ENSO‐based probabilistic forecasts of March–May U.S. tornado and hail activity
Extended logistic regression is used to predict March–May severe convective storm (SCS) activity based on the preceding December–February (DJF) El Niño–Southern Oscillation (ENSO) state. The spatially resolved probabilistic forecasts are verified against U.S. tornado counts, hail events, and two env...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lepore, Chiara [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Rechteinformationen: |
Nutzungsrecht: © 2017. The Authors. |
|---|
| Schlagwörter: |
seasonal probabilistic forecast |
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| Übergeordnetes Werk: |
Enthalten in: Geophysical research letters - Washington, DC : Union, 1974, 44(2017), 17, Seite 9093-9101 |
|---|---|
| Übergeordnetes Werk: |
volume:44 ; year:2017 ; number:17 ; pages:9093-9101 |
| Links: |
|---|
| DOI / URN: |
10.1002/2017GL074781 |
|---|
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OLC1996982389 |
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| 520 | |a Extended logistic regression is used to predict March–May severe convective storm (SCS) activity based on the preceding December–February (DJF) El Niño–Southern Oscillation (ENSO) state. The spatially resolved probabilistic forecasts are verified against U.S. tornado counts, hail events, and two environmental indices for severe convection. The cross‐validated skill is positive for roughly a quarter of the U.S. Overall, indices are predicted with more skill than are storm reports, and hail events are predicted with more skill than tornado counts. Skill is higher in the cool phase of ENSO (La Niña like) when overall SCS activity is higher. SCS forecasts based on the predicted DJF ENSO state from coupled dynamical models initialized in October of the previous year extend the lead time with only a modest reduction in skill compared to forecasts based on the observed DJF ENSO state. Probability forecasts of U.S. March‐May severe thunderstorm activity show skill over about a quarter of the country Forecasts are based on either the observed or predicted ENSO state during the preceding December‐February Hail event numbers are predicted more skillfully than tornado counts | ||
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| 650 | 4 | |a seasonal probabilistic forecast | |
| 650 | 4 | |a ENSO | |
| 650 | 4 | |a severe thunderstorms | |
| 650 | 4 | |a Convection | |
| 650 | 4 | |a Hail | |
| 650 | 4 | |a Severe convection | |
| 650 | 4 | |a Tornadoes | |
| 650 | 4 | |a La Nina | |
| 650 | 4 | |a Storms | |
| 650 | 4 | |a Southern Oscillation | |
| 650 | 4 | |a Ocean currents | |
| 650 | 4 | |a Reduction | |
| 650 | 4 | |a Lead time | |
| 650 | 4 | |a El Nino | |
| 650 | 4 | |a El Nino-Southern Oscillation event | |
| 650 | 4 | |a El Nino phenomena | |
| 700 | 1 | |a Tippett, Michael K |4 oth | |
| 700 | 1 | |a Allen, John T |4 oth | |
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10.1002/2017GL074781 doi PQ20171228 (DE-627)OLC1996982389 (DE-599)GBVOLC1996982389 (PRQ)p1369-521ab132e404f31165061e5947aa7ead2da435663db1710a012efba755bcbdd80 (KEY)0026932820170000044001709093ensobasedprobabilisticforecastsofmarchmayustornado DE-627 ger DE-627 rakwb eng 550 DNB 38.70 bkl Lepore, Chiara verfasserin aut ENSO‐based probabilistic forecasts of March–May U.S. tornado and hail activity 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Extended logistic regression is used to predict March–May severe convective storm (SCS) activity based on the preceding December–February (DJF) El Niño–Southern Oscillation (ENSO) state. The spatially resolved probabilistic forecasts are verified against U.S. tornado counts, hail events, and two environmental indices for severe convection. The cross‐validated skill is positive for roughly a quarter of the U.S. Overall, indices are predicted with more skill than are storm reports, and hail events are predicted with more skill than tornado counts. Skill is higher in the cool phase of ENSO (La Niña like) when overall SCS activity is higher. SCS forecasts based on the predicted DJF ENSO state from coupled dynamical models initialized in October of the previous year extend the lead time with only a modest reduction