Evaluation of Potential Predictability of Indian Summer Monsoon Rainfall in ECMWF's Fifth-Generation Seasonal Forecast System (SEAS5)
Forecasts of Indian summer monsoon rainfall (ISMR: June to September, JJAS) are issued prior to the onset of rainy season. Thus, an assessment of both potential and actual forecast skills for Indian summer monsoon rainfall should be based on a longer lead time. Based upon the European Center for Med...
Gespeichert in:
| Veröffentlicht in: | Pure and applied geophysics 2022-12, Vol.179 (12), p.4639-4655 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 4655 |
|---|---|
| container_issue | 12 |
| container_start_page | 4639 |
| container_title | Pure and applied geophysics |
| container_volume | 179 |
| creator | Attada, Raju Ehsan, Muhammad Azhar Pillai, Prasanth A. |
| description | Forecasts of Indian summer monsoon rainfall (ISMR: June to September, JJAS) are issued prior to the onset of rainy season. Thus, an assessment of both potential and actual forecast skills for Indian summer monsoon rainfall should be based on a longer lead time. Based upon the European Center for Medium Range Weather Forecasts (ECMWF) fifth-generation seasonal forecast system (SEAS5), two lead times are considered: one with an April initial condition (IC) and the other with a May IC from 1981 through 2019 (39 years). Our results show that SEAS5 successfully represents the spatial patterns and variations in the mean JJAS precipitation in the ISMR region compared with the observed rainfall patterns. However, there seem to be significant discrepancies in the simulation of mean precipitation, particularly over topographical regions. SEAS5 is capable of reproducing the observed annual precipitation cycle in India. Moreover, the model is able to better predict the realistic ISMR teleconnections with El Niño-Southern Oscillation and the Indian Ocean Dipole at May ICs. The resulting forecasts across the region are characterized by moderate significant potential and actual skill in both leads, and it decreases as lead time increases. The predictability of SEAS5 is directly related to its ability to correctly predict the forcing of the tropical sea surface temperature and its teleconnections. In spite of this, both lead forecasts have a significant number of unpredicted events and false alarms. This study highlights model discrepancies, shows poor performance in predicting ISMR, and highlights the need for further research on this crucial issue of social relevance. |
| doi_str_mv | 10.1007/s00024-022-03184-9 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2755976423</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2755976423</sourcerecordid><originalsourceid>FETCH-LOGICAL-c249t-a400b0566cc97a4c84ef05a6ed7b54a1be5eb96cd02bf68329b3c28c45b8317b3</originalsourceid><addsrcrecordid>eNp9kE9LHDEYh4NU6Fb9Aj0FerA9RN_8m5kcZdlVQak4lh5Dks3YyGyiSbawH6Dfu6Mj9NZTDu_veQgPQp8pnFGA9rwAABMEGCPAaSeIOkALKhgQRXnzAS0AOCdCSv4RfSrlCYC2rVQL9Gf124w7U0OKOA34LlUfazAjvst-E1w1Noyh7l9v13ETTMT9brv1Gd-mWNIE3ZsQBzOOOES8Wt7-XJ8WvA5D_UUuffR5NvfelBQn6zpl70ypuN-X6rf4a7-66OW3Y3Q4OYo_eX-P0I_16mF5RW6-X14vL26IY0JVYgSABdk0zqnWCNcJP4A0jd-0VgpDrZfeqsZtgNmh6ThTljvWOSFtx2lr-RH6Mnufc3rZ-VL1U9rl6WNFs1ZK1TaC8WnF5pXLqZTsB_2cw9bkvaagX3PrObeecuu33FpNEJ-hMo3jo8__1P-h_gI9TYL7</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2755976423</pqid></control><display><type>article</type><title>Evaluation of Potential Predictability of Indian Summer Monsoon Rainfall in ECMWF's Fifth-Generation Seasonal Forecast System (SEAS5)</title><source>SpringerLink Journals</source><creator>Attada, Raju ; Ehsan, Muhammad Azhar ; Pillai, Prasanth A.</creator><creatorcontrib>Attada, Raju ; Ehsan, Muhammad Azhar ; Pillai, Prasanth A.