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149
Ensemble data assimilation with the ncep global forecast system
, 2007
"... Realdata experiments with an ensemble data assimilation system using the NCEP Global Forecast System model were performed and compared with the NCEP Global Data Assimilation System (GDAS). All observations in the operational data stream were assimilated for the period 1 January–10 February 2004, ex ..."
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Cited by 51 (7 self)
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Realdata experiments with an ensemble data assimilation system using the NCEP Global Forecast System model were performed and compared with the NCEP Global Data Assimilation System (GDAS). All observations in the operational data stream were assimilated for the period 1 January–10 February 2004, except satellite radiances. Because of computational resource limitations, the comparison was done at lower resolution (triangular truncation at wavenumber 62 with 28 levels) than the GDAS realtime NCEP operational runs (triangular truncation at wavenumber 254 with 64 levels). The ensemble data assimilation system outperformed the reducedresolution version of the NCEP threedimensional variational data assimilation system (3DVAR), with the biggest improvement in datasparse regions. Ensemble data assimilation analyses yielded a 24h improvement in forecast skill in the Southern Hemisphere extratropics relative to the NCEP 3DVAR system (the 48h forecast from the ensemble data assimilation system was as accurate as the 24h forecast from the 3DVAR system). Improvements in the datarich Northern Hemisphere, while still statistically significant, were more modest. It remains to be seen whether the improvements seen in the Southern Hemisphere will be retained when satellite radiances are assimilated. Three different parameterizations of background errors unaccounted for in the data assimilation system (including
4DVar or Ensemble Kalman Filter?
 TELLUS
, 2007
"... We consider the relative advantages of two advanced data assimilation systems, 4DVar and ensemble Kalman filter (EnKF), currently in use or under consideration for operational implementation. With the Lorenz model, we explore the impact of tuning assimilation parameters such as the assimilation wi ..."
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Cited by 42 (5 self)
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We consider the relative advantages of two advanced data assimilation systems, 4DVar and ensemble Kalman filter (EnKF), currently in use or under consideration for operational implementation. With the Lorenz model, we explore the impact of tuning assimilation parameters such as the assimilation window length and background error covariance in 4DVar, variance inflation in EnKF, and the effect of model errors and reduced observation coverage. For short assimilation windows EnKF gives more accurate analyses. Both systems reach similar levels of accuracy if long windows are used for 4DVar. For infrequent observations, when ensemble perturbations grow nonlinearly and become nonGaussian, 4DVar attains lower errors than EnKF. If the model is imperfect, the 4DVar with long windows requires weak constraint. Similar results are obtained with a quasigeostrophic channel model. EnKF experiments made with the primitive equations SPEEDY model provide comparisons with 3DVar and guidance on model error and ‘observation localization’. Results obtained using operational models and both simulated and real observations indicate that currently EnKF is becoming competitive with 4DVar, and that the experience acquired with each of these methods can be used to improve the other. A table summarizes the pros and cons of the two methods.
2009: Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter
 Quarterly Journal of the Royal Meteorological Society
"... Covariance inflation plays an important role within ensemble Kalman filter (EnKF) in preventing filter divergence and handling model errors. However the inflation factor needs to be tuned and tuning a parameter in EnKF is expensive. Wang and Bishop (2003), followed by Miyoshi (2005), adaptively esti ..."
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Cited by 38 (1 self)
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Covariance inflation plays an important role within ensemble Kalman filter (EnKF) in preventing filter divergence and handling model errors. However the inflation factor needs to be tuned and tuning a parameter in EnKF is expensive. Wang and Bishop (2003), followed by Miyoshi (2005), adaptively estimated the inflation factor from the innovation statistics. Although the results were satisfactory it is clear that this inflation factor estimation method relies on the accuracy of the estimated observation error covariance, which in practice is not perfectly known. In this study we propose to estimate the inflation factor and observational errors simultaneously within the EnKF. Our results for the Lorenz96 model show that without accurate observation error statistics, a scheme for adaptively estimating inflation alone does not work appropriately. By contrast, the simultaneous approach works very well in the perfect model scenario and in the presence of random model errors or small systematic model bias. For an imperfect model with large model bias, our algorithm may require the application of an additional method to remove the bias.
