Imperial College London
Alternate

Professor Joanna D. Haigh

Faculty of Natural Sciences, Department of Physics

Distinguished Research Fellow
 
 
 
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Contact

 

+44 (0)20 7594 7770j.haigh Website

 
 
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Assistant

 

Mr Luke Kratzmann +44 (0)20 7594 7770

 
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Location

 

Blackett LaboratorySouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Thomas:2021:10.5194/wcd-2021-1,
author = {Thomas, C and Voulgarakis, A and Lim, G and Haigh, J and Nowack, P},
doi = {10.5194/wcd-2021-1},
journal = {Weather and Climate Dynamics},
title = {An unsupervised learning approach to identifying blocking events:the case of European summer},
url = {http://dx.doi.org/10.5194/wcd-2021-1},
volume = {2},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Atmospheric blocking events are mid-latitudeweather patterns, which obstruct the usual path of the polar jet streams. They are often associated with heat wavesin summer and cold snaps in winter. Despite being centralfeatures of mid-latitude synoptic-scale weather, there is nowell-defined historical dataset of blocking events. Variousblocking indices (BIs) have thus been suggested for automatically identifying blocking events in observational and inclimate model data. However, BIs show significant regionaland seasonal differences so that several indices are typicallyapplied in combination to ensure scientific robustness. Here,we introduce a new BI using self-organizing maps (SOMs),an unsupervised machine learning approach, and compare itsdetection skill to some of the most widely applied BIs. Toenable this intercomparison, we first create a new groundtruth time series classification of European blocking basedon expert judgement. We then demonstrate that our method(SOM-BI) has several key advantages over previous BIs because it exploits all of the spatial information provided in theinput data and reduces the dependence on arbitrary thresholds. Using ERA5 reanalysis data (1979–2019), we find thatthe SOM-BI identifies blocking events with a higher precision and recall than other BIs. In particular, SOM-BI alreadyperforms well using only around 20 years of training data sothat observational records are long enough to train our newmethod. We present case studies of the 2003 and 2019 European heat waves and highlight that well-defined groups ofSOM nodes can be an effective tool to diagnose such weatherevents, although the domain-based approach can still lead toerrors in the identification of certain events in a fashion similar to the other BIs. We further test the red blocking detectionskill of SOM-BI depending on the meteorological variableused to study blocking, including geopotential height, sealevel pressure and four variables related to potential vorticity,and t
AU  - Thomas,C
AU  - Voulgarakis,A
AU  - Lim,G
AU  - Haigh,J
AU  - Nowack,P
DO  - 10.5194/wcd-2021-1
PY  - 2021///
SN  - 2698-4016
TI  - An unsupervised learning approach to identifying blocking events:the case of European summer
T2  - Weather and Climate Dynamics
UR  - http://dx.doi.org/10.5194/wcd-2021-1
UR  - https://wcd.copernicus.org/articles/2/581/2021/
UR  - http://hdl.handle.net/10044/1/96463
VL  - 2
ER  -
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