</creatorcontrib><description>Forecasts of Indian summer monsoon rainfall (ISMR: June to September, JJAS) are issued prior to the onset of rainy season. Thus, an assessment of both potential and actual forecast skills for Indian summer monsoon rainfall should be based on a longer lead time. Based upon the European Center for Medium Range Weather Forecasts (ECMWF) fifth-generation seasonal forecast system (SEAS5), two lead times are considered: one with an April initial condition (IC) and the other with a May IC from 1981 through 2019 (39 years). Our results show that SEAS5 successfully represents the spatial patterns and variations in the mean JJAS precipitation in the ISMR region compared with the observed rainfall patterns. However, there seem to be significant discrepancies in the simulation of mean precipitation, particularly over topographical regions. SEAS5 is capable of reproducing the observed annual precipitation cycle in India. Moreover, the model is able to better predict the realistic ISMR teleconnections with El Niño-Southern Oscillation and the Indian Ocean Dipole at May ICs. The resulting forecasts across the region are characterized by moderate significant potential and actual skill in both leads, and it decreases as lead time increases. The predictability of SEAS5 is directly related to its ability to correctly predict the forcing of the tropical sea surface temperature and its teleconnections. In spite of this, both lead forecasts have a significant number of unpredicted events and false alarms. This study highlights model discrepancies, shows poor performance in predicting ISMR, and highlights the need for further research on this crucial issue of social relevance.</description><identifier>ISSN: 0033-4553</identifier><identifier>EISSN: 1420-9136</identifier><identifier>DOI: 10.1007/s00024-022-03184-9</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Annual precipitation ; Dipoles ; Earth and Environmental Science ; Earth Sciences ; El Nino ; El Nino phenomena ; False alarms ; Geophysics/Geodesy ; Lead time ; Mathematical models ; Mean precipitation ; Monsoon rainfall ; Monsoons ; Performance prediction ; Precipitation ; Rain ; Rainfall ; Rainfall forecasting ; Rainfall patterns ; Rainy season ; Sea surface ; Sea surface temperature ; Seasonal forecasting ; Southern Oscillation ; Summer ; Summer monsoon ; Surface temperature ; Teleconnections ; Weather forecasting ; Wet season ; Wind</subject><ispartof>Pure and applied geophysics, 2022-12, Vol.179 (12), p.4639-4655</ispartof><rights>The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c249t-a400b0566cc97a4c84ef05a6ed7b54a1be5eb96cd02bf68329b3c28c45b8317b3</citedby><cites>FETCH-LOGICAL-c249t-a400b0566cc97a4c84ef05a6ed7b54a1be5eb96cd02bf68329b3c28c45b8317b3</cites><orcidid>0000-0002-6194-4561</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00024-022-03184-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00024-022-03184-9$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Attada, Raju</creatorcontrib><creatorcontrib>Ehsan, Muhammad Azhar</creatorcontrib><creatorcontrib>Pillai, Prasanth A.</creatorcontrib><title>Evaluation of Potential Predictability of Indian Summer Monsoon Rainfall in ECMWF's Fifth-Generation Seasonal Forecast System (SEAS5)</title><title>Pure and applied geophysics</title><addtitle>Pure Appl. Geophys</addtitle><description>Forecasts of Indian summer monsoon rainfall (ISMR: June to September, JJAS) are issued prior to the onset of rainy season. Thus, an assessment of both potential and actual forecast skills for Indian summer monsoon rainfall should be based on a longer lead time. Based upon the European Center for Medium Range Weather Forecasts (ECMWF) fifth-generation seasonal forecast system (SEAS5), two lead times are considered: one with an April initial condition (IC) and the other with a May IC from 1981 through 2019 (39 years). Our results show that SEAS5 successfully represents the spatial patterns and variations in the mean JJAS precipitation in the ISMR region compared with the observed rainfall patterns. However, there seem to be significant discrepancies in the simulation of mean precipitation, particularly over topographical regions. SEAS5 is capable of reproducing the observed annual precipitation cycle in India. Moreover, the model is able to better predict the realistic ISMR teleconnections with El Niño-Southern Oscillation and the Indian Ocean Dipole at May ICs. The resulting forecasts across the region are characterized by moderate significant potential and actual skill in both leads, and it decreases as lead time increases. The predictability of SEAS5 is directly related to its ability to correctly predict the forcing of the tropical sea surface temperature and its teleconnections. In spite of this, both lead forecasts have a significant number of unpredicted events and false alarms. This study highlights model discrepancies, shows poor performance in predicting ISMR, and highlights the need for further research on this crucial issue of social relevance.