MATHEMATICAL STRATEGIES FOR FILTERING TURBULENT DYNAMICAL SYSTEMS
"... Abstract. The modus operandi of modern applied mathematics in developing very recent mathematical strategies for filtering turbulent dynamical systems is emphasized here. The approach involves the synergy of rigorous mathematical guidelines, exactly solvable nonlinear models with physical insight, a ..."
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Cited by 32 (15 self)
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Abstract. The modus operandi of modern applied mathematics in developing very recent mathematical strategies for filtering turbulent dynamical systems is emphasized here. The approach involves the synergy of rigorous mathematical guidelines, exactly solvable nonlinear models with physical insight, and novel cheap algorithms with judicious model errors to filter turbulent signals with many degrees of freedom. A large number of new theoretical and computational phenomena such as “catastrophic filter divergence ” in finite ensemble filters are reviewed here with the intention to introduce mathematicians, applied mathematicians, and scientists to this remarkable emerging scientific discipline with increasing practical importance. 1. Introduction. Filtering
A local ensemble transform Kalman filter data assimilation system for the NCEP global model. Tellus, 59A, to appear. Preprint available at http://www.weatherchaos.umd.edu/publications.php
, 2007
"... This paper describes the Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme and its implementation on the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model at the University of Maryland. Numerical results are shown for both simulated observ ..."
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Cited by 31 (5 self)
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This paper describes the Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme and its implementation on the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model at the University of Maryland. Numerical results are shown for both simulated observations and observations of the real atmosphere. The role of flowdependent information in data assimilation is discussed based on the results of the numerical experiments. Preliminary assimilation results with AMSUA radiance observations are also presented. 1
Global ensemble predictions of 2009’s tropical cyclones initialized with an ensemble Kalman filter
 WEATHER REV
, 2011
"... Verification was performed on ensemble forecasts of 2009 Northern Hemisphere summer tropical cyclones (TCs) from two experimental global numerical weather prediction ensemble prediction systems (EPSs). The first model was a highresolution version (T382L64) of the National Centers for Environmental ..."
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Cited by 30 (4 self)
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Verification was performed on ensemble forecasts of 2009 Northern Hemisphere summer tropical cyclones (TCs) from two experimental global numerical weather prediction ensemble prediction systems (EPSs). The first model was a highresolution version (T382L64) of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). The secondmodel was a 30km version of the experimental NOAA/ Earth System Research Laboratory’s Flowfollowing finitevolume Icosahedral Model (FIM). Both models were initialized with the first 20 members of a 60member ensemble Kalman filter (EnKF) using the T382L64 GFS. The GFS–EnKF assimilated the full observational data stream that was normally assimilated into the NCEP operational Global Statistical Interpolation (GSI) data assimilation, plus humansynthesized ‘‘observations’ ’ of tropical cyclone central pressure and position produced at the National Hurricane Center and the Joint Typhoon Warning Center. The forecasts from the two experimental ensembles were compared against four operational EPSs from the European Centre for MediumRangeWeather Forecasts (ECMWF), NCEP, the Canadian Meteorological Centre (CMC), and the Met Office (UKMO). The errors of GFS–EnKF ensemble track forecasts were competitive with those from the ECMWF ensemble system, and the overall spread of the ensemble tracks was consistent inmagnitudewith the track error.
2008: Accelerating the spinup of Ensemble Kalman Filtering
"... Ensemble Kalman Filter (EnKF) has that disadvantage that the spinup time needed to reach its asymptotic level of accuracy is longer than the corresponding spinup time in variational methods (3DVar or 4DVar). This is because the ensemble has to fulfill two independent requirements, namely that th ..."
