Transcript Descriptive Analysis of the Global Climate System and Predictive

Comparing Predictive Power in
Climate Data: Clustering Matters
Karsten Steinhaeuser
University of Minnesota
joint work with
Nitesh Chawla & Auroop Ganguly
12th International Symposium on
Spatial and Temporal Databases
Minneapolis, MN
August 24, 2011
Outline
• Motivation
• Networks Primer
• From Data to Networks
• Motivating Networks in Climate Science
• Descriptive Analysis and Predictive Modeling
• Empirical Evaluation & Comparison
• Conclusions
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Mining Complex Data
• Complex spatio-temporal data pose unique challenges
• Tobler’s First Law of Geography:
“Everything is related, but near
things more than distant.”
– But are all near things equally related?
– Are there phenomena explained by
interactions among distant things?
(teleconnections)
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Networks Primer
What is a Network?
• Oxford English Dictionary:
network, n.: Any netlike or complex
system or collection of interrelated
things, as topographical features,
lines of transportation, or
telecommunications routes
(esp. telephone lines).
• My working definition:
Any set of items that are connected or related to each other.
(“items” and “connections” can be concrete or abstract)
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Networks Primer
Community Detection in Networks
• Identify groups of nodes
that are relatively more tightly
connected to each other than
to other nodes in the network
• Computationally challenging
problem for real-world networks
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From Data to Networks
• Networks are pervasive in
social science, technology,
and nature
• Many datasets explicitly
define network structure
• But networks can also represent other types of data,
framework for identifying relationships, patterns, etc.
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Motivating Networks in Climate
Uncertainty derives from many known and
often many more unknown sources
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Motivating Networks in Climate
• Projections of climate
rely on many factors
– Understanding of the
physical processes
– Ability to implement
this understanding in
computational models
– Assumptions about
the future
Source: IPCC SRES and AR-4
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Motivating Networks in Climate
• Some processes well-understood and modeled,
others much less credible
• Comparison to observations shows varying skills
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Motivating Networks in Climate
• Models cannot capture some features/processes
• Comparison to observations illustrates severe
geographic variability, topographic bias
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Motivating Networks in Climate
Research Question:
Can we characterize the credible variables,
identify relationships to the relatively less
credible variables, and leverage them to
improve or refine our understanding?
Answer:
Stay Tuned…
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Knowledge Discovery for Climate
Data Storage &
Management
Knowledge
Discovery
System Inputs
Observations
GCM outputs
HPSS
Complex
Networks
Oracle DB
Data Mining
HighPerformance
Computing
HPCC
Lens - Jaguar
Visualization
ARM Data
ArcGIS
Basic OLAP
Capabilities
PowerWall
System Outputs
Novel Insights
Decision Support
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Historical Climate Data
• NCEP/NCAR Reanalysis (proxy for observation)
• Monthly for 60 years (1948-2007) on 5ºx5º grid
• Seven variables:
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Raw
Data
De-Seasonalize
Sea surface temperature (SST)
Sea level pressure (SLP)
Geopotential height (GH)
Precipitable water (PW)
Relative Humidity (RH)
Horizontal wind speed (HWS)
Vertical wind speed (VWS)
Anomaly
Series
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Network Construction
• View global climate system as a
collection of interacting oscillators
[Tsonis & Roebber, 2004]
– Vertices represent locations in
space
– Edges denote correlation in
variability
• Link strength estimated by
correlation, low-weight edges are
pruned from the network
• Construct networks only for
locations over the oceans
– Relatively better captured by models
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Geographic Properties
• Examine network structure in spatial context
– Link lengths computed as great-circle distance
– Compare autocorrelation / de-correlation lengths for
different variables, interpret within the domain
Autocorrelation
Teleconnection
Sea Level Pressure
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Precipitable Water
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Vertical Wind Speed
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Clustering Climate Networks
• Apply community detection to
partition networks
– Use Walktrap algorithm
[Pons & Latapy, 2006]
– Efficient and works well
for dense networks
Sea Level Pressure
• Visualize spatial pattern using
GIS tools
• Cluster structure suggests
relationships within the climate
system
Precipitable Water
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Update!
Research Question:
Can we characterize the credible variables,
identify relationships to the relatively less
credible variables, and leverage them to
improve or refine our understanding?
Revised Answer:
Yes… but that’s not all.
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Descriptive  Predictive
• Network representation is able to capture
interactions, reveal patterns in climate
– Validate existing assumptions / knowledge
– Suggest potentially new insights or hypotheses
for climate science
• Want to extract the relationships between
atmospheric dynamics over ocean and land
– i.e., “Learn” physical phenomena from the data
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Predictive Modeling
• Use network clusters as candidate predictors
• Create response variables for target regions
around the globe (illustrated below)
• Build regression
model relating
ocean clusters
to land climate
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Illustrative Example
• Predictive model for air temperature in Peru
– Long-term variability highly predictable due to
well-documented relation to El Nino
• Small number of clusters have majority of skill
– Feature selection (blue line) improves predictions
Raw Data
All Clusters
Feature Selection
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Update!
Research Question:
Can we characterize the credible variables,
identify relationships to the relatively less
credible variables, and leverage them to
improve or refine our understanding?
Revised Answer:
Yes and Yes… but wait, there’s more.
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Results on Train/Test
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Predictive Skill
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Update!
Research Question:
Can we characterize the credible variables,
identify relationships to the relatively less
credible variables, and leverage them to
improve or refine our understanding?
Revised Answer:
Yes, Yes, and Yes.
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Variations / Extensions
• Compare network approach to traditional
clustering methods
– k-means, k-medoids, spectral, EM, etc.
• Compare different types of predictive models
– (linear) regression, regression trees, neural nets,
support vector regression
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Compare Clustering Methods
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Compare Predictive Models
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Refining Model Projections
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Conclusions
• Networks capture behavior of the climate system
• Clusters (or “communities”) derived from these
networks have useful predictive skill
– Statistically significantly better than predictors based
on clusters derived using traditional methods
• Potential for advancing climate science
– Understanding of physical processes
– Complement climate model simulations
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Upcoming Events
1. First International Workshop on Climate Informatics,
New York, NY, August 26, 2011
http://www.nyas.org/climateinformatics
2. NASA Conference on Intelligent Data Understanding
(CIDU), Mountain View, CA, Oct 19-21, 2011
https://c3.ndc.nasa.gov/dashlink/projects/43/
3. IEEE ICDM Workshop on Knowledge Discovery from
Climate Data, Vancouver, Canada, December 10, 2011
http://www.nd.edu/~dial/climkd11/
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Thanks & Questions
Contact
[email protected]
Personal Homepage
http://www.nd.edu/~ksteinha
NSF Expeditions on
Understanding Climate Change
http://climatechange.cs.umn.edu
This work was supported in part by the National Science Foundation
under Grants OCI-1029584 and BCS-0826958. This research was
also funded in part by the project entitled “Uncertainty Assessment
and Reduction for Climate Extremes and Climate Change Impacts”
under the initiative “Understanding Climate Change Impact: Energy,
Carbon, and Water Initiative” within the Laboratory Directed
Research and Development (LDRD) Program of the Oak Ridge
National Laboratory, managed by UT-Battelle, LLC for the U.S.
Department of Energy under Contract DE-AC05-00OR22725.
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