Sea-level pressure–air temperature teleconnections during northern hemisphere winter

Abstract The relationship between sea-level pressure (SLP) and 1,000 hPa air temperature (AT) in winter is investigated over the northern hemisphere, and a statistical forecasting of one of the two parameters from the other is attempted on a monthly basis. Mean monthly SLP and 1,000 hPa level AT val...
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Autor*in:	Papadimas, C. D. [verfasserIn] Bartzokas, A. [verfasserIn] Lolis, C. J. [verfasserIn] Hatzianastassiou, N. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	Canonical Correlation North Atlantic Oscillation Canonical Correlation Analysis Canonical Variate Prediction Skill

Übergeordnetes Werk:	Enthalten in: Theoretical and applied climatology - Wien [u.a.] : Springer, 1948, 108(2011), 1-2 vom: 21. Sept., Seite 173-189
Übergeordnetes Werk:	volume:108 ; year:2011 ; number:1-2 ; day:21 ; month:09 ; pages:173-189

Links:	Volltext

DOI / URN:	10.1007/s00704-011-0523-8

Katalog-ID:	SPR007327781

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100	1		\|a Papadimas, C. D. \|e verfasserin \|4 aut
245	1	0	\|a Sea-level pressure–air temperature teleconnections during northern hemisphere winter
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520			\|a Abstract The relationship between sea-level pressure (SLP) and 1,000 hPa air temperature (AT) in winter is investigated over the northern hemisphere, and a statistical forecasting of one of the two parameters from the other is attempted on a monthly basis. Mean monthly SLP and 1,000 hPa level AT values at 563 grid points over the northern hemisphere are utilized for January, February and March, for the period 1949–2002. At first, factor analysis is applied to the data sets as a dimensionality reduction tool. Then, canonical correlation analysis is applied to the resultant factor scores time series for the five SLP–AT pairs: SLP(J)–AT(J), SLP(J)–AT(F), SLP(J)–AT(M), AT(J)–SLP(F) and AT(J)–SLP(M), and the synchronous and time-lag connections between the two parameters are investigated. The areas characterized by a satisfactory monthly or/and bi-monthly forecasting ability are detected. The most satisfactory results refer to the areas affected by the Southern Oscillation. It is found that the SLP teleconnection between the areas of the eastern and the western Pacific in January is related to the regime of AT in the central Pacific Ocean, in both February and March. Also, SLP over the Aleutian and Icelandic lows in January is related to AT over their southwestern and southeastern neighbouring areas in February and March. Finally, it appears that there is also ability for monthly/bi-monthly statistical prediction for some areas affected by the well-known oscillations of North Atlantic Oscillation and Pacific/North American Oscillation. A validation of the statistical prediction methodology is carried out, using real-time series of AT and SLP parameters for some characteristic cases. The results show that the statistical prediction presents a remarkable success. The success rate varies from 67% to 83% for the analysis period 1949–2002 and from 71% to 86% for the recent period 2003–2009.
650		4	\|a Canonical Correlation \|7 (dpeaa)DE-He213
650		4	\|a North Atlantic Oscillation \|7 (dpeaa)DE-He213
650		4	\|a Canonical Correlation Analysis \|7 (dpeaa)DE-He213
650		4	\|a Canonical Variate \|7 (dpeaa)DE-He213
650		4	\|a Prediction Skill \|7 (dpeaa)DE-He213
700	1		\|a Bartzokas, A. \|e verfasserin \|4 aut
700	1		\|a Lolis, C. J. \|e verfasserin \|4 aut
700	1		\|a Hatzianastassiou, N. \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Theoretical and applied climatology \|d Wien [u.a.] : Springer, 1948 \|g 108(2011), 1-2 vom: 21. Sept., Seite 173-189 \|w (DE-627)25490968X \|w (DE-600)1463177-5 \|x 1434-4483 \|7 nnns
773	1	8	\|g volume:108 \|g year:2011 \|g number:1-2 \|g day:21 \|g month:09 \|g pages:173-189
856	4	0	\|u https://dx.doi.org/10.1007/s00704-011-0523-8 \|z lizenzpflichtig \|3 Volltext
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936	b	k	\|a 38.82 \|q ASE
951			\|a AR
952			\|d 108 \|j 2011 \|e 1-2 \|b 21 \|c 09 \|h 173-189

Indexfelder

author_variant	c d p cd cdp a b ab c j l cj cjl n h nh
matchkey_str	article:14344483:2011----::elvlrsueitmeaueeeoncindrnnr
hierarchy_sort_str	2011
bklnumber	38.82
publishDate	2011