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Cited by 18 (4 self)
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Ensemble Kalman Filter (EnKF) has that disadvantage that the spinup time needed to reach its asymptotic level of accuracy is longer than the corresponding spinup time in variational methods (3DVar or 4DVar). This is because the ensemble has to fulfill two independent requirements, namely that the mean be close to the true state, and that the ensemble perturbations represent the “errors of the day”. As a result, there are cases such as radar observations of a severe storm, where EnKF may spinup too slowly to be useful. A scheme is proposed to accelerate the spinup of EnKF applying a nocost Ensemble Kalman Smoother, and using the observations more than once in each assimilation window in order to maximize the initial extraction of information. The performance of this scheme is tested with the Local Ensemble Transform Kalman Filter (LETKF) implemented in a Quasigeostrophic model, which requires a very long spinup time when initialized from a cold start. Results show that with the new “running in place” scheme the LETKF spinsup and converges to the optimal level of error at least as fast as 3DVar or 4DVar. Additional computations (24 iterations for each window) are only required during the initial spinup, since the scheme naturally returns to the original LETKF after spinup is achieved. 1.
Asynchronous data assimilation with the EnKF
 Tellus
"... This study revisits the problem of assimilation of asynchronous observations, or fourdimensional data assimilation, with the ensemble Kalman filter (EnKF). We show that for a system with perfect model and linear dynamics the ensemble Kalman smoother (EnKS) provides a simple and efficient solution f ..."
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Cited by 16 (7 self)
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This study revisits the problem of assimilation of asynchronous observations, or fourdimensional data assimilation, with the ensemble Kalman filter (EnKF). We show that for a system with perfect model and linear dynamics the ensemble Kalman smoother (EnKS) provides a simple and efficient solution for the problem: one just needs to use the ensemble observations (that is, the forecast observations for each ensemble member) from the time of observation during the update, for each assimilated observation. This recipe can be used for assimilating both past and future data; in the context of assimilating generic asynchronous observations we refer to it as the asynchronous EnKF. The asynchronous EnKF is essentially equivalent to the fourdimensional variational data assimilation (4DVar). It requires only one forward integration of the system to obtain and store the data necessary for the analysis, and therefore is feasible for largescale applications. Unlike 4DVar, the asynchronous EnKF requires no tangent linear or adjoint model. 1.
Fourdimensional local ensemble transform Kalman filter: numerical experiments with a global circulation model. Tellus 59A
, 2007
"... We present an efficient variation of the Local Ensemble Kalman Filter (Ott et al. 2002, 2004) and the results of perfect model tests with the Lorenz96 model. This scheme is locally analogous to performing the Ensemble Transform Kalman Filter (Bishop et al. 2001). We also include a fourdimensional ..."
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Cited by 14 (4 self)
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We present an efficient variation of the Local Ensemble Kalman Filter (Ott et al. 2002, 2004) and the results of perfect model tests with the Lorenz96 model. This scheme is locally analogous to performing the Ensemble Transform Kalman Filter (Bishop et al. 2001). We also include a fourdimensional extension of the scheme to allow for asynchronous observations. 1.
Estimating observation impact without adjoint model in an ensemble Kalman
, 2008
"... Abstract We propose an ensemble sensitivity method to calculate observation impacts similar to Like the adjoint method, the ensemble sensitivity method is able to detect observations that have large random errors or biases. This sensitivity could be routinely calculated in an ensemble Kalman filte ..."
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Cited by 13 (4 self)
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Abstract We propose an ensemble sensitivity method to calculate observation impacts similar to Like the adjoint method, the ensemble sensitivity method is able to detect observations that have large random errors or biases. This sensitivity could be routinely calculated in an ensemble Kalman filter, thus providing a powerful tool to monitor the quality of observations and give quantitative estimations of observation impacts on the forecasts.