Verification of Seasonal Ensemble Forecasts Based on the INM-CM5 Earth System Model

The issues related to the verification of ensemble seasonal forecasts obtained using a new technology implemented on the basis of the INM-CM5 Earth system model are considered. The forecast objects are global gridded ( ) fields of ensemble mean anomalies and probabilities of three gradations of the...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Russian meteorology and hydrology 2024-07, Vol.49 (7), p.587-597
Hauptverfasser: Khan, V. M., Kruglova, E. N., Tishchenko, V. A., Kulikova, I. A., Subbotin, A. V., Gritsun, A. S., Volodin, E. M., Tarasevich, M. A., Bragina, V. V.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 597
container_issue 7
container_start_page 587
container_title Russian meteorology and hydrology
container_volume 49
creator Khan, V. M.
Kruglova, E. N.
Tishchenko, V. A.
Kulikova, I. A.
Subbotin, A. V.
Gritsun, A. S.
Volodin, E. M.
Tarasevich, M. A.
Bragina, V. V.
description The issues related to the verification of ensemble seasonal forecasts obtained using a new technology implemented on the basis of the INM-CM5 Earth system model are considered. The forecast objects are global gridded ( ) fields of ensemble mean anomalies and probabilities of three gradations of the anomalies (below normal, normal, above normal) for each of the five characteristics: 500 hPa geopotential height ( ), mean sea level pressure (SLP), 850 hPa air temperature ( ), surface air temperature ( ), and precipitation (Prec) for six periods of time averaging (at monthly intervals for the 1st, 2nd, 3rd, 4th months and at two seasonal intervals: season 1 (the 1st–3rd months) and season 2 (the 2nd–4th months)). It is noted that forecast skill scores vary greatly depending on a region, a season, and a meteorological parameter. The best results for all parameters were obtained for the tropics, where the main sources of long-term predictability of the atmosphere are located. In extratropical latitudes, the quality of forecasts, especially at the monthly intervals, decreases and approaches the level of climate forecasts. The skill of precipitation forecasts is significantly inferior to the quality of forecasts of other meteorological parameters. It is noted that the success in ensemble seasonal probabilistic and deterministic forecasting of the main meteorological elements with the INM-CM5 model, both on a global scale and in individual regions, is comparable with the skill scores of similar forecasts of the foreign meteorological centers participating in the LC MME-WMO project. The introduction of the new climate model into the scientific and operational practice of the Hydrometcenter of Russia/North Eurasia Climate Centre will contribute to improving the quality of monthly and seasonal forecasts and developing specialized climate services.
doi_str_mv 10.3103/S1068373924070033
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3124606100</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3124606100</sourcerecordid><originalsourceid>FETCH-LOGICAL-c198t-ad23b9e65f37c184da68bc457670f740fa85133866f27282f84d1fba2b5218503</originalsourceid><addsrcrecordid>eNp1kD9PwzAUxC0EEqXwAdgsMQee_zsjVG2p1MIQYI2cxKap0rjY7tBvT6oiMSCmd9L97vR0CN0SuGcE2ENBQGqmWE45KADGztCI5IxnGnJ5PujBzo7-JbqKcQMgJOVqhIoPG1rX1ia1vsfe4cKa6HvT4Wkf7bbqLJ75YGsTU8RPJtoGD1xaW7x4WWWTlcBTE9IaF4eY7BavfGO7a3ThTBftzc8do_fZ9G3ynC1f54vJ4zKrSa5TZhrKqtxK4ZiqieaNkbqquVBSgVMcnNGCMKaldFRRTd2AEFcZWglKtAA2Rnen3l3wX3sbU7nx-zD8HktGKJcgCRwpcqLq4GMM1pW70G5NOJQEyuN25Z_thgw9ZeLA9p82_Db_H_oGESBtug</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3124606100</pqid></control><display><type>article</type><title>Verification of Seasonal Ensemble Forecasts Based on the INM-CM5 Earth System Model</title><source>SpringerLink Journals - AutoHoldings</source><creator>Khan, V. M. ; Kruglova, E. N. ; Tishchenko, V. A. ; Kulikova, I. A. ; Subbotin, A. V. ; Gritsun, A. S. ; Volodin, E. M. ; Tarasevich, M. A. ; Bragina, V. V.</creator><creatorcontrib>Khan, V. M. ; Kruglova, E. N. ; Tishchenko, V. A. ; Kulikova, I. A. ; Subbotin, A. V. ; Gritsun, A. S. ; Volodin, E. M. ; Tarasevich, M. A. ; Bragina, V. V.