Implementation of a dynamic equation constraint based on the steady state momentum equations within the WRF hybrid ensemble‐3DVar data assimilation system and test with radar T‐TREC wind assimilation for tropical Cyclone Chanthu (2010)
Proper dynamic equation constraints in data assimilation (DA) systems can help improve balance of analyzed atmospheric state. The formulation of ensemble‐variational DA algorithms allows for easy incorporation of such constraints, but their impacts within such DA systems have been little studied. A...
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| Autor*in: |
Li, Xin [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Rechteinformationen: |
Nutzungsrecht: © 2015. American Geophysical Union. All Rights Reserved. |
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Enthalten in: Journal of geophysical research / D - Washington, DC : Union, 1984, 120(2015), 9, Seite 4017-4039 |
|---|---|
| Übergeordnetes Werk: |
volume:120 ; year:2015 ; number:9 ; pages:4017-4039 |
| Links: |
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| DOI / URN: |
10.1002/2014JD022706 |
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| 245 | 1 | 0 | |a Implementation of a dynamic equation constraint based on the steady state momentum equations within the WRF hybrid ensemble‐3DVar data assimilation system and test with radar T‐TREC wind assimilation for tropical Cyclone Chanthu (2010) |
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| 520 | |a Proper dynamic equation constraints in data assimilation (DA) systems can help improve balance of analyzed atmospheric state. The formulation of ensemble‐variational DA algorithms allows for easy incorporation of such constraints, but their impacts within such DA systems have been little studied. A dynamic constraint based on the steady momentum equations is incorporated into the WRF (Weather Research and Forecasting) hybrid ensemble‐three‐dimensional variational (3DVar) (En3DVar) DA system as a weak constraint. The constraint aims at improving the coupling and balance among wind and thermodynamic state variables, especially when few state variables are directly observed. The scheme is applied to the assimilation of radar T‐TREC (Typhoon‐Tracking Radar Echo by Correlation) winds at a convection‐allowing resolution, for landfalling typhoon, Chanthu (2010), when it was within the range of a coastal radar. Parallel experiments using the 3DVar and En3DVar with and without the dynamic constraint are run to examine the impact of the constraint. The flow‐dependent ensemble covariance used in En3DVar helps to update unobserved pressure and temperature fields in a dynamically more consistent way compared to the static covariance; the added dynamic constraint produces more accurate pressure within the typhoon. The pressure field improved by the dynamic constraint also leads to better temperature and moisture analyses within the variational minimization through flow‐dependent cross covariance. En3DVar analysis with the dynamic constraint produces the best intensity forecast for the typhoon, in terms of the minimum sea level pressure and maximum surface wind speed. Additional sensitivity experiments examine the impact of the weight of the dynamic constraint. Hybrid ensemble‐3DVar is more effective than 3DVar in radar assimilation for TC Including dynamic constraint in hybrid En3DVar produces better TC analysis Hybrid En3DVar analysis with dynamic constraint produces the best TC forecast | ||
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| 650 | 4 | |a dynamic constraint | |
| 650 | 4 | |a hybrid assimilation | |
| 650 | 4 | |a typhoon forecast | |
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| 650 | 4 | |a Meteorology | |
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| 700 | 1 | |a Ming, Jie |4 oth | |
| 700 | 1 | |a Xue, Ming |4 oth | |
| 700 | 1 | |a Wang, Yuan |4 oth | |
| 700 | 1 | |a Zhao, Kun |4 oth | |
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10.1002/2014JD022706 doi PQ20160617 (DE-627)OLC195705753X (DE-599)GBVOLC195705753X (PRQ)p2139-6682cb1966799f97de3d5095d04981c44c6b7f9def1cda86b20743c021cc11a40 (KEY)0137985220150000120000904017implementationofadynamicequationconstraintbasedont DE-627 ger DE-627 rakwb eng 550 DNB Li, Xin verfasserin aut Implementation of a dynamic equation constraint based on the steady state momentum equations within the WRF hybrid ensemble‐3DVar data assimilation system and test with radar T‐TREC wind assimilation for tropical Cyclone Chanthu (2010) 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Proper dynamic equation constraints in data assimilation (DA) systems can help improve balance of analyzed atmospheric state. The formulation of ensemble‐variational DA algorithms allows for easy incorporation of such constraints, but their impacts within such DA systems have been little