self-organizing maps (soms): proposed rmip diagnosis
DESCRIPTION
Self-Organizing Maps (SOMs): Proposed RMIP Diagnosis. W. J. Gutowski, Jr. Iowa State University. with thanks to: H. Wei , F. Otieno , Z. Pan, R. W. Arritt, E. S. Takle. RMIP(December 2001). Outline. Background Example 1: Precipitation simulation versus observations - PowerPoint PPT PresentationTRANSCRIPT
Self-Organizing Maps (SOMs):Self-Organizing Maps (SOMs):Proposed RMIP DiagnosisProposed RMIP Diagnosis
W. J. Gutowski, Jr.W. J. Gutowski, Jr.Iowa State UniversityIowa State University
RMIP (December 2001)
with thanks to: with thanks to: H. WeiH. Wei, , F. OtienoF. Otieno, Z. Pan, R. W. Arritt, E. S. Takle, Z. Pan, R. W. Arritt, E. S. Takle
OutlineOutline
BackgroundBackground
Example 1: Precipitation simulation Example 1: Precipitation simulation versus observationsversus observations
Example 2: RMIP sea-level pressure Example 2: RMIP sea-level pressure behaviorbehavior
SummarySummaryRMIP (December 2001)
Self-Organizing Maps:Background
Set of maps
• Show characteristic data structures
• Trained to distribution of data
• Give 2-D projection of higher order map space
• Are approximately continuous
• Basis in Artificial Neural Nets
Find closest map ...Find closest map ...(here - smallest RMS (here - smallest RMS
difference)difference)
… … and it nudge and it nudge toward sampletoward sample
Nudge also a Nudge also a surrounding surrounding
region ...region ...
… … that decreases that decreases with iterationwith iteration
… … that decreases that decreases with iterationwith iteration
Nudge also a Nudge also a surrounding surrounding
region ...region ...
… … that decreases that decreases with iterationwith iteration
Nudge also a Nudge also a surrounding surrounding
region ...region ...
OutlineOutline
Background
Example 1: Precipitation simulation Example 1: Precipitation simulation versus observationsversus observations
Example 2: RMIP sea-level pressure behavior
SummaryRMIP (December 2001)
Simulations
Model Observed GCM-control GCM-Scenario
RegCM2 NCEPReanalysis(1979-1988)
HadleyCentre(~1990’s)
HadleyCentre(2040-2050)
HIRHAM(DMI)
“ “ “
OutlineOutline
Background
Example 1: Precipitation simulation versus observations
Example 2: RMIP sea-level pressure Example 2: RMIP sea-level pressure behaviorbehavior
SummaryRMIP (December 2001)
RMIP Sea-level Pressure
Input:
ISU MM5 simulation
Sampled daily at 12 UTC
18 months 549 samples
Unadjusted (e.g., no filtering,
scaling, etc.)
0
500
1000
1500
2000
100
101
102
103
104
105
Mean Sq. Error [hPa*hPa]
Iterations
SOM set: SOM set: Mean Sq. Error vs. IterationsMean Sq. Error vs. Iterations
Alternatives
Band-, low-, high-pass filter
Multiple data types
Input “maps” in (x,t), (y,t), (x,t), etc.
...
OutlineOutline
Background
Example 1: Precipitation simulation versus observations
Example 2: RMIP sea-level pressure behavior
SummarySummaryRMIP (December 2001)
Summary
(1) Self-Organizing Maps provide characteristic
spatial distributions
evolution in time
frequency of occurrence
RMIP (December 2001)
Summary
(1) Self-Organizing Maps provide characteristic
spatial distributions
evolution in time
frequency of occurrence
(2) Caution: SOMs are a tool, not an answer.
The user must supply interpretation and care in use.
RMIP (December 2001)
Summary
(1) Self-Organizing Maps provide characteristic
spatial distributions
evolution in time
frequency of occurrence
(2) Caution: SOMs are a tool, not an answer.
The user must supply interpretation and care in use.
(3) SOMs wealth of information
about complex systems at their “natural” scale.