</creatorcontrib><description>The issues related to the verification of ensemble seasonal forecasts obtained using a new technology implemented on the basis of the INM-CM5 Earth system model are considered. The forecast objects are global gridded ( ) fields of ensemble mean anomalies and probabilities of three gradations of the anomalies (below normal, normal, above normal) for each of the five characteristics: 500 hPa geopotential height ( ), mean sea level pressure (SLP), 850 hPa air temperature ( ), surface air temperature ( ), and precipitation (Prec) for six periods of time averaging (at monthly intervals for the 1st, 2nd, 3rd, 4th months and at two seasonal intervals: season 1 (the 1st–3rd months) and season 2 (the 2nd–4th months)). It is noted that forecast skill scores vary greatly depending on a region, a season, and a meteorological parameter. The best results for all parameters were obtained for the tropics, where the main sources of long-term predictability of the atmosphere are located. In extratropical latitudes, the quality of forecasts, especially at the monthly intervals, decreases and approaches the level of climate forecasts. The skill of precipitation forecasts is significantly inferior to the quality of forecasts of other meteorological parameters. It is noted that the success in ensemble seasonal probabilistic and deterministic forecasting of the main meteorological elements with the INM-CM5 model, both on a global scale and in individual regions, is comparable with the skill scores of similar forecasts of the foreign meteorological centers participating in the LC MME-WMO project. The introduction of the new climate model into the scientific and operational practice of the Hydrometcenter of Russia/North Eurasia Climate Centre will contribute to improving the quality of monthly and seasonal forecasts and developing specialized climate services.</description><identifier>ISSN: 1068-3739</identifier><identifier>EISSN: 1934-8096</identifier><identifier>DOI: 10.3103/S1068373924070033</identifier><language>eng</language><publisher>Moscow: Pleiades Publishing</publisher><subject>Air temperature ; Anomalies ; Atmospheric Sciences ; Climate ; Climate models ; Dynamic height ; Earth and Environmental Science ; Earth Sciences ; Ensemble forecasting ; Forecasting skill ; Geopotential ; Geopotential height ; Intervals ; Mean sea level ; Meteorological parameters ; Meteorology ; Parameters ; Precipitation ; Precipitation forecasting ; Sea level pressure ; Seasonal forecasting ; Seasons ; Surface temperature ; Surface-air temperature relationships ; Tropical environments ; Verification ; Weather forecasting</subject><ispartof>Russian meteorology and hydrology, 2024-07, Vol.49 (7), p.587-597</ispartof><rights>Pleiades Publishing, Ltd. 2024</rights><rights>Pleiades Publishing, Ltd. 2024.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c198t-ad23b9e65f37c184da68bc457670f740fa85133866f27282f84d1fba2b5218503</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.3103/S1068373924070033$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.3103/S1068373924070033$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Khan, V. M.</creatorcontrib><creatorcontrib>Kruglova, E. N.</creatorcontrib><creatorcontrib>Tishchenko, V. A.</creatorcontrib><creatorcontrib>Kulikova, I. A.</creatorcontrib><creatorcontrib>Subbotin, A. V.</creatorcontrib><creatorcontrib>Gritsun, A. S.</creatorcontrib><creatorcontrib>Volodin, E. M.</creatorcontrib><creatorcontrib>Tarasevich, M. A.</creatorcontrib><creatorcontrib>Bragina, V. V.</creatorcontrib><title>Verification of Seasonal Ensemble Forecasts Based on the INM-CM5 Earth System Model</title><title>Russian meteorology and hydrology</title><addtitle>Russ. Meteorol. Hydrol</addtitle><description>The issues related to the verification of ensemble seasonal forecasts obtained using a new technology implemented on the basis of the INM-CM5 Earth system model are considered. The forecast objects are global gridded ( ) fields of ensemble mean anomalies and probabilities of three gradations of the anomalies (below normal, normal, above normal) for each of the five characteristics: 500 hPa geopotential height ( ), mean sea level pressure (SLP), 850 hPa air temperature ( ), surface air temperature ( ), and precipitation (Prec) for six periods of time averaging (at monthly intervals for the 1st, 2nd, 3rd, 4th months and at two seasonal intervals: season 1 (the 1st–3rd months) and season 2 (the 2nd–4th months)). It is noted that forecast skill scores vary greatly depending on a region, a season, and a meteorological parameter. The best results for all parameters were obtained for the tropics, where the main sources of long-term predictability of the atmosphere are located. In extratropical latitudes, the