Assimilating the LAI Data to the VEGAS Model Using the Local Ensemble Transform Kalman Filter: An Observing System Simulation Experiment

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.

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Assimilation of satellite data to optimize large-scale hydrological model parameters: a case study for the SWOT mission

Hydrology and Earth System Sciences ◽

10.5194/hess-18-4485-2014 ◽

2014 ◽

Vol 18 (11) ◽

pp. 4485-4507 ◽

Cited By ~ 24

Author(s):

V. Pedinotti ◽

A. Boone ◽

S. Ricci ◽

S. Biancamaria ◽

N. Mognard

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Water Surface ◽

Large Scale ◽

Simulation Experiment ◽

Hydrological Model ◽

System Simulation ◽

Water Levels ◽

Model Parameters ◽

Observing System Simulation Experiment

Abstract. During the last few decades, satellite measurements have been widely used to study the continental water cycle, especially in regions where in situ measurements are not readily available. The future Surface Water and Ocean Topography (SWOT) satellite mission will deliver maps of water surface elevation (WSE) with an unprecedented resolution and provide observation of rivers wider than 100 m and water surface areas greater than approximately 250 x 250 m over continental surfaces between 78° S and 78° N. This study aims to investigate the potential of SWOT data for parameter optimization for large-scale river routing models. The method consists in applying a data assimilation approach, the extended Kalman filter (EKF) algorithm, to correct the Manning roughness coefficients of the ISBA (Interactions between Soil, Biosphere, and Atmosphere)-TRIP (Total Runoff Integrating Pathways) continental hydrologic system. Parameters such as the Manning coefficient, used within such models to describe water basin characteristics, are generally derived from geomorphological relationships, which leads to significant errors at reach and large scales. The current study focuses on the Niger Basin, a transboundary river. Since the SWOT observations are not available yet and also to assess the proposed assimilation method, the study is carried out under the framework of an observing system simulation experiment (OSSE). It is assumed that modeling errors are only due to uncertainties in the Manning coefficient. The true Manning coefficients are then supposed to be known and are used to generate synthetic SWOT observations over the period 2002–2003. The impact of the assimilation system on the Niger Basin hydrological cycle is then quantified. The optimization of the Manning coefficient using the EKF (extended Kalman filter) algorithm over an 18-month period led to a significant improvement of the river water levels. The relative bias of the water level is globally improved (a 30% reduction). The relative bias of the Manning coefficient is also reduced (40% reduction) and it converges towards an optimal value. Discharge is also improved by the assimilation, but to a lesser extent than for the water levels (7%). Moreover, the method allows for a better simulation of the occurrence and intensity of flood events in the inner delta and shows skill in simulating the maxima and minima of water storage anomalies, especially in the groundwater and the aquifer reservoirs. The application of the assimilation method in the framework of an observing system simulation experiment allows evaluating the skill of the EKF algorithm to improve hydrological model parameters and to demonstrate SWOT's promising potential for global hydrology issues. However, further studies (e.g., considering multiple error sources and the difference between synthetic and real observations) are needed to achieve the evaluation of the method.

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Improving quantitative precipitation nowcasting with a local ensemble transform Kalman filter radar data assimilation system: observing system simulation experiments

Tellus A Dynamic Meteorology and Oceanography ◽

10.3402/tellusa.v66.21804 ◽

2014 ◽

Vol 66 (1) ◽

pp. 21804 ◽

Cited By ~ 10

Author(s):

Chih-Chien Tsai ◽

Shu-Chih Yang ◽

Yu-Chieng Liou

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

System Simulation ◽

Radar Data ◽

Simulation Experiments ◽

Ensemble Transform Kalman Filter ◽

Data Assimilation System ◽

Ensemble Transform ◽

Radar Data Assimilation ◽

Assimilation System

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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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Observing System Simulation Experiment for Remote Sensing of Snow at P-Band

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9324240 ◽

2020 ◽

Author(s):

Xiaolan Xu ◽

Rashmi Shah ◽

Simon Yueh ◽

Steve Margulis

Keyword(s):

Remote Sensing ◽

Simulation Experiment ◽

System Simulation ◽

Observing System Simulation Experiment

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Local ensemble transform Kalman filter data assimilation system for the global semi-Lagrangian atmospheric model

Russian Journal of Numerical Analysis and Mathematical Modelling ◽

10.1515/rnam-2013-0023 ◽

2013 ◽

Vol 28 (4) ◽

Cited By ~ 2

Author(s):

A. Shlyaeva ◽

M. Tolstykh ◽

V. Mizyak ◽

V. Rogutov

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Atmospheric Model ◽

Ensemble Transform Kalman Filter ◽

Data Assimilation System ◽

Ensemble Transform ◽

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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