Sub-seasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models
Heatwaves are extreme near-surface temperature events that can have substantial impacts on ecosystems and society. Early Warning Systems help to reduce these impacts by helping communities prepare for hazardous climate-related events. However, state-of-the-art prediction systems can often not make a...
Published in: | Artificial Intelligence for the Earth Systems |
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Online Access: | https://serval.unil.ch/notice/serval:BIB_E00CAD4A5128 https://doi.org/10.1175/aies-d-22-0038.1 https://serval.unil.ch/resource/serval:BIB_E00CAD4A5128.P001/REF.pdf http://nbn-resolving.org/urn/resolver.pl?urn=urn:nbn:ch:serval-BIB_E00CAD4A51281 |
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ftunivlausanne:oai:serval.unil.ch:BIB_E00CAD4A5128 2024-02-11T10:06:17+01:00 Sub-seasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models Weirich Benet, Elizabeth Pyrina, Maria Jiménez-Esteve, Bernat Fraenkel, Ernest Cohen, Judah Domeisen, Daniela I.V. 2023-01-09 application/pdf https://serval.unil.ch/notice/serval:BIB_E00CAD4A5128 https://doi.org/10.1175/aies-d-22-0038.1 https://serval.unil.ch/resource/serval:BIB_E00CAD4A5128.P001/REF.pdf http://nbn-resolving.org/urn/resolver.pl?urn=urn:nbn:ch:serval-BIB_E00CAD4A51281 eng eng info:eu-repo/semantics/altIdentifier/doi/10.1175/aies-d-22-0038.1 info:eu-repo/semantics/altIdentifier/pissn/2769-7525 info:eu-repo/grantAgreement/SNF//PP00P2_170523/// info:eu-repo/grantAgreement/SNF//PP00P2_198896/// info:eu-repo/grantAgreement/ERC//847456/// info:eu-repo/grantAgreement/OTHER//AGS-1657748, PLR-1901352, and ARCSS-2115068/// info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:serval-BIB_E00CAD4A51281 https://serval.unil.ch/notice/serval:BIB_E00CAD4A5128 doi:10.1175/aies-d-22-0038.1 https://serval.unil.ch/resource/serval:BIB_E00CAD4A5128.P001/REF.pdf http://nbn-resolving.org/urn/resolver.pl?urn=urn:nbn:ch:serval-BIB_E00CAD4A51281 info:eu-repo/semantics/openAccess CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Artificial Intelligence for the Earth Systems, pp. 1-52 info:eu-repo/semantics/article article info:eu-repo/semantics/acceptedVersion 2023 ftunivlausanne https://doi.org/10.1175/aies-d-22-0038.1 2024-01-22T00:56:58Z Heatwaves are extreme near-surface temperature events that can have substantial impacts on ecosystems and society. Early Warning Systems help to reduce these impacts by helping communities prepare for hazardous climate-related events. However, state-of-the-art prediction systems can often not make accurate forecasts of heatwaves more than two weeks in advance, which are required for advance warnings. We therefore investigate the potential of statistical and machine learning methods to understand and predict central European summer heatwaves on timescales of several weeks. As a first step, we identify the most important regional atmospheric and surface predictors based on previous studies and supported by a correlation analysis: 2-m air temperature, 500-hPa geopotential, precipitation, and soil moisture in central Europe, as well as Mediterranean and North Atlantic sea surface temperatures, and the North Atlantic jet stream. Based on these predictors, we apply machine learning methods to forecast two targets: summer temperature anomalies and the probability of heatwaves for 1–6 weeks lead time at weekly resolution. For each of these two target variables, we use both a linear and a random forest model. The performance of these statistical models decays with lead time, as expected, but outperforms persistence and climatology at all lead times. For lead times longer than two weeks, our machine learning models compete with the ensemble mean of the European Centre for Medium-Range Weather Forecasts’ hindcast system. We thus show that machine learning can help improve sub-seasonal forecasts of summer temperature anomalies and heatwaves. Article in Journal/Newspaper North Atlantic Université de Lausanne (UNIL): Serval - Serveur académique lausannois Artificial Intelligence for the Earth Systems 1 52 |
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Open Polar |
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Université de Lausanne (UNIL): Serval - Serveur académique lausannois |
op_collection_id |
ftunivlausanne |
language |
English |
description |
Heatwaves are extreme near-surface temperature events that can have substantial impacts on ecosystems and society. Early Warning Systems help to reduce these impacts by helping communities prepare for hazardous climate-related events. However, state-of-the-art prediction systems can often not make accurate forecasts of heatwaves more than two weeks in advance, which are required for advance warnings. We therefore investigate the potential of statistical and machine learning methods to understand and predict central European summer heatwaves on timescales of several weeks. As a first step, we identify the most important regional atmospheric and surface predictors based on previous studies and supported by a correlation analysis: 2-m air temperature, 500-hPa geopotential, precipitation, and soil moisture in central Europe, as well as Mediterranean and North Atlantic sea surface temperatures, and the North Atlantic jet stream. Based on these predictors, we apply machine learning methods to forecast two targets: summer temperature anomalies and the probability of heatwaves for 1–6 weeks lead time at weekly resolution. For each of these two target variables, we use both a linear and a random forest model. The performance of these statistical models decays with lead time, as expected, but outperforms persistence and climatology at all lead times. For lead times longer than two weeks, our machine learning models compete with the ensemble mean of the European Centre for Medium-Range Weather Forecasts’ hindcast system. We thus show that machine learning can help improve sub-seasonal forecasts of summer temperature anomalies and heatwaves. |
format |
Article in Journal/Newspaper |
author |
Weirich Benet, Elizabeth Pyrina, Maria Jiménez-Esteve, Bernat Fraenkel, Ernest Cohen, Judah Domeisen, Daniela I.V. |
spellingShingle |
Weirich Benet, Elizabeth Pyrina, Maria Jiménez-Esteve, Bernat Fraenkel, Ernest Cohen, Judah Domeisen, Daniela I.V. Sub-seasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models |
author_facet |
Weirich Benet, Elizabeth Pyrina, Maria Jiménez-Esteve, Bernat Fraenkel, Ernest Cohen, Judah Domeisen, Daniela I.V. |
author_sort |
Weirich Benet, Elizabeth |
title |
Sub-seasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models |
title_short |
Sub-seasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models |
title_full |
Sub-seasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models |
title_fullStr |
Sub-seasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models |
title_full_unstemmed |
Sub-seasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models |
title_sort |
sub-seasonal prediction of central european summer heatwaves with linear and random forest machine learning models |
publishDate |
2023 |
url |
https://serval.unil.ch/notice/serval:BIB_E00CAD4A5128 https://doi.org/10.1175/aies-d-22-0038.1 https://serval.unil.ch/resource/serval:BIB_E00CAD4A5128.P001/REF.pdf http://nbn-resolving.org/urn/resolver.pl?urn=urn:nbn:ch:serval-BIB_E00CAD4A51281 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Artificial Intelligence for the Earth Systems, pp. 1-52 |
op_relation |
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op_rights |
info:eu-repo/semantics/openAccess CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1175/aies-d-22-0038.1 |
container_title |
Artificial Intelligence for the Earth Systems |
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1 |
op_container_end_page |
52 |
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1790603908093050880 |