Principal Investigator: Peter Jan van Leeuwen

Professor in Data Assimilation and Nonlinear Dynamics, Colorado State University.

Dr Van Leeuwen obtained three BSc’s in Physics, Astrophysics, and Physical Chemistry from the University of Leiden, Netherlands, an MSc in the physics of the Early University at the University of Amsterdam, Netherlands, and his PhD in Coastal Fluid Dynamics from the Technical University Delft, Netherlands. He was Professor in Data Assimilation at the University of Reading, United Kingdom, leading the 35-scientists strong Data Assimilation Research Centre (DARC). During this time he was head of the Data Assimilation Theme in the National Centre for Earth Observation (NCEO), and later Interim Director of NCEO. NCEO is consortium of 13 research institutes and universities, with a budget of about a £5M/year. He won an Advanced Investigator Grant from the European Research Council, the highest personal research award in the European Union of €2.6M. He recently moved to Colorado State University, where he leads a group of 6 PhD students and two postdocs. He has a rich research history, ranging from the physics of the Early Universe via coastal ocean waves and the connection between the Indian and the Atlantic Ocean, geophysical fluid dynamics and data assimilation, to cloud physics. Specifically, he was a major developer of the Ensemble Kalman Filter and (local) efficient Particle Filters, and inventor of the Ensemble Kalman Smoother and Particle Flow Filters, and he worked on all aspects of data assimilation, including Gaussian and non-Gaussian representation errors and how to estimate and use them in the ECMWF system, model error estimation, synchronization in data assimilation, randomized preconditioners for variational methods, machine learning in nonlinear data assimilation, and more, and is co-author on two data-assimilation books. He also developed an analytical expression for autoconversion of cloud droplets to rain droplets and confirmed this experession using parameter estimation and machine learning based on aircraft observations, and derived a first complete uncertainty estimate for deep learning estimates. His research group works on new data-assimilation methods for highly nonlinear systems, and uses these to understand the interaction between the Indian and the Atlantic Oceans, Hurricane rapid intensification, and to estimate new paramaterizations in coupled ocean-atmosphere models. The group also works on high-resolution air-sea interactions in the tropics and their influence on large-scale tropical models (MJO,ENSO), on trying to understand the causal connections in stratocumulus clouds using a newly developed fully nonlinear causal discovery method, on trying to identify the basic physics of cloud organization into sugar, flower, gravel, and fish clouds structures.