The role of constant optimal forcing in correcting forecast models

Abstract In this paper, the role of constant optimal forcing (COF) in correcting forecast models was numerically studied using the well-known Lorenz 63 model. The results show that when we only consider model error caused by parameter error, which also changes with the development of state variables...
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Autor*in:	Feng, Fan [verfasserIn] Duan, WanSuo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2013

Schlagwörter:	predictability prediction error model error optimal forcing

Übergeordnetes Werk:	Enthalten in: Science in China - Heidelberg : Springer, 1997, 56(2013), 3 vom: 17. Jan., Seite 434-443
Übergeordnetes Werk:	volume:56 ; year:2013 ; number:3 ; day:17 ; month:01 ; pages:434-443

Links:	Volltext

DOI / URN:	10.1007/s11430-012-4568-z

Katalog-ID:	SPR019239076

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allfields	10.1007/s11430-012-4568-z doi (DE-627)SPR019239076 (SPR)s11430-012-4568-z-e DE-627 ger DE-627 rakwb eng 550 ASE 38.00 bkl Feng, Fan verfasserin aut The role of constant optimal forcing in correcting forecast models 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, the role of constant optimal forcing (COF) in correcting forecast models was numerically studied using the well-known Lorenz 63 model. The results show that when we only consider model error caused by parameter error, which also changes with the development of state variables in a numerical model, the impact of such model error on forecast uncertainties can be offset by superimposing COF on the tendency equations in the numerical model. The COF can also offset the impact of model error caused by stochastic processes. In reality, the forecast results of numerical models are simultaneously influenced by parameter uncertainty and stochastic process as well as their interactions. Our results indicate that COF is also able to significantly offset the impact of such hybrid model error on forecast results. In summary, although the variation in the model error due to physical process is time-dependent, the superimposition of COF on the numerical model is an effective approach to reducing the influence of model error on forecast results. Therefore, the COF method may be an effective approach to correcting numerical models and thus improving the forecast capability of models. predictability (dpeaa)DE-He213 prediction error (dpeaa)DE-He213 model error (dpeaa)DE-He213 optimal forcing (dpeaa)DE-He213 Duan, WanSuo verfasserin aut Enthalten in Science in China Heidelberg : Springer, 1997 56(2013), 3 vom: 17. Jan., Seite 434-443 (DE-627)385614748 (DE-600)2142896-7 1862-2801 nnns volume:56 year:2013 number:3 day:17 month:01 pages:434-443 https://dx.doi.org/10.1007/s11430-012-4568-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE 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_65 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_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 38.00 ASE AR 56 2013 3 17 01 434-443
spelling	10.1007/s11430-012-4568-z doi (DE-627)SPR019239076 (SPR)s11430-012-4568-z-e DE-627 ger DE-627 rakwb eng 550 ASE 38.00 bkl Feng, Fan verfasserin aut The role of constant optimal forcing in correcting forecast models 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, the role of constant optimal forcing (COF) in correcting forecast models was numerically studied using the well-known Lorenz 63 model. The results show that when we only consider model error caused by parameter error, which also changes with the development of state variables in a numerical model, the impact of such model error on forecast uncertainties can be offset by superimposing COF on the tendency equations in the numerical model. The COF can also offset the impact of model error caused by stochastic processes. In reality, the forecast results of numerical models are simultaneously influenced by parameter uncertainty and stochastic process as well as their interactions. Our results indicate that COF is also able to significantly offset the impact of such hybrid model error on forecast results. In summary, although the variation in the model error due to physical process is time-dependent, the superimposition of COF on the numerical model is an effective approach to reducing the influence of model error on forecast results. Therefore, the COF method may be an effective approach to correcting numerical models and thus improving the forecast capability of models. predictability (dpeaa)DE-He213 prediction error (dpeaa)DE-He213 model error (dpeaa)DE-He213 optimal forcing (dpeaa)DE-He213 Duan, WanSuo verfasserin aut Enthalten in Science in China Heidelberg : Springer, 1997 56(2013), 3 vom: 17. Jan., Seite 434-443 (DE-627)385614748 (DE-600)2142896-7 1862-2801 nnns volume:56 year:2013 number:3 day:17 month:01 pages:434-443 https://dx.doi.org/10.1007/s11430-012-4568-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE 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_65 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_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 38.00 ASE AR 56 2013 3 17 01 434-443
