Outcomes of the WMO Prize Challenge to Improve Subseasonal to Seasonal Predictions Using Artificial Intelligence
There is a high demand and expectation for subseasonal to seasonal (S2S) prediction, which provides forecasts beyond 2 weeks, but less than 3 months ahead. To assess the potential benefit of artificial intelligence (AI) methods for S2S prediction through better postprocessing of ensemble prediction...
Gespeichert in:
| Veröffentlicht in: | Bulletin of the American Meteorological Society 2022-12, Vol.103 (12), p.E2878-E2886 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | E2886 |
|---|---|
| container_issue | 12 |
| container_start_page | E2878 |
| container_title | Bulletin of the American Meteorological Society |
| container_volume | 103 |
| creator | Vitart, F. Robertson, A. W. Spring, A. Pinault, F. Roškar, R. Cao, W. Bech, S. Bienkowski, A. Caltabiano, N. De Coning, E. Denis, B. Dirkson, A. Dramsch, J. Dueben, P. Gierschendorf, J. Kim, H. S. Nowak, K. Landry, D. Lledó, L. Palma, L. Rasp, S. Zhou, S. |
| description | There is a high demand and expectation for subseasonal to seasonal (S2S) prediction, which provides forecasts beyond 2 weeks, but less than 3 months ahead. To assess the potential benefit of artificial intelligence (AI) methods for S2S prediction through better postprocessing of ensemble prediction system outputs, the World Meteorological Organization (WMO) coordinated a prize challenge in 2021 to improve subseasonal prediction. The goal of this competition was to produce the most skillful forecasts of precipitation and 2-m temperature globally averaged over forecast weeks 3 and 4 and over weeks 5 and 6 for the year 2020 using artificial intelligence techniques. The top three submissions, described in this article, succeeded in producing S2S forecasts significantly more skillful than the bias-corrected ECMWF operational reference forecasts, particularly for precipitation, through improved calibration of the ECMWF raw forecast outputs or multimodel combination. These forecast improvements should benefit the use of S2S forecasts in applications. |
| doi_str_mv | 10.1175/BAMS-D-22-0046.1 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2807030624</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2807030624</sourcerecordid><originalsourceid>FETCH-LOGICAL-c313t-28d9260573441e423c5dae6678952a23be703c8d55aa2bdbc5506c197dcb70663</originalsourceid><addsrcrecordid>eNo1kN1PwjAUxRujiYi--9jE5-Ftu67jEcEPEggkSHxsuu4OSsaG7Waif71b0Kf7cc69OfkRcs9gxJiSj0-T5SaaRZxHAHEyYhdkwCSHCGKlLskAAESngLomNyEc-lGkbEBOq7ax9REDrQva7JF-LFd07d0P0unelCVWO6RNTefHk6-_kG7aLKAJdWXKfr3579cec2cbV1eBboOrdnTiG1c46zpxXjVYlm6HlcVbclWYMuDdXx2S7cvz-_QtWqxe59PJIrKCiSbiaT7mCUgl4phhzIWVucEkUelYcsNFhgqETXMpjeFZnlkpIbFsrHKbKUgSMSQP579d7s8WQ6MPdeu7qEHzFLpjSHjcueDssr4OwWOhT94djf_WDHTPVfdc9Uxzrnuumolfl_RrYw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2807030624</pqid></control><display><type>article</type><title>Outcomes of the WMO Prize Challenge to Improve Subseasonal to Seasonal Predictions Using Artificial Intelligence</title><source>American Meteorological Society</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Vitart, F. ; Robertson, A. W. ; Spring, A. ; Pinault, F. ; Roškar, R. ; Cao, W. ; Bech, S. ; Bienkowski, A. ; Caltabiano, N. ; De Coning, E. ; Denis, B. ; Dirkson, A. ; Dramsch, J. ; Dueben, P. ; Gierschendorf, J. ; Kim, H. S. ; Nowak, K. ; Landry, D. ; Lledó, L. ; Palma, L. ; Rasp, S. ; Zhou, S.</creator><creatorcontrib>Vitart, F. ; Robertson, A. W. ; Spring, A. ; Pinault, F. ; Roškar, R. ; Cao, W. ; Bech, S. ; Bienkowski, A. ; Caltabiano, N. ; De Coning, E. ; Denis, B. ; Dirkson, A. ; Dramsch, J. ; Dueben, P. ; Gierschendorf, J. ; Kim, H. S. ; Nowak, K. ; Landry, D. ; Lledó, L. ; Palma, L. ; Rasp, S. ; Zhou, S.