in skill compared to forecasts based on the observed DJF ENSO state. Probability forecasts of U.S. March‐May severe thunderstorm activity show skill over about a quarter of the country Forecasts are based on either the observed or predicted ENSO state during the preceding December‐February Hail event numbers are predicted more skillfully than tornado counts Nutzungsrecht: © 2017. The Authors. seasonal probabilistic forecast ENSO severe thunderstorms Convection Hail Severe convection Tornadoes La Nina Storms Southern Oscillation Ocean currents Reduction Lead time El Nino El Nino-Southern Oscillation event El Nino phenomena Tippett, Michael K oth Allen, John T oth Enthalten in Geophysical research letters Washington, DC : Union, 1974 44(2017), 17, Seite 9093-9101 (DE-627)129095109 (DE-600)7403-2 (DE-576)01443122X 0094-8276 nnns volume:44 year:2017 number:17 pages:9093-9101 http://dx.doi.org/10.1002/2017GL074781 Volltext http://onlinelibrary.wiley.com/doi/10.1002/2017GL074781/abstract https://search.proquest.com/docview/1942631739 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_47 GBV_ILN_62 GBV_ILN_154 GBV_ILN_601 GBV_ILN_2279 38.70 AVZ AR 44 2017 17 9093-9101 |
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10.1002/2017GL074781 doi PQ20171228 (DE-627)OLC1996982389 (DE-599)GBVOLC1996982389 (PRQ)p1369-521ab132e404f31165061e5947aa7ead2da435663db1710a012efba755bcbdd80 (KEY)0026932820170000044001709093ensobasedprobabilisticforecastsofmarchmayustornado DE-627 ger DE-627 rakwb eng 550 DNB 38.70 bkl Lepore, Chiara verfasserin aut ENSO‐based probabilistic forecasts of March–May U.S. tornado and hail activity 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Extended logistic regression is used to predict March–May severe convective storm (SCS) activity based on the preceding December–February (DJF) El Niño–Southern Oscillation (ENSO) state. The spatially resolved probabilistic forecasts are verified against U.S. tornado counts, hail events, and two environmental indices for severe convection. The cross‐validated skill is positive for roughly a quarter of the U.S. Overall, indices are predicted with more skill than are storm reports, and hail events are predicted with more skill than tornado counts. Skill is higher in the cool phase of ENSO (La Niña like) when overall SCS activity is higher. SCS forecasts based on the predicted DJF ENSO state from coupled dynamical models initialized in October of the previous year extend the lead time with only a modest reduction in skill compared to forecasts based on the observed DJF ENSO state. Probability forecasts of U.S. March‐May severe thunderstorm activity show skill over about a quarter of the country Forecasts are based on either the observed or predicted ENSO state during the preceding December‐February Hail event numbers are predicted more skillfully than tornado counts Nutzungsrecht: © 2017. The Authors. seasonal probabilistic forecast ENSO severe thunderstorms Convection Hail Severe convection Tornadoes La Nina Storms Southern Oscillation Ocean currents Reduction Lead time El Nino El Nino-Southern Oscillation event El Nino phenomena Tippett, Michael K oth Allen, John T oth Enthalten in Geophysical research letters Washington, DC : Union, 1974 44(2017), 17, Seite 9093-9101 (DE-627)129095109 (DE-600)7403-2 (DE-576)01443122X 0094-8276 nnns volume:44 year:2017 number:17 pages:9093-9101 http://dx.doi.org/10.1002/2017GL074781 Volltext http://onlinelibrary.wiley.com/doi/10.1002/2017GL074781/abstract https://search.proquest.com/docview/1942631739 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_47 GBV_ILN_62 GBV_ILN_154 GBV_ILN_601 GBV_ILN_2279 38.70 AVZ AR 44 2017 17 9093-9101 |
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Enthalten in Geophysical research letters 44(2017), 17, Seite 9093-9101 volume:44 year:2017 number:17 pages:9093-9101 |
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550 DNB 38.70 bkl ENSO‐based probabilistic forecasts of March–May U.S. tornado and hail activity seasonal probabilistic forecast ENSO severe thunderstorms Convection Hail Severe convection Tornadoes La Nina Storms Southern Oscillation Ocean currents Reduction Lead time El Nino El Nino-Southern Oscillation event El Nino phenomena |
