A comparison of limited‐area 3DVAR and ETKF‐En3DVAR data assimilation using radar observations at convective scale for the prediction of Typhoon Saomai (2006)

The ensemble transform Kalman filter based ensemble 3D variational ( ETKF‐En3DVAR ) data assimilation ( DA ) system is employed to evaluate the potential value of assimilating radar radial wind ( V r ) data for the analysis and forecasting of T yphoon S aomai (2006). The DA system conducted cycling...
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Autor*in:	Shen, Feifei [verfasserIn] Xue, Ming Min, Jinzhong

Format:	Artikel
Sprache:	Englisch

Erschienen:	2017

Rechteinformationen:	Nutzungsrecht: © 2017 Royal Meteorological Society

Schlagwörter:	hybrid assimilation model WRF radar radial velocity Data collection Computational fluid dynamics Data processing Mathematical models Radar data Error detection Rainfall Kalman filters Kalman filter Localization Correlation Position (location) Data assimilation Covariance Data Analysis Radar Assimilation Rain Typhoons

Übergeordnetes Werk:	Enthalten in: Meteorological applications - Bognor Regis : Wiley, 1994, 24(2017), 4, Seite 628-641
Übergeordnetes Werk:	volume:24 ; year:2017 ; number:4 ; pages:628-641

Links:	Volltext Link aufrufen Link aufrufen

DOI / URN:	10.1002/met.1663

Katalog-ID:	OLC1998312445

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520			\|a The ensemble transform Kalman filter based ensemble 3D variational ( ETKF‐En3DVAR ) data assimilation ( DA ) system is employed to evaluate the potential value of assimilating radar radial wind ( V r ) data for the analysis and forecasting of T yphoon S aomai (2006). The DA system conducted cycling assimilation every 30 min when S aomai started to enter radar coverage. Within the DA cycles, the control analysis was updated by the ETKF‐En3DVAR algorithm whereas the forecast ensemble perturbations in the hybrid scheme were updated by the ETKF algorithm. The benefits from the use of the flow‐dependent ensemble covariance are explored by comparing the analysis increments, analysis and subsequent forecasts from the hybrid scheme with those from a pure 3DVAR using static background error covariance. Sensitivity to the horizontal correlation scale in the 3DVAR and the vertical covariance localization in the hybrid are also explored. The reduced horizontal correlation scale in the 3DVAR yields much more reasonable circulation analyses than the default scale. The vertical covariance localization scale specified in terms of geometric height instead of model levels allows for desirable spreading of V r data to the surface. It seems that the assimilation with the hybrid method leads to further improved vortex intensity forecast and track forecast of the typhoon compared to those in the analyses from the global forecast system and 3DVAR . The results also indicated that the hybrid has a significant effect on the 12 h accumulated rainfall forecasts. Such improvements for analysis and forecast are probably due to the use of the flow‐dependent background error covariance.
