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.

Join the Land DA Community!

To share information and opportunities relevant to the land DA community (e.g. papers, workshops, conferences, job advertisements, funding opportunities, ideas for discussion/collaboration, code, GitHub repos, best practices/pitfalls/methods advice etc), please subscribe to the Land DA Community listserv!