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ENSO‐based probabilistic forecasts of March–May U.S. tornado and hail activity |
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enso‐based probabilistic forecasts of march–may u.s. tornado and hail activity |
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ENSO‐based probabilistic forecasts of March–May U.S. tornado and hail activity |
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Extended logistic regression is used to predict March–May severe convective storm (SCS) activity based on the preceding December–February (DJF) El Niño–Southern Oscillation (ENSO) state. The spatially resolved probabilistic forecasts are verified against U.S. tornado counts, hail events, and two environmental indices for severe convection. The cross‐validated skill is positive for roughly a quarter of the U.S. Overall, indices are predicted with more skill than are storm reports, and hail events are predicted with more skill than tornado counts. Skill is higher in the cool phase of ENSO (La Niña like) when overall SCS activity is higher. SCS forecasts based on the predicted DJF ENSO state from coupled dynamical models initialized in October of the previous year extend the lead time with only a modest reduction in skill compared to forecasts based on the observed DJF ENSO state. Probability forecasts of U.S. March‐May severe thunderstorm activity show skill over about a quarter of the country Forecasts are based on either the observed or predicted ENSO state during the preceding December‐February Hail event numbers are predicted more skillfully than tornado counts |
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Extended logistic regression is used to predict March–May severe convective storm (SCS) activity based on the preceding December–February (DJF) El Niño–Southern Oscillation (ENSO) state. The spatially resolved probabilistic forecasts are verified against U.S. tornado counts, hail events, and two environmental indices for severe convection. The cross‐validated skill is positive for roughly a quarter of the U.S. Overall, indices are predicted with more skill than are storm reports, and hail events are predicted with more skill than tornado counts. Skill is higher in the cool phase of ENSO (La Niña like) when overall SCS activity is higher. SCS forecasts based on the predicted DJF ENSO state from coupled dynamical models initialized in October of the previous year extend the lead time with only a modest reduction in skill compared to forecasts based on the observed DJF ENSO state. Probability forecasts of U.S. March‐May severe thunderstorm activity show skill over about a quarter of the country Forecasts are based on either the observed or predicted ENSO state during the preceding December‐February Hail event numbers are predicted more skillfully than tornado counts |
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Extended logistic regression is used to predict March–May severe convective storm (SCS) activity based on the preceding December–February (DJF) El Niño–Southern Oscillation (ENSO) state. The spatially resolved probabilistic forecasts are verified against U.S. tornado counts, hail events, and two environmental indices for severe convection. The cross‐validated skill is positive for roughly a quarter of the U.S. Overall, indices are predicted with more skill than are storm reports, and hail events are predicted with more skill than tornado counts. Skill is higher in the cool phase of ENSO (La Niña like) when overall SCS activity is higher. SCS forecasts based on the predicted DJF ENSO state from coupled dynamical models initialized in October of the previous year extend the lead time with only a modest reduction in skill compared to forecasts based on the observed DJF ENSO state. Probability forecasts of U.S. March‐May severe thunderstorm activity show skill over about a quarter of the country Forecasts are based on either the observed or predicted ENSO state during the preceding December‐February Hail event numbers are predicted more skillfully than tornado counts |
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ENSO‐based probabilistic forecasts of March–May U.S. tornado and hail activity |
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