540			\|a Nutzungsrecht: © 2017 Royal Meteorological Society
650		4	\|a hybrid assimilation
650		4	\|a model
650		4	\|a WRF
650		4	\|a radar radial velocity
650		4	\|a Data collection
650		4	\|a Computational fluid dynamics
650		4	\|a Data processing
650		4	\|a Mathematical models
650		4	\|a Radar data
650		4	\|a Error detection
650		4	\|a Rainfall
650		4	\|a Kalman filters
650		4	\|a Kalman filter
650		4	\|a Localization
650		4	\|a Correlation
650		4	\|a Position (location)
650		4	\|a Data assimilation
650		4	\|a Covariance
650		4	\|a Data
650		4	\|a Analysis
650		4	\|a Radar
650		4	\|a Assimilation
650		4	\|a Rain
650		4	\|a Typhoons
700	1		\|a Xue, Ming \|4 oth
700	1		\|a Min, Jinzhong \|4 oth
773	0	8	\|i Enthalten in \|t Meteorological applications \|d Bognor Regis : Wiley, 1994 \|g 24(2017), 4, Seite 628-641 \|w (DE-627)188146865 \|w (DE-600)1280578-6 \|w (DE-576)334708931 \|x 1350-4827 \|7 nnns
773	1	8	\|g volume:24 \|g year:2017 \|g number:4 \|g pages:628-641
856	4	1	\|u http://dx.doi.org/10.1002/met.1663 \|3 Volltext
856	4	2	\|u http://onlinelibrary.wiley.com/doi/10.1002/met.1663/abstract
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allfields	10.1002/met.1663 doi PQ20171228 (DE-627)OLC1998312445 (DE-599)GBVOLC1998312445 (PRQ)p1313-ccb5955d62085bf0ba874954689b053a8b4aaa91e36c3f8cc279917f38e86ad3 (KEY)0238303920170000024000400628comparisonoflimitedarea3dvarandetkfen3dvardataassi DE-627 ger DE-627 rakwb eng 550 ZDB Shen, Feifei verfasserin aut A comparison of limited‐area 3DVAR and ETKF‐En3DVAR data assimilation using radar observations at convective scale for the prediction of Typhoon Saomai (2006) 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The ensemble transform Kalman filter based ensemble 3D variational ( ETKF‐En3DVAR ) data assimilation ( DA ) system is employed to evaluate the potential value of assimilating radar radial wind ( V r ) data for the analysis and forecasting of T yphoon S aomai (2006). The DA system conducted cycling assimilation every 30 min when S aomai started to enter radar coverage. Within the DA cycles, the control analysis was updated by the ETKF‐En3DVAR algorithm whereas the forecast ensemble perturbations in the hybrid scheme were updated by the ETKF algorithm. The benefits from the use of the flow‐dependent ensemble covariance are explored by comparing the analysis increments, analysis and subsequent forecasts from the hybrid scheme with those from a pure 3DVAR using static background error covariance. Sensitivity to the horizontal correlation scale in the 3DVAR and the vertical covariance localization in the hybrid are also explored. The reduced horizontal correlation scale in the 3DVAR yields much more reasonable circulation analyses than the default scale. The vertical covariance localization scale specified in terms of geometric height instead of model levels allows for desirable spreading of V r data to the surface. It seems that the assimilation with the hybrid method leads to further improved vortex intensity forecast and track forecast of the typhoon compared to those in the analyses from the global forecast system and 3DVAR . The results also indicated that the hybrid has a significant effect on the 12 h accumulated rainfall forecasts. Such improvements for analysis and forecast are probably due to the use of the flow‐dependent background error covariance. Nutzungsrecht: © 2017 Royal Meteorological Society hybrid assimilation model WRF radar radial velocity Data collection Computational fluid dynamics Data processing Mathematical models Radar data Error detection Rainfall Kalman filters Kalman filter Localization Correlation Position (location) Data assimilation Covariance Data Analysis Radar Assimilation Rain Typhoons Xue, Ming oth Min, Jinzhong oth Enthalten in Meteorological applications Bognor Regis : Wiley, 1994 24(2017), 4, Seite 628-641 (DE-627)188146865 (DE-600)1280578-6 (DE-576)334708931 1350-4827 nnns volume:24 year:2017 number:4 pages:628-641 http://dx.doi.org/10.1002/met.1663 Volltext http://onlinelibrary.wiley.com/doi/10.1002/met.1663/abstract https://search.proquest.com/docview/1947367120 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_22 GBV_ILN_601 AR 24 2017 4 628-641
