A Local Ensemble Transform Kalman Filter Data Assimilation System for the Global FSU Atmospheric Model

Abstract. We developed a Carbon data assimilation system to estimate the surface carbon fluxes using the Local Ensemble Transform Kalman Filter and atmospheric transfer model of GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological fields based on the Goddard Earth Observing System Model, Version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. [2011, 2012], who estimated the surface carbon fluxes in an Observing System Simulation Experiment (OSSE) mode, as evolving parameters in the assimilation of the atmospheric CO2, using a short assimilation window of 6 hours. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO2 transport. After introducing new techniques such as variable localization, and increased observation weights near the surface, they obtained accurate carbon fluxes at grid point resolution. We developed a new version of the LETKF related to the Running-in-Place (RIP) method used to accelerate the spin-up of EnKF data assimilation [Kalnay and Yang, 2010; Wang et al., 2013, Yang et al., 2014]. Like RIP, the new assimilation system uses the no-cost smoothing algorithm for the LETKF [Kalnay et al., 2007b], which allows shifting at no cost the Kalman Filter solution forward or backward within an assimilation window. In the new scheme a long observation window (e.g., 7-days or longer) is used to create an LETKF ensemble at 7-days. Then, the RIP smoother is used to obtain an accurate final analysis at 1-day. This analysis has the advantage of being based on a short assimilation window, which makes it more accurate, and of having been exposed to the future 7-days observations, which accelerates the spin up. The assimilation and observation windows are then shifted forward by one day, and the process is repeated. This reduces significantly the analysis error, suggesting that this method could be used in other data assimilation problems.

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Convective-Scale Data Assimilation and Precipitation Prediction with a Local Ensemble Transform Kalman Filter Radar Assimilation System Over Complex Terrain: A Thorough Investigation with the Heavy Rainfall in Taiwan on 16 June 2008

10.1007/978-3-030-77722-7_21 ◽

2021 ◽

pp. 543-579

Author(s):

Shu-Chih Yang ◽

Hsiang-Wen Cheng ◽

Pin-Ying Wu ◽

Zih-Mao Huang ◽

Chih-Chien Tsai

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Complex Terrain ◽

Heavy Rainfall ◽

Precipitation Prediction ◽

Convective Scale ◽

Ensemble Transform Kalman Filter ◽

Scale Data ◽

Ensemble Transform ◽

Assimilation System

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Estimating Surface Carbon Fluxes Based on a Local Ensemble Transform Kalman Filter with a Short Assimilation Window and a Long Observation Window

10.5194/gmd-2017-269 ◽

2017 ◽

Author(s):

Yun Liu ◽

Eugenia Kalnay ◽

Ning Zeng ◽

Ghassem Asrar ◽

Zhaohui Chen ◽

...

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Grid Point ◽

Carbon Fluxes ◽

Transfer Model ◽

Observation Window ◽

Surface Carbon ◽

Ensemble Transform Kalman Filter ◽

Ensemble Transform ◽

Assimilation System

Abstract. We developed a Carbon data assimilation system to estimate the surface carbon fluxes using the Local Ensemble Transform Kalman Filter and atmospheric transfer model of GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological fields based on the Goddard Earth Observing System Model, Version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. (2011, 2012), who estimated the surface carbon fluxes in an Observing System Simulation Experiment (OSSE) mode, as evolving parameters in the assimilation of the atmospheric CO2, using a short assimilation window of 6 hours. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO2 transport. After introducing new techniques such as “variable localization”, and increased observation weights near the surface, they obtained accurate carbon fluxes at grid point resolution. We developed a new version of the LETKF related to the “Running-in-Place” (RIP) method used to accelerate the spin-up of EnKF data assimilation (Kalnay and Yang, 2010; Wang et al., 2013, Yang et al., 2014). Like RIP, the new assimilation system uses the “no-cost smoothing” algorithm for the LETKF (Kalnay et al., 2007b), which allows shifting at no cost the Kalman Filter solution forward or backward within an assimilation window. In the new scheme a long “observation window” (e.g., 7-days or longer) is used to create an LETKF ensemble at 7-days. Then, the RIP smoother is used to obtain an accurate final analysis at 1-day. This analysis has the advantage of being based on a short assimilation window, which makes it more accurate, and of having been exposed to the future 7-days observations, which accelerates the spin up. The assimilation and observation windows are then shifted forward by one day, and the process is repeated. This reduces significantly the analysis error, suggesting that this method could be used in other data assimilation problems. The newly developed assimilation method can be used with other Earth system models, especially for greater use of observations in conjunction with models.

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Multi-Model Ensemble Approaches to Data Assimilation Using the 4D-Local Ensemble Transform Kalman Filter

10.21236/ada542670 ◽

2010 ◽

Author(s):

Kayo Ide

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Model Ensemble ◽

Ensemble Transform Kalman Filter ◽

Ensemble Transform

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An ensemble Kalman filter data assimilation system for the martian atmosphere: Implementation and simulation experiments

Icarus ◽

10.1016/j.icarus.2010.03.034 ◽

2010 ◽

Vol 209 (2) ◽

pp. 470-481 ◽

Cited By ~ 24

Author(s):

Matthew J. Hoffman ◽

Steven J. Greybush ◽

R. John Wilson ◽

Gyorgyi Gyarmati ◽

Ross N. Hoffman ◽

...

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Ensemble Kalman Filter ◽

Martian Atmosphere ◽

Simulation Experiments ◽

Data Assimilation System ◽

Assimilation System

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Gain Form of the Ensemble Transform Kalman Filter and Its Relevance to Satellite Data Assimilation with Model Space Ensemble Covariance Localization

Monthly Weather Review ◽

10.1175/mwr-d-17-0102.1 ◽

2017 ◽

Vol 145 (11) ◽

pp. 4575-4592 ◽

Cited By ~ 15

Author(s):

Craig H. Bishop ◽

Jeffrey S. Whitaker ◽

Lili Lei

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Satellite Data ◽

Localization Length ◽

Model Space ◽

Ensemble Size ◽

Length Scales ◽

Ensemble Transform Kalman Filter ◽

Ensemble Transform ◽

Expanded Ensemble

To ameliorate suboptimality in ensemble data assimilation, methods have been introduced that involve expanding the ensemble size. Such expansions can incorporate model space covariance localization and/or estimates of climatological or model error covariances. Model space covariance localization in the vertical overcomes problematic aspects of ensemble-based satellite data assimilation. In the case of the ensemble transform Kalman filter (ETKF), the expanded ensemble size associated with vertical covariance localization would also enable the simultaneous update of entire vertical columns of model variables from hyperspectral and multispectral satellite sounders. However, if the original formulation of the ETKF were applied to an expanded ensemble, it would produce an analysis ensemble that was the same size as the expanded forecast ensemble. This article describes a variation on the ETKF called the gain ETKF (GETKF) that takes advantage of covariances from the expanded ensemble, while producing an analysis ensemble that has the required size of the unexpanded forecast ensemble. The approach also yields an inflation factor that depends on the localization length scale that causes the GETKF to perform differently to an ensemble square root filter (EnSRF) using the same expanded ensemble. Experimentation described herein shows that the GETKF outperforms a range of alternative ETKF-based solutions to the aforementioned problems. In cycling data assimilation experiments with a newly developed storm-track version of the Lorenz-96 model, the GETKF analysis root-mean-square error (RMSE) matches the EnSRF RMSE at shorter than optimal localization length scales but is superior in that it yields smaller RMSEs for longer localization length scales.

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