allfields_unstemmed	10.1007/s11430-012-4568-z doi (DE-627)SPR019239076 (SPR)s11430-012-4568-z-e DE-627 ger DE-627 rakwb eng 550 ASE 38.00 bkl Feng, Fan verfasserin aut The role of constant optimal forcing in correcting forecast models 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, the role of constant optimal forcing (COF) in correcting forecast models was numerically studied using the well-known Lorenz 63 model. The results show that when we only consider model error caused by parameter error, which also changes with the development of state variables in a numerical model, the impact of such model error on forecast uncertainties can be offset by superimposing COF on the tendency equations in the numerical model. The COF can also offset the impact of model error caused by stochastic processes. In reality, the forecast results of numerical models are simultaneously influenced by parameter uncertainty and stochastic process as well as their interactions. Our results indicate that COF is also able to significantly offset the impact of such hybrid model error on forecast results. In summary, although the variation in the model error due to physical process is time-dependent, the superimposition of COF on the numerical model is an effective approach to reducing the influence of model error on forecast results. Therefore, the COF method may be an effective approach to correcting numerical models and thus improving the forecast capability of models. predictability (dpeaa)DE-He213 prediction error (dpeaa)DE-He213 model error (dpeaa)DE-He213 optimal forcing (dpeaa)DE-He213 Duan, WanSuo verfasserin aut Enthalten in Science in China Heidelberg : Springer, 1997 56(2013), 3 vom: 17. Jan., Seite 434-443 (DE-627)385614748 (DE-600)2142896-7 1862-2801 nnns volume:56 year:2013 number:3 day:17 month:01 pages:434-443 https://dx.doi.org/10.1007/s11430-012-4568-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE 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_65 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_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 38.00 ASE AR 56 2013 3 17 01 434-443
allfieldsGer	10.1007/s11430-012-4568-z doi (DE-627)SPR019239076 (SPR)s11430-012-4568-z-e DE-627 ger DE-627 rakwb eng 550 ASE 38.00 bkl Feng, Fan verfasserin aut The role of constant optimal forcing in correcting forecast models 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, the role of constant optimal forcing (COF) in correcting forecast models was numerically studied using the well-known Lorenz 63 model. The results show that when we only consider model error caused by parameter error, which also changes with the development of state variables in a numerical model, the impact of such model error on forecast uncertainties can be offset by superimposing COF on the tendency equations in the numerical model. The COF can also offset the impact of model error caused by stochastic processes. In reality, the forecast results of numerical models are simultaneously influenced by parameter uncertainty and stochastic process as well as their interactions. Our results indicate that COF is also able to significantly offset the impact of such hybrid model error on forecast results. In summary, although the variation in the model error due to physical process is time-dependent, the superimposition of COF on the numerical model is an effective approach to reducing the influence of model error on forecast results. Therefore, the COF method may be an effective approach to correcting numerical models and thus improving the forecast capability of models. predictability (dpeaa)DE-He213 prediction error (dpeaa)DE-He213 model error (dpeaa)DE-He213 optimal forcing (dpeaa)DE-He213 Duan, WanSuo verfasserin aut Enthalten in Science in China Heidelberg : Springer, 1997 56(2013), 3 vom: 17. Jan., Seite 434-443 (DE-627)385614748 (DE-600)2142896-7 1862-2801 nnns volume:56 year:2013 number:3 day:17 month:01 pages:434-443 https://dx.doi.org/10.1007/s11430-012-4568-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE 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_65 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_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 38.00 ASE AR 56 2013 3 17 01 434-443
allfieldsSound	10.1007/s11430-012-4568-z doi (DE-627)SPR019239076 (SPR)s11430-012-4568-z-e DE-627 ger DE-627 rakwb eng 550 ASE 38.00 bkl Feng, Fan verfasserin aut The role of constant optimal forcing in correcting forecast models 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, the role of constant optimal forcing (COF) in correcting forecast models was numerically studied using the well-known Lorenz 63 model. The results show that when we only consider model error caused by parameter error, which also changes with the development of state variables in a numerical model, the impact of such model error on forecast uncertainties can be offset by superimposing COF on the tendency equations in the numerical model. The COF can also offset the impact of model error caused by stochastic processes. In reality, the forecast results of numerical models are simultaneously influenced by parameter uncertainty and stochastic process as well as their interactions. Our results indicate that COF is also able to significantly offset the impact of such hybrid model error on forecast results. In summary, although the variation in the model error due to physical process is time-dependent, the superimposition of COF on the numerical model is an effective approach to reducing the influence of model error on forecast results. Therefore, the COF method may be an effective approach to correcting numerical models and thus improving the forecast capability of models. predictability (dpeaa)DE-He213 prediction error (dpeaa)DE-He213 model error (dpeaa)DE-He213 optimal forcing (dpeaa)DE-He213 Duan, WanSuo verfasserin aut Enthalten in Science in China Heidelberg : Springer, 1997 56(2013), 3 vom: 17. Jan., Seite 434-443 (DE-627)385614748 (DE-600)2142896-7 1862-2801 nnns volume:56 year:2013 number:3 day:17 month:01 pages:434-443 https://dx.doi.org/10.1007/s11430-012-4568-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE 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_65 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_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 38.00 ASE AR 56 2013 3 17 01 434-443