spelling	10.1002/met.1663 doi PQ20171228 (DE-627)OLC1998312445 (DE-599)GBVOLC1998312445 (PRQ)p1313-ccb5955d62085bf0ba874954689b053a8b4aaa91e36c3f8cc279917f38e86ad3 (KEY)0238303920170000024000400628comparisonoflimitedarea3dvarandetkfen3dvardataassi DE-627 ger DE-627 rakwb eng 550 ZDB Shen, Feifei verfasserin aut A comparison of limited‐area 3DVAR and ETKF‐En3DVAR data assimilation using radar observations at convective scale for the prediction of Typhoon Saomai (2006) 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The ensemble transform Kalman filter based ensemble 3D variational ( ETKF‐En3DVAR ) data assimilation ( DA ) system is employed to evaluate the potential value of assimilating radar radial wind ( V r ) data for the analysis and forecasting of T yphoon S aomai (2006). The DA system conducted cycling assimilation every 30 min when S aomai started to enter radar coverage. Within the DA cycles, the control analysis was updated by the ETKF‐En3DVAR algorithm whereas the forecast ensemble perturbations in the hybrid scheme were updated by the ETKF algorithm. The benefits from the use of the flow‐dependent ensemble covariance are explored by comparing the analysis increments, analysis and subsequent forecasts from the hybrid scheme with those from a pure 3DVAR using static background error covariance. Sensitivity to the horizontal correlation scale in the 3DVAR and the vertical covariance localization in the hybrid are also explored. The reduced horizontal correlation scale in the 3DVAR yields much more reasonable circulation analyses than the default scale. The vertical covariance localization scale specified in terms of geometric height instead of model levels allows for desirable spreading of V r data to the surface. It seems that the assimilation with the hybrid method leads to further improved vortex intensity forecast and track forecast of the typhoon compared to those in the analyses from the global forecast system and 3DVAR . The results also indicated that the hybrid has a significant effect on the 12 h accumulated rainfall forecasts. Such improvements for analysis and forecast are probably due to the use of the flow‐dependent background error covariance. Nutzungsrecht: © 2017 Royal Meteorological Society hybrid assimilation model WRF radar radial velocity Data collection Computational fluid dynamics Data processing Mathematical models Radar data Error detection Rainfall Kalman filters Kalman filter Localization Correlation Position (location) Data assimilation Covariance Data Analysis Radar Assimilation Rain Typhoons Xue, Ming oth Min, Jinzhong oth Enthalten in Meteorological applications Bognor Regis : Wiley, 1994 24(2017), 4, Seite 628-641 (DE-627)188146865 (DE-600)1280578-6 (DE-576)334708931 1350-4827 nnns volume:24 year:2017 number:4 pages:628-641 http://dx.doi.org/10.1002/met.1663 Volltext http://onlinelibrary.wiley.com/doi/10.1002/met.1663/abstract https://search.proquest.com/docview/1947367120 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_22 GBV_ILN_601 AR 24 2017 4 628-641
allfields_unstemmed	10.1002/met.1663 doi PQ20171228 (DE-627)OLC1998312445 (DE-599)GBVOLC1998312445 (PRQ)p1313-ccb5955d62085bf0ba874954689b053a8b4aaa91e36c3f8cc279917f38e86ad3 (KEY)0238303920170000024000400628comparisonoflimitedarea3dvarandetkfen3dvardataassi DE-627 ger DE-627 rakwb eng 550 ZDB Shen, Feifei verfasserin aut A comparison of limited‐area 3DVAR and ETKF‐En3DVAR data assimilation using radar observations at convective scale for the prediction of Typhoon Saomai (2006) 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The ensemble transform Kalman filter based ensemble 3D variational ( ETKF‐En3DVAR ) data assimilation ( DA ) system is employed to evaluate the potential value of assimilating radar radial wind ( V r ) data for the analysis and forecasting of T yphoon S aomai (2006). The DA system conducted cycling assimilation every 30 min when S aomai started to enter radar coverage. Within the DA cycles, the control analysis was updated by the ETKF‐En3DVAR algorithm whereas the forecast ensemble perturbations in the hybrid scheme were updated by the ETKF algorithm. The benefits from the use of the flow‐dependent ensemble covariance are explored by comparing the analysis increments, analysis and subsequent forecasts from the hybrid scheme with those from a pure 3DVAR using static background error covariance. Sensitivity to the horizontal correlation scale in the 3DVAR and the vertical covariance localization in the hybrid are also explored. The reduced horizontal correlation scale in the 3DVAR yields much more reasonable circulation analyses than the default scale. The vertical covariance localization scale specified in terms of geometric height instead of model levels allows for desirable spreading of V r data to the surface. It seems that the assimilation with the hybrid method leads to further improved vortex intensity forecast