Results 1  10
of
42
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 ..."
Abstract

Cited by 51 (7 self)
 Add to MetaCart
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
2006a: Tests of an ensemble Kalman filter for mesoscale and regionalscale data assimilation. Part I: Perfect model experiments
 Mon. Wea. Rev
"... In Part I of this twopart work, the feasibility of using an ensemble Kalman filter (EnKF) for mesoscale and regionalscale data assimilation through various observing system simulation experiments was demonstrated assuming a perfect forecast model for a winter snowstorm event that occurred on 24–2 ..."
Abstract

Cited by 42 (7 self)
 Add to MetaCart
In Part I of this twopart work, the feasibility of using an ensemble Kalman filter (EnKF) for mesoscale and regionalscale data assimilation through various observing system simulation experiments was demonstrated assuming a perfect forecast model for a winter snowstorm event that occurred on 24–26 January 2000. The current study seeks to explore the performance of the EnKF for the same event in the presence of significant model errors due to physical parameterizations by assimilating synthetic sounding and surface observations with typical temporal and spatial resolutions. The EnKF performance with imperfect models is also examined for a warmseason mesoscale convective vortex (MCV) event that occurred on 10–13 June 2003. The significance of model error in both warm and coldseason events is demonstrated when the use of different cumulus parameterization schemes within different ensembles results in significantly different forecasts in terms of both ensemble mean and spread. Nevertheless, the EnKF performed reasonably well in most experiments with the imperfect model assumption (though its performance can sometimes be significantly degraded). As in Part I, where the perfect model assumption was utilized, most analysis error reduction comes from larger scales. Results show that using a combination of different physical parameterization schemes in the ensemble forecast can significantly improve filter performance. A multischeme
Ensemblebased atmospheric data assimilation
, 2004
"... Ensemblebased data assimilation techniques are being explored as possible alternatives to current operational analysis techniques such as 3 or 4dimensional variational assimilation. Ensemblebased assimilation techniques utilize an ensemble of parallel data assimilation and forecast cycles. The ..."
Abstract

Cited by 42 (2 self)
 Add to MetaCart
Ensemblebased data assimilation techniques are being explored as possible alternatives to current operational analysis techniques such as 3 or 4dimensional variational assimilation. Ensemblebased assimilation techniques utilize an ensemble of parallel data assimilation and forecast cycles. The backgrounderror covariances are estimated using the forecast ensemble and are used to produce an ensemble of analyses. The backgrounderror covariances are flow dependent and often have very complicated structure, providing a very different adjustment to the observations than are seen from methods such as 3 dimensional variational assimilation. Though computationally expensive, ensemblebased techniques are relatively easy to code, since no adjoint nor tangentlinear models are required, and previous tests in simple models suggest that dramatic improvements over existing operational methods may be possible. A review of the ensemblebased assimilation is provided here, starting from the basic concepts of Bayesian assimilation. Without some simplification, full Bayesian assimilation is computationally impossible for model states of large dimension. Assuming normality of error statistics and linearity of error growth, the state and its error covariance may be predicted optimally using Kalman filter (KF) techniques. The ensemble Kalman filter (EnKF) is then described. The EnKF is an approximation to the KF in that backgrounderror covariances are estimated from a finite ensemble of forecasts. However, no assumptions about linearity of error growth are made. Recent algorithmic variants on the standard EnKF are also described, as well as methods for simplifying the computations and increasing the accuracy. Examples of ensemblebased assimilations are provided in simple and more realistic dynamical systems.
Local ensemble Kalman filtering in the presence of model bias. Tellus 58A
, 2006
"... We modify the local ensemble Kalman filter (LEKF) to incorporate the effect of forecast model bias. The method is based on augmentation of the atmospheric state by estimates of the model bias, and we consider different ways of modeling (i.e. parameterizing) the model bias. We evaluate the effectiven ..."
Abstract

