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COST733 Working Group2 -
Implementation and development of weather types
classification methodsAndreas PhilippJudit Bartholy
Michel ErpicumSylvie Jourdain
Thomas KrennertSpyros Lykoudis
Krystyna Pianko-KluczynskaPiia Post
Domingo Rassilla Álvarez
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I.) A retrospect of WG2
II.) Relevance of classification algorithms
III.) Randomcent as a benchmark method
IV.) Randomcent for classification tuning
Outline
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I.) Retrospect of WG2Development of cost733cat
1.) WG1 phase (09/2005 to 09/2006)
2.) WG2 collection phase (2007)
3.) version 1.0 (01.02.2008) version 1.1 (17.02.2008) version 1.2 (12.07.2008)
4.) version 2.0 (21.11.2009)
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I.) Retrospect of WG2Development of cost733cat
1.) WG1 phase (to 09/2006) results of questionnaire:→ daily scale→ spatial scale→ circulation data→ selected methods
(Huth et al. 2007)
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I.) Retrospect of WG2Development of cost733cat
1.) WG1 phase (to 09/2006) results of questionnaire:→ daily scale→ spatial scale→ circulation data→ selected methods
(Huth et al. 2007)
![Page 6: COST733 Working Group2cost733.met.no/Presentations/Philipp.pdf · 2011-01-11 · COST733 Working Group2-Implementation and development of weather types classification methods Andreas](https://reader034.vdocuments.net/reader034/viewer/2022042315/5f032ee17e708231d407f373/html5/thumbnails/6.jpg)
I.) Retrospect of WG2Development of cost733cat
1.) WG1 phase (to 09/2006) results of questionnaire:→ daily scale→ spatial scale→ circulation data→ selected methods
(Huth et al. 2007)
![Page 7: COST733 Working Group2cost733.met.no/Presentations/Philipp.pdf · 2011-01-11 · COST733 Working Group2-Implementation and development of weather types classification methods Andreas](https://reader034.vdocuments.net/reader034/viewer/2022042315/5f032ee17e708231d407f373/html5/thumbnails/7.jpg)
I.) Retrospect of WG2Development of cost733cat
1.) WG1 phase (to 09/2006) results of questionnaire:→ daily scale→ spatial scale→ circulation data→ selected methods
(Huth et al. 2007)
![Page 8: COST733 Working Group2cost733.met.no/Presentations/Philipp.pdf · 2011-01-11 · COST733 Working Group2-Implementation and development of weather types classification methods Andreas](https://reader034.vdocuments.net/reader034/viewer/2022042315/5f032ee17e708231d407f373/html5/thumbnails/8.jpg)
I.) Retrospect of WG2Development of cost733cat
2.) WG2 collection phase (2006/2007)
- 25 methods selected- daily ERA40 12:00 SLP data distributed - definition of the 12 domains- calculation by authors- upload on directory cost733.org- many authors - various formats- need for versioning system
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The COST733 spatial domains
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I.) Retrospect of WG2Development of cost733cat
2.) WG2 collection phase (2006/2007)
- 25 methods selected- definition of the 12 domains- daily ERA40 12:00 SLP data distributed - calculation by authors- upload on directory cost733.org- many authors - various formats- need for versioning system
![Page 11: COST733 Working Group2cost733.met.no/Presentations/Philipp.pdf · 2011-01-11 · COST733 Working Group2-Implementation and development of weather types classification methods Andreas](https://reader034.vdocuments.net/reader034/viewer/2022042315/5f032ee17e708231d407f373/html5/thumbnails/11.jpg)
I.) Retrospect of WG2Development of cost733cat
3.) Version 1.0 to 1.2 (02-07/2008)
- numbers of types → 9, 18, 27- SLP and full year (not seasonal)- method revisions (e.g. E)- 27 methods/catalogs (+ KH)- single file version - also available through wiki
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I.) Retrospect of WG2V1 composite plots
3.) Version 1.0 to 1.2 (02-07/2008)
- numbers of types → 9, 18, 27- SLP and full year (not seasonal)- method revisions (e.g. E)- more methods again (e.g. KH)- 27 methods/catalogs- also available through wiki- single file version - nomenclature
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Retrospect of WG2V1 composite plots
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I.) Retrospect of WG2Development of cost733cat
4.) Version 2.0 (11/2009)
- more meteorological parameters- seasonal variants / sequences of days- new nomenclature- recalculations with cost733class- 23 methods/catalogs (original and reproduced)- 5 method groups established- more than 5000 single catalogs
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I.) Retrospect of WG2Development of cost733cat
4.) Version 2.0 (11/2009)
- more meteorological parameters- Seasonal variants / sequences of days- recalculations with cost733class- 23 methods/catalogs (original and reproduced)- Quality checks- new nomenclature- 5 method groups established
![Page 16: COST733 Working Group2cost733.met.no/Presentations/Philipp.pdf · 2011-01-11 · COST733 Working Group2-Implementation and development of weather types classification methods Andreas](https://reader034.vdocuments.net/reader034/viewer/2022042315/5f032ee17e708231d407f373/html5/thumbnails/16.jpg)
I.) Retrospect of WG2Development of cost733cat
![Page 17: COST733 Working Group2cost733.met.no/Presentations/Philipp.pdf · 2011-01-11 · COST733 Working Group2-Implementation and development of weather types classification methods Andreas](https://reader034.vdocuments.net/reader034/viewer/2022042315/5f032ee17e708231d407f373/html5/thumbnails/17.jpg)
Retrospect of WG2Development of cost733cat
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Retrospect of WG2Development of cost733cat
Mean Seasonal Cycle of type frequencies
Long term time series of type requencies
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II.) Relevance of classification algorithmsfor properties of the resulting frequencies
SUB THR PCA LDR OPT
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SUB THR PCA LDR OPT
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II.) Relevance of the classification Algorithms for applications
Feedback from WG3 and WG4 concerning:temperature, precipitation and other applications:
→ no general significant differences!