and track forecast of the typhoon compared to those in the analyses from the global forecast system and 3DVAR . The results also indicated that the hybrid has a significant effect on the 12 h accumulated rainfall forecasts. Such improvements for analysis and forecast are probably due to the use of the flow‐dependent background error covariance. Nutzungsrecht: © 2017 Royal Meteorological Society hybrid assimilation model WRF radar radial velocity Data collection Computational fluid dynamics Data processing Mathematical models Radar data Error detection Rainfall Kalman filters Kalman filter Localization Correlation Position (location) Data assimilation Covariance Data Analysis Radar Assimilation Rain Typhoons Xue, Ming oth Min, Jinzhong oth Enthalten in Meteorological applications Bognor Regis : Wiley, 1994 24(2017), 4, Seite 628-641 (DE-627)188146865 (DE-600)1280578-6 (DE-576)334708931 1350-4827 nnns volume:24 year:2017 number:4 pages:628-641 http://dx.doi.org/10.1002/met.1663 Volltext http://onlinelibrary.wiley.com/doi/10.1002/met.1663/abstract https://search.proquest.com/docview/1947367120 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_22 GBV_ILN_601 AR 24 2017 4 628-641
allfieldsGer	10.1002/met.1663 doi PQ20171228 (DE-627)OLC1998312445 (DE-599)GBVOLC1998312445 (PRQ)p1313-ccb5955d62085bf0ba874954689b053a8b4aaa91e36c3f8cc279917f38e86ad3 (KEY)0238303920170000024000400628comparisonoflimitedarea3dvarandetkfen3dvardataassi DE-627 ger DE-627 rakwb eng 550 ZDB Shen, Feifei verfasserin aut A comparison of limited‐area 3DVAR and ETKF‐En3DVAR data assimilation using radar observations at convective scale for the prediction of Typhoon Saomai (2006) 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The ensemble transform Kalman filter based ensemble 3D variational ( ETKF‐En3DVAR ) data assimilation ( DA ) system is employed to evaluate the potential value of assimilating radar radial wind ( V r ) data for the analysis and forecasting of T yphoon S aomai (2006). The DA system conducted cycling assimilation every 30 min when S aomai started to enter radar coverage. Within the DA cycles, the control analysis was updated by the ETKF‐En3DVAR algorithm whereas the forecast ensemble perturbations in the hybrid scheme were updated by the ETKF algorithm. The benefits from the use of the flow‐dependent ensemble covariance are explored by comparing the analysis increments, analysis and subsequent forecasts from the hybrid scheme with those from a pure 3DVAR using static background error covariance. Sensitivity to the horizontal correlation scale in the 3DVAR and the vertical covariance localization in the hybrid are also explored. The reduced horizontal correlation scale in the 3DVAR yields much more reasonable circulation analyses than the default scale. The vertical covariance localization scale specified in terms of geometric height instead of model levels allows for desirable spreading of V r data to the surface. It seems that the assimilation with the hybrid method leads to further improved vortex intensity forecast and track forecast of the typhoon compared to those in the analyses from the global forecast system and 3DVAR . The results also indicated that the hybrid has a significant effect on the 12 h accumulated rainfall forecasts. Such improvements for analysis and forecast are probably due to the use of the flow‐dependent background error covariance. Nutzungsrecht: © 2017 Royal Meteorological Society hybrid assimilation model WRF radar radial velocity Data collection Computational fluid dynamics Data processing Mathematical models Radar data Error detection Rainfall Kalman filters Kalman filter Localization Correlation Position (location) Data assimilation Covariance Data Analysis Radar Assimilation Rain Typhoons Xue, Ming oth Min, Jinzhong oth Enthalten in Meteorological applications Bognor Regis : Wiley, 1994 24(2017), 4, Seite 628-641 (DE-627)188146865 (DE-600)1280578-6 (DE-576)334708931 1350-4827 nnns volume:24 year:2017 number:4 pages:628-641 http://dx.doi.org/10.1002/met.1663 Volltext http://onlinelibrary.wiley.com/doi/10.1002/met.1663/abstract https://search.proquest.com/docview/1947367120 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_22 GBV_ILN_601 AR 24 2017 4 628-641