Cited by 29 (7 self)
 Add to MetaCart
We modify the local ensemble Kalman filter (LEKF) to incorporate the effect of forecast model bias. The method is based on augmentation of the atmospheric state by estimates of the model bias, and we consider different ways of modeling (i.e. parameterizing) the model bias. We evaluate the effectiveness of the proposed augmented state ensemble Kalman filter through numerical experiments incorporating various model biases into the model of Lorenz and Emanuel. Our results highlight the critical role played by the selection of a good parameterization model for representing the form of the possible bias in the forecast model. In particular, we find that forecasts can be greatly improved provided that a good model parameterizing the model bias is used to augment the state in the Kalman filter. 1.
A comparison of hybrid ensemble transform Kalman filterOptimum Interpolation and ensemble squareroot filter analysis schemes
, 2007
"... A hybrid ensemble transform Kalman filter (ETKF)–optimum interpolation (OI) analysis scheme is described and compared with an ensemble square root filter (EnSRF) analysis scheme. A twolayer primitive equation model was used under perfectmodel assumptions. A simplified observation network was used ..."
Abstract

Cited by 21 (6 self)
 Add to MetaCart
A hybrid ensemble transform Kalman filter (ETKF)–optimum interpolation (OI) analysis scheme is described and compared with an ensemble square root filter (EnSRF) analysis scheme. A twolayer primitive equation model was used under perfectmodel assumptions. A simplified observation network was used, and the OI method utilized a static background error covariance constructed from a large inventory of historical forecast errors. The hybrid scheme updated the ensemble mean using a hybridized ensemble and static backgrounderror covariance. The ensemble perturbations in the hybrid scheme were updated by the ETKF scheme. The EnSRF ran parallel data assimilation cycles for each member and serially assimilated the observations. The EnSRF backgrounderror covariance was estimated fully from the ensemble. For 50member ensembles, the analyses from the hybrid scheme were as accurate or nearly as accurate as those from the EnSRF, depending on the norm. For 20member ensembles, the analyses from the hybrid scheme were more accurate than analyses from the EnSRF under certain norms. Both hybrid and EnSRF analyses were more accurate than the analyses from the OI. Further reducing the ensemble size to five members, the EnSRF exhibited filter divergence, whereas the analyses from the hybrid scheme were still better than those updated by the OI. Additionally, the hybrid scheme was less prone to spurious gravity
Data Assimilation for Geophysical Fluids
"... The ultimate purpose of environmental studies is the forecast of its natural evolution. A prerequisite before a prediction is to retrieve at best the state of the environment. Data assimilation is the ensemble of techniques which, starting from heterogeneous information, permit to retrieve the initi ..."
Abstract

Cited by 8 (1 self)
 Add to MetaCart
The ultimate purpose of environmental studies is the forecast of its natural evolution. A prerequisite before a prediction is to retrieve at best the state of the environment. Data assimilation is the ensemble of techniques which, starting from heterogeneous information, permit to retrieve the initial state of a flow. In the first part, the mathematical models governing geophysical flows are presented together with the networks of observations of the atmosphere and of the ocean. In variational methods, we seek for the minimum of a functional estimating the discrepancy between the solution of the model and the observation. The derivation of the optimality system, using the adjoint state, permits to compute a gradient which is used in the optimization. The definition of the cost function permits to take into account the available statistical information through the choice of metrics in the space of observation and in the space of the initial condition. Some examples are presented on simplified models, especially an application in oceanography. Among the tools of optimal control, the adjoint model permits to carry out sensitivity studies, but if we look for the sensitivity of the prediction with respect to the observations, then a secondorder analysis should be considered. One of the first methods used for assimilating data in oceanography is the nudging method, adding a forcing term in the equations. A variational variant of nudging method is described and also a socalled Computational Methods for the Atmosphere and the Oceans
An ensemble Kalman filter for shortterm forecasting of tropospheric ozone concentrations
, 2005
"... An airquality forecasting system based on the pair ‘NWP model MM5–chemistry transport model CAMx’ is proposed. A version of the ensemble Kalman Filter has been developed. The modelerror covariance matrix is parametrized with the help of a covariance function and represented by an ensemble formed a ..."
Abstract

Cited by 8 (1 self)
 Add to MetaCart
An airquality forecasting system based on the pair ‘NWP model MM5–chemistry transport model CAMx’ is proposed. A version of the ensemble Kalman Filter has been developed. The modelerror covariance matrix is parametrized with the help of a covariance function and represented by an ensemble formed as a random selection from leading eigenvectors. The performance of the system is tested on the case of an ozone episode in June 2001. As a source of observations, the AirBase database has been used. Starting the forecast from analysed concentration fields improves the quality of forecast of the next day’s ozone concentration maxima.
Simultaneous Estimation of Microphysical Parameters and the Atmospheric State Using Simulated Polarimetric Radar Data and an Ensemble Kalman Filter in the Presence of an Observation Operator Error
, 2008
"... The impacts of polarimetric radar data on the estimation of uncertain microphysical parameters are investigated through observing system simulation experiments when the effects of uncertain parameters on the observation operators are also considered. Five fundamental microphysical parameters (i.e., ..."
Abstract