allfields	10.1007/s00704-011-0523-8 doi (DE-627)SPR007327781 (SPR)s00704-011-0523-8-e DE-627 ger DE-627 rakwb eng 550 ASE 38.82 bkl Papadimas, C. D. verfasserin aut Sea-level pressure–air temperature teleconnections during northern hemisphere winter 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The relationship between sea-level pressure (SLP) and 1,000 hPa air temperature (AT) in winter is investigated over the northern hemisphere, and a statistical forecasting of one of the two parameters from the other is attempted on a monthly basis. Mean monthly SLP and 1,000 hPa level AT values at 563 grid points over the northern hemisphere are utilized for January, February and March, for the period 1949–2002. At first, factor analysis is applied to the data sets as a dimensionality reduction tool. Then, canonical correlation analysis is applied to the resultant factor scores time series for the five SLP–AT pairs: SLP(J)–AT(J), SLP(J)–AT(F), SLP(J)–AT(M), AT(J)–SLP(F) and AT(J)–SLP(M), and the synchronous and time-lag connections between the two parameters are investigated. The areas characterized by a satisfactory monthly or/and bi-monthly forecasting ability are detected. The most satisfactory results refer to the areas affected by the Southern Oscillation. It is found that the SLP teleconnection between the areas of the eastern and the western Pacific in January is related to the regime of AT in the central Pacific Ocean, in both February and March. Also, SLP over the Aleutian and Icelandic lows in January is related to AT over their southwestern and southeastern neighbouring areas in February and March. Finally, it appears that there is also ability for monthly/bi-monthly statistical prediction for some areas affected by the well-known oscillations of North Atlantic Oscillation and Pacific/North American Oscillation. A validation of the statistical prediction methodology is carried out, using real-time series of AT and SLP parameters for some characteristic cases. The results show that the statistical prediction presents a remarkable success. The success rate varies from 67% to 83% for the analysis period 1949–2002 and from 71% to 86% for the recent period 2003–2009. Canonical Correlation (dpeaa)DE-He213 North Atlantic Oscillation (dpeaa)DE-He213 Canonical Correlation Analysis (dpeaa)DE-He213 Canonical Variate (dpeaa)DE-He213 Prediction Skill (dpeaa)DE-He213 Bartzokas, A. verfasserin aut Lolis, C. J. verfasserin aut Hatzianastassiou, N. verfasserin aut Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 108(2011), 1-2 vom: 21. Sept., Seite 173-189 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:108 year:2011 number:1-2 day:21 month:09 pages:173-189 https://dx.doi.org/10.1007/s00704-011-0523-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.82 ASE AR 108 2011 1-2 21 09 173-189
spelling	10.1007/s00704-011-0523-8 doi (DE-627)SPR007327781 (SPR)s00704-011-0523-8-e DE-627 ger DE-627 rakwb eng 550 ASE 38.82 bkl Papadimas, C. D. verfasserin aut Sea-level pressure–air temperature teleconnections during northern hemisphere winter 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The relationship between sea-level pressure (SLP) and 1,000 hPa air temperature (AT) in winter is investigated over the northern hemisphere, and a statistical forecasting of one of the two parameters from the other is attempted on a monthly basis. Mean monthly SLP and 1,000 hPa level AT values at 563 grid points over the northern hemisphere are utilized for January, February and March, for the period 1949–2002. At first, factor analysis is applied to the data sets as a dimensionality reduction tool. Then, canonical correlation analysis is applied to the resultant factor scores time series for the five SLP–AT pairs: SLP(J)–AT(J), SLP(J)–AT(F), SLP(J)–AT(M), AT(J)–SLP(F) and AT(J)–SLP(M), and the synchronous and time-lag connections between the two parameters are investigated. The areas characterized by a satisfactory monthly or/and bi-monthly forecasting ability are detected. The most satisfactory results refer to the areas affected by the Southern Oscillation. It is found that the SLP teleconnection between the areas of the eastern and the western Pacific in January is related to the regime of AT in the central Pacific Ocean, in both February and March. Also, SLP over the Aleutian and Icelandic lows in January is related to AT over their southwestern and southeastern neighbouring areas in February and March. Finally, it appears that there is also ability for monthly/bi-monthly statistical prediction for some areas affected by the well-known oscillations of North Atlantic Oscillation and Pacific/North American Oscillation. A validation of the statistical prediction methodology is carried out, using real-time series of AT and SLP parameters for some characteristic cases. The results show that