→ does the method matter?
→ try (pseudo)randomly generated classifications
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III.) Randomcent
1.) select some dailypatterns by randomas key day
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III.) Randomcent
1.) select some dailypatterns by randomas key day
2.) assign each dayto the most similarkey day
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III.) Randomcent
1.) select some dailypatterns by randomas key day
2.) assign each dayto the most similarkey day
3.) do that 1000times and comparethose to the deliberate methods
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III.) Randomcent: Explained variance of 2mT
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III.) Randomcent: Explained variance of PRC
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Counting 95th percentile exceedencefor 2mT in all seasons and all domains
CAPo PXKPXKoPTSoLNDPCTKRZ
LWToLIT
LIToWLK
GWT
LIT LITo PTSo CAPoKRZ
LITLITo
WLKWLKo
PTSoCAPo
~09types
~18types
~27types
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~09types
~18types
~27types
Counting 95th percentile exceedencefor PRC in all seasons and all domains
PXEPXEo
LWTo PXK PXKoCAPo
LWTo PXK PXKoCAPo
CAPo
JCTGWT
LWTo
PXE
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III.) Randomcent
→ A benchmark method for classifications
→ A method for testing classification tuningparameters excluding the (possibly random)influence of the classification algorithm:
Many (1000) realizations of RAC allow a robust estimatewhether a certain tuning parameter is good in generalor only by chance!
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IV.) RAC for testingclassification tuning parameters
RAC-variants done with cost733class software:
- meteorological parameter (input variables)- PCA preprocessing (scores)- time filtering of input data (high-/low-pass)- sequence length (1-10-day sequences)- distance metrics (Euclid, Pearson, Minkowski)
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RAC testing meteorological parameter for 2mT D07
Thickness 500-850
Z500
Z300
Vort500SLP
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RAC testing meteorological parameter for PRC D07
SLP
Thickness 500-850
Z500
Z300
Vort500
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RAC testing number of PCs for 2mT D06
3PCs2PCs
4PCs5PCs
1PCs
NoPCs
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RAC testing number of PCs for PRC D06
3PCs
2PCs
4PCs
1PC NoPCs
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RAC testing time filter period for 2mT (Yea) D06
No filter
Low-Pass 5-15 days
High-Pass 5days
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RAC testing time filter period for 2mT (DJF) D07
Low Pass 5-15 days
No filterLow-Pass 5 days
High-Pass 90 days
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RAC testing sequence length for 2mT D07
S02
S03
S04S01
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RAC testing distance metrics for PRC D06
Euclidean
Manhatten
Chebychev Pearson correlation
Minkowski3 to 9
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Conclusions- we haven't found/developed the ultimate classification
- but from the methodological point of view we have now much more insight into classification procedures
- for applications it seems not possible to highlight single method RAC can be used as benchmark
- data preprocessig/selection is more important, testing is simplified by cost733class software, RAC shows systematic rules
- optimum parameter search in conditioned classification still has a large potential to improve applications → Downscaling
- further developments should distinguish between pure circulation classifications for basic (regime) research and conditioned weather type classification for application purposes
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Classification tuning forapplications in synoptic climatologyOPS Optimal Parameter selection→ not only optimize assignment of days to classes
for optimizing downscaling skill but
→ also optimize:→ number of classes→ weight of grid points→ weight of parameters→ sequences→ time filtering
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Bump-hunting for regimesin the PDF-plane→ Circulation
Dynamics