Cited by 5 (3 self)
 Add to MetaCart
The impacts of polarimetric radar data on the estimation of uncertain microphysical parameters are investigated through observing system simulation experiments when the effects of uncertain parameters on the observation operators are also considered. Five fundamental microphysical parameters (i.e., the intercept parameters of rain, snow, and hail and the bulk densities of snow and hail) are estimated individually or collectively using the ensemble square root Kalman filter. The differential reflectivity Z DR, specific differential phase KDP, and radar reflectivity at horizontal polarization ZH are used individually or in combinations for the parameter estimation while the radial velocity and ZH are used for the state estimation. In the process, the parameter values estimated in the previous analysis cycles are used in the forecast model and in observation operators in the ensuing assimilation cycle. Analyses are first performed that examine the sensitivity of various observations to the microphysical parameters with and without observation operator error. The results are used to help interpret the filter behaviors in parameter estimation. The experiments in which either a single or all five parameters contain initial errors reveal difficulties in estimating certain parameters using Z H alone when observation operator error is involved. Additional polarimetric measurements are found to be beneficial for both parameter and state estimation in general. It is found that the polarimetric data are more
Controlling overestimation of error covariance in ensemble Kalman filters with sparse observations: A variance limiting Kalman filter, Monthly Weather Review 139
, 2011
"... The problem of an ensemble Kalman filter when only partial observations are available is considered. In particular, the situation is investigated where the observational space consists of variables that are directly observable with known observational error, and of variables of which only their clim ..."
Abstract

Cited by 3 (2 self)
 Add to MetaCart
The problem of an ensemble Kalman filter when only partial observations are available is considered. In particular, the situation is investigated where the observational space consists of variables that are directly observable with known observational error, and of variables of which only their climatic variance and mean are given. To limit the variance of the latter poorly resolved variables a variancelimiting Kalman filter (VLKF) is derived in a variational setting. TheVLKF for a simple linear toymodel is analyzed and its range of optimal performance is determined. The VLKF is explored in an ensemble transform setting for the Lorenz96 system, and it is shown that incorporating the information of the variance of some unobservable variables can improve the skill and also increase the stability of the data assimilation procedure. 1.
A Comparison of the Hybrid and EnSRF Analysis Schemes in the Presence of Model Errors due to Unresolved Scales
, 2009
"... A hybrid analysis scheme is compared with an ensemble square root filter (EnSRF) analysis scheme in the presence of model errors as a followup to a previous perfectmodel comparison. In the hybrid scheme, the ensemble perturbations are updated by the ensemble transform Kalman filter (ETKF) and the ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
A hybrid analysis scheme is compared with an ensemble square root filter (EnSRF) analysis scheme in the presence of model errors as a followup to a previous perfectmodel comparison. In the hybrid scheme, the ensemble perturbations are updated by the ensemble transform Kalman filter (ETKF) and the ensemble mean is updated with a hybrid ensemble and static backgrounderror covariance. The experiments were conducted with a twolayer primitive equation model. The true state was a T127 simulation. Data assimilation experiments were conducted at T31 resolution (3168 complex spectral coefficients), assimilating imperfect observations drawn from the T127 nature run. By design, the magnitude of the truncation error was large, which provided a test on the ability of both schemes to deal with model error. Additive noise was used to parameterize model errors in the background ensemble for both schemes. In the first set of experiments, additive noise was drawn from a large inventory of historical forecast errors; in the second set of experiments, additive noise was drawn from a large inventory of differences between forecasts and analyses. The static covariance was computed correspondingly from the two inventories. The hybrid analysis was statistically significantly more accurate than the EnSRF analysis. The improvement of the hybrid over the EnSRF was smaller when differences of forecasts and analyses were used to form the random noise and the static covariance. The EnSRF analysis was more sensitive to the size of the ensemble than the hybrid. A series of tests was conducted to understand why the EnSRF performed worse than the hybrid. It was shown that the inferior performance of the EnSRF was likely due to the sampling error in the estimation of the modelerror covariance in the mean update and the lessbalanced EnSRF initial conditions resulting from the extra localizations used in the EnSRF. 1.