the statistical prediction presents a remarkable success. The success rate varies from 67% to 83% for the analysis period 1949–2002 and from 71% to 86% for the recent period 2003–2009. Canonical Correlation (dpeaa)DE-He213 North Atlantic Oscillation (dpeaa)DE-He213 Canonical Correlation Analysis (dpeaa)DE-He213 Canonical Variate (dpeaa)DE-He213 Prediction Skill (dpeaa)DE-He213 Bartzokas, A. verfasserin aut Lolis, C. J. verfasserin aut Hatzianastassiou, N. verfasserin aut Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 108(2011), 1-2 vom: 21. Sept., Seite 173-189 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:108 year:2011 number:1-2 day:21 month:09 pages:173-189 https://dx.doi.org/10.1007/s00704-011-0523-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.82 ASE AR 108 2011 1-2 21 09 173-189
allfields_unstemmed	10.1007/s00704-011-0523-8 doi (DE-627)SPR007327781 (SPR)s00704-011-0523-8-e DE-627 ger DE-627 rakwb eng 550 ASE 38.82 bkl Papadimas, C. D. verfasserin aut Sea-level pressure–air temperature teleconnections during northern hemisphere winter 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The relationship between sea-level pressure (SLP) and 1,000 hPa air temperature (AT) in winter is investigated over the northern hemisphere, and a statistical forecasting of one of the two parameters from the other is attempted on a monthly basis. Mean monthly SLP and 1,000 hPa level AT values at 563 grid points over the northern hemisphere are utilized for January, February and March, for the period 1949–2002. At first, factor analysis is applied to the data sets as a dimensionality reduction tool. Then, canonical correlation analysis is applied to the resultant factor scores time series for the five SLP–AT pairs: SLP(J)–AT(J), SLP(J)–AT(F), SLP(J)–AT(M), AT(J)–SLP(F) and AT(J)–SLP(M), and the synchronous and time-lag connections between the two parameters are investigated. The areas characterized by a satisfactory monthly or/and bi-monthly forecasting ability are detected. The most satisfactory results refer to the areas affected by the Southern Oscillation. It is found that the SLP teleconnection between the areas of the eastern and the western Pacific in January is related to the regime of AT in the central Pacific Ocean, in both February and March. Also, SLP over the Aleutian and Icelandic lows in January is related to AT over their southwestern and southeastern neighbouring areas in February and March. Finally, it appears that there is also ability for monthly/bi-monthly statistical prediction for some areas affected by the well-known oscillations of North Atlantic Oscillation and Pacific/North American Oscillation. A validation of the statistical prediction methodology is carried out, using real-time series of AT and SLP parameters for some characteristic cases. The results show that the statistical prediction presents a remarkable success. The success rate varies from 67% to 83% for the analysis period 1949–2002 and from 71% to 86% for the recent period 2003–2009. Canonical Correlation (dpeaa)DE-He213 North Atlantic Oscillation (dpeaa)DE-He213 Canonical Correlation Analysis (dpeaa)DE-He213 Canonical Variate (dpeaa)DE-He213 Prediction Skill (dpeaa)DE-He213 Bartzokas, A. verfasserin aut Lolis, C. J. verfasserin aut Hatzianastassiou, N. verfasserin aut Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 108(2011), 1-2 vom: 21. Sept., Seite 173-189 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:108 year:2011 number:1-2 day:21 month:09 pages:173-189 https://dx.doi.org/10.1007/s00704-011-0523-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.82 ASE AR 108 2011 1-2 21 09 173-189
allfieldsGer	10.1007/s00704-011-0523-8 doi (DE-627)SPR007327781 (SPR)s00704-011-0523-8-e DE-627 ger DE-627 rakwb eng 550 ASE 38.82 bkl Papadimas, C. D. verfasserin aut Sea-level pressure–air temperature teleconnections during northern hemisphere winter 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The relationship between sea-level pressure (SLP) and 1,000 hPa air temperature (AT) in winter is investigated over the northern hemisphere, and a statistical forecasting of one of the two parameters from the other is attempted on a monthly basis. Mean monthly SLP and 1,000 hPa level AT values at 563 grid points over the northern hemisphere are utilized for January, February and March, for the period 1949–2002. At first, factor analysis is applied to the data sets as a dimensionality reduction tool. Then, canonical correlation analysis is applied to the resultant factor scores time series for the five SLP–AT pairs: SLP(J)–AT(J), SLP(J)–AT(F), SLP(J)–AT(M), AT(J)–SLP(F) and AT(J)–SLP(M), and the synchronous and time-lag connections between the two parameters are investigated. The areas characterized by a satisfactory monthly or/and bi-monthly forecasting ability are detected. The most satisfactory results refer to the areas affected by the Southern Oscillation. It is found that the SLP teleconnection between the areas of the eastern and the western Pacific in January is related to the regime of AT in the central Pacific Ocean, in both February and March. Also, SLP over the Aleutian and Icelandic lows in January is related to AT over their southwestern and southeastern neighbouring areas in February and March. Finally, it appears that there is also ability for monthly/bi-monthly statistical prediction for some areas affected by the well-known oscillations of North Atlantic Oscillation and Pacific/North American Oscillation. A validation of the statistical prediction methodology is carried out, using real-time series of AT and SLP parameters for some characteristic cases. The results show that the statistical prediction presents a remarkable success. The success rate varies from 67% to 83% for the analysis period 1949–2002 and from 71% to 86% for the recent period 2003–2009. Canonical Correlation (dpeaa)DE-He213 North Atlantic Oscillation (dpeaa)DE-He213 Canonical Correlation Analysis (dpeaa)DE-He213 Canonical Variate (dpeaa)DE-He213 Prediction Skill (dpeaa)DE-He213 Bartzokas, A. verfasserin aut Lolis, C. J. verfasserin aut Hatzianastassiou, N. verfasserin aut Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 108(2011), 1-2 vom: 21. Sept., Seite 173-189 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:108 year:2011 number:1-2 day:21 month:09 pages:173-189 https://dx.doi.org/10.1007/s00704-011-0523-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.82 ASE AR 108 2011 1-2 21 09 173-189
allfieldsSound	10.1007/s00704-011-0523-8 doi (DE-627)SPR007327781 (SPR)s00704-011-0523-8-e DE-627 ger DE-627 rakwb eng 550 ASE 38.82 bkl Papadimas, C. D. verfasserin aut Sea-level pressure–air temperature teleconnections during northern hemisphere winter 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The relationship between sea-level pressure (SLP) and 1,000 hPa air temperature (AT) in winter is investigated over the northern hemisphere, and a statistical forecasting of one of the two parameters from the other is attempted on a monthly basis. Mean monthly SLP and 1,000 hPa level AT values at 563 grid points over the northern hemisphere are utilized for January, February and March, for the period 1949–2002. At first, factor analysis is applied to the data sets as a dimensionality reduction tool. Then, canonical correlation analysis is applied to the resultant factor scores time series for the five SLP–AT pairs: SLP(J)–AT(J), SLP(J)–AT(F), SLP(J)–AT(M), AT(J)–SLP(F) and AT(J)–SLP(M), and the synchronous and time-lag connections between the two parameters are investigated. The areas characterized by a satisfactory monthly or/and bi-monthly forecasting ability are detected. The most satisfactory results refer to the areas affected by the Southern Oscillation. It is found that the SLP teleconnection between the areas of the eastern and the western Pacific in January is related to the regime of AT in the central Pacific Ocean, in both February and March. Also, SLP over the Aleutian and Icelandic lows in January is related to AT over their southwestern and southeastern neighbouring areas in February and March. Finally, it appears that there is also ability for monthly/bi-monthly statistical prediction for some areas affected by the well-known oscillations of North Atlantic Oscillation and Pacific/North American Oscillation. A validation of the statistical prediction methodology is carried out, using real-time series of AT and SLP parameters for some characteristic cases. The results show that the statistical prediction presents a remarkable success. The success rate varies from 67% to 83% for the analysis period 1949–2002 and from 71% to 86% for the recent period 2003–2009. Canonical Correlation (dpeaa)DE-He213 North Atlantic Oscillation (dpeaa)DE-He213 Canonical Correlation Analysis (dpeaa)DE-He213 Canonical Variate (dpeaa)DE-He213 Prediction Skill (dpeaa)DE-He213 Bartzokas, A. verfasserin aut Lolis, C. J. verfasserin aut Hatzianastassiou, N. verfasserin aut Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 108(2011), 1-2 vom: 21. Sept., Seite 173-189 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:108 year:2011 number:1-2 day:21 month:09 pages:173-189 https://dx.doi.org/10.1007/s00704-011-0523-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.82 ASE AR 108 2011 1-2 21 09 173-189
