KU Leuven is looking for an enthusiastic postdoctoral researcher with experience in (i) land and/or atmospheric modeling and (ii) satellite-based earth observations (EO). You will participate in a cutting-edge and international research project “METEORI” (funded by Belspo) to improve our understanding of the effect of irrigation on land-atmosphere coupling, and you will assimilate EO data to enhance the representation of irrigation in Earth system modeling. You will be part of the Department of Earth and Environmental Sciences, Division Soil and Water Management, Research Group “Land Surface Remote Sensing, Modeling and Data Assimilation”. Research stays at FZ Jülich (Germany) are possible. Your direct supervisors will be prof. Gabriëlle De Lannoy (KU Leuven), drs. Zdenko Heyvaert (KU Leuven/ECMWF), and prof. Stefan Kollet (FZ Jülich).
The impact of irrigation on the atmosphere has mostly been studied using models. However, EO data offer the possibility to identify areas with irrigation and improve our estimates of the land surface. The land surface interacts with the lower atmosphere through land-atmosphere coupling. METEORI will for the first time apply multi-sensor, multi-frequency, and multivariate data assimilation over agricultural areas to leverage EO in a coupled land-atmosphere model. More specifically, the irrigated land will a priori be classified using EO data. Over these areas, microwave-based retrievals of soil moisture, optical-based retrievals of vegetation and infrared-based retrievals of soil temperature will be assimilated into the Noah-MP model embedded within the NASA Land Information System (LIS). The successful candidate will extend the LIS software to facilitate the assimilation of satellite products that have not yet been implemented. Following an ‘offline’ (land surface only) evaluation, Noah-MP will be used together with the Weather Research & Forecasting (WRF) atmospheric model within the NASA Unified WRF (NU-WRF) to perform ‘online’ (land surface and atmosphere) simulations. The inclusion of EO data into the coupled land-atmosphere simulations will alter the coupling strength compared to model-only simulations. This coupling strength will be quantified using novel methods that involve machine learning or deep learning. METEORI will improve both the understanding of land-atmosphere coupling and the skill of local and regional meteorological forecasts, both directly over agricultural areas and downstream.
The postdoc is expected to work in a broad international context, and collaborate with PhD and MSc students.