studied. A dynamic constraint based on the steady momentum equations is incorporated into the WRF (Weather Research and Forecasting) hybrid ensemble‐three‐dimensional variational (3DVar) (En3DVar) DA system as a weak constraint. The constraint aims at improving the coupling and balance among wind and thermodynamic state variables, especially when few state variables are directly observed. The scheme is applied to the assimilation of radar T‐TREC (Typhoon‐Tracking Radar Echo by Correlation) winds at a convection‐allowing resolution, for landfalling typhoon, Chanthu (2010), when it was within the range of a coastal radar. Parallel experiments using the 3DVar and En3DVar with and without the dynamic constraint are run to examine the impact of the constraint. The flow‐dependent ensemble covariance used in En3DVar helps to update unobserved pressure and temperature fields in a dynamically more consistent way compared to the static covariance; the added dynamic constraint produces more accurate pressure within the typhoon. The pressure field improved by the dynamic constraint also leads to better temperature and moisture analyses within the variational minimization through flow‐dependent cross covariance. En3DVar analysis with the dynamic constraint produces the best intensity forecast for the typhoon, in terms of the minimum sea level pressure and maximum surface wind speed. Additional sensitivity experiments examine the impact of the weight of the dynamic constraint. Hybrid ensemble‐3DVar is more effective than 3DVar in radar assimilation for TC Including dynamic constraint in hybrid En3DVar produces better TC analysis Hybrid En3DVar analysis with dynamic constraint produces the best TC forecast Nutzungsrecht: © 2015. American Geophysical Union. All Rights Reserved. dynamic constraint hybrid assimilation typhoon forecast Data assimilation Meteorology Radar Ming, Jie oth Xue, Ming oth Wang, Yuan oth Zhao, Kun oth Enthalten in Journal of geophysical research / D Washington, DC : Union, 1984 120(2015), 9, Seite 4017-4039 (DE-627)130444391 (DE-600)710256-2 (DE-576)015978818 2169-897X nnns volume:120 year:2015 number:9 pages:4017-4039 http://dx.doi.org/10.1002/2014JD022706 Volltext http://onlinelibrary.wiley.com/doi/10.1002/2014JD022706/abstract http://search.proquest.com/docview/1685159112 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_62 GBV_ILN_154 AR 120 2015 9 4017-4039 |
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10.1002/2014JD022706 doi PQ20160617 (DE-627)OLC195705753X (DE-599)GBVOLC195705753X (PRQ)p2139-6682cb1966799f97de3d5095d04981c44c6b7f9def1cda86b20743c021cc11a40 (KEY)0137985220150000120000904017implementationofadynamicequationconstraintbasedont DE-627 ger DE-627 rakwb eng 550 DNB Li, Xin verfasserin aut Implementation of a dynamic equation constraint based on the steady state momentum equations within the WRF hybrid ensemble‐3DVar data assimilation system and test with radar T‐TREC wind assimilation for tropical Cyclone Chanthu (2010) 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Proper dynamic equation constraints in data assimilation (DA) systems can help improve balance of analyzed atmospheric state. The formulation of ensemble‐variational DA algorithms allows for easy incorporation of such constraints, but their impacts within such DA systems have been little studied. A dynamic constraint based on the steady momentum equations is incorporated into the WRF (Weather Research and Forecasting) hybrid ensemble‐three‐dimensional variational (3DVar) (En3DVar) DA system as a weak constraint. The constraint aims at improving the coupling and balance among wind and thermodynamic state variables, especially when few state variables are directly observed. The scheme is applied to the assimilation of radar T‐TREC (Typhoon‐Tracking Radar Echo by Correlation) winds at a convection‐allowing resolution, for landfalling typhoon, Chanthu (2010), when it was within the range of a coastal radar. Parallel experiments using the 3DVar and En3DVar with and without the dynamic constraint are run to examine the impact of the constraint. The flow‐dependent ensemble covariance used in En3DVar helps to update unobserved pressure and temperature fields in a dynamically more consistent way compared to the static covariance; the added dynamic constraint produces more accurate pressure within the typhoon. The pressure field improved by the dynamic constraint also leads to better temperature and moisture analyses within the variational minimization through flow‐dependent cross covariance. En3DVar analysis with the dynamic constraint produces the best intensity forecast for the typhoon, in terms of the minimum sea level pressure and maximum surface wind speed. Additional sensitivity experiments examine the impact of the weight of the dynamic constraint. Hybrid ensemble‐3DVar is more effective than 3DVar in