Tackling Technical Challenges in Land Data Assimilation
virtual workshop

June 14-16, 2021 ⋅ 9:00-12:00 EDT // 15:00 – 18:00 CEST

Organizers: Natasha MacBean (1), Jana Kolassa (2), Andy Fox (3), Tristan Quaife (4), Hannah Liddy (5)
(1) Indiana University, (2) NASA GMAO, (3) Joint Center for Satellite Data Assimilation, (4) University of Reading, (5) Columbia University/NASA GISS
† Organized by the AIMES Land Data Assimilation Working Group

Download Workshop Flyer

Workshop Overview: There is growing consensus that land surface models need to be confronted with a wide range of data to constrain uncertainty in parameters, initialize surface states, and address model structural uncertainty. However, there are limited opportunities at scientific meetings to specifically discuss the challenges faced by modeling teams when implementing data assimilation (DA) techniques. To strengthen communication between modeling groups, this workshop will 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. We welcome participation from a broad range of research interests including land surface states and fluxes (carbon, energy, and water cycles to crop, fire, and land management), timescales (daily, seasonal to subseasonal, centennial/millennial), and scientific and practical applications (improving understanding of carbon-climate feedbacks, weather prediction, agricultural forecasting, and climate change impacts). The outcome of this workshop is to increase collaboration and coordination within the land DA community to tackle technical challenges and promote the routine use of DA tools in the wider modeling community.

Workshop Agenda: This workshop is focused on the technical challenges of data assimilation, and we have a great lineup of technically-focused, thought-provoking talks that allow ample time for discussion. The second half of each day will be dedicated to making connections between land DA communities, increasing knowledge exchange to tackle land DA challenges, and building a collaborative land DA community inclusive of all backgrounds and career stages.

Monday, June 14: Applicability of data assimilation approaches across different land modeling communities

9:00 AM EDT Welcome from the Co-Chairs: Introduction to the workshop context and goals

9:10 AM EDT Speaker 1: Patricia De Rosnay (ECMWF) – Technical challenges of coupled land-atmosphere data assimilation for operational Numerical Weather Prediction and reanalyses
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9:25 AM EDT Speaker 2: Eunjee Lee (NASA GSFC) – Effect of land initialization on the skill of forecasting carbon fluxes on sub-seasonal to seasonal (S2S) time scales
9:40 AM EDT Speaker 3: Bertrand Bonan (CNRM) – Monitoring land surface variables with LDAS-Monde: focus on assimilation approaches and applications to kilometric-scale spatial resolutions
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9:55 AM EDT Break (5 minutes)
10:00 AM EDT Speaker 4: Marko Scholze (Lund University) – Experiences on terrestrial model parameter optimisation based from the Carbon Cycle Data Assimilation System using multiple observations
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10:15 AM EDT Speaker 5: Breo Gomez (UK Met Office) – Differences between atmospheric and land data assimilation and challenges for strong coupling
10:30 AM EDT Speaker 6: Sujay Kumar (NASA GSFC) – Land hydrology data assimilation – Are we on the right track?
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10:45 AM EDT  Introduction to Break Out Groups 
10:50 AM EDT  Break (10 minutes) 
11:00 AM EDT Break Out Groups
11:45 AM EDT Plenary Discussion/Report Backs
11:55 AM EDT Co-chair wrap up

Tuesday, June 15: Emerging techniques

9:00 AM EDT Welcome from the Co-Chairs: Introduction to Day 2 
9:05 AM EDT Speaker 1: Jianzhi Dong (MIT) – The added value of brightness temperature assimilation for global soil moisture estimation
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9:20 AM EDT Speaker 2: Ewan Pinnington (University of Reading) – Hybrid Data Assimilation Methods for Land Surface Modelling
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9:35 AM EDT Speaker 3: Istem Fer (Finnish Meteorological Institute) – Gaussian process emulators for efficient Bayesian calibration of process-based models
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9:50 AM EDT Break (5 minutes)
9:55 AM EDT

Speaker 4: Joanne Waller (UK Met Office) Estimating the full observation error covariance matrix
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10:10 AM EDT Speaker 5: Moha El Gharamti (NCAR/UCAR) – Enhanced Streamflow Forecasting using Ensemble Data Assimilation
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10:25 AM EDT Speaker 6: Anthony Bloom (NASA JPL/Caltech) – Using an ever-growing Earth Observation record to infer and predict terrestrial C and H2O dynamics
10:40 AM EDT  Introduction to Break Out Groups 
10:45 AM EDT  Break (10 minutes) 
10:55 AM EDT Break Out Groups
11:40 AM EDT Plenary Discussion/Report Backs
11:55 AM EDT Co-chair wrap up

Wednesday, June 16: Challenges in dealing with observations

9:00 AM EDT Welcome from the Co-Chairs: Introduction to Day 3
9:05 AM EDT Speaker 1: Nina Raoult (LSCE) – Using the temporal dynamics of surface soil moisture to deal with biases when calibrating land surface models
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9:20 AM EDT Speaker 2: Susan Steele-Dunne (Delft University of Technology) – Towards constraining water and carbon cycle processes with radar data through assimilation
9:35 AM EDT Speaker 3: Jina Jeong (Vrije Universiteit Amsterdam) – Using the International Tree-Ring Data Bank (ITRDB) records as century-long benchmarks for land-surface models
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9:50 AM EDT Break (5 minutes)
9:55 AM EDT Speaker 4: Ann Raiho (NASA GSFC/University of Maryland) – Advances and challenges for using paleoecological data for state data assimilation within a forest gap model
10:10 AM EDT Speaker 5: Clara Draper (NOAA) – Time scales in land data assimilation
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10:25 AM EDT Speaker 6: Manuela Girotto (UC Berkeley) – Technical challenges of assimilating observations with large spatiotemporal resolutions
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10:40 AM EDT  Introduction to Break Out Groups 
10:45 AM EDT  Break (10 minutes) 
10:55 AM EDT Break Out Groups
11:40 AM EDT Plenary Discussion/Report Backs
11:55 AM EDT

Co-chair wrap up: Next step

 

In addition to each day’s central theme, the workshop will include cross-cutting themes addressing issues related to error characterization and the different spatial and temporal scales over which we assimilate data.

Download Workshop Agenda and Abstract Booklet