</description><subject>Annual precipitation</subject><subject>Dipoles</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>El Nino</subject><subject>El Nino phenomena</subject><subject>False alarms</subject><subject>Geophysics/Geodesy</subject><subject>Lead time</subject><subject>Mathematical models</subject><subject>Mean precipitation</subject><subject>Monsoon rainfall</subject><subject>Monsoons</subject><subject>Performance prediction</subject><subject>Precipitation</subject><subject>Rain</subject><subject>Rainfall</subject><subject>Rainfall forecasting</subject><subject>Rainfall patterns</subject><subject>Rainy season</subject><subject>Sea surface</subject><subject>Sea surface temperature</subject><subject>Seasonal forecasting</subject><subject>Southern Oscillation</subject><subject>Summer</subject><subject>Summer monsoon</subject><subject>Surface temperature</subject><subject>Teleconnections</subject><subject>Weather forecasting</subject><subject>Wet season</subject><subject>Wind</subject><issn>0033-4553</issn><issn>1420-9136</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kE9LHDEYh4NU6Fb9Aj0FerA9RN_8m5kcZdlVQak4lh5Dks3YyGyiSbawH6Dfu6Mj9NZTDu_veQgPQp8pnFGA9rwAABMEGCPAaSeIOkALKhgQRXnzAS0AOCdCSv4RfSrlCYC2rVQL9Gf124w7U0OKOA34LlUfazAjvst-E1w1Noyh7l9v13ETTMT9brv1Gd-mWNIE3ZsQBzOOOES8Wt7-XJ8WvA5D_UUuffR5NvfelBQn6zpl70ypuN-X6rf4a7-66OW3Y3Q4OYo_eX-P0I_16mF5RW6-X14vL26IY0JVYgSABdk0zqnWCNcJP4A0jd-0VgpDrZfeqsZtgNmh6ThTljvWOSFtx2lr-RH6Mnufc3rZ-VL1U9rl6WNFs1ZK1TaC8WnF5pXLqZTsB_2cw9bkvaagX3PrObeecuu33FpNEJ-hMo3jo8__1P-h_gI9TYL7</recordid><startdate>20221201</startdate><enddate>20221201</enddate><creator>Attada, Raju</creator><creator>Ehsan, Muhammad Azhar</creator><creator>Pillai, Prasanth A.</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TG</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</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>H8D</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>L7M</scope><scope>M2P</scope><scope>P5Z</scope><scope>P62</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PYCSY</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0002-6194-4561</orcidid></search><sort><creationdate>20221201</creationdate><title>Evaluation of Potential Predictability of Indian Summer Monsoon Rainfall in ECMWF's Fifth-Generation Seasonal Forecast System (SEAS5)</title><author>Attada, Raju ; Ehsan, Muhammad Azhar ; Pillai, Prasanth A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c249t-a400b0566cc97a4c84ef05a6ed7b54a1be5eb96cd02bf68329b3c28c45b8317b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Annual precipitation</topic><topic>Dipoles</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>El Nino</topic><topic>El Nino phenomena</topic><topic>False alarms</topic><topic>Geophysics/Geodesy</topic><topic>Lead time</topic><topic>Mathematical models</topic><topic>Mean precipitation</topic><topic>Monsoon rainfall</topic><topic>Monsoons</topic><topic>Performance prediction</topic><topic>Precipitation</topic><topic>Rain</topic><topic>Rainfall</topic><topic>Rainfall forecasting</topic><topic>Rainfall patterns</topic><topic>Rainy season</topic><topic>Sea surface</topic><topic>Sea surface temperature</topic><topic>Seasonal forecasting</topic><topic>Southern Oscillation</topic><topic>Summer</topic><topic>Summer monsoon</topic><topic>Surface temperature</topic><topic>Teleconnections</topic><topic>Weather forecasting</topic><topic>Wet season</topic><topic>Wind</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Attada, Raju</creatorcontrib><creatorcontrib>Ehsan, Muhammad Azhar</creatorcontrib><creatorcontrib>Pillai, Prasanth A.