radar assimilation for TC Including dynamic constraint in hybrid En3DVar produces better TC analysis Hybrid En3DVar analysis with dynamic constraint produces the best TC forecast Nutzungsrecht: © 2015. American Geophysical Union. All Rights Reserved. dynamic constraint hybrid assimilation typhoon forecast Data assimilation Meteorology Radar Ming, Jie oth Xue, Ming oth Wang, Yuan oth Zhao, Kun oth Enthalten in Journal of geophysical research / D Washington, DC : Union, 1984 120(2015), 9, Seite 4017-4039 (DE-627)130444391 (DE-600)710256-2 (DE-576)015978818 2169-897X nnns volume:120 year:2015 number:9 pages:4017-4039 http://dx.doi.org/10.1002/2014JD022706 Volltext http://onlinelibrary.wiley.com/doi/10.1002/2014JD022706/abstract http://search.proquest.com/docview/1685159112 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_62 GBV_ILN_154 AR 120 2015 9 4017-4039 |
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10.1002/2014JD022706 doi PQ20160617 (DE-627)OLC195705753X (DE-599)GBVOLC195705753X (PRQ)p2139-6682cb1966799f97de3d5095d04981c44c6b7f9def1cda86b20743c021cc11a40 (KEY)0137985220150000120000904017implementationofadynamicequationconstraintbasedont DE-627 ger DE-627 rakwb eng 550 DNB Li, Xin verfasserin aut Implementation of a dynamic equation constraint based on the steady state momentum equations within the WRF hybrid ensemble‐3DVar data assimilation system and test with radar T‐TREC wind assimilation for tropical Cyclone Chanthu (2010) 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Proper dynamic equation constraints in data assimilation (DA) systems can help improve balance of analyzed atmospheric state. The formulation of ensemble‐variational DA algorithms allows for easy incorporation of such constraints, but their impacts within such DA systems have been little studied. A dynamic constraint based on the steady momentum equations is incorporated into the WRF (Weather Research and Forecasting) hybrid ensemble‐three‐dimensional variational (3DVar) (En3DVar) DA system as a weak constraint. The constraint aims at improving the coupling and balance among wind and thermodynamic state variables, especially when few state variables are directly observed. The scheme is applied to the assimilation of radar T‐TREC (Typhoon‐Tracking Radar Echo by Correlation) winds at a convection‐allowing resolution, for landfalling typhoon, Chanthu (2010), when it was within the range of a coastal radar. Parallel experiments using the 3DVar and En3DVar with and without the dynamic constraint are run to examine the impact of the constraint. The flow‐dependent ensemble covariance used in En3DVar helps to update unobserved pressure and temperature fields in a dynamically more consistent way compared to the static covariance; the added dynamic constraint produces more accurate pressure within the typhoon. The pressure field improved by the dynamic constraint also leads to better temperature and moisture analyses within the variational minimization through flow‐dependent cross covariance. En3DVar analysis with the dynamic constraint produces the best intensity forecast for the typhoon, in terms of the minimum sea level pressure and maximum surface wind speed. Additional sensitivity experiments examine the impact of the weight of the dynamic constraint. Hybrid ensemble‐3DVar is more effective than 3DVar in radar assimilation for TC Including dynamic constraint in hybrid En3DVar produces better TC analysis Hybrid En3DVar analysis with dynamic constraint produces the best TC forecast Nutzungsrecht: © 2015. American Geophysical Union. All Rights Reserved. dynamic constraint hybrid assimilation typhoon forecast Data assimilation Meteorology Radar Ming, Jie oth Xue, Ming oth Wang, Yuan oth Zhao, Kun oth Enthalten in Journal of geophysical research / D Washington, DC : Union, 1984 120(2015), 9, Seite 4017-4039 (DE-627)130444391 (DE-600)710256-2 (DE-576)015978818 2169-897X nnns volume:120 year:2015 number:9 pages:4017-4039 http://dx.doi.org/10.1002/2014JD022706 Volltext http://onlinelibrary.wiley.com/doi/10.1002/2014JD022706/abstract http://search.proquest.com/docview/1685159112 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_62 GBV_ILN_154 AR 120 2015 9 4017-4039 |
