Land Data Assimilation Working Group

Overview

There is a growing consensus that land models need to be confronted with a wide range of data to constrain uncertainty in parameters, initialize surface states, and address model structural uncertainty. To this end, different land modeling teams are implementing a range of data assimilation techniques. However, these teams are facing a number of technical challenges and issues implementing data assimilation methods with land models. Their efforts are further frustrated by the fact that there are limited opportunities to discuss these challenges at regular scientific meetings. To address this issue, the AIMES Land Data Assimilation Working Group is in the process of building a land data assimilation community within which we can share knowledge and collaborate on synergistic activities to overcome land data assimilation related challenges.

Objectives and Goals

This working group seeks to bring together a community of data assimilation scientists working with land surface models to share the latest tools and techniques to better quantify and constrain uncertainty in carbon-climate and land-atmosphere feedbacks and promote the use of these methods to the wider modeling community.

Working Group Steering Committee

Natasha MacBean is an Assistant Professor in the Department of Geography & Environment and Department of Biology at Western University, Canada. She is a leading expert on using a wide range of in situ and satellite datasets within a data assimilation framework to test, develop, and constrain terrestrial biosphere model (TBM) carbon cycle processes and parametric uncertainty. Her research also includes studies investigating satellite greening trends and the impact of satellite land cover mapping uncertainties on TBM carbon, water and energy budget estimates.

Andy Fox is a Senior Research Scientist at Morgan State University in Goddard Earth Sciences Technology and Research II (GESTAR-II) and works at the Global Modeling and Assimilation Office (GMAO) of NASA Goddard Space Flight Center developing the next generation land reanalysis system. Prior to this, he worked at the Joint Center for Satellite Data Assimilation (JCSDA) in Boulder, Colorado, from July 2019 leading efforts in land surface model data assimilation as Land Project Lead. Andy has conducted research in this field for the last two decades through a variety of projects focused on the roles of land surface processes in the coupled Earth System. Originally from Manchester, UK, Andy has degrees in Geography and Hydrology from the University of Oxford, the University of Colorado – Boulder, and a PhD from the University of Cambridge. He has worked as a researcher at a number of universities in the UK and in the US and maintains strong connections with NCAR where he was a visiting scientist for several years developing a data assimilation system for the Community Land Model.

Jana Kolassa is a researcher and scientific land model developer at the Global Modeling and Assimilation Office (GMAO) at the NASA Goddard Space Flight Center. She obtained her PhD in Environmental Science and Remote Sensing from the Université Pierre et Marie Curie in 2013. Before joining the GMAO as a full-time researcher, she worked as a postdoctoral fellow at Columbia University in New York and through the NASA Postdoctoral Program. Her research focusses on improving the representation on land-atmosphere interaction in global Earth System Models through (1) improvements in process representation and (2) data assimilation. She is currently developing the next version of the NASA Catchment-CN model and is leading a project that aims to investigate the potential of SMAP soil moisture data assimilation to improve the forecast of tropical cyclone landfall behavior in the GMAO’s operational GEOS model.

Tristan Quaife is Associate Professor of Carbon Cycle Science and the University of Reading, UK, and an investigator in the UK’s National Centre for Earth Observation. His research focuses on the interface between models and observations, especially those from satellites, and a central theme to his work has been using Data Assimilation to integrate EO and field data with models of the land surface, often using complex, non-linear observation operators. His DA work has covered Ensemble Kalman Filters, Particle Filters and 4DVAR. Most recently his group has been working on hybrid data assimilation techniques (in particular the iterative ensemble Kalman smoother) to assimilate EO data into the JULES land surface model. This has resulted in an operational soil moisture product for Africa that integrates NASA SMAP data with JULES.

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