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...
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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 |
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) 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. 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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. 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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> |
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ispartof | Russian meteorology and hydrology, 2024-07, Vol.49 (7), p.587-597 |
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language | eng |
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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 |
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