data
DESCRIPTION
When data and model are in isolation. Data. Model. We are getting …. Soil carbon modeled in CMIP5 vs. HWSD. Integrated Data-Model Approaches to Carbon Cycle Research. Data. Model. Prediction. Panelists. Mike Kuperberg , DOE program officer: DOE's - PowerPoint PPT PresentationTRANSCRIPT
Data Model
When data and model are in isolation
We are getting …
Soil carbon modeled in CMIP5 vs. HWSD
Data Model
Prediction
Integrated Data-Model Approaches to Carbon Cycle Research
Mike Kuperberg, DOE program officer: DOE's perspective on the data-model integration Yiqi Luo, University of Oklahoma: Challenges and opportunities in data-model integrationAnthony Walker, ORNL: Benchmark analysis of models against data from FACE experiments Sasha Hararuk, University of Oklahoma: Evaluation and
improvement of global land models against soil carbon data.
Robert Cook, ORNL: Ecoinformatics and cyberinfrastructure to promote data-model integration
Panelists
A new philosophy of research
Modeling activities guide field research activities, which in turn informs modeling activities. This cyclical processing of information should maximize the financial and scientific investments and result in high quality predictive models
Field research Modeling
Scientific inquiry
Process thinkingData
Gain best knowledge from imperfect data and imperfect models
?
1. Benchmarking: Data used to evaluate model performance
2.Data assimilation: Multiple streams of data ingested into model to improve its performance
3. Parameterization: Data used to parameterize models
4.Process representation: New algorithms to represent processes instead of a black-box approach
Experiment results model
Benchmarking
Problem:
If an incomplete set of variables are used for benchmarking,
…benchmarking can give false confidence when models predict with
some accuracy but for the wrong reasons
How do CLM-CASA’ and CABLE simulate Soil C?
IGBP-DIS data
Data assimilation to improve soil C simulation by two global models:
IGBP-DIS data
Changes in temporal dynamics: CLM-CASA’
5,270 Pg
5,780 Pg
11,100 Pg
3/1/2012
5th NSF RCN FORECAST meetingForecast of Resource and Environmental Change using
data Assimilation Science and Technology
1. Model strengths and deficiencies: Effective communication from modeling to experimental communities?
2. Data model products: What are the data model products directly useful for model improvement?
3. Infrastructure: Data assimilation techniques, data model products, cyberinfrastructure, visualization, and analytic tools.
4. Possible national center(s): Infrastructure development and coordination of activities
Strategies to promote experiment-model interactions
DAAC, CDIAC NCAR CLM
Field research Modeling
Scientific inquiry
Process thinkingData
‹#›
Users can access observational data and convert to their specified format, spatial resolution, spatial extent, and temporal extent.
Pilot Study: Integrate Observations with Models using “Access Broker”
Original Observational
DataFTP/HTTP/…
SCRIP (regrid)
Data
Process
Customized Observational
Data Request for Data
17
Original MODIS Data
MODIS Web Service
Model-Data Comparison Framework
Data Assimilation Framework
Stefano Nativi et al.
Field research Modeling
Scientific inquiry
Process thinkingData
National center for experiment-model integration (NCEMI)