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</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>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>Aerospace Database</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>Advanced Technologies Database with Aerospace</collection><collection>Science Database</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>ProQuest Central China</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Pure and applied geophysics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Attada, Raju</au><au>Ehsan, Muhammad Azhar</au><au>Pillai, Prasanth A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluation of Potential Predictability of Indian Summer Monsoon Rainfall in ECMWF's Fifth-Generation Seasonal Forecast System (SEAS5)</atitle><jtitle>Pure and applied geophysics</jtitle><stitle>Pure Appl. Geophys</stitle><date>2022-12-01</date><risdate>2022</risdate><volume>179</volume><issue>12</issue><spage>4639</spage><epage>4655</epage><pages>4639-4655</pages><issn>0033-4553</issn><eissn>1420-9136</eissn><abstract>Forecasts of Indian summer monsoon rainfall (ISMR: June to September, JJAS) are issued prior to the onset of rainy season. Thus, an assessment of both potential and actual forecast skills for Indian summer monsoon rainfall should be based on a longer lead time. Based upon the European Center for Medium Range Weather Forecasts (ECMWF) fifth-generation seasonal forecast system (SEAS5), two lead times are considered: one with an April initial condition (IC) and the other with a May IC from 1981 through 2019 (39 years). Our results show that SEAS5 successfully represents the spatial patterns and variations in the mean JJAS precipitation in the ISMR region compared with the observed rainfall patterns. However, there seem to be significant discrepancies in the simulation of mean precipitation, particularly over topographical regions. SEAS5 is capable of reproducing the observed annual precipitation cycle in India. Moreover, the model is able to better predict the realistic ISMR teleconnections with El Niño-Southern Oscillation and the Indian Ocean Dipole at May ICs. The resulting forecasts across the region are characterized by moderate significant potential and actual skill in both leads, and it decreases as lead time increases. The predictability of SEAS5 is directly related to its ability to correctly predict the forcing of the tropical sea surface temperature and its teleconnections. In spite of this, both lead forecasts have a significant number of unpredicted events and false alarms. This study highlights model discrepancies, shows poor performance in predicting ISMR, and highlights the need for further research on this crucial issue of social relevance.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s00024-022-03184-9</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0002-6194-4561</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0033-4553 |
| ispartof | Pure and applied geophysics, 2022-12, Vol.179 (12), p.4639-4655 |
| issn | 0033-4553 1420-9136 |
| language | eng |
| recordid | cdi_proquest_journals_2755976423 |
| source | SpringerLink Journals |
| subjects | Annual precipitation Dipoles Earth and Environmental Science Earth Sciences El Nino El Nino phenomena False alarms Geophysics/Geodesy Lead time Mathematical models Mean precipitation Monsoon rainfall Monsoons Performance prediction Precipitation Rain Rainfall Rainfall forecasting Rainfall patterns Rainy season Sea surface Sea surface temperature Seasonal forecasting Southern Oscillation Summer Summer monsoon Surface temperature Teleconnections Weather forecasting Wet season Wind |
| title | Evaluation of Potential Predictability of Indian Summer Monsoon Rainfall in ECMWF's Fifth-Generation Seasonal Forecast System (SEAS5) |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T11%3A27%3A01IST&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=Evaluation%20of%20Potential%20Predictability%20of%20Indian%20Summer%20Monsoon%20Rainfall%20in%20ECMWF's%20Fifth-Generation%20Seasonal%20Forecast%20System%20(SEAS5)&rft.jtitle=Pure%20and%20applied%20geophysics&rft.au=Attada,%20Raju&rft.date=2022-12-01&rft.volume=179&rft.issue=12&rft.spage=4639&rft.epage=4655&rft.pages=4639-4655&rft.issn=0033-4553&rft.eissn=1420-9136&rft_id=info:doi/10.1007/s00024-022-03184-9&rft_dat=%3Cproquest_cross%3E2755976423%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=2755976423&rft_id=info:pmid/&rfr_iscdi=true |