language	English
source	Enthalten in Science in China 56(2013), 3 vom: 17. Jan., Seite 434-443 volume:56 year:2013 number:3 day:17 month:01 pages:434-443
sourceStr	Enthalten in Science in China 56(2013), 3 vom: 17. Jan., Seite 434-443 volume:56 year:2013 number:3 day:17 month:01 pages:434-443
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	predictability prediction error model error optimal forcing
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container_title	Science in China
authorswithroles_txt_mv	Feng, Fan @@aut@@ Duan, WanSuo @@aut@@
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author	Feng, Fan
spellingShingle	Feng, Fan ddc 550 bkl 38.00 misc predictability misc prediction error misc model error misc optimal forcing The role of constant optimal forcing in correcting forecast models
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topic_title	550 ASE 38.00 bkl The role of constant optimal forcing in correcting forecast models predictability (dpeaa)DE-He213 prediction error (dpeaa)DE-He213 model error (dpeaa)DE-He213 optimal forcing (dpeaa)DE-He213
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title	The role of constant optimal forcing in correcting forecast models
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title_sort	role of constant optimal forcing in correcting forecast models
title_auth	The role of constant optimal forcing in correcting forecast models
abstract	Abstract In this paper, the role of constant optimal forcing (COF) in correcting forecast models was numerically studied using the well-known Lorenz 63 model. The results show that when we only consider model error caused by parameter error, which also changes with the development of state variables in a numerical model, the impact of such model error on forecast uncertainties can be offset by superimposing COF on the tendency equations in the numerical model. The COF can also offset the impact of model error caused by stochastic processes. In reality, the forecast results of numerical models are simultaneously influenced by parameter uncertainty and stochastic process as well as their interactions. Our results indicate that COF is also able to significantly offset the impact of such hybrid model error on forecast results. In summary, although the variation in the model error due to physical process is time-dependent, the superimposition of COF on the numerical model is an effective approach to reducing the influence of model error on forecast results. Therefore, the COF method may be an effective approach to correcting numerical models and thus improving the forecast capability of models.
abstractGer	Abstract In this paper, the role of constant optimal forcing (COF) in correcting forecast models was numerically studied using the well-known Lorenz 63 model. The results show that when we only consider model error caused by parameter error, which also changes with the development of state variables in a numerical model, the impact of such model error on forecast uncertainties can be offset by superimposing COF on the tendency equations in the numerical model. The COF can also offset the impact of model error caused by stochastic processes. In reality, the forecast results of numerical models are simultaneously influenced by parameter uncertainty and stochastic process as well as their interactions. Our results indicate that COF is also able to significantly offset the impact of such hybrid model error on forecast results. In summary, although the variation in the model error due to physical process is time-dependent, the superimposition of COF on the numerical model is an effective approach to reducing the influence of model error on forecast results. Therefore, the COF method may be an effective approach to correcting numerical models and thus improving the forecast capability of models.
abstract_unstemmed	Abstract In this paper, the role of constant optimal forcing (COF) in correcting forecast models was numerically studied using the well-known Lorenz 63 model. The results show that when we only consider model error caused by parameter error, which also changes with the development of state variables in a numerical model, the impact of such model error on forecast uncertainties can be offset by superimposing COF on the tendency equations in the numerical model. The COF can also offset the impact of model error caused by stochastic processes. In reality, the forecast results of numerical models are simultaneously influenced by parameter uncertainty and stochastic process as well as their interactions. Our results indicate that COF is also able to significantly offset the impact of such hybrid model error on forecast results. In summary, although the variation in the model error due to physical process is time-dependent, the superimposition of COF on the numerical model is an effective approach to reducing the influence of model error on forecast results. Therefore, the COF method may be an effective approach to correcting numerical models and thus improving the forecast capability of models.
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title_short	The role of constant optimal forcing in correcting forecast models
url	https://dx.doi.org/10.1007/s11430-012-4568-z
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