possibly also parameters, in another compartment (e.g., land surface).

Data Assimilation is an integral tool to enable precise forecasts and becomes increasingly important to derive the values of uncertain parameters due to lack of observations. Numerical models of Earth system compartments are coupled in order to simulate physically consistent water and energy fluxes in the subsurface-landsurface-atmosphere system. Such model systems become increasingly important to analyze and understand the complex processes at boundaries of terrestrial compartments and interdependencies of states across these boundaries. As such, data assimilation for these coupled systems needs to be developed.