</creatorcontrib><description>There is a high demand and expectation for subseasonal to seasonal (S2S) prediction, which provides forecasts beyond 2 weeks, but less than 3 months ahead. To assess the potential benefit of artificial intelligence (AI) methods for S2S prediction through better postprocessing of ensemble prediction system outputs, the World Meteorological Organization (WMO) coordinated a prize challenge in 2021 to improve subseasonal prediction. The goal of this competition was to produce the most skillful forecasts of precipitation and 2-m temperature globally averaged over forecast weeks 3 and 4 and over weeks 5 and 6 for the year 2020 using artificial intelligence techniques. The top three submissions, described in this article, succeeded in producing S2S forecasts significantly more skillful than the bias-corrected ECMWF operational reference forecasts, particularly for precipitation, through improved calibration of the ECMWF raw forecast outputs or multimodel combination. These forecast improvements should benefit the use of S2S forecasts in applications.</description><identifier>ISSN: 0003-0007</identifier><identifier>EISSN: 1520-0477</identifier><identifier>DOI: 10.1175/BAMS-D-22-0046.1</identifier><language>eng</language><publisher>Boston: American Meteorological Society</publisher><subject>Artificial intelligence ; Climate change ; Collaboration ; Data science ; Meteorological satellites ; Neural networks ; Precipitation ; Predictions ; Software ; Weather ; Weather forecasting</subject><ispartof>Bulletin of the American Meteorological Society, 2022-12, Vol.103 (12), p.E2878-E2886</ispartof><rights>Copyright American Meteorological Society 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c313t-28d9260573441e423c5dae6678952a23be703c8d55aa2bdbc5506c197dcb70663</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,781,785,3682,27929,27930</link.rule.ids></links><search><creatorcontrib>Vitart, F.</creatorcontrib><creatorcontrib>Robertson, A. W.</creatorcontrib><creatorcontrib>Spring, A.</creatorcontrib><creatorcontrib>Pinault, F.</creatorcontrib><creatorcontrib>Roškar, R.</creatorcontrib><creatorcontrib>Cao, W.</creatorcontrib><creatorcontrib>Bech, S.</creatorcontrib><creatorcontrib>Bienkowski, A.</creatorcontrib><creatorcontrib>Caltabiano, N.</creatorcontrib><creatorcontrib>De Coning, E.</creatorcontrib><creatorcontrib>Denis, B.</creatorcontrib><creatorcontrib>Dirkson, A.</creatorcontrib><creatorcontrib>Dramsch, J.</creatorcontrib><creatorcontrib>Dueben, P.</creatorcontrib><creatorcontrib>Gierschendorf, J.</creatorcontrib><creatorcontrib>Kim, H. S.</creatorcontrib><creatorcontrib>Nowak, K.</creatorcontrib><creatorcontrib>Landry, D.</creatorcontrib><creatorcontrib>Lledó, L.</creatorcontrib><creatorcontrib>Palma, L.</creatorcontrib><creatorcontrib>Rasp, S.</creatorcontrib><creatorcontrib>Zhou, S.</creatorcontrib><title>Outcomes of the WMO Prize Challenge to Improve Subseasonal to Seasonal Predictions Using Artificial Intelligence</title><title>Bulletin of the American Meteorological Society</title><description>There is a high demand and expectation for subseasonal to seasonal (S2S) prediction, which provides forecasts beyond 2 weeks, but less than 3 months ahead. To assess the potential benefit of artificial intelligence (AI) methods for S2S prediction through better postprocessing of ensemble prediction system outputs, the World Meteorological Organization (WMO) coordinated a prize challenge in 2021 to improve subseasonal prediction. The goal of this competition was to produce the most skillful forecasts of precipitation and 2-m temperature globally averaged over forecast weeks 3 and 4 and over weeks 5 and 6 for the year 2020 using artificial intelligence techniques. The top three submissions, described in this article, succeeded in producing S2S forecasts significantly more skillful than the bias-corrected ECMWF operational reference forecasts, particularly for precipitation, through improved calibration of the ECMWF raw forecast outputs or multimodel combination. These forecast improvements should benefit the use of S2S forecasts in applications.