quality of forecasts, especially at the monthly intervals, decreases and approaches the level of climate forecasts. The skill of precipitation forecasts is significantly inferior to the quality of forecasts of other meteorological parameters. It is noted that the success in ensemble seasonal probabilistic and deterministic forecasting of the main meteorological elements with the INM-CM5 model, both on a global scale and in individual regions, is comparable with the skill scores of similar forecasts of the foreign meteorological centers participating in the LC MME-WMO project. The introduction of the new climate model into the scientific and operational practice of the Hydrometcenter of Russia/North Eurasia Climate Centre will contribute to improving the quality of monthly and seasonal forecasts and developing specialized climate services.</description><subject>Air temperature</subject><subject>Anomalies</subject><subject>Atmospheric Sciences</subject><subject>Climate</subject><subject>Climate models</subject><subject>Dynamic height</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Ensemble forecasting</subject><subject>Forecasting skill</subject><subject>Geopotential</subject><subject>Geopotential height</subject><subject>Intervals</subject><subject>Mean sea level</subject><subject>Meteorological parameters</subject><subject>Meteorology</subject><subject>Parameters</subject><subject>Precipitation</subject><subject>Precipitation forecasting</subject><subject>Sea level pressure</subject><subject>Seasonal forecasting</subject><subject>Seasons</subject><subject>Surface temperature</subject><subject>Surface-air temperature relationships</subject><subject>Tropical environments</subject><subject>Verification</subject><subject>Weather forecasting</subject><issn>1068-3739</issn><issn>1934-8096</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp1kD9PwzAUxC0EEqXwAdgsMQee_zsjVG2p1MIQYI2cxKap0rjY7tBvT6oiMSCmd9L97vR0CN0SuGcE2ENBQGqmWE45KADGztCI5IxnGnJ5PujBzo7-JbqKcQMgJOVqhIoPG1rX1ia1vsfe4cKa6HvT4Wkf7bbqLJ75YGsTU8RPJtoGD1xaW7x4WWWTlcBTE9IaF4eY7BavfGO7a3ThTBftzc8do_fZ9G3ynC1f54vJ4zKrSa5TZhrKqtxK4ZiqieaNkbqquVBSgVMcnNGCMKaldFRRTd2AEFcZWglKtAA2Rnen3l3wX3sbU7nx-zD8HktGKJcgCRwpcqLq4GMM1pW70G5NOJQEyuN25Z_thgw9ZeLA9p82_Db_H_oGESBtug</recordid><startdate>20240701</startdate><enddate>20240701</enddate><creator>Khan, V. M.</creator><creator>Kruglova, E. N.</creator><creator>Tishchenko, V. A.</creator><creator>Kulikova, I. A.</creator><creator>Subbotin, A. V.</creator><creator>Gritsun, A. S.</creator><creator>Volodin, E. M.</creator><creator>Tarasevich, M. A.</creator><creator>Bragina, V. V.</creator><general>Pleiades Publishing</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7TG</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>KL.</scope><scope>L.G</scope></search><sort><creationdate>20240701</creationdate><title>Verification of Seasonal Ensemble Forecasts Based on the INM-CM5 Earth System Model</title><author>Khan, V. M. ; Kruglova, E. N. ; Tishchenko, V. A. ; Kulikova, I. A. ; Subbotin, A. V. ; Gritsun, A. S. ; Volodin, E. M. ; Tarasevich, M. A. ; Bragina, V. V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c198t-ad23b9e65f37c184da68bc457670f740fa85133866f27282f84d1fba2b5218503</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Air temperature</topic><topic>Anomalies</topic><topic>Atmospheric Sciences</topic><topic>Climate</topic><topic>Climate models</topic><topic>Dynamic height</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Ensemble forecasting</topic><topic>Forecasting skill</topic><topic>Geopotential</topic><topic>Geopotential height</topic><topic>Intervals</topic><topic>Mean sea level</topic><topic>Meteorological parameters</topic><topic>Meteorology</topic><topic>Parameters</topic><topic>Precipitation</topic><topic>Precipitation forecasting</topic><topic>Sea level pressure</topic><topic>Seasonal forecasting</topic><topic>Seasons</topic><topic>Surface temperature</topic><topic>Surface-air temperature relationships</topic><topic>Tropical environments</topic><topic>Verification</topic><topic>Weather forecasting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Khan, V. M.</creatorcontrib><creatorcontrib>Kruglova, E. N.</creatorcontrib><creatorcontrib>Tishchenko, V. A.</creatorcontrib><creatorcontrib>Kulikova, I. A.</creatorcontrib><creatorcontrib>Subbotin, A. V.</creatorcontrib><creatorcontrib>Gritsun, A. S.</creatorcontrib><creatorcontrib>Volodin, E. M.</creatorcontrib><creatorcontrib>Tarasevich, M. A.