The Local Ensemble Transform Kalman Filter (LETKF) with a Global NWP Model on the Cubed Sphere

2016 ◽  
Vol 173 (7) ◽  
pp. 2555-2570 ◽  
Author(s):  
Seoleun Shin ◽  
Ji-Sun Kang ◽  
Youngsoon Jo
Keyword(s):  
Kalman Filter ◽  
Ensemble Transform Kalman Filter ◽  
Cubed Sphere ◽  
Nwp Model ◽  
Ensemble Transform

Real Data Assimilation Using the Local Ensemble Transform Kalman Filter (LETKF) System for a Global Non-hydrostatic NWP model on the Cubed-sphere

2018 ◽  
Vol 54 (S1) ◽  
pp. 351-360 ◽  
Author(s):  
Seoleun Shin ◽  
Jeon-Ho Kang ◽  
Hyoung-Wook Chun ◽  
Sihye Lee ◽  
Kwangjae Sung ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Real Data ◽  
Ensemble Transform Kalman Filter ◽  
Cubed Sphere ◽  
Nwp Model ◽  
Ensemble Transform

scholarly journals EnVAR for ICON-LAM: observations and quality control

2021 ◽  
Author(s):  
Mareike Burba ◽  
Sven Ulbrich ◽  
Stefanie Hollborn ◽  
Roland Potthast ◽  
Peter Knippertz
Keyword(s):  
Quality Control ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Future Development ◽  
Variational Data Assimilation ◽  
Limited Area ◽  
Deterministic Analysis ◽  
Ensemble Transform Kalman Filter ◽  
Nwp Model ◽  
Ensemble Transform

<p>The German Weather Service (DWD) introduces the regional NWP model ICON-LAM (ICON Limited Area Mode) in 2021 to replace the COSMO model. For the ICON-LAM data assimilation, a novel EnVAR (Ensemble VARiational data assimilation) setup is currently evaluated in comparison to the operational deterministic run of KENDA-LETKF (Local Ensemble Transform Kalman Filter). This requires special care as the observation handling differs for the global assimilation (via EnVAR) and the regional assimilation (KENDA). Furthermore, the variational quality control for the regional EnVAR may require a setup differing from the global setup. We will give an introduction to the observation processing in DWD's data assimilation framework (DACE).</p><p>For future development, we give an outlook on how a regional EnVAR can be used for a regional deterministic analysis by using a global ICON ensemble in combination with a regional deterministic ICON-LAM run. This is potentially of interest for DWD's partners with smaller computational capacities, because a regional EnVAR analysis is computationally less expensive than running a full KENDA ensemble assimilation cycle.</p>

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

2017 ◽  
Vol 145 (11) ◽  
pp. 4575-4592 ◽  
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.

An Iterative Ensemble Transform Kalman Filter Based on the Smoothing and the Filtering Steps

2021 ◽  
Author(s):  
Li Chen ◽  
Xiao Ding ◽  
Dunbiao Niu ◽  
Zhujun Cao ◽  
Enbin Song
Keyword(s):  
Kalman Filter ◽  
Ensemble Transform Kalman Filter ◽  
Ensemble Transform

scholarly journals Estimating surface carbon fluxes based on a local ensemble transform Kalman filter with a short assimilation window and a long observation window: an observing system simulation experiment test in GEOS-Chem 10.1

2019 ◽  
Vol 12 (7) ◽  
pp. 2899-2914
Author(s):  
Yun Liu ◽  
Eugenia Kalnay ◽  
Ning Zeng ◽  
Ghassem Asrar ◽  
Zhaohui Chen ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Simulation Experiment ◽  
System Simulation ◽  
Carbon Fluxes ◽  
Observation Window ◽  
Surface Carbon ◽  
Ensemble Transform Kalman Filter ◽  
Observing System Simulation Experiment ◽  
Ensemble Transform ◽  
Assimilation System

Abstract. We developed a carbon data assimilation system to estimate surface carbon fluxes using the local ensemble transform Kalman filter (LETKF) and atmospheric transport model GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological field 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) as evolving parameters in the assimilation of the atmospheric CO2, using a short assimilation window of 6 h. 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 surface carbon fluxes at grid-point resolution. We developed a new version of the local ensemble transform Kalman filter related to the “running-in-place” (RIP) method used to accelerate the spin-up of ensemble Kalman filter (EnKF) data assimilation (Kalnay and Yang, 2010; Wang et al., 2013; Yang et al., 2012). Like RIP, the new assimilation system uses the “no cost smoothing” algorithm for the LETKF (Kalnay et al., 2007b), which allows shifting the Kalman filter solution forward or backward within an assimilation window at no cost. In the new scheme a long “observation window” (e.g., 7 d or longer) is used to create a LETKF ensemble at 7 d. Then, the RIP smoother is used to obtain an accurate final analysis at 1 d. This new approach 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 d observations, which improves the analysis and accelerates the spin-up. The assimilation and observation windows are then shifted forward by 1 d, and the process is repeated. This reduces significantly the analysis error, suggesting that the newly developed assimilation method can be used with other Earth system models, especially in order to make greater use of observations in conjunction with models.

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

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.

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