</description><subject>Artificial intelligence</subject><subject>Climate change</subject><subject>Collaboration</subject><subject>Data science</subject><subject>Meteorological satellites</subject><subject>Neural networks</subject><subject>Precipitation</subject><subject>Predictions</subject><subject>Software</subject><subject>Weather</subject><subject>Weather forecasting</subject><issn>0003-0007</issn><issn>1520-0477</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNo1kN1PwjAUxRujiYi--9jE5-Ftu67jEcEPEggkSHxsuu4OSsaG7Waif71b0Kf7cc69OfkRcs9gxJiSj0-T5SaaRZxHAHEyYhdkwCSHCGKlLskAAESngLomNyEc-lGkbEBOq7ax9REDrQva7JF-LFd07d0P0unelCVWO6RNTefHk6-_kG7aLKAJdWXKfr3579cec2cbV1eBboOrdnTiG1c46zpxXjVYlm6HlcVbclWYMuDdXx2S7cvz-_QtWqxe59PJIrKCiSbiaT7mCUgl4phhzIWVucEkUelYcsNFhgqETXMpjeFZnlkpIbFsrHKbKUgSMSQP579d7s8WQ6MPdeu7qEHzFLpjSHjcueDssr4OwWOhT94djf_WDHTPVfdc9Uxzrnuumolfl_RrYw</recordid><startdate>20221201</startdate><enddate>20221201</enddate><creator>Vitart, F.</creator><creator>Robertson, A. W.</creator><creator>Spring, A.</creator><creator>Pinault, F.</creator><creator>Roškar, R.</creator><creator>Cao, W.</creator><creator>Bech, S.</creator><creator>Bienkowski, A.</creator><creator>Caltabiano, N.</creator><creator>De Coning, E.</creator><creator>Denis, B.</creator><creator>Dirkson, A.</creator><creator>Dramsch, J.</creator><creator>Dueben, P.</creator><creator>Gierschendorf, J.</creator><creator>Kim, H. S.</creator><creator>Nowak, K.</creator><creator>Landry, D.</creator><creator>Lledó, L.</creator><creator>Palma, L.</creator><creator>Rasp, S.</creator><creator>Zhou, S.</creator><general>American Meteorological Society</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QH</scope><scope>7TG</scope><scope>7TN</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8AF</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>M2O</scope><scope>M2P</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>R05</scope><scope>S0X</scope></search><sort><creationdate>20221201</creationdate><title>Outcomes of the WMO Prize Challenge to Improve Subseasonal to Seasonal Predictions Using Artificial Intelligence</title><author>Vitart, F. ; Robertson, A. W. ; Spring, A. ; Pinault, F. ; Roškar, R. ; Cao, W. ; Bech, S. ; Bienkowski, A. ; Caltabiano, N. ; De Coning, E. ; Denis, B. ; Dirkson, A. ; Dramsch, J. ; Dueben, P. ; Gierschendorf, J. ; Kim, H. S. ; Nowak, K. ; Landry, D. ; Lledó, L. ; Palma, L. ; Rasp, S. ; Zhou, S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c313t-28d9260573441e423c5dae6678952a23be703c8d55aa2bdbc5506c197dcb70663</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Artificial intelligence</topic><topic>Climate change</topic><topic>Collaboration</topic><topic>Data science</topic><topic>Meteorological satellites</topic><topic>Neural networks</topic><topic>Precipitation</topic><topic>Predictions</topic><topic>Software</topic><topic>Weather</topic><topic>Weather forecasting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Vitart, F.</creatorcontrib><creatorcontrib>Robertson, A. W.</creatorcontrib><creatorcontrib>Spring, A.</creatorcontrib><creatorcontrib>Pinault, F.</creatorcontrib><creatorcontrib>Roškar, R.</creatorcontrib><creatorcontrib>Cao, W.</creatorcontrib><creatorcontrib>Bech, S.</creatorcontrib><creatorcontrib>Bienkowski, A.</creatorcontrib><creatorcontrib>Caltabiano, N.</creatorcontrib><creatorcontrib>De Coning, E.</creatorcontrib><creatorcontrib>Denis, B.</creatorcontrib><creatorcontrib>Dirkson, A.</creatorcontrib><creatorcontrib>Dramsch, J.</creatorcontrib><creatorcontrib>Dueben, P.</creatorcontrib><creatorcontrib>Gierschendorf, J.</creatorcontrib><creatorcontrib>Kim, H. S.</creatorcontrib><creatorcontrib>Nowak, K.</creatorcontrib><creatorcontrib>Landry, D.</creatorcontrib><creatorcontrib>Lledó, L.</creatorcontrib><creatorcontrib>Palma, L.</creatorcontrib><creatorcontrib>Rasp, S.