Download Discovery of Climate Indices using Clustering

Discovery of Climate Indices
using Clustering
Michael Steinbach
Steven Klooster
Christopher Potter
Rohit Bhingare, School of Informatics
University of Edinburgh
Overview
• Aim: Applying Clustering to the task of finding interesting
patterns in earth science data.
• Key interests and research goals
• Climate Indices
• Using SVD analysis to find Spatial/Temporal Patterns
• Using Clustering for discovery of indices
• Conclusion and Future Work
Key Interest


Find global climate patterns of interest to Earth
Scientists
Finding connection between the ocean/atmosphere
and land.
Average Monthly Temperature
NINO 1+2 Index
The El Nino Climate Phenomenon
• El Nino is the anomalous warming of the eastern
tropical region of the Pacific.
Normal Year: Trade winds
push warm ocean water west,
cool water rises in its place
El Nino Year: Trade winds
ease, switch direction,
warmest water moves east.
Climate Indices
• A climate index is a time series of temperature or
pressure
– Connecting the Ocean/Atmosphere and the Land
– Commonly based on Sea Surface Temperature (SST)
or Sea Level Pressure (SLP)
• Why climate indices?
– They extract climate variability at a regional or global
scale into a single time series.
– They are well-accepted by Earth scientists.
– They are related to well-known climate phenomena
such as El Nino.
Finding Patterns using SVD and
Clustering
• SVD Analysis:
– Impressive for finding the strongest patterns falling
into independent subspaces.
– All discovered signals must be orthogonal (difficult to
attach physical interpretation)
– Weaker signals may be masked by stronger signals.
• Use of Clustering:
– The centroids of clusters summarize the behaviour of
the ocean/atmosphere in those regions.
Clustering Based Methodology
• The SNN Procedure:
– Apply the SNN clustering on the SST (or SLP)
data over a specific time period.
– Eliminate all the clusters with poor areaweighted correlation.
– The cluster centroids of remaining clusters
are potential climate indices :
<G0, G1, G2, G3>
Clusters with correlation to
known indices
G0
G2
G1
G3
Conclusion
• Clustering plays a useful role in the
discovery
of
interesting ecosystem
patterns.
• Clustering is used to discover previously
unknown relationships between regions of
the land and sea.
Future Work
• Can all climate indices be represented using
clusters?
• Extending the research to land and ocean
variables - Many more opportunities for data
mining/data analysis in Earth Science data.
Earth Observing System: Detecting
patterns such as finding relationships
between
fire
frequency
and
elevation as well as topographic
position