language	English
source	Enthalten in Theoretical and applied climatology 108(2011), 1-2 vom: 21. Sept., Seite 173-189 volume:108 year:2011 number:1-2 day:21 month:09 pages:173-189
sourceStr	Enthalten in Theoretical and applied climatology 108(2011), 1-2 vom: 21. Sept., Seite 173-189 volume:108 year:2011 number:1-2 day:21 month:09 pages:173-189
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Canonical Correlation North Atlantic Oscillation Canonical Correlation Analysis Canonical Variate Prediction Skill
dewey-raw	550
isfreeaccess_bool	false
container_title	Theoretical and applied climatology
authorswithroles_txt_mv	Papadimas, C. D. @@aut@@ Bartzokas, A. @@aut@@ Lolis, C. J. @@aut@@ Hatzianastassiou, N. @@aut@@
publishDateDaySort_date	2011-09-21T00:00:00Z
hierarchy_top_id	25490968X
dewey-sort	3550
id	SPR007327781
language_de	englisch
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author	Papadimas, C. D.
spellingShingle	Papadimas, C. D. ddc 550 bkl 38.82 misc Canonical Correlation misc North Atlantic Oscillation misc Canonical Correlation Analysis misc Canonical Variate misc Prediction Skill Sea-level pressure–air temperature teleconnections during northern hemisphere winter
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topic_title	550 ASE 38.82 bkl Sea-level pressure–air temperature teleconnections during northern hemisphere winter Canonical Correlation (dpeaa)DE-He213 North Atlantic Oscillation (dpeaa)DE-He213 Canonical Correlation Analysis (dpeaa)DE-He213 Canonical Variate (dpeaa)DE-He213 Prediction Skill (dpeaa)DE-He213
topic	ddc 550 bkl 38.82 misc Canonical Correlation misc North Atlantic Oscillation misc Canonical Correlation Analysis misc Canonical Variate misc Prediction Skill
topic_unstemmed	ddc 550 bkl 38.82 misc Canonical Correlation misc North Atlantic Oscillation misc Canonical Correlation Analysis misc Canonical Variate misc Prediction Skill
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title	Sea-level pressure–air temperature teleconnections during northern hemisphere winter
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title_full	Sea-level pressure–air temperature teleconnections during northern hemisphere winter
author_sort	Papadimas, C. D.
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author_browse	Papadimas, C. D. Bartzokas, A. Lolis, C. J. Hatzianastassiou, N.
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title_sort	sea-level pressure–air temperature teleconnections during northern hemisphere winter
title_auth	Sea-level pressure–air temperature teleconnections during northern hemisphere winter
abstract	Abstract The relationship between sea-level pressure (SLP) and 1,000 hPa air temperature (AT) in winter is investigated over the northern hemisphere, and a statistical forecasting of one of the two parameters from the other is attempted on a monthly basis. Mean monthly SLP and 1,000 hPa level AT values at 563 grid points over the northern hemisphere are utilized for January, February and March, for the period 1949–2002. At first, factor analysis is applied to the data sets as a dimensionality reduction tool. Then, canonical correlation analysis is applied to the resultant factor scores time series for the five SLP–AT pairs: SLP(J)–AT(J), SLP(J)–AT(F), SLP(J)–AT(M), AT(J)–SLP(F) and AT(J)–SLP(M), and the synchronous and time-lag connections between the two parameters are investigated. The areas characterized by a satisfactory monthly or/and bi-monthly forecasting ability are detected. The most satisfactory results refer to the areas affected by the Southern Oscillation. It is found that the SLP teleconnection between the areas of the eastern and the western Pacific in January is related to the regime of AT in the central Pacific Ocean, in both February and March. Also, SLP over the Aleutian and Icelandic lows in January is related to AT over their southwestern and southeastern neighbouring areas in February and March. Finally, it appears that there is also ability for monthly/bi-monthly statistical prediction for some areas affected by the well-known oscillations of North Atlantic Oscillation and Pacific/North American Oscillation. A validation of the statistical prediction methodology is carried out, using real-time series of AT and SLP parameters for some characteristic cases. The results show that the statistical prediction presents a remarkable success. The success rate varies from 67% to 83% for the analysis period 1949–2002 and from 71% to 86% for the recent period 2003–2009.
