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. Many land modeling (sub)groups are using data assimilation (DA) to quantify and reduce uncertainty in their land model predictions, and thus to improve their near-term weather forecasts or longer-term climate projections. This work is often highly technically challenging, with many groups working in relative isolation over many months and years to find solutions to these challenges. At the same time, this technical work often is not published in peer reviewed journals, and there limited opportunities to discuss these challenges at regular scientific meetings.
With the objective of building a land DA community in mind, in June 2021 they held a virtual workshop on “Tackling Technical Challenges in Land Data Assimilation”. A summary of the meeting and planned goals and activities for the future of the community can be found here.
The AIMES Land DA Working Group is working on several initiatives to continue building the land DA community and to foster collaborative activities to address some of the technical challenges we face. More on these endeavors coming soon!