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10.1002/2014JD022706 doi PQ20160617 (DE-627)OLC195705753X (DE-599)GBVOLC195705753X (PRQ)p2139-6682cb1966799f97de3d5095d04981c44c6b7f9def1cda86b20743c021cc11a40 (KEY)0137985220150000120000904017implementationofadynamicequationconstraintbasedont DE-627 ger DE-627 rakwb eng 550 DNB Li, Xin verfasserin aut Implementation of a dynamic equation constraint based on the steady state momentum equations within the WRF hybrid ensemble‐3DVar data assimilation system and test with radar T‐TREC wind assimilation for tropical Cyclone Chanthu (2010) 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Proper dynamic equation constraints in data assimilation (DA) systems can help improve balance of analyzed atmospheric state. The formulation of ensemble‐variational DA algorithms allows for easy incorporation of such constraints, but their impacts within such DA systems have been little studied. A dynamic constraint based on the steady momentum equations is incorporated into the WRF (Weather Research and Forecasting) hybrid ensemble‐three‐dimensional variational (3DVar) (En3DVar) DA system as a weak constraint. The constraint aims at improving the coupling and balance among wind and thermodynamic state variables, especially when few state variables are directly observed. The scheme is applied to the assimilation of radar T‐TREC (Typhoon‐Tracking Radar Echo by Correlation) winds at a convection‐allowing resolution, for landfalling typhoon, Chanthu (2010), when it was within the range of a coastal radar. Parallel experiments using the 3DVar and En3DVar with and without the dynamic constraint are run to examine the impact of the constraint. The flow‐dependent ensemble covariance used in En3DVar helps to update unobserved pressure and temperature fields in a dynamically more consistent way compared to the static covariance; the added dynamic constraint produces more accurate pressure within the typhoon. The pressure field improved by the dynamic constraint also leads to better temperature and moisture analyses within the variational minimization through flow‐dependent cross covariance. En3DVar analysis with the dynamic constraint produces the best intensity forecast for the typhoon, in terms of the minimum sea level pressure and maximum surface wind speed. Additional sensitivity experiments examine the impact of the weight of the dynamic constraint. Hybrid ensemble‐3DVar is more effective than 3DVar in radar assimilation for TC Including dynamic constraint in hybrid En3DVar produces better TC analysis Hybrid En3DVar analysis with dynamic constraint produces the best TC forecast Nutzungsrecht: © 2015. American Geophysical Union. All Rights Reserved. dynamic constraint hybrid assimilation typhoon forecast Data assimilation Meteorology Radar Ming, Jie oth Xue, Ming oth Wang, Yuan oth Zhao, Kun oth Enthalten in Journal of geophysical research / D Washington, DC : Union, 1984 120(2015), 9, Seite 4017-4039 (DE-627)130444391 (DE-600)710256-2 (DE-576)015978818 2169-897X nnns volume:120 year:2015 number:9 pages:4017-4039 http://dx.doi.org/10.1002/2014JD022706 Volltext http://onlinelibrary.wiley.com/doi/10.1002/2014JD022706/abstract http://search.proquest.com/docview/1685159112 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_62 GBV_ILN_154 AR 120 2015 9 4017-4039 |
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10.1002/2014JD022706 doi PQ20160617 (DE-627)OLC195705753X (DE-599)GBVOLC195705753X (PRQ)p2139-6682cb1966799f97de3d5095d04981c44c6b7f9def1cda86b20743c021cc11a40 (KEY)0137985220150000120000904017implementationofadynamicequationconstraintbasedont DE-627 ger DE-627 rakwb eng 550 DNB Li, Xin verfasserin aut Implementation of a dynamic equation constraint based on the steady state momentum equations within the WRF hybrid ensemble‐3DVar data assimilation system and test with radar T‐TREC wind assimilation for tropical Cyclone Chanthu (2010) 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Proper dynamic equation constraints in data assimilation (DA) systems can help improve balance of analyzed atmospheric state. The formulation of ensemble‐variational DA algorithms allows for easy incorporation of such constraints, but their impacts within such DA systems have been little studied. A dynamic constraint based on the steady momentum equations is incorporated into the WRF (Weather Research and Forecasting) hybrid ensemble‐three‐dimensional variational (3DVar) (En3DVar) DA system as a weak constraint. The constraint aims at improving the coupling and balance among wind and thermodynamic state variables, especially when few state variables are directly observed. The scheme is applied to the assimilation of radar T‐TREC (Typhoon‐Tracking Radar Echo by Correlation) winds at a convection‐allowing resolution, for landfalling typhoon, Chanthu (2010), when it was within the range of a coastal radar. Parallel experiments using the 3DVar and En3DVar with and without the dynamic constraint are run to examine the impact of the constraint. The flow‐dependent ensemble covariance used in En3DVar helps to update unobserved pressure and temperature fields in a dynamically more consistent way compared to the static covariance; the added dynamic constraint produces more accurate pressure within the typhoon. The pressure field improved by the dynamic constraint also leads to better temperature and moisture analyses within the variational minimization through flow‐dependent cross covariance. En3DVar analysis with the dynamic constraint produces