allfieldsSound	10.1002/met.1663 doi PQ20171228 (DE-627)OLC1998312445 (DE-599)GBVOLC1998312445 (PRQ)p1313-ccb5955d62085bf0ba874954689b053a8b4aaa91e36c3f8cc279917f38e86ad3 (KEY)0238303920170000024000400628comparisonoflimitedarea3dvarandetkfen3dvardataassi DE-627 ger DE-627 rakwb eng 550 ZDB Shen, Feifei verfasserin aut A comparison of limited‐area 3DVAR and ETKF‐En3DVAR data assimilation using radar observations at convective scale for the prediction of Typhoon Saomai (2006) 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The ensemble transform Kalman filter based ensemble 3D variational ( ETKF‐En3DVAR ) data assimilation ( DA ) system is employed to evaluate the potential value of assimilating radar radial wind ( V r ) data for the analysis and forecasting of T yphoon S aomai (2006). The DA system conducted cycling assimilation every 30 min when S aomai started to enter radar coverage. Within the DA cycles, the control analysis was updated by the ETKF‐En3DVAR algorithm whereas the forecast ensemble perturbations in the hybrid scheme were updated by the ETKF algorithm. The benefits from the use of the flow‐dependent ensemble covariance are explored by comparing the analysis increments, analysis and subsequent forecasts from the hybrid scheme with those from a pure 3DVAR using static background error covariance. Sensitivity to the horizontal correlation scale in the 3DVAR and the vertical covariance localization in the hybrid are also explored. The reduced horizontal correlation scale in the 3DVAR yields much more reasonable circulation analyses than the default scale. The vertical covariance localization scale specified in terms of geometric height instead of model levels allows for desirable spreading of V r data to the surface. It seems that the assimilation with the hybrid method leads to further improved vortex intensity forecast and track forecast of the typhoon compared to those in the analyses from the global forecast system and 3DVAR . The results also indicated that the hybrid has a significant effect on the 12 h accumulated rainfall forecasts. Such improvements for analysis and forecast are probably due to the use of the flow‐dependent background error covariance. Nutzungsrecht: © 2017 Royal Meteorological Society hybrid assimilation model WRF radar radial velocity Data collection Computational fluid dynamics Data processing Mathematical models Radar data Error detection Rainfall Kalman filters Kalman filter Localization Correlation Position (location) Data assimilation Covariance Data Analysis Radar Assimilation Rain Typhoons Xue, Ming oth Min, Jinzhong oth Enthalten in Meteorological applications Bognor Regis : Wiley, 1994 24(2017), 4, Seite 628-641 (DE-627)188146865 (DE-600)1280578-6 (DE-576)334708931 1350-4827 nnns volume:24 year:2017 number:4 pages:628-641 http://dx.doi.org/10.1002/met.1663 Volltext http://onlinelibrary.wiley.com/doi/10.1002/met.1663/abstract https://search.proquest.com/docview/1947367120 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_22 GBV_ILN_601 AR 24 2017 4 628-641
language	English
source	Enthalten in Meteorological applications 24(2017), 4, Seite 628-641 volume:24 year:2017 number:4 pages:628-641
sourceStr	Enthalten in Meteorological applications 24(2017), 4, Seite 628-641 volume:24 year:2017 number:4 pages:628-641
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	hybrid assimilation model WRF radar radial velocity Data collection Computational fluid dynamics Data processing Mathematical models Radar data Error detection Rainfall Kalman filters Kalman filter Localization Correlation Position (location) Data assimilation Covariance Data Analysis Radar Assimilation Rain Typhoons
dewey-raw	550
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container_title	Meteorological applications
authorswithroles_txt_mv	Shen, Feifei @@aut@@ Xue, Ming @@oth@@ Min, Jinzhong @@oth@@
publishDateDaySort_date	2017-01-01T00:00:00Z
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It seems that the assimilation with the hybrid method leads to further improved vortex intensity forecast and track forecast of the typhoon compared to those in the analyses from the global forecast system and 3DVAR . The results also indicated that the hybrid has a significant effect on the 12 h accumulated rainfall forecasts. 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author	Shen, Feifei
spellingShingle	Shen, Feifei ddc 550 misc hybrid assimilation misc model misc WRF misc radar radial velocity misc Data collection misc Computational fluid dynamics misc Data processing misc Mathematical models misc Radar data misc Error detection misc Rainfall misc Kalman filters misc Kalman filter misc Localization misc Correlation misc Position (location) misc Data assimilation misc Covariance misc Data misc Analysis misc Radar misc Assimilation misc Rain misc Typhoons A comparison of limited‐area 3DVAR and ETKF‐En3DVAR data assimilation using radar observations at convective scale for the prediction of Typhoon Saomai (2006)