abstractGer	Abstract The relationship between sea-level pressure (SLP) and 1,000 hPa air temperature (AT) in winter is investigated over the northern hemisphere, and a statistical forecasting of one of the two parameters from the other is attempted on a monthly basis. Mean monthly SLP and 1,000 hPa level AT values at 563 grid points over the northern hemisphere are utilized for January, February and March, for the period 1949–2002. At first, factor analysis is applied to the data sets as a dimensionality reduction tool. Then, canonical correlation analysis is applied to the resultant factor scores time series for the five SLP–AT pairs: SLP(J)–AT(J), SLP(J)–AT(F), SLP(J)–AT(M), AT(J)–SLP(F) and AT(J)–SLP(M), and the synchronous and time-lag connections between the two parameters are investigated. The areas characterized by a satisfactory monthly or/and bi-monthly forecasting ability are detected. The most satisfactory results refer to the areas affected by the Southern Oscillation. It is found that the SLP teleconnection between the areas of the eastern and the western Pacific in January is related to the regime of AT in the central Pacific Ocean, in both February and March. Also, SLP over the Aleutian and Icelandic lows in January is related to AT over their southwestern and southeastern neighbouring areas in February and March. Finally, it appears that there is also ability for monthly/bi-monthly statistical prediction for some areas affected by the well-known oscillations of North Atlantic Oscillation and Pacific/North American Oscillation. A validation of the statistical prediction methodology is carried out, using real-time series of AT and SLP parameters for some characteristic cases. The results show that the statistical prediction presents a remarkable success. The success rate varies from 67% to 83% for the analysis period 1949–2002 and from 71% to 86% for the recent period 2003–2009.
abstract_unstemmed	Abstract The relationship between sea-level pressure (SLP) and 1,000 hPa air temperature (AT) in winter is investigated over the northern hemisphere, and a statistical forecasting of one of the two parameters from the other is attempted on a monthly basis. Mean monthly SLP and 1,000 hPa level AT values at 563 grid points over the northern hemisphere are utilized for January, February and March, for the period 1949–2002. At first, factor analysis is applied to the data sets as a dimensionality reduction tool. Then, canonical correlation analysis is applied to the resultant factor scores time series for the five SLP–AT pairs: SLP(J)–AT(J), SLP(J)–AT(F), SLP(J)–AT(M), AT(J)–SLP(F) and AT(J)–SLP(M), and the synchronous and time-lag connections between the two parameters are investigated. The areas characterized by a satisfactory monthly or/and bi-monthly forecasting ability are detected. The most satisfactory results refer to the areas affected by the Southern Oscillation. It is found that the SLP teleconnection between the areas of the eastern and the western Pacific in January is related to the regime of AT in the central Pacific Ocean, in both February and March. Also, SLP over the Aleutian and Icelandic lows in January is related to AT over their southwestern and southeastern neighbouring areas in February and March. Finally, it appears that there is also ability for monthly/bi-monthly statistical prediction for some areas affected by the well-known oscillations of North Atlantic Oscillation and Pacific/North American Oscillation. A validation of the statistical prediction methodology is carried out, using real-time series of AT and SLP parameters for some characteristic cases. The results show that the statistical prediction presents a remarkable success. The success rate varies from 67% to 83% for the analysis period 1949–2002 and from 71% to 86% for the recent period 2003–2009.
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container_issue	1-2
title_short	Sea-level pressure–air temperature teleconnections during northern hemisphere winter
url	https://dx.doi.org/10.1007/s00704-011-0523-8
remote_bool	true
author2	Bartzokas, A. Lolis, C. J. Hatzianastassiou, N.
author2Str	Bartzokas, A. Lolis, C. J. Hatzianastassiou, N.
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doi_str	10.1007/s00704-011-0523-8
up_date	2024-07-04T02:50:44.123Z
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Sea-level pressure–air temperature teleconnections during northern hemisphere winter

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