Post on 31-Dec-2015
Tucson Arizona, May 16-17, 2006
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A. The EO’s: Role of Modeling (Science/Models in the Context of the EO’s & Enabling
Technologies)
B. Mobilizing Theory in Principle: Cyber-Infrastructure
C. “Wild Seeds”: Modeling in a Non-EO Context
D. Reconciling Theory With Observation(Reconciling Theory with Data)
E. EO’s and Model Applications for Environmental Stewardship(Model Applications & Matters Arising)
F. Educational & Learning Opportunities
A. The EO’s: Role of Modeling (Science/Models in the Context of the EO’s & Enabling
Technologies)
B. Mobilizing Theory in Principle: Cyber-Infrastructure
C. “Wild Seeds”: Modeling in a Non-EO Context
D. Reconciling Theory With Observation(Reconciling Theory with Data)
E. EO’s and Model Applications for Environmental Stewardship(Model Applications & Matters Arising)
F. Educational & Learning Opportunities
Session Chair: Hoshin Gupta (University of Arizona)
Grand Challenges of the Future for Environmental ModelingEnvironmental Observatories Modeling Workshop
May 16-17, 2006, Tucson, Arizona
Grand Challenges of the Future for Environmental ModelingEnvironmental Observatories Modeling Workshop
May 16-17, 2006, Tucson, Arizona
Reconciling Theory With Observation(Reconciling Theory with Data)Reconciling Theory With Observation(Reconciling Theory with Data)
Tucson Arizona, May 16-17, 2006
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Grand Challenges
“What new technologies for observing, simulating, and telecommunicating will emerge over the next 5-10 years?
How will they change the grand challenges for modeling?
What will those challenges be?
How might those challenges be pursued?
Grand Challenges
“What new technologies for observing, simulating, and telecommunicating will emerge over the next 5-10 years?
How will they change the grand challenges for modeling?
What will those challenges be?
How might those challenges be pursued?
Certainly we can discuss how NSF can support ever more efficient ways (technologies) to help us do the modeling equivalent of “business as usual” …
But I hope we will spend some time on …What are the grand challenges? How might we approach them?
Tucson Arizona, May 16-17, 2006
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Models are complex assemblies of multiple, constituent hypotheses… that must be tested … against the new streams of field data …
Working out novel ways of conducting these tests, will be a major scientific challenge associated with the EO’s
ACTSReconciling Theory With Observation
Theories <---> Models <---> ACTS <---> ObservationsTheories <---> Models <---> ACTS <---> Observations
How on earth are we going to take “large” models …
and large volumes of data …
juxtapose them …
and make sense of this juxtaposition ???
How on earth are we going to take “large” models …
and large volumes of data …
juxtapose them …
and make sense of this juxtaposition ???
Tucson Arizona, May 16-17, 2006
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To be effective, an intervention must be introduced at the correct logical level
If a problem seems unsolvable … consider that you may have a meta-problem.
JOHN GALL(Systemantics: The Underground Text of Systems
Lore)
ACTSReconciling Theory With Observation
Tucson Arizona, May 16-17, 2006
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DATA IS NOT INFORMATION !!!
DataBase
Information AContext A
Information BContext B
Prior InformationPrior Knowledge
Analysis
Tucson Arizona, May 16-17, 2006
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DATA IS NOT INFORMATION !!!
Information is obtained by viewing data in a certain context (perceptual filtering).
There may be (are always) multiple plausible contexts …
Extraction -- “What kind of information does this data contain?”
“How do we extract it?”
SIGNAL (Data) ---> SIGNATURES (Information)
We should generally be interested in “minimal” representations of Information … … they indicate how much complexity we can resolve
RInfo << RData
Tucson Arizona, May 16-17, 2006
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Confront Models with Information !!!
Perceptual
Conceptual
Symbolic
Computational
DATA
FITTINGVERIFICATIONVALIDATION
FITTINGVERIFICATIONVALIDATION
INFORMATION
PAR. ESTIMATIONEVALUATION
FAULT DETECTION
DIAGNOSIS
PAR. ESTIMATIONEVALUATION
FAULT DETECTION
DIAGNOSIS
? Assumptions ?
Tucson Arizona, May 16-17, 2006
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Classical Approach to Model Evaluation
Is
I-S-OMeasurement Os
N-Dim Time Series
RD
Om
I-S-OSimulation
N-Dim Time Series
RD
ModelModel
Structure (Eqns)Parameters ()
R
SystemSystem
Form & FunctionInvariants
RS
-
C()
=Om- Os
Measure of Closeness(Single Criterion based on residuals)
R1
OO
timetime
t
t
Fundamentally WeakHas Little Diagnostic Power
Tucson Arizona, May 16-17, 2006
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Diagnostic Approach to Model Evaluation
I-S-OData ModelModelI-S-O
Simulation
Is
SystemSystem
RC R?
Measures of Closeness
(Diagnostics)
1M - 1
S
cM - c
S
RD RD R
RS >> RD >> RC
Structure (Eqns)Parameters ()
Form & FunctionInvariants
RS
Model Generated “Data”(N-Dim Time Series)
Observed Data(N-Dim Time Series)
I-S-O Structures
I-S-O Indices (S)
RC
RC
Indices(Pattern
Properties)
Signatures(Pattern Extracts)
I-S-O Structures
I-S-O Indices (M)
RC
Signatures(Pattern Extracts)
Indices(Pattern
Properties)
Model Identification <--> Search for Isomorphisms
M()
S
1
2
Tucson Arizona, May 16-17, 2006
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Grand Challenges
DATA (Large Volumes) ---> INFORMATION
DIAGNOSTICS APPROACH TO SYSTEMS IDENTIFICATION
Grand Challenges
DATA (Large Volumes) ---> INFORMATION
DIAGNOSTICS APPROACH TO SYSTEMS IDENTIFICATION
Tucson Arizona, May 16-17, 2006
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Ed Rastetter - Marine Biological Lab, Woods HoleDeveloping and Testing Mechanistic Models of Terrestrial Carbon Cycling Using Time Series Data
Hans Graber - University of MiamiReal-Time Forecasting System of Winds, Waves and Surges in Tropical Cyclones
Three Breakout Sessions
THIS MORNING