RMIP (December 2001)
AcknowledgmentsAcknowledgments
Electric Power Research Institute Electric Power Research Institute (EPRI)(EPRI)
U.S. National Oceanic and Atmospheric U.S. National Oceanic and Atmospheric Administration (NOAA)Administration (NOAA)
RMIP (December 2001)
Simulations
Model Observed GCM-control GCM-Scenario
RegCM2 NCEPReanalysis(1979-1988)
HadleyCentre(~1990’s)
HadleyCentre(2040-2050)
HIRHAM(DMI)
“ “ “
PrecipitationPrecipitation
– VEMAP - monthly; 0.5˚ x 0.5˚VEMAP - monthly; 0.5˚ x 0.5˚
– Higgins et al. - hourly; 2˚ x 2.5˚Higgins et al. - hourly; 2˚ x 2.5˚
Circulation – NCEP/NCAR reanalysisCirculation – NCEP/NCAR reanalysis
Precipitable water – NCEP/NCAR reanalysisPrecipitable water – NCEP/NCAR reanalysis
Runoff - UNH 0.5˚ climatologyRunoff - UNH 0.5˚ climatology
Observations
SASAS (September 2001)
OutlineOutline
10-yr RCM simulations10-yr RCM simulations
Precipitation analysisPrecipitation analysis
Error evaluationError evaluation
Conclusions: Sources of error?Conclusions: Sources of error?
Bias: average differenceBias: average difference
Bias Score: intensity “spectrum”Bias Score: intensity “spectrum”
SOM: SOM: pattern differencepattern difference
Diagnoses
-40
0
1979 1981 1983 1985 1987Year
WINTER SPRING
SUMMER FALL
Season’s Bias by Year~ south-central US ~
(“Fall” = Sep-Oct-Nov, etc.)
SASAS (September 2001)
OutlineOutline
10-yr RCM simulations10-yr RCM simulations
Precipitation analysisPrecipitation analysis
Error evaluationError evaluation
Conclusions: Sources of error?Conclusions: Sources of error?
Water Balance AnalysisWater Balance Analysis
ΔE =ΔP −(P −E)m +Ro
Observed runoff
Model output
Precipitation error
Evapotranspiration error
From terrestrial balance:
Water Balance AnalysisWater Balance Analysis
ΔE=ΔP −(P −E)m +Ro +dWdt o
Observed runoff
Model output
Precipitation error
Evapotranspiration error
From terrestrial balance:
Subsurface storage
Water Balance AnalysisWater Balance Analysis
ΔC=ΔP −ΔE
Precipitation error
Evapotranspiration error
From atmospheric balance:
Vapor convergence error
Water Balance AnalysisWater Balance Analysis
ΔE=ΔP −(P −E)m +Ro +dWdt o
For Sep-Oct-Nov:
Plausible values?• model’s root-zone storage: => -0.5 mm/d• (P-E)m - Ro: => -0.1 mm/d
Error: S-O-N averageError: S-O-N average
-3
-2
-1
0
1
-1 -0.5 0 0.5 1
P E C
Storage [mm/d]
Δ ΔΔP E C
SASAS (September 2001)
OutlineOutline
10-yr RCM simulations10-yr RCM simulations
Precipitation analysisPrecipitation analysis
Error evaluationError evaluation
Conclusions: Sources of error?Conclusions: Sources of error?
SASAS (September 2001)
Evapotranspiration error from:Evapotranspiration error from:
Temperature error?Temperature error?
SASAS (September 2001)
Evapotranspiration error from:Evapotranspiration error from:
Temperature error? Apparently no.Temperature error? Apparently no.
Surface moisture error?Surface moisture error?
SASAS (September 2001)
Not resolved by Not resolved by
60 km grid.60 km grid.
Large source of Large source of
water?water?
The region has substantial wetlands:The region has substantial wetlands:
AcknowledgmentsAcknowledgments
Primary Funding: Primary Funding: U.S. National Oceanic and Atmospheric U.S. National Oceanic and Atmospheric
Administration (NOAA)Administration (NOAA)
Additional Support: Additional Support: Electric Power Research Institute (EPRI)Electric Power Research Institute (EPRI)
SASAS (September 2001)