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)
High-resolution spatial and temporal measurements in the surface layer (Department of Energy Early Career)
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
NSF Cyberinfrastructure for Sustained Scientific Innovation (CSSI) - Data and Software: Software for a new machine learning based parameterization
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.