Research Projects

Project investigating the combination of physics and machine learning as well as causality for Earth system observations and modeling in collaboration with Prof. Eyring (DLR), Prof. Reichstein (MPI-Jena) and Prof. Camps-Valls (U. Valencia)

We are using high resolution spatial and temporal measurements of turbulence using Distributed Temperature Sensing (DTS). Some of the questions we have answered are: is Taylor’s hypothesis valid in the atmospheric boundary layer or does it need to be corrected.

Project to estimate transpiration from remote sensing using multiple data sources and physical modeling

We are using remote solar induced fluorescence (SIF) to retrieve surface carbon and water fluxes.

Machine learning is used instead of radiative transfer model inversion to retrieve soil moisture for data assimilation into land-surface models.

Drought project to investigate the role and regulation of vegetation on droughts

Project to investigate the role of cloud feedback on tropical forests

Project to build an infrastructure for machine learning used for climate parameterizations

We are using remotely sensed solar induced fluorescence (SIF) as a proxy for photosynthesis to investigate the coupling between the biosphere and the atmosphere in observations compared to models.

Using high-resolution turbulence models combined with observations we are reinvestigating surface layer turbulent transport, with a focus on the role of coherent structures.

We are using cloud resolving models and large eddy simulations to investigate the causes of the transition between shallow and deep convection.