Land Data Assimilation Working Group
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
Andy Fox is the Land Project lead at the inter-agency Joint Center for Satellite Data Assimilation. He has worked with many land models and DA systems in the past, from site to global spatial scales, and over short to long timescales. Whilst previously focused on studying the terrestrial carbon cycle with climate models in the university research community, his work now largely focuses on developing operational land DA systems for NOAA’s Unified Forecast System and National Water Model using the Joint Effort for Data Assimilation Integration. He is particularly interested in developing techniques and infrastructure for utilizing the latest generation of remote sensing observations of the land surface, and preparing for future observation capabilities.
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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News and Events
The AIMES Land Data Assimilation Working Group organized a virtual kickoff workshop on June 14-16, 2021. Click here to view the workshop presentations!
The AIMES Land Data Assimilation Working Group is organizing a virtual kickoff workshop to bring together land DA scientists to highlight a range of DA methods used within the community, discuss challenges facing different modeling communities, and identify strategies for addressing those challenges. The workshop will be held June 14-16, 2021. Learn more and register here.