</creatorcontrib><creatorcontrib>Bragina, V. V.</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Meteorological &amp; Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy &amp; Non-Living Resources</collection><collection>Meteorological &amp; Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) Professional</collection><jtitle>Russian meteorology and hydrology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Khan, V. M.</au><au>Kruglova, E. N.</au><au>Tishchenko, V. A.</au><au>Kulikova, I. A.</au><au>Subbotin, A. V.</au><au>Gritsun, A. S.</au><au>Volodin, E. M.</au><au>Tarasevich, M. A.</au><au>Bragina, V. V.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Verification of Seasonal Ensemble Forecasts Based on the INM-CM5 Earth System Model</atitle><jtitle>Russian meteorology and hydrology</jtitle><stitle>Russ. Meteorol. Hydrol</stitle><date>2024-07-01</date><risdate>2024</risdate><volume>49</volume><issue>7</issue><spage>587</spage><epage>597</epage><pages>587-597</pages><issn>1068-3739</issn><eissn>1934-8096</eissn><abstract>The issues related to the verification of ensemble seasonal forecasts obtained using a new technology implemented on the basis of the INM-CM5 Earth system model are considered. The forecast objects are global gridded ( ) fields of ensemble mean anomalies and probabilities of three gradations of the anomalies (below normal, normal, above normal) for each of the five characteristics: 500 hPa geopotential height ( ), mean sea level pressure (SLP), 850 hPa air temperature ( ), surface air temperature ( ), and precipitation (Prec) for six periods of time averaging (at monthly intervals for the 1st, 2nd, 3rd, 4th months and at two seasonal intervals: season 1 (the 1st–3rd months) and season 2 (the 2nd–4th months)). It is noted that forecast skill scores vary greatly depending on a region, a season, and a meteorological parameter. The best results for all parameters were obtained for the tropics, where the main sources of long-term predictability of the atmosphere are located. In extratropical latitudes, the quality of forecasts, especially at the monthly intervals, decreases and approaches the level of climate forecasts. The skill of precipitation forecasts is significantly inferior to the quality of forecasts of other meteorological parameters. It is noted that the success in ensemble seasonal probabilistic and deterministic forecasting of the main meteorological elements with the INM-CM5 model, both on a global scale and in individual regions, is comparable with the skill scores of similar forecasts of the foreign meteorological centers participating in the LC MME-WMO project. The introduction of the new climate model into the scientific and operational practice of the Hydrometcenter of Russia/North Eurasia Climate Centre will contribute to improving the quality of monthly and seasonal forecasts and developing specialized climate services.</abstract><cop>Moscow</cop><pub>Pleiades Publishing</pub><doi>10.3103/S1068373924070033</doi><tpages>11</tpages></addata></record>
fulltext fulltext
identifier ISSN: 1068-3739
ispartof Russian meteorology and hydrology, 2024-07, Vol.49 (7), p.587-597
issn 1068-3739
1934-8096
language eng
recordid cdi_proquest_journals_3124606100
source SpringerLink Journals - AutoHoldings
subjects Air temperature
Anomalies
Atmospheric Sciences
Climate
Climate models
Dynamic height
Earth and Environmental Science
Earth Sciences
Ensemble forecasting
Forecasting skill
Geopotential
Geopotential height
Intervals
Mean sea level
Meteorological parameters
Meteorology
Parameters
Precipitation
Precipitation forecasting
Sea level pressure
Seasonal forecasting
Seasons
Surface temperature
Surface-air temperature relationships
Tropical environments
Verification
Weather forecasting
title Verification of Seasonal Ensemble Forecasts Based on the INM-CM5 Earth System Model
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T01%3A16%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=Verification%20of%20Seasonal%20Ensemble%20Forecasts%20Based%20on%20the%20INM-CM5%20Earth%20System%20Model&rft.jtitle=Russian%20meteorology%20and%20hydrology&rft.au=Khan,%20V.%20M.&rft.date=2024-07-01&rft.volume=49&rft.issue=7&rft.spage=587&rft.epage=597&rft.pages=587-597&rft.issn=1068-3739&rft.eissn=1934-8096&rft_id=info:doi/10.3103/S1068373924070033&rft_dat=%3Cproquest_cross%3E3124606100%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=3124606100&rft_id=info:pmid/&rfr_iscdi=true