the best intensity forecast for the typhoon, in terms of the minimum sea level pressure and maximum surface wind speed. Additional sensitivity experiments examine the impact of the weight of the dynamic constraint. Hybrid ensemble‐3DVar is more effective than 3DVar in radar assimilation for TC Including dynamic constraint in hybrid En3DVar produces better TC analysis Hybrid En3DVar analysis with dynamic constraint produces the best TC forecast Nutzungsrecht: © 2015. American Geophysical Union. All Rights Reserved. dynamic constraint hybrid assimilation typhoon forecast Data assimilation Meteorology Radar Ming, Jie oth Xue, Ming oth Wang, Yuan oth Zhao, Kun oth Enthalten in Journal of geophysical research / D Washington, DC : Union, 1984 120(2015), 9, Seite 4017-4039 (DE-627)130444391 (DE-600)710256-2 (DE-576)015978818 2169-897X nnns volume:120 year:2015 number:9 pages:4017-4039 http://dx.doi.org/10.1002/2014JD022706 Volltext http://onlinelibrary.wiley.com/doi/10.1002/2014JD022706/abstract http://search.proquest.com/docview/1685159112 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_62 GBV_ILN_154 AR 120 2015 9 4017-4039 |
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Li, Xin |
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Li, Xin ddc 550 misc dynamic constraint misc hybrid assimilation misc typhoon forecast misc Data assimilation misc Meteorology misc Radar Implementation of a dynamic equation constraint based on the steady state momentum equations within the WRF hybrid ensemble‐3DVar data assimilation system and test with radar T‐TREC wind assimilation for tropical Cyclone Chanthu (2010) |
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550 DNB Implementation of a dynamic equation constraint based on the steady state momentum equations within the WRF hybrid ensemble‐3DVar data assimilation system and test with radar T‐TREC wind assimilation for tropical Cyclone Chanthu (2010) dynamic constraint hybrid assimilation typhoon forecast Data assimilation Meteorology Radar |
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Implementation of a dynamic equation constraint based on the steady state momentum equations within the WRF hybrid ensemble‐3DVar data assimilation system and test with radar T‐TREC wind assimilation for tropical Cyclone Chanthu (2010) |
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Implementation of a dynamic equation constraint based on the steady state momentum equations within the WRF hybrid ensemble‐3DVar data assimilation system and test with radar T‐TREC wind assimilation for tropical Cyclone Chanthu (2010) |
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implementation of a dynamic equation constraint based on the steady state momentum equations within the wrf hybrid ensemble‐3dvar data assimilation system and test with radar t‐trec wind assimilation for tropical cyclone chanthu (2010) |
| title_auth |
Implementation of a dynamic equation constraint based on the steady state momentum equations within the WRF hybrid ensemble‐3DVar data assimilation system and test with radar T‐TREC wind assimilation for tropical Cyclone Chanthu (2010) |
| abstract |
Proper dynamic equation constraints in data assimilation (DA) systems can help improve balance of analyzed atmospheric state. The formulation of ensemble‐variational DA algorithms allows for easy incorporation of such constraints, but their impacts within such DA systems have been little studied. A dynamic constraint based on the steady momentum equations is incorporated into the WRF (Weather Research and Forecasting) hybrid ensemble‐three‐dimensional variational (3DVar) (En3DVar) DA system as a weak constraint. The constraint aims at improving the coupling and balance among wind and thermodynamic state variables, especially when few state variables are directly observed. The scheme is applied to the assimilation of radar T‐TREC (Typhoon‐Tracking Radar Echo by Correlation) winds at a convection‐allowing resolution, for landfalling typhoon, Chanthu (2010), when it was within the range of a coastal radar. Parallel experiments using the 3DVar and En3DVar with and without the dynamic constraint are run to examine the impact of the constraint. The flow‐dependent ensemble covariance used in En3DVar helps to update unobserved pressure and temperature fields in a dynamically more consistent way compared to the static covariance; the added dynamic constraint produces more accurate pressure within the typhoon. The pressure field improved by the dynamic constraint also leads to better temperature and moisture analyses within the variational minimization through flow‐dependent cross covariance. En3DVar analysis with the dynamic constraint produces the best intensity forecast for the typhoon, in terms of the minimum sea level pressure and maximum surface wind speed. Additional sensitivity experiments examine the impact of the weight of the dynamic constraint. Hybrid ensemble‐3DVar is more effective than 3DVar in radar assimilation for TC Including dynamic constraint in hybrid En3DVar produces better TC analysis Hybrid En3DVar analysis with dynamic constraint produces the best TC forecast |