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topic_title	550 ZDB A comparison of limited‐area 3DVAR and ETKF‐En3DVAR data assimilation using radar observations at convective scale for the prediction of Typhoon Saomai (2006) hybrid assimilation model WRF radar radial velocity Data collection Computational fluid dynamics Data processing Mathematical models Radar data Error detection Rainfall Kalman filters Kalman filter Localization Correlation Position (location) Data assimilation Covariance Data Analysis Radar Assimilation Rain Typhoons
topic	ddc 550 misc hybrid assimilation misc model misc WRF misc radar radial velocity misc Data collection misc Computational fluid dynamics misc Data processing misc Mathematical models misc Radar data misc Error detection misc Rainfall misc Kalman filters misc Kalman filter misc Localization misc Correlation misc Position (location) misc Data assimilation misc Covariance misc Data misc Analysis misc Radar misc Assimilation misc Rain misc Typhoons
topic_unstemmed	ddc 550 misc hybrid assimilation misc model misc WRF misc radar radial velocity misc Data collection misc Computational fluid dynamics misc Data processing misc Mathematical models misc Radar data misc Error detection misc Rainfall misc Kalman filters misc Kalman filter misc Localization misc Correlation misc Position (location) misc Data assimilation misc Covariance misc Data misc Analysis misc Radar misc Assimilation misc Rain misc Typhoons
topic_browse	ddc 550 misc hybrid assimilation misc model misc WRF misc radar radial velocity misc Data collection misc Computational fluid dynamics misc Data processing misc Mathematical models misc Radar data misc Error detection misc Rainfall misc Kalman filters misc Kalman filter misc Localization misc Correlation misc Position (location) misc Data assimilation misc Covariance misc Data misc Analysis misc Radar misc Assimilation misc Rain misc Typhoons
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title	A comparison of limited‐area 3DVAR and ETKF‐En3DVAR data assimilation using radar observations at convective scale for the prediction of Typhoon Saomai (2006)
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title_full	A comparison of limited‐area 3DVAR and ETKF‐En3DVAR data assimilation using radar observations at convective scale for the prediction of Typhoon Saomai (2006)
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title_sort	comparison of limited‐area 3dvar and etkf‐en3dvar data assimilation using radar observations at convective scale for the prediction of typhoon saomai (2006)
title_auth	A comparison of limited‐area 3DVAR and ETKF‐En3DVAR data assimilation using radar observations at convective scale for the prediction of Typhoon Saomai (2006)
abstract	The ensemble transform Kalman filter based ensemble 3D variational ( ETKF‐En3DVAR ) data assimilation ( DA ) system is employed to evaluate the potential value of assimilating radar radial wind ( V r ) data for the analysis and forecasting of T yphoon S aomai (2006). The DA system conducted cycling assimilation every 30 min when S aomai started to enter radar coverage. Within the DA cycles, the control analysis was updated by the ETKF‐En3DVAR algorithm whereas the forecast ensemble perturbations in the hybrid scheme were updated by the ETKF algorithm. The benefits from the use of the flow‐dependent ensemble covariance are explored by comparing the analysis increments, analysis and subsequent forecasts from the hybrid scheme with those from a pure 3DVAR using static background error covariance. Sensitivity to the horizontal correlation scale in the 3DVAR and the vertical covariance localization in the hybrid are also explored. The reduced horizontal correlation scale in the 3DVAR yields much more reasonable circulation analyses than the default scale. The vertical covariance localization scale specified in terms of geometric height instead of model levels allows for desirable spreading of V r data to the surface. It seems that the assimilation with the hybrid method leads to further improved vortex intensity forecast and track forecast of the typhoon compared to those in the analyses from the global forecast system and 3DVAR . The results also indicated that the hybrid has a significant effect on the 12 h accumulated rainfall forecasts. Such improvements for analysis and forecast are probably due to the use of the flow‐dependent background error covariance.
