Identifying Teleconnection Patterns from Point Correlation

Maps using Self Organizing Maps

Freja Hunt, Joel Hirschi and Bablu SinhaNational Oceanography Center, Southampton, UK

8th April 2011 EGU General Assembly Vienna

Correlation Maps

Wallace & Gutzler 1981

Correlate a grid point with every other grid point on the map for all grid points

Identifying Teleconnection Patterns from Point Correlation Maps using Self Organizing Maps Freja Hunt

Self Organizing Maps (SOMs)What is a SOM?

An unsupervised non-linear neural networkFinds representative patterns in the dataResults are arranged topologically

Similar results are close together, different results are far apart

Examples of SOMs in teleconnectionsLeloup et al 2008 ENSO, Johnson et al 2008 NAO

Simple example

Identifying Teleconnection Patterns from Point Correlation Maps using Self Organizing Maps Freja Hunt

Current data from a moored buoy in Loch Shieldaig, Scotland

Actual data willbe shown in RED

SOM data willbe shown in BLUE

Data courtesy of the British Oceanographic Data CentreIdentifying Teleconnection Patterns from Point Correlation Maps using Self Organizing Maps

Freja Hunt

How do Self Organizing Maps work?

InitializationHow many patterns?

Starting patterns

Identifying Teleconnection Patterns from Point Correlation Maps using Self Organizing Maps Freja Hunt

How do Self Organizing Maps work?

How do SOMs work?2. Locate SOM pattern most similar to data

pattern

1. Present each data pattern to SOM

2. Locate BMU3. Update BMU – learning rate4. Update neighbors -

neighborhood function5. Learning rate and

neighborhood function reduce over time

BMU

Identifying Teleconnection Patterns from Point Correlation Maps using Self Organizing Maps Freja Hunt

How do SOMs work?3. Update BMU to more closely resemble the

data pattern

1. Present each data pattern to SOM

2. Locate BMU3. Update BMU – learning rate4. Update neighbors -

neighborhood function5. Learning rate and

neighborhood function reduce over time

BMU

Identifying Teleconnection Patterns from Point Correlation Maps using Self Organizing Maps Freja Hunt

How do SOMs work?4. Update neighboring SOM patterns

1. Present each data pattern to SOM

2. Locate BMU3. Update BMU – learning rate4. Update neighbors -

neighborhood function5. Learning rate and

neighborhood function reduce over time

Neig

hbor

hood

func

tion

Identifying Teleconnection Patterns from Point Correlation Maps using Self Organizing Maps Freja Hunt

How do SOMs work?1. Present each data pattern to

SOM2. Locate BMU3. Update BMU – learning rate4. Update neighbors -

neighborhood function5. Learning rate and

neighborhood function reduce over time

1. Iteratively present each data pattern to SOM

Identifying Teleconnection Patterns from Point Correlation Maps using Self Organizing Maps Freja Hunt

Comparison• Compare original data patterns with SOM patterns

• For each data pattern find its BMU

• Add up number of times each SOM pattern is BMU to get ‘hits’• Frequency of occurrence

Identifying Teleconnection Patterns from Point Correlation Maps using Self Organizing Maps Freja Hunt

How do Self Organizing Maps work?

Identifying Teleconnection Patterns from Point Correlation Maps using Self Organizing Maps Freja Hunt

Percentage frequency occurrence of each SOM pattern in the original data

How do Self Organizing Maps work?

Correlation Map SOMsGridded data setPoint correlation maps for each grid pointnx by ny correlation mapsPresent correlation maps to SOM rather than raw

dataAdvantages:

Correlation maps already highlight related regionsSOM summarizes patternsNo requirement for orthogonality

Identifying Teleconnection Patterns from Point Correlation Maps using Self Organizing Maps Freja Hunt

Idealized Self Organizing Maps

Rectangular domainSimple north-south oscillationPlus east-west oscillation in northern halfAdd noiseConstruct point correlation maps for each grid pointPresent to 4 x 4 SOMAlso SOM from raw data

Identifying Teleconnection Patterns from Point Correlation Maps using Self Organizing Maps Freja Hunt

Idealized SOMN-S oscillation, + E-W oscillation, no noise

Red = positiveBlue = negativeGreen = zero

Identifying Teleconnection Patterns from Point Correlation Maps using Self Organizing Maps Freja Hunt

Idealized SOMN-S oscillation, + E-W oscillation, + white noise

Red = positiveBlue = negativeGreen = zero

Identifying Teleconnection Patterns from Point Correlation Maps using Self Organizing Maps Freja Hunt

Idealized SOMN-S oscillation, + E-W oscillation, + random walk

Red = positiveBlue = negativeGreen = zero

Identifying Teleconnection Patterns from Point Correlation Maps using Self Organizing Maps Freja Hunt

Temperature SOM20 x 40 SOM - NCEP/NCAR monthly 2m temperature

anomalies1.1948 to 11.2008

Identifying Teleconnection Patterns from Point Correlation Maps using Self Organizing Maps Freja Hunt

ENSO TypeNAO Type

ConclusionsCorrelation maps + SOMs effectively identify and

summarize teleconnectionsAdvantage over raw data as relationships already

definedAdvantage over EOFs as no orthogonalityFlexible method – use comparison stage in many

different ways to get different insights into large datasets

Validating model behavior

Identifying Teleconnection Patterns from Point Correlation Maps using Self Organizing Maps Freja Hunt