| abstractGer |
Proper dynamic equation constraints in data assimilation (DA) systems can help improve balance of analyzed atmospheric state. The formulation of ensemble‐variational DA algorithms allows for easy incorporation of such constraints, but their impacts within such DA systems have been little studied. A dynamic constraint based on the steady momentum equations is incorporated into the WRF (Weather Research and Forecasting) hybrid ensemble‐three‐dimensional variational (3DVar) (En3DVar) DA system as a weak constraint. The constraint aims at improving the coupling and balance among wind and thermodynamic state variables, especially when few state variables are directly observed. The scheme is applied to the assimilation of radar T‐TREC (Typhoon‐Tracking Radar Echo by Correlation) winds at a convection‐allowing resolution, for landfalling typhoon, Chanthu (2010), when it was within the range of a coastal radar. Parallel experiments using the 3DVar and En3DVar with and without the dynamic constraint are run to examine the impact of the constraint. The flow‐dependent ensemble covariance used in En3DVar helps to update unobserved pressure and temperature fields in a dynamically more consistent way compared to the static covariance; the added dynamic constraint produces more accurate pressure within the typhoon. The pressure field improved by the dynamic constraint also leads to better temperature and moisture analyses within the variational minimization through flow‐dependent cross covariance. En3DVar analysis with the dynamic constraint produces the best intensity forecast for the typhoon, in terms of the minimum sea level pressure and maximum surface wind speed. Additional sensitivity experiments examine the impact of the weight of the dynamic constraint. Hybrid ensemble‐3DVar is more effective than 3DVar in radar assimilation for TC Including dynamic constraint in hybrid En3DVar produces better TC analysis Hybrid En3DVar analysis with dynamic constraint produces the best TC forecast |
| abstract_unstemmed |
Proper dynamic equation constraints in data assimilation (DA) systems can help improve balance of analyzed atmospheric state. The formulation of ensemble‐variational DA algorithms allows for easy incorporation of such constraints, but their impacts within such DA systems have been little studied. A dynamic constraint based on the steady momentum equations is incorporated into the WRF (Weather Research and Forecasting) hybrid ensemble‐three‐dimensional variational (3DVar) (En3DVar) DA system as a weak constraint. The constraint aims at improving the coupling and balance among wind and thermodynamic state variables, especially when few state variables are directly observed. The scheme is applied to the assimilation of radar T‐TREC (Typhoon‐Tracking Radar Echo by Correlation) winds at a convection‐allowing resolution, for landfalling typhoon, Chanthu (2010), when it was within the range of a coastal radar. Parallel experiments using the 3DVar and En3DVar with and without the dynamic constraint are run to examine the impact of the constraint. The flow‐dependent ensemble covariance used in En3DVar helps to update unobserved pressure and temperature fields in a dynamically more consistent way compared to the static covariance; the added dynamic constraint produces more accurate pressure within the typhoon. The pressure field improved by the dynamic constraint also leads to better temperature and moisture analyses within the variational minimization through flow‐dependent cross covariance. En3DVar analysis with the dynamic constraint produces the best intensity forecast for the typhoon, in terms of the minimum sea level pressure and maximum surface wind speed. Additional sensitivity experiments examine the impact of the weight of the dynamic constraint. Hybrid ensemble‐3DVar is more effective than 3DVar in radar assimilation for TC Including dynamic constraint in hybrid En3DVar produces better TC analysis Hybrid En3DVar analysis with dynamic constraint produces the best TC forecast |
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Implementation of a dynamic equation constraint based on the steady state momentum equations within the WRF hybrid ensemble‐3DVar data assimilation system and test with radar T‐TREC wind assimilation for tropical Cyclone Chanthu (2010) |
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http://dx.doi.org/10.1002/2014JD022706 http://onlinelibrary.wiley.com/doi/10.1002/2014JD022706/abstract http://search.proquest.com/docview/1685159112 |
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