</creatorcontrib><creatorcontrib>Zhou, S.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Aqualine</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>STEM Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>eLibrary</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Research Library</collection><collection>Science Database</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>University of Michigan</collection><collection>SIRS Editorial</collection><jtitle>Bulletin of the American Meteorological Society</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Vitart, F.</au><au>Robertson, A. W.</au><au>Spring, A.</au><au>Pinault, F.</au><au>Roškar, R.</au><au>Cao, W.</au><au>Bech, S.</au><au>Bienkowski, A.</au><au>Caltabiano, N.</au><au>De Coning, E.</au><au>Denis, B.</au><au>Dirkson, A.</au><au>Dramsch, J.</au><au>Dueben, P.</au><au>Gierschendorf, J.</au><au>Kim, H. S.</au><au>Nowak, K.</au><au>Landry, D.</au><au>Lledó, L.</au><au>Palma, L.</au><au>Rasp, S.</au><au>Zhou, S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Outcomes of the WMO Prize Challenge to Improve Subseasonal to Seasonal Predictions Using Artificial Intelligence</atitle><jtitle>Bulletin of the American Meteorological Society</jtitle><date>2022-12-01</date><risdate>2022</risdate><volume>103</volume><issue>12</issue><spage>E2878</spage><epage>E2886</epage><pages>E2878-E2886</pages><issn>0003-0007</issn><eissn>1520-0477</eissn><abstract>There is a high demand and expectation for subseasonal to seasonal (S2S) prediction, which provides forecasts beyond 2 weeks, but less than 3 months ahead. To assess the potential benefit of artificial intelligence (AI) methods for S2S prediction through better postprocessing of ensemble prediction system outputs, the World Meteorological Organization (WMO) coordinated a prize challenge in 2021 to improve subseasonal prediction. The goal of this competition was to produce the most skillful forecasts of precipitation and 2-m temperature globally averaged over forecast weeks 3 and 4 and over weeks 5 and 6 for the year 2020 using artificial intelligence techniques. The top three submissions, described in this article, succeeded in producing S2S forecasts significantly more skillful than the bias-corrected ECMWF operational reference forecasts, particularly for precipitation, through improved calibration of the ECMWF raw forecast outputs or multimodel combination. These forecast improvements should benefit the use of S2S forecasts in applications.</abstract><cop>Boston</cop><pub>American Meteorological Society</pub><doi>10.1175/BAMS-D-22-0046.1</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0003-0007 |
| ispartof | Bulletin of the American Meteorological Society, 2022-12, Vol.103 (12), p.E2878-E2886 |
| issn | 0003-0007 1520-0477 |
| language | eng |
| recordid | cdi_proquest_journals_2807030624 |
| source | American Meteorological Society; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Artificial intelligence Climate change Collaboration Data science Meteorological satellites Neural networks Precipitation Predictions Software Weather Weather forecasting |
| title | Outcomes of the WMO Prize Challenge to Improve Subseasonal to Seasonal Predictions Using Artificial Intelligence |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-13T04%3A54%3A33IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Outcomes%20of%20the%20WMO%20Prize%20Challenge%20to%20Improve%20Subseasonal%20to%20Seasonal%20Predictions%20Using%20Artificial%20Intelligence&rft.jtitle=Bulletin%20of%20the%20American%20Meteorological%20Society&rft.au=Vitart,%20F.&rft.date=2022-12-01&rft.volume=103&rft.issue=12&rft.spage=E2878&rft.epage=E2886&rft.pages=E2878-E2886&rft.issn=0003-0007&rft.eissn=1520-0477&rft_id=info:doi/10.1175/BAMS-D-22-0046.1&rft_dat=%3Cproquest_cross%3E2807030624%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2807030624&rft_id=info:pmid/&rfr_iscdi=true |