A multimodel comparison of stratospheric ozone data assimilation based on an ensemble Kalman filter approach

Nakamura, Tetsu; Akiyoshi, Hideharu; Deushi, Makoto; Miyazaki, Kazuyuki; Kobayashi, Chiaki; Shibata, Kiyotaka; Iwasaki, Toshiki

doi:10.1002/jgrd.50338

Cited by 4 publications

(5 citation statements)

References 63 publications

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“…Recently Sekiyama et al (2011) constructed a total ozone assimilation system on the basis of a four-dimensional local ensemble transform Kalman filter (LETKF). Nakamura et al (2013) applied the EnKF to stratospheric ozone data assimilation using a multi-model approach. Meanwhile some developments based on the EnKF have begun to address tropospheric composition (Constantinescu et al, 2007a;Liu et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Comparison of the ensemble Kalman filter and 4D-Var assimilation methods using a stratospheric tracer transport model

et al. 2014

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Abstract. An ensemble Kalman filter (EnKF) assimilation method is applied to the tracer transport using the same stratospheric transport model as in the four-dimensional variational (4D-Var) assimilation system BASCOE (Belgian Assimilation System for Chemical ObsErvations). This EnKF version of BASCOE was built primarily to avoid the large costs associated with the maintenance of an adjoint model. The EnKF developed in BASCOE accounts for two adjustable parameters: a parameter α controlling the model error term and a parameter r controlling the observational error. The EnKF system is shown to be markedly sensitive to these two parameters, which are adjusted based on the monitoring of a χ 2 test measuring the misfit between the control variable and the observations. The performance of the EnKF and 4D-Var versions was estimated through the assimilation of Aura-MLS (microwave limb sounder) ozone observations during an 8-month period which includes the formation of the 2008 Antarctic ozone hole. To ensure a proper comparison, despite the fundamental differences between the two assimilation methods, both systems use identical and carefully calibrated input error statistics. We provide the detailed procedure for these calibrations, and compare the two sets of analyses with a focus on the lower and middle stratosphere where the ozone lifetime is much larger than the observational update frequency. Based on the observation-minusforecast statistics, we show that the analyses provided by the two systems are markedly similar, with biases less than 5 % and standard deviation errors less than 10 % in most of the stratosphere. Since the biases are markedly similar, they most probably have the same causes: these can be deficiencies in the model and in the observation data set, but not in the assimilation algorithm nor in the error calibration. The remarkably similar performance also shows that in the context of stratospheric transport, the choice of the assimilation method can be based on application-dependent factors, such as CPU cost or the ability to generate an ensemble of forecasts.

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Section: Introductionmentioning

confidence: 99%

Comparison of the ensemble Kalman filter and 4D-Var assimilation methods using a stratospheric tracer transport model

et al. 2014

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“…The application of EnKFs enables an evaluation of uncertainties of the analysis fields and leads to a probabilistic prediction through ensemble forecasting. LETKF has been applied to various simulations, such as weather forecast modeling (e.g., Miyoshi and Aranami 2006;Miyoshi and Yamane 2007;Miyoshi and Kunii 2012;Kunii 2013) and chemistry transport modeling (e.g., Sekiyama et al 2010Sekiyama et al , 2011aKang et al 2011;Miyazaki et al 2012;Nakamura et al 2013).…”

Section: Preparation Of Meteorological Analysesmentioning

confidence: 99%

Horizontal Resolution Dependence of Atmospheric Simulations of the Fukushima Nuclear Accident Using 15-km, 3-km, and 500-m Grid Models

Sekiyama¹,

Kunii²,

Kajino³

et al. 2015

Journal of the Meteorological Society of Japan

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We investigated the horizontal resolution dependence of atmospheric radionuclide (Cs-137) simulations of the Fukushima nuclear accident on March 15, 2011. We used Eulerian and Lagrangian transport models with low-(15-km), medium-(3-km), and high-(500-m) resolutions; both models were driven by the same meteorological analysis that was prepared by our data assimilation system (NHM-LETKF) for each horizontal resolution. This preparation was necessary for the resolution-dependent investigation, excluding any interpolation or averaging of meteorological fields. In the results, the 15-km grid analysis could not reproduce Fukushima's mountainous topography in detail, and consequently failed to depict a complex wind structure over mountains and valleys. In reality, the Cs-137 plume emitted from the Fukushima Daiichi Nuclear Power Plant (FDNPP) was mostly blocked by Mt. Azuma and other mountains along the Naka-dori valley after crossing over Abukuma Mountains on March 15, 2011. However, the 15-km grid simulations could not represent the blockage of the Cs-137 plume, which unnaturally spread through the Naka-dori valley. In contrast, the 3-km and 500-m grid simulations produced very similar Cs-137 concentrations and depositions, and successfully produced the plume blockage and deposition along the Naka-dori valley. In conclusion, low-resolution (15-km grid or greater) atmospheric models should be avoided for assessing the Fukushima nuclear accident when a regional analysis is needed. Meanwhile, it is reasonable to use 3-km grid models instead of 500-m grid models due to their similarities and the high computational burden of 500-m grid model simulations.

