Model Uncertainty
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. Model uncertainty can be reduced by sampled isolation, parameter constraints, and ensemble approaches, and arises from internal variability, data errors, and the parameterization of sub-grid scale processes. One example of a tool for addressing uncertainty in model structures and parameterizations is data assimilation. The potential of improving model uncertainty is untapped and possible effects on future models are unknown. Here we feature a series of talks given at the 2021 virtual workshop on ‘Tackling Technical Challenges in Land Data Assimilation’ the talks feature a range of DA methods used within the community spanning techniques used by numerical weather prediction and land surface modeling.
Introduction and Orientation to the Webinar Series
Speakers: Andy Fox (Joint Center for Satellite Data Assimilation), Hannah Liddy (AIMES), Jana Kolassa (NASA GSFC), Natasha MacBean (Indiana University), Tristan Quaife (Univesity of Reading)
Date aired: 14-16 June 2021
Technical challenges of coupled land-atmosphere data assimilation for operational NWP and reanalyses
Speaker: Patricia De Rosnay (ECMWF)
Date aired: 14-16 June 2021
About: Patricia De Rosnay gives a presentation called: Technical challenges of coupled land-atmosphere data assimilation for operational Numerical Weather Prediction and reanalyses.
Topic: Applicability of data assimilation approaches across different land modeling groups
Date aired: 14-16 June 2021
About: Bertran Bonan from CNRM gives a presentation on Monitoring land surface variables with LDAS-Monde: focus on assimilation approaches and applications to kilometric-scale spatial resolutions.
Topic: Applicability of data assimilation approaches across different land modeling groups
Experiences on terrestrial model parameter optimisation based from CCDAS using multiple observations
Speaker: Marko Scholze (Lund University)
Date aired: 14-16 June 2021
About: Marko Scholze from Lund University gives a presentation on: Experiences on terrestrial model parameter optimisation based from the Carbon Cycle Data Assimilation System using multiple observations.
Topic: Applicability of data assimilation approaches across different land modeling groups
Land hydrology data assimilation – Are we on the right track?
Speaker: Sujay Kumar (NASA GSFC)
Date aired: 14-16 June 2021
About: Sujay Kumar from NASA GSFC gives a presentation: Land hydrology data assimilation – Are we on the right track?
Topic: Applicability of data assimilation approaches across different land modeling groups
The added value of brightness temperature assimilation for global soil moisture estimation
Speaker: Jianzhi Dong (MIT)
Date aired: 14-16 June 2021
About: Jianzhi Dong from MIT gives a presentation called: The added value of brightness temperature assimilation for global soil moisture estimation.
Topic: Emerging techniques in land data assimilation
Hybrid data assimilation methods for land surface modelling
Speaker: Ewan Pinnington (University of Reading)
Date aired: 14-16 June 2021
About: Ewan Pinnington from the University of Reading gives a presentation on Hybrid data assimilation methods for land surface modelling.
Topic: Emerging techniques in land data assimilation
Gaussian process emulators for efficient Bayesian calibration of process-based models
Speaker: Istem Fer (Finnish Meteorological Institute)
Date aired: 14-16 June 2021
About: Istem Fer from the Finnish Meteorological Institute presented on Gaussian process emulators for efficient Bayesian calibration of process-based models.
Topic: Emerging techniques in land data assimilation
Estimating the full observation error covariance matrix
Speaker: Joanne Waller (UK Met Office)
Date aired: 14-16 June 2021
About: Joanne Waller from the UK Met Office gives her presentation on: Estimating the full observation error covariance matrix.
Topic: Emerging techniques in land data assimilation
Enhanced streamflow forecasting using ensemble data assimilation
Speaker: Moha El Gharamti (NCAR/UCAR)
Date aired: 14-16 June 2021
About: Moha El Gharamti from NCAR/UCAR presents on Enhanced streamflow forecasting using ensemble data assimilation.
Topic: Emerging techniques in land data assimilation
Using the temporal dynamics of surface soil moisture to deal with biases when calibrating LSMs
Speaker: Nina Raoult (LSCE)
Date aired: 14-16 June 2021
About: Nina Raoult from LSCE gives a presentation on using the temporal dynamics of surface soil moisture to deal with biases when calibrating land surface models.
Topic: Challenges in dealing with observations
Using the ITRDB records as century-long benchmarks for land-surface models
Speaker: Jina Jeong (Vrije Universiteit Amsterdam)
Date aired: 14-16 June 2021
About: Jina Jeong from Vrije Universiteit Amsterdam gave a presentation on: Using the International Tree-Ring Data Bank (ITRDB) records as century-long benchmarks for land-surface models.
Topic: Challenges in dealing with observations
Want to learn more?
If you are interested in model uncertainty, take a look at our webpage for the Land Data Assimilation Working Group. You’ll find information on our webinar series as well as recent developments in the field.