abstractGer	The ensemble transform Kalman filter based ensemble 3D variational ( ETKF‐En3DVAR ) data assimilation ( DA ) system is employed to evaluate the potential value of assimilating radar radial wind ( V r ) data for the analysis and forecasting of T yphoon S aomai (2006). The DA system conducted cycling assimilation every 30 min when S aomai started to enter radar coverage. Within the DA cycles, the control analysis was updated by the ETKF‐En3DVAR algorithm whereas the forecast ensemble perturbations in the hybrid scheme were updated by the ETKF algorithm. The benefits from the use of the flow‐dependent ensemble covariance are explored by comparing the analysis increments, analysis and subsequent forecasts from the hybrid scheme with those from a pure 3DVAR using static background error covariance. Sensitivity to the horizontal correlation scale in the 3DVAR and the vertical covariance localization in the hybrid are also explored. The reduced horizontal correlation scale in the 3DVAR yields much more reasonable circulation analyses than the default scale. The vertical covariance localization scale specified in terms of geometric height instead of model levels allows for desirable spreading of V r data to the surface. It seems that the assimilation with the hybrid method leads to further improved vortex intensity forecast and track forecast of the typhoon compared to those in the analyses from the global forecast system and 3DVAR . The results also indicated that the hybrid has a significant effect on the 12 h accumulated rainfall forecasts. Such improvements for analysis and forecast are probably due to the use of the flow‐dependent background error covariance.
abstract_unstemmed	The ensemble transform Kalman filter based ensemble 3D variational ( ETKF‐En3DVAR ) data assimilation ( DA ) system is employed to evaluate the potential value of assimilating radar radial wind ( V r ) data for the analysis and forecasting of T yphoon S aomai (2006). The DA system conducted cycling assimilation every 30 min when S aomai started to enter radar coverage. Within the DA cycles, the control analysis was updated by the ETKF‐En3DVAR algorithm whereas the forecast ensemble perturbations in the hybrid scheme were updated by the ETKF algorithm. The benefits from the use of the flow‐dependent ensemble covariance are explored by comparing the analysis increments, analysis and subsequent forecasts from the hybrid scheme with those from a pure 3DVAR using static background error covariance. Sensitivity to the horizontal correlation scale in the 3DVAR and the vertical covariance localization in the hybrid are also explored. The reduced horizontal correlation scale in the 3DVAR yields much more reasonable circulation analyses than the default scale. The vertical covariance localization scale specified in terms of geometric height instead of model levels allows for desirable spreading of V r data to the surface. It seems that the assimilation with the hybrid method leads to further improved vortex intensity forecast and track forecast of the typhoon compared to those in the analyses from the global forecast system and 3DVAR . The results also indicated that the hybrid has a significant effect on the 12 h accumulated rainfall forecasts. Such improvements for analysis and forecast are probably due to the use of the flow‐dependent background error covariance.
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title_short	A comparison of limited‐area 3DVAR and ETKF‐En3DVAR data assimilation using radar observations at convective scale for the prediction of Typhoon Saomai (2006)
url	http://dx.doi.org/10.1002/met.1663 http://onlinelibrary.wiley.com/doi/10.1002/met.1663/abstract https://search.proquest.com/docview/1947367120
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up_date	2024-07-04T04:45:24.504Z
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