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“…In the present study, the EnKF and the 4D-Var are both tuned to provide their best performance while using the same spectral formulation for the prescribed background error covariance. First of all, the background error covariance is calibrated within the 4D-Var using the National Meteorological Center (NMC) method (Parrish and Derber, 1992). The calibrated errors are passed to the EnKF to generate the initial ensemble and the model error term.…”

Section: S Skachko Et Al: Enkf and 4d-var Using A Stratospheric Tracer Transport Modelmentioning

confidence: 99%

“…In the present study, the spatial correlation matrix considers Gaussian correlations in the horizontal and in the vertical directions with correlation length scales fixed to L h 0 = 800 km horizontally and L v 0 = 1 level vertically. The vertical profile of the background standard deviation matrix is estimated using the NMC method (Parrish and Derber, 1992) and is shown in Fig. 1.…”

Section: Ensemble Initialization and Model Error Generationmentioning

confidence: 99%

“…The matrix of the 4D-Var system has been calibrated using the NMC method (Parrish and Derber, 1992;Rabier et al, 1998;Bannister, 2008). For this purpose, a 6-month assimilation experiment (May-October 2008) has been performed assuming a matrix set-up as 30 % of the background field and a matrix assuming Gaussian correlations with correlation length scales L h 0 = 800 km horizontally and L v 0 = 1 level vertically.…”

Section: D-varmentioning

confidence: 99%

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Comparison of the ensemble Kalman filter and 4D-Var assimilation methods using a stratospheric tracer transport model

Skachko¹,

Errera²,

Ménard³

et al. 2014

Preprint

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Abstract. The Ensemble Kalman filter (EnKF) assimilation method is applied to the tracer transport using the same stratospheric transport model as in the 4D-Var assimilation system BASCOE. This EnKF version of BASCOE was built primarily to avoid the large costs associated with the maintenance of an adjoint model. The EnKF developed in BASCOE accounts for two adjustable parameters: a parameter α controlling the model error term and a parameter r controlling the observational error. The EnKF system is shown to be markedly sensitive to these two parameters, which are adjusted based on the monitoring of a χ2-test measuring the misfit between the control variable and the observations. The performance of the EnKF and 4D-Var versions was estimated through the assimilation of Aura-MLS ozone observations during an 8 month period which includes the formation of the 2008 Antarctic ozone hole. To ensure a proper comparison, despite the fundamental differences between the two assimilation methods, both systems use identical and carefully calibrated input error statistics. We provide the detailed procedure for these calibrations, and compare the two sets of analyses with a focus on the lower and middle stratosphere where the ozone lifetime is much larger than the observational update frequency. Based on the Observation-minus-Forecast statistics, we show that the analyses provided by the two systems are markedly similar, with biases smaller than 5% and standard deviation errors smaller than 10% in most of the stratosphere. Since the biases are markedly similar, they have most probably the same causes: these can be deficiencies in the model and in the observation dataset, but not in the assimilation algorithm nor in the error calibration. The remarkably similar performance also shows that in the context of stratospheric transport, the choice of the assimilation method can be based on application-dependent factors, such as CPU cost or the ability to generate an ensemble of forecasts.

show abstract

A multimodel comparison of stratospheric ozone data assimilation based on an ensemble Kalman filter approach

Cited by 4 publications

References 63 publications

Comparison of the ensemble Kalman filter and 4D-Var assimilation methods using a stratospheric tracer transport model

Comparison of the ensemble Kalman filter and 4D-Var assimilation methods using a stratospheric tracer transport model

Horizontal Resolution Dependence of Atmospheric Simulations of the Fukushima Nuclear Accident Using 15-km, 3-km, and 500-m Grid Models

Comparison of the ensemble Kalman filter and